An Architectural Approach to Instructional Design 9781135118822, 9780415807388

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AN ARCHITECTURAL APPROACH TO INSTRUCTIONAL DESIGN An Architectural Approach to Instructional Design is organized around a groundbreaking new way of conceptualizing instructional design practice. Both practical and theoretically sound, this approach is drawn from current international trends in architectural, digital, and industrial design, and focuses on the structural and functional properties of the artifact being designed rather than the processes used to design it. Harmonious with existing systematic design models, the architectural approach expands the scope of design discourse by introducing new depth into the conversation and merging current knowledge with proven systematic techniques. An architectural approach is the natural result of increasing technological complexity and escalating user expectations. As the complexity of design problems increases, specialties evolve their own design languages, theories, processes, tools, literature, organizations, and standards. An Architectural Approach to Instructional Design describes the implications for theory and practice, providing a powerful and commercially relevant introduction for all students of instructional design. Andrew S. Gibbons is Department Chair of Instructional Psychology and Technology at Brigham Young University, Provo, Utah.

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AN ARCHITECTURAL APPROACH TO INSTRUCTIONAL DESIGN

Andrew S. Gibbons Brigham Young University

First published 2014 by Routledge 711 Third Avenue, New York, NY 10017 and by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2014 Taylor & Francis The right of Andrew S. Gibbons to be identified as author of this work has been asserted by him/her in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloguing-in-Publication Data Gibbons, Andrew S. An architectural approach to instructional design / Andrew S. Gibbons. pages cm Includes bibliographical references and index. 1. Instructional systems—Design I. Title. LB1028.38.G53 2013 371.3—dc23 2013008718

ISBN: 978-0-415-80738-8 (hbk) ISBN: 978-0-415-80739-5 (pbk) ISBN: 978-0-203-07520-3 (ebk) Typeset in Minion Pro by Apex CoVantage, LLC

To my patient, patient wife, Marsha

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Contents

Preface Acknowledgments

ix xiii

Part I: Fundamentals 1. 2. 3. 4. 5. 6. 7.

Introduction Design Layers Design Process Systems in Design The New Contexts of Instructional Design: Instruction, Learning, Technology, and Design Instructional Design and Theory Operational Principles and Design Languages

3 17 49 83 111 145 173

Part II: Design in Layers 8. 9. 10. 11. 12. 13. 14.

Design Within the Message Layer Design Within the Control Layer Design Within the Representation Layer Design Within the Content Layer Design Within the Strategy Layer Design Within the Data Management Layer Design Within the Media-Logic Layer

203 227 255 279 299 323 341

Part III: The Designer’s Value-Added 15. 16.

Layers and Modularity Adding Value to the Organization

Appendix A: Target Population Analysis Appendix B: Current Training and Resources Analysis Appendix C: Evaluation Planning Appendix D: Management Planning Appendix E: Implementation Planning References Index Author Biography

363 385 411 417 423 427 437 441 455 465 vii

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Preface

Traditionally, architecture has been associated with buildings, but in our age it has become synonymous with the concept of structure in general—structures both built and natural. Today we talk about “digital” architecture and “genetic” architecture in the same breath; for example, algorithmic digital structures have been the key to unraveling genomic structures. Exploration and exploitation of structure is a passion in our modern world. We use highly structured tools to pry secrets out of nature and then apply the structures we have found to human-made artifacts. In this context, an architect is not just a designer of buildings: anyone involved in studying and applying principles of structure is an architect, whatever the domain of the activity. Therefore, we speak of information architects, social architects, computer architects, software architects, visual architects, and economic architects. This book is about instructional architects. It is not about designing mechanical structures; it is about designing experience structures for learning. Moreover, it is not about the particular style of experience structure, but about the architecture of experience structures—the framework of design ideas and processes that allows a designer to create experiences of any style, using any set of structural elements. If this were a book about building design, it would not be specifically about the design of skyscrapers or homes, it would be about designing skyscrapers, homes, schools, public buildings, malls, memorials, libraries, and museums. And it wouldn’t be about designing in a classical, rococo, or modern style, it would be about designing in the style of the designer’s choice. How we instruct has changed. Some would question whether the term “instruction” is still relevant in a world where self-directed choice is the expected standard. Learning and problem solving in connected ways in teams of learners has become common, and the image of the lone student toiling away in a media carrel seems out of place. At the same time, learners are more and more demanding personalization to their needs and interests, in a world where knowledge is expanding and time is shrinking. Tailoring instruction to the momentary need insofar as possible is becoming more important rather than less, and the value of mentoring by a more advanced learning companion is increasingly attractive. The places that we use for instruction have also changed. In many cases one does not have to be in a particular place to engage in the joint learning experience we call instruction. We engage in instruction in schools, in offices, in public places, and on airplanes. This is in part because our conceptions of learning have changed. Learning is no longer treated as a strictly intellectual process, nor are reception theories (“fill the vessel”) any longer taken seriously. Learning today is seen as a process that involves the whole person, including intellect, emotion, action, and intention. It is also considered a highly social activity, and the implications for that are still emerging. The learner is perceived as a more active and involved agent in the learning process. ix

x • Preface

The way we design is changing. Formulaic approaches to design that are present in the early years in every design field are increasingly sharing the stage with approaches that consider the nature of design thinking. Considerations of process are becoming less prominent, and considerations of the nature of what is designed are on the rise. Most design fields are recognizing that how we design is not particular to a given field, but that design can be studied across fields to yield principles and theories that bear fruit in all fields, without supplanting the domain theories of the particular field. Finally, the technologies we use to provide experiences (in all of their new places) are in constant churn. No sooner do designers (and users) become used to new technologies than we find that they have changed—something has become obsolete, and something else has taken over its function— and so we have to reexamine our media principles to incorporate new possibilities. In the last few decades, the world of the instructional designer has become a new place, and there is no end in sight to the changes in any of these areas: instruction, learning, design, and technology. This book is about how instructional designers—instructional architects—employ specialized design languages to supply the content of designs. By filling design frameworks with the structures implied by design language terms taken from formal and personal instructional theories, designers set up systems capable of transmitting information and energies through experience that can lead to learning if the designer’s choices match the learner’s. Something additional should be said about theory, because it is a frightening issue for some designers, particularly those who labor under heavy deadlines and expectations for high-volume production. Theory is not a choice, really, because every choice of a designer constitutes an expression of the designer’s personal theories of instruction, which are usually based on personal theories of how people learn. The question for the designer is, “What are the theories that I trust? My own, or those of the theorists?” By being more clear about what theory is and how it enters into a designer’s work, this book hopes to remove the fear factor and replace it with a set of conceptual tools that will in the long run give the designer more confidence in navigating and making theoretical claims. An amusing example shows that this is not a recent problem. In an 1840 book on mast-making for sea-going ships (Cock, 1840), the author laments: Among the many necessary qualities which constitute a well-regulated see-going [sic] vessel, there is not one of greater importance, or one that should require greater research or attention, than that of producing correct rules for the station of the masts . . . There is too much truth in the observation, that with many ship-builders and ship-Masters, their inquiry and attention is not that everything on deck should give place to the best station to the vessel’s masts, with the first question with them is the placing of hatchways, the station of the windlass, the stowing of the long boat, yes, even the placing of the cook house appears of greater importance. It is . . . evident that not the least calculation is made as to the form of the vessel’s body, whether she is fullest forward or abaft. Full vessels forward require their masts proportionably forward, and the reverse if full abaft. How frequently do we hear of vessels steering hard, or that they are kept out of the wind with difficulty, while others carry a lee helm, and will scarce come up with the wind; with a variety of complaints proceeding from inattention to this important subject, which shows the necessity of enquiry as to the possibility of producing rules, founded upon principles derived from vessels themselves, as to quality or class. —(Cock, 1840, pp. 3–4) Instructional design is entering a new period of development as a profession. Traditional methods of design and concepts of theory have improved the level of practice, but it is time to examine progress in other design fields to see what lessons can be learned from them.

Preface • xi

Finally, the change that is of perhaps the most importance is the position of the instructional designer with respect to the organization that sponsors the designer’s work. New patterns of organizational use of design, new economic incentives, and the new consumption patterns of the increasingly connected learner create a new, dynamic environment within which the designer works and is rewarded. This book places the new environment of instruction, learning, design, and technology into perspective within this larger field of professional service. The expert designer obeys the same laws and invokes the same natural forces as the beginner, but in the mind of the expert designer there exist better conceptual processes, a better orientation to the landscape of designing, and more ideas to work with.

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Acknowledgments

Over many years of writing, friends and colleagues have lent patient ears and in many cases manuscript reviews that improved this work. I list many of them, apologizing for those that I may have omitted. My only excuse is the length of time it took to write and refine these ideas. Thanks to: Barbara Bichelmeyer, Elizabeth Boling, Katy Campbell, Peter Fairweather, Dexter Fletcher, Michael Heufner, Janette Hill, Jon Nelson, Daren Olson, Fred O’Neal, Russ Osguthorpe, Rick Schwier, Kennon Smith, Michael Spector, and many former students at Utah State University and Brigham Young University. Workshop participants at yearly meetings of the Association for Educational Communications and Technology (AECT) have asked important and thoughtful questions, and David Wiley was a great support in presenting the workshops. At this point, he has received so many recitations of this book’s content that he qualifies as a kind of “cloud” storage for it. His challenging questions over a number of years have been invaluable. The participants at the AECT 2012 Summer Research Symposium, and especially Brad Hokanson, were very encouraging in response to some of these ideas, and their input was very helpful. Fellow faculty members in the Instructional Psychology and Technology Department at Brigham Young University have shown great reserve and patience during frequent outbursts about layers during faculty meetings. A large number of anonymous reviewers gave extensive commentary on each of the chapters herein, but in particular, reviewers who gave their identities to their reviews were especially appreciated: James Osler, Gordon Rowland, and Brent Wilson. Thanks to the Routledge editorial staff, Rebecca Pearce, Development Editor, and Alex Masulis, Senior Editor. The errors are mine, the thanks are theirs.

xiii

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I

Fundamentals

The chapters in this section introduce several new terms that are essential to understanding the ideas in the remainder of the book. A layered approach to instructional design is described that focuses attention on product function rather than design process. A history of design approaches provides perspective on how instructional designers think today about design processes. This places the concept of multiple mutually influencing systems back as a central issue of design. The use of theory by designers is introduced in a way that requires some new categories of theory to be considered. Chapter 1: Introduction This chapter describes how technological fields are overtaken by change in ways that reorganize their concepts and practices, causing practitioners to accept new ways of thinking about what they do and how they do it. The idea of layers is introduced as a way of describing how changes in one area of a design ripple out into other areas of the design during and after periods of layer change. The instructional designer is described as an architect, whose focus is on all of the layers of a design. Chapter 2: Design Layers This chapter draws lessons from the history of the computer as an instructional medium. The concept of layered design is applied to instructional designs. Seven layers of a generic design are identified and described briefly. The design layer concept, as applied in other design fields, is used to bolster the argument for its application to the design of instruction. Chapter 3: Design Process This chapter names eight perspective points from which instructional design can be viewed, each having its own implications for design practice and designer career preparation. Four of the views are discussed in this chapter. Two views are described in Chapter 4, and the two remaining views are described in Chapter 7. Chapter 4: Systems in Design This chapter places instructional design in historical perspective by describing how the systems approach overtook the social sciences, including education, following World War II and during the Cold War. It describes the adoption of the concept of the systems approach within the instructional 1

2 • Fundamentals

design community and shows how the rigor of the systems approach was replaced over time as engineering models grew. The common features of several instructional design models are compared, and the manner of applying them is described. Chapter 5: The New Contexts of Instructional Design This chapter describes developments in several areas that provide new meaning to common terms in the designer’s basic vocabulary. The new instruction: Instruction is in essence a conversation. It is challenging today to design and implement instructional conversations. A possible way around this impasse is to recognize that conversations involve a more detailed structure—architecture—than current instructional design concepts describe. The new learning: New ways of describing learning are connected more directly to instructional design. Learning is currently described as more than a merely rational, intellectual activity. It is a process involving intellectual and emotional responses to a dynamic situation and constantly shifting learning goals of both the learner and the designer. Learners are seen as active agents who decide whether to remain engaged in learning experiences; they are expected to take an active part in choosing goals, negotiating strategic paths, and otherwise becoming more active learners. The new technology: Technology involves much more than hardware and devices. It consists of the harnessing of natural forces and energies in service to human ends. The study of technology therefore includes processes related to accomplishing this: measurement, intervention planning, and artifact design. Technological theory pertains to these activities and exists as a theoretical species apart from scientific theory, a point enlarged upon in Chapter 6. The new design: Applying technology requires design, which is typified in this part of the chapter in a number of different ways, each of which holds implications for instructional designers. Design is a topic to be studied in its own right. It represents one of the ways humans pursue new knowledge. A designer must understand this in order to understand how to apply theory to designing and how to gain new theory from designing. Chapter 6: Instructional Design and Theory This chapter recognizes that theory is difficult for many designers to deal with for many reasons. It identifies two main types of theory that are important to the instructional designer: scientific theory and technological theory. It describes a further division of technological theory into two sub-types: design theory and domain theory. Design theories are theories about how designs are generated. Domain (instructional) theories, in contrast, supply intellectual concepts to populate designs. Layer theory is a design theory. Single domain (instructional) theories are insufficient to inform an entire design. Instead, multiple domain theories are applied to a design through layers with which they are associated. Chapter 7: Operational Principles and Design Languages This chapter describes the concept of an operational principle as a source of design structure. Operational principles are abstract ideas about how energy and information are transferred and transformed by designed artifacts—what one theorist has described as “what makes the technological device work”. Human-made designs work according to operational principles that bring about the arrangement and balancing of natural forces. Design languages are also described in this chaper, as the basis for comprehending and communicating design concepts. Design languages grow automatically as technology advances. This chapter describes how awareness of design languages and their subtle interaction with design thinking can give a designer advantage.

1

Introduction

The reason why new concepts in any branch of science are hard to grasp is always the same; contemporary scientists try to picture the new concept in terms of ideas which existed before. —(Freeman Dyson, 1958) The Changing World of Instructional Design In 1953, James D. Finn, a leading figure in the field of audio-visual instruction (AVI), issued a worried assessment of the profession. According to Finn, “the most fundamental . . . characteristic of a profession is that the skills involved are founded upon a body of intellectual theory and research” (Finn, 1953, p. 8). Finn was concerned with what he considered to be the lack of an adequate theory base in AVI. Finn was also concerned about the research and theory that AVI “adapts from other academic areas of study” (Januszewski, 1994, p. 310). Finn lamented that the audio-visual field is in the peculiar position of having much of its research carried on by workers in other disciplines using hypotheses unknown to many audio-visual workers, and representing results in journals that audio-visual people do not read and at meetings that audio-visual people do not attend. —(Finn,1953, pp.15–16). We should ask whether in the modern field of instructional design and technology (IDT) the state of affairs has improved since Finn’s writing. There has certainly been a great deal of academic writing since then, but has it coalesced into a coherent body of guidance for the instructional designer? Consider the following hypothetical: today an average rocket scientist would not hesitate to design a piece of instruction, but most instructional designers would not attempt to design a rocket. It would seem that the depth of knowledge required to build and fly a rocket outstrips the depth of knowledge required to build acceptable instruction. We should ask whether the research supporting instructional design has been sufficiently balanced to inform all of the areas of decision-making necessary to support the design of instruction. When we find it is not, we should ask whether we have a way to decide what kind of research is needed and whether we have the kinds of research methodology that will allow us to ask such questions. A rapid expansion of knowledge will most certainly occur over the next decade for the instructional designer in what is becoming the new environment of instructional design. What will that new environment be like? It will involve a changed landscape in almost every respect: 3

4 • Fundamentals

• Increased commercial involvement in the design and marketing of educational products will raise the acceptable standard for products and escalate user expectations through competition. • Rapidly evolving delivery system concepts (laptop, tablet, smart phone), increased social media connection, and sprawling Wi-Fi connectivity will increase the number of users and the number of places where use can take place. • Innovative instructional concepts, new understanding of learning processes, and new design processes will speed the design and production of adaptive artifacts matched with the learner’s patterns of use and preferred patterns of learning. • Easy-to-use tools for media development will increase the number of amateur producers, but competition among deep-pocket commercial vendors will lift product concepts beyond the reach of the individual producer in volume and production values. This will force design and production specializations to deepen and proliferate. It was only twenty-five years ago that the main selling point of the most popular design and production tool for authoring computer-based instruction was that a single producer—a teacher, an instructor, or a college professor—could produce all of the elements of a self-contained computerized lesson. The very terms of that claim reveal the many conceptual and practical changes that have taken place since. We would no longer believe that a single piece of software would be adequate for development, that a single medium would be used, that instruction would take the form of a lesson, and that a single person would possess all of the necessary skills to design and create a credible computerized product. These beliefs are the residue of a former time in which the medium and the package of instruction were the focus of instructional designs. None of these things would be assumed today. Instead, an acceptable standard would include multi-media (including live instruction), optionally scheduled and located, multi-sourced, and experientially centered learning. Moreover, it would be assumed that many hands and minds had been involved in designing and producing the . . . the temptation is to use the word “product”, but that term is also becoming outdated. A preferred term might be “instructional environment”. Even the term “instruction” implies the wrong thing to many who consider themselves instructional designers. Is it “instruction” or “designed formal and informal learning experiences” that “influence” and “support” (not “cause”) learning? These distinctions may seem trivial, but they all pertain to the concept of what the designer thinks is being designed. Later parts of this chapter and later chapters will show that the concept in the designer’s mind regarding what is being designed is paramount. The changes taking place in these traditional terms are significant, which is one of the messages this work hopes to convey. So much of our knowledge about designing is in flux that every term from the inherited lexicon of instructional design is under discussion. Rather than becoming more narrow and focused, the concepts and principles that guide instructional design have become more diverse and blurred. Perhaps the greatest current challenge is the question of the “how” of instructional design. How does design take place? How can it take place? How do we describe alternative approaches to design to a new generation of designers? How do we converse about design with a generation trained to a different set of understandings? How do we explain the order and process of decision-making in design? These are the questions Finn raised that are still awaiting answers. How do we advance our knowledge about designing along a broad front? And how do designers talk among themselves about design? These are questions of this book. When Change Comes Designing instruction has become more complex. We do not seek the complexity, but rising standards and expectations force us to deal with it; complexity is a natural by-product of technological

Introduction • 5

progress. Technologies mature in the manner of a wildfire: starting from a small center, their concerns expand in every direction. Their margins grow outward in a non-linear expansion whose very volume at the fringes produces the complexity. We could wish that over time progress would result in more simplicity, but that only happens as simplifying patterns are detected within the complexity that allow a new synthesis. Every technology follows this law; only a technology that is not advancing stays simple. Progress and change can overtake technological fields and turn them on their heads almost overnight. There are many excellent examples of this, including the invention of electrical distribution systems following the invention of the electrical light bulb (Bazerman, 2002), and the invention of new skyscraper structures which followed on the heels of advances in steel-making (Misa, 1995). The most compact, interesting, and relevant example for the instructional design community, however, is the coming of sound to the movies (Gomery, 2005; LoBrutto, 1994). Change Overtakes the Movies Between 1927 and 1931 every major film studio changed over from making silent films to making “talkies”. The motive for the change was economic and deliberately sudden; the impact on the lives, professions, and technical processes and tools of a community of filmmakers was earthshaking, and things never again returned to “normal”. It will be useful to trace the impact of this change on all areas of the filmmaking and distribution technology, because similar changes are in store for the educational technologist. What was the impact of the adoption of sound? Some workers were affected immediately. Overnight the value of title-writers, piano and organ players, pit orchestras, and vaudeville acts plummeted. Many actors found that their reputations had depended more on their looks than their voice (does “Singing in the Rain” come to mind?). Some professions changed radically. Writers had to learn to create dialogue instead of scene descriptions. Only some writers were able to make the change, and the need for training in this new skill was immediate. More than just job descriptions, totally new professions were created overnight (dialog coach, diction coach, recordist, sound engineer, sound editor, sound effects or Foley specialist, and so forth). The conceptual definition of the product changed. Filmmakers began to realize that they were making something new and more powerful. Before sound, films had been primarily a visual experience. With sound, films began to change, to give more personal experiences in which the sound track, always improving in quality, played an increasingly critical part in delineating personalities, portraying more subtle plots, and weaving more intimate and personal stories the viewer could relate to at a deeper level. The local-talent music that previously had done its best in the theater to create an atmosphere became an orchestra on film that settled into the background as an amplifier of shifting emotional textures. The concept of “movie” itself began to change in a way that continues to be explored today. The physical form of the product changed. Sound-physically-off-film in the form of phonograph records and other playback devices became sound-physically-on-film in the form of an optical track located physically right next to the visual frames. Some companies that had spent millions of dollars to develop various technologies for adding sound to film became irrelevant—and out of business— overnight once the winning technology was chosen. Production equipment and processes changed. New recording equipment was added to the studio, and a new sound recording room. New microphone equipment was added to the set, and new microphone handlers began to learn new techniques for positioning the mike to obtain even, consistent sound. Homemade, invented solutions often had to be worked out on the spot, as the clock ticked. New editing equipment began to be operated by new sound editors, and libraries of sounds began to accumulate, to be catalogued by a new sound staff. Rooms on wheels were devised to

6 • Fundamentals

insulate the clackety noise of cameras from the microphone. Within two years every major film studio constructed enormous “sound stages” to house the new equipment and to protect filming from the invasive noise of the outside world. Eventually, quiet cameras were developed, signaling the impact of the change in the film world on other industries. The process for the design of movies changed. Where scene descriptions punctuated with visual titles had sufficed as a script during silent filmmaking, dialogue was now required for a talkie. Some silent scripts had included occasional lines to be mouthed by the actors, but these were lost in a sea of scene description. For talkies, the proportion of dialogue to description was reversed: occasional descriptions would appear within the linear flow of scenes made up largely of dialogue. New physical product standards had to form so that films made by different studios would play universally. This included decisions about film size, frame rate, physical placement of visual and audio tracks, and quality of film and sound images. In some cases favored personal choices had to be neutralized by adherence to economically motivated standards. Professional education in diverse skills and knowledge was instituted literally overnight. The number of people who needed training in diverse technical areas created a land-office business for those with the right kinds of knowledge in everything from acting to the physics of sound and the technical details of electricity. The nature of the delivery venue—the theater—changed. Chain-owned theaters were hastily upgraded to the new sound technology. Small independent theaters had to wait in line to be retrofitted, while their customers drove on improved roads to more distant towns to watch sound movies in theaters owned by the large chains. The cost of the new equipment was in some cases more than a theater could afford, so many small theaters went out of business. New technical problems appeared in place of the old ones, and the specialists called in to solve local malfunctions represented a new professional group. Camera operators in theaters became skilled in operating and troubleshooting the new sound equipment and camera systems. Because there were theaters in small towns across the U.S., this group of technical specialists with augmented skills became a large workforce. Professional organizations were founded to train and sometimes represent the interests of blocks of the new specialist workforce groups created by sound. At first, professional organizations stepped in with training and education, often from the level of fundamentals and continuing into advanced topics. Textbooks were published. Journals emerged. Professional societies began to offer workshops, conferences, and courses of training for a wide range of skills needed. Highly trained specialists in the new technology became instructors and then gurus who could consult across studio lines. Groups of disgruntled workers who felt they were being mistreated by the studios became new members of a unionized workforce. Specialists created new languages, jargon, and pidgins for communication. Language terms were created or adapted in order to capture emerging intellectual concepts of the new craft; jargons and slang terms allowed specialists to communicate in shorthand; and pidgins allowed specialists from different fields to communicate across their specialty boundaries to accomplish creative work jointly. All the while, research questions multiplied like rabbits. The quality of the sound systems took a while to settle in, and a period of debugging within individual theaters was the first concern. However, as theatergoers became used to the increasingly stable operation of the sound equipment, they began to hope for more—more quality, more personality, more innovative uses of sound, more emotion, and more enjoyment. Research and development on how to improve the impact of movie sound took place in corporate R&D labs, film studios, and universities, especially those located close to movie studios. The rising tide of change floated all boats. As the quality of the sound improved, the technical aspects of all of the other areas of movie-making had to improve with it.

Introduction • 7

An avalanche of change was triggered by the introduction of the talkies. New economic values appeared, and outdated values disappeared. There were new winners, and many who had prospered under the old system became the new losers. Consolidation took place among the powerful, and the independent was marginalized. Finally, where there had been ten researchable questions, a hundred new questions appeared. R&D activities within corporations and universities increased. Departments were established, and a new field of research and theorizing had taken form. Application Exercise Identify a technological field that experienced rapid and radical change similar to that experienced by the movie industry. Using available books and online resources, try to identify the impact of change on the following: • Changes in career categories • Changes in the concept of the product in people’s minds In the mind of the designer In the mind of the consumer In the mind of the business entrepreneur. • Changes in the physical form of the product • Changes in production tools and processes • Changes in the design process • Changes in product standards • Changes in the delivery system • Changes in professional training • Changes in how the product was sold • Changes in how the product was used • Changes in how the product was paid for • Changes in professional organizations • Changes in professional publications • Changes in technical jargon • Changes in research questions.   

Topics you might consider looking at include the introduction of: • • • • • • •

Digital photography The cell phone The Internet The word processor The portable music player The electric light The airplane.

Parallels with Instructional Design The purpose of this extended recitation is to illustrate the extent of the changes that overtook a single industry quite rapidly owing to a change in technology. The movie example was chosen from among a number that could have been used because both movies and technology-based instruction involve the production of non-trivial message-bearing media that require careful design, planning, and production.

8 • Fundamentals

Might similar forces be accumulating to bring about similar tectonic changes in the design, creation, and delivery of education and training? Consider the degree of turmoil surrounding the education and training environment: the rise of the Internet, increasing commercial interest in producing online educational and training materials, increasing economic and political pressures on traditional schools from kindergarten to post-doctorate, the increased value of personnel training for organizations striving to keep up with an increasingly competitive market, the wide availability of low-cost, low-maintenance, more portable and user-friendly hardware, the emergence of software development tools compatible with the skills of the average user, and a changing population of learners adept at operating the new technologies and socially enculturated in their use to an advanced degree. The revolution in movie-making was economically motivated. Do similar conditions exist today which favor the tipping of educational uses of technology toward new, perhaps commercial, sources and product forms? That question is not the question of this work, but the ability of instructional designers to survive and thrive in a changing educational and training environment created by that possibility is. The technical and theoretical world of the instructional designer has undergone, and continues to undergo, major shifts, changing the landscape in which future designers will carry out their careers. Regardless of the economic future and who will employ designers, it is imperative that career designers of all ages and experience levels continually update their views of what they do, how they do it, and what their value proposition is in a changing world. In particular, it is important for designers to assess the impact of revolutionary changes in four major areas on their practice: the revolution in delivery technologies, in the technology of design, in what we know about learning, and what we know about instructing. In all of these areas change has been more rapid in the last twenty to thirty years than at any time before. The definitions and perspectives within all of these areas have undergone major changes. A later chapter (Chapter 5) describes these changes in more detail. For the present, we need to consider the implications of living as a designer in this rapidly changing world and question whether there are not perspectives that allow designers to organize their understanding of design at a deeper, less changeable level. This work assumes that it is helpful for instructional designers to see themselves as instructional architects, capable of thinking and communicating in more nuanced ways about the unchanging universals of the learning environments they design. Instructional Designers as Architects of Systems This book sees instructional designers as architects—designers of systems that instruct. It is written for the designer who is engaged in or looks forward to professional design practice and hopes to continue the improvement of skills, knowledge, and design values over the course of a career. It is targeted to those who have the goal of becoming more insightful designers and who may at some point themselves add new knowledge about design gained from experience and/or research. It is not a how-to book in the sense of giving step-by-step directions for instructional design. It does provide challenging ideas that lead directly to useful principles for designing in new ways. These urge us to advance our knowledge of instructional design processes to match advances in hard technologies and theories of learning. We can learn to design instruction as well as rocket scientists can design rockets, but that will involve entertaining new ideas about design itself. A systems theme asserts itself at several points throughout this work. It is suggested by the title’s invocation of the architectural metaphor—the “architecture” of instructional design. Architects design structures by combining simple, elemental ideas and by dealing with multiple functional layers in which different kinds of primary structures participate at every layer (Ching, 2007, p. xii; Brand,

Introduction • 9

1994, Chapter 2). The structures that architects create are physical, but they have an aesthetic component: great architectural designs are devised as much to create emotional experiences and proclaim messages as to shelter persons. Instructional designs should acknowledge this principle as well. The structures designed by architects are systems: living, not static, structures. These systems perform functions, have a life cycle, and change over time to adapt to their environment; they are organic. If properly designed, they can be said in a sense to “live”. They create emotional and intellectual experiences and serve important purposes while at the same time serving as symbols with meanings to those who use them. Through daily use, architectural designs, no matter how grand, become absorbed into the daily lives and culture of their users. As the saying goes, we make them, and then they make us. Architects, therefore, take part in creating our cultures as much as in representing them. Instructional designers design physical artifacts, but as with built structures, the artifacts are not an end but rather a means of creating experiences. Confusing the physical artifact with the learning experience is a mistake easily made. Making it leads to mechanical, mass produced instruction whose success is measured in terms of low cost rather than intellectual and emotional impact. Instructional designers design things that, like architectural works, inspire both the heart and the mind, so instructional designs, like architectural designs, shape our cultures of learning and knowledge. Structure It is the structural nature of architectural designs that is of most importance to the architectural theme of this work. Although architects strive for unity of impression and holistic effect, their designs consist of primary elements arranged in new combinations, in a manner that strives for new patterns, new statements, and new impressions. Ching, in his widely read book on architecture (Ching, 2007), describes how the most complicated and ornate architectural designs are founded on simple manipulations of basic shapes, in a way that gives them an underlying coherence, organization, and unity. The structures created in this way not only give an outer physical impression, but they also have an inner integrity which balances the invisible forces of gravity, ground pressure, and lateral thrust which act on built structures, allowing them to preserve form and to perform functions within a constantly changing environment. This harmony of inner and outer structure and how to achieve the integrity of both represents the first theme of this work. The second structural theme of this book addresses another structure—the structure of the design process itself. Architects apply decision-making processes that possess an inner structure. Though on the surface creative design processes may seem unpredictable and unruly, if they are viewed more closely they possess a strong underlying logic. That logic is based on one or more primary values that are held constant in the designer’s mind, while other values take shape around them. Jane Darke (1979) describes these values as the primary generators of a design. She describes the redesign of Coventry Cathedral after its near-complete destruction during the Second World War. The redesign was intended to portray the resilience of life rising out of the ruins of war, so the two symbols chosen as the primary generator for the redesign were the undamaged altar of the cathedral and the theme of a Phoenix rising from the ashes. Together these served as the primary values that were held constant as the remainder of the redesign decisions revolved around them. These first decisions, taken as the primary generators of the design, determined not only the nature of the other structures included in the design, but as well the order in which subsequent design decisions were made. These two ideas of structure—one about the inner structure of the design, and the second about the inner discipline of design making—are the primary generators of this book, and everything in the book revolves around them.

10 • Fundamentals

The Inner Structure of Designs The discussion of the inner structure of designs in later chapters will center on the concept of design layers. The theory of design layers holds that the inner structure of a design can be described in terms of functional layers that the designer chooses. The layers serve multiple purposes, but most importantly they serve as the entry point into the design for the philosophical, experimental, personal, and folk-wisdom principles and theories of the designer. They are, in effect, where the rubber meets the road. Designs are made up of combinations of these layers, and no instructional design consists purely of any one of them. The chapters that follow discuss the concept of layers as it applies to instructional design. Brand (1994) supplies an archetypal example of the layering principle from architecture. In Brand’s view, the design of a building consists of layers of design that have been integrated and harmonized. Each layer of the design represents a function carried out by a group of related design elements. Brand identifies six layers: site, structure, skin, services, spaces, and stuff. The structure layer, which is likely to contain steel girders or a wooden frame, performs the function of conveying the force of gravity to the ground. It also protects the building from the destabilizing lateral forces of wind and earthquakes. The spaces layer defines the shape and size of inner spaces of the building. To the building user, this layer appears in the form of walls, barriers, and other space-defining arrangements. The services layer lies between the skin layer—the outer layer of the building—and the spaces layer. The services layer provides (among other things) electrical power, communications connections, air conditioning, heating, ventilation, and plumbing. These are invisible to the user except where they emerge through the surfaces of the space layer in the form of electrical outlets, switches, heating vents, and lighting fixtures. Layers are functional subdivisions of a design. Each layer has its own set of associated functions to perform. In technological fields related to design, systems of layers evolve over time naturally as technologies mature. A system of layers does not exist in a scientific sense of being a truth; layers represent utility to the designer—tools for thinking about the inner structure and functioning of the designed artifact. An example will show how layers evolve within a design field. Early in the nineteenth century the skin and spaces layers of a building were inseparable. The outside wall was also the inside wall. The services layer, if there was one, existed in the form of exposed pipes and conduit inside the interior spaces. The disappearance of the services layer to its place between the walls where we find it today was made possible by the introduction of “balloon frame” construction in which the outer wall of a building was separated from the inner wall by a space filled with “studs” and “cross-pieces” (O’Donnell, 1889; Peterson, 2000). The invention of balloon construction opened a space between the skin and the spaces layers into which electrical systems and plumbing could migrate to form what today is the mostly invisible services layer. The original purpose of balloon construction was to provide insulation from outside temperatures and protection against the rapid spread of fire (the function of the cross-pieces). However, it also supplied the unanticipated opportunity for a new layer—the services layer—of building design to develop and disappear over time behind the walls, as electrical, plumbing, and other technologies matured at a later date. Later chapters of this work describe the application of layer design theory to the design of instructional artifacts. At present, it is important to realize that the architectural layers of a building are only one example of many that could have been used to illustrate the concept of design layers. For example, Donald Schön, in a system that will be described in more detail later, defines at least twelve layers of a building’s design. Design layering is deliberately applied in the structuring of commercial product lines (Ericsson and Erixon, 1999), business organization (Wieringa et al., 2003), software architecture (Fowler 2003), and computer designs (Uyemura, 1999), where the application of layer

Introduction • 11

concepts has provided the economic basis of the entire personal computer industry (Baldwin and Clark, 2000), and arguably for many other industries as well (Baldwin and Clark, 2005). Layers are emerging in the design theory of many fields. The concept of design layers is described here in terms of seven recommended layers as a thought tool for instructional designers, but seven is an arbitrary number, and once the concept of layers becomes apparent, other layers emerge in the designer’s thinking. Application Exercise Analyze an existing instructional product (technology-based) or some live instruction. Look for some of the structural elements of its architecture. Try to “see” things you didn’t notice before: • • • •

Repeating patterns of strategy Expected roles and responsibilities of participants Expected locations or possible locations where instruction is supposed to/or can take place Subject-matter structures (concepts, main ideas, processes, relationships .  .  . what kinds of things can you “see”? • The infrastructure required (trace the whole system from source to users) • The goals of the instruction and how well they are matched with instructional techniques. Consider all of these things as manifestations of both surface and hidden structures. How many hidden structures can you see? Living Designs The architectural theme is frequently invoked in modern scientific and technological literature. It is found in sciences like business, psychology, biology, genomics, economics, linguistics, and materials science, and it is found in virtually all of the fields of engineering, and technology. The architectural metaphor has become prominent in all aspects of computer science, including the design of software, operating systems, computers, computer chips, business enterprise systems, programming languages, and networks. The architectural metaphor describes not only the structural plan of the designed thing, but also how the designed thing interacts with, contributes to, and adapts within a changing environment. The layperson might say that architects and engineers devise structures which mechanically bear loads and convey forces. However, designed things have a life cycle and are in some ways analogous to living things: A building . . . is protected by the skin of its façade, supported by the skeleton of its columns, beams, and slabs, and rests on the feet of its foundations. Like most human bodies, most buildings have full lives, and then they die. —(Levy et al., 1994, pp. 13–14) Architects design systems. The systems of a modern building include the layers of structure named by Brand and their interactions. Brand’s main idea is that buildings have life. His idea is summed up in the curious title of his book: How Buildings Learn. At first this would appear to be an oxymoron; buildings can’t learn. Then the second part of the title provides the key insight: What Happens After They’re Built. What does happen after a building is built? Immediately it begins to age. Brand’s insight

12 • Fundamentals

is that different parts or layers of the building’s design (and construction) age at different rates; one part of the building becomes obsolete or deteriorates before another. According to Brand, the layers of a building’s design must be able to slide past each other (Brand uses the term “shear”) without destroying each other and the whole edifice. Before balloon construction (pre-1889), if you changed the outer wall of a building, you were at the same time destroying the inner wall. This highlights an important aspect of the balloon construction technology: if the layers of the building’s design are kept independent of each other, then over time as one layer ages, it can be changed with only minor disruption to the other layers. The degree of disruption depends on how successful the architect was in maintaining the separation. On the other hand, the building has to make an overall impression, and so the layers of the building have to be integrated into a system that is in harmony with itself. Moreover, if the building is to live out its life cycle within its surrounding environment, it has to be in harmony with that environment, both taking value from the environment, and giving value back to it. Design by layers allows the building system to age and change gracefully. At least one well-known structure, the Centre Pompidou in Paris, shows that layers can be reversed in relation to each other without becoming incoherent as layers. Instructional designers devise structures—edifices—of time, of space, of goals, of information, and of action. Instructional “architects” synthesize their structures for the purpose of conveying emotional energy and intellectual content to learners in subtle ways which bring them together, as willing agents, in mutually beneficial learning exchanges. The systems they design must have certain organic, living qualities in order to survive, especially in this time of tumultuous change: they must possess adaptivity, generativity, and scalability. • Adaptivity to learner needs and interests, to new environmental circumstances, and to new uses. • Generativity that assumes that some part of the experience can be generated at the time of use on an as-needed basis rather than being pre-packaged. • Scalability that accommodates increases in volume of design, production, and use without loss of function and without proportional increases in cost. Architects assume there will be a living quality in the thing designed. Di Palma (2006) states: “architects, theorists, and critics have, throughout history, turned to nature and natural metaphors for inspiration or justification” (p. 385). Architecture can imitate nature because nature is composed of organic systems and architects design system[s] whose components work together to support life . . . It is this that makes the organic term applicable not to just natural bodies, but to social bodies too . . . Thus the term organic incorporates ideas of life in a very wide sense—stretching from the life of an individual to the life of a collectivity, or a society, as a whole. —(p. 386) Turner (2006) also associates the organic systems metaphor with the rise of cyberculture, which is one of the main social features of today’s connected world. Application Exercise Describe how the following actions would influence a system comprised of you as an instructor and a class of about fifteen learners. Describe the effects on the system that consists of you and the learners. Couch your description of the influence in terms of:

Introduction • 13

• Likely initial reactions of everyone to the situation • Likely secondary actions to the initial reaction • Actions that might be take to restore a positive, productive learning environment. Situation #1—The power goes off in the computer lab where you are conducting instruction. Situation #2—One of the learners says out loud in a disruptive manner, “I don’t see how this is going to teach me anything!” Situation #3—One of the learners, seeing that other learners don’t understand, asks, “Do you mind if I try to explain it?” Consider these situations from the point of view in which you and the learners influence each other’s emotions, desire to stay engaged, goals, roles, and responsibilities. What’s This Book About? A later chapter shows that this book deals with design theory—a specific type of theory about how designs can be seen in structural or architectural terms. The idea of theory may suggest to some an uncomfortable level of technical complexity. Maybe, some feel, we should just design what comes naturally and not trouble ourselves with so many theoretical details. The theory issue comes back into focus, however, when we realize that every design has—either on purpose or by default—theoretical foundations. The theory may come from a scientific or technological source or from a designer’s personal theories about people and how their learning can be supported. Many designers have the opinion that theory is something “out there”, something that can complicate things to the point of paralysis. Consider, however, the notion that everything we plan—every design—involves theory. This includes planning of vacations, new homes, and daily schedules. If this is so, then a designer needs to become aware of it and take control of the theory, not be intimidated by it. At that point theories of many kinds become the designer’s tools rather than the designer’s nightmare. Having given this pep talk about “theory is your friend”, it has to be said that there are some types of theory that this book specifically does not deal with because there are already numerous books on them: (1) learning theory, and (2) instructional theory. What else remains, you might ask. The answer is design theory. Learning theory and instructional theory pertain only to instructional design. In contrast, design theory—theory about how designs evolve—is shared across many design fields. Treatments of just design theory for instructional designers are almost non-existent, hence this book. A later chapter describes how instructional theory and design theory have become entangled in the instructional design literature, impeding progress in both. This is a book about design theory for instructional designers. It can be used in conjunction with books on learning theory and instructional theory to create a well-rounded, balanced instructional design curriculum. It invites instructional designers to see themselves as part of a larger design community that cuts across disciplinary lines: one from which there is much to learn. The subject matter of this work is the cognitive tools and techniques instructional designers can use as they create designs, regardless of their favorite learning or instructional theory. The work is organized to provide insights into the characteristics which instructional designs share with designs from other fields. It invites instructional designers to consider ideas that could be imported from other design fields to broaden and deepen their theory base. Why This Book Now? This book puts new ideas about design theory into perspective with ideas, traditions, and practices in instructional design accumulated over half a century. The older ideas must not go away. But over

14 • Fundamentals

time we have learned new things, and older ideas have to be harmonized with newer ones. This work is written for an audience of designers who consider design to be a career choice, including designers of technology-based instruction, live instructors, and those who blend technology-based with live instruction. It is not a step-by-step book, but its principles are practically applicable to designers who are in search of long-term advancement. The career instructional designer will increase in importance over the coming decades because: • In the future design skills will become as important as, if not more important than, media production skills. Designers who can speak both to the concerns of their organization and to those of their design team will increase in value. • Product concepts and standards are changing. In the past, value was placed on producing packaged products. However, instructional designers are more and more challenged to design things that don’t look like traditional instruction: product families, interfaces, networks, community projects, and discourses (see Krippendorff, 2005, Chapter 1). These new categories of artifact require designers with greater insight and imagination. This trend toward greater diversity of artifact types will continue. • Instructional designers represent a value-adding profession—a fact that most corporations, military, government organizations, and public and higher education organizations are institutionalizing with the appointment of Chief Learning Officers (CLOs) and the establishment of corporate learning organizations. The contribution of the designer to such organizations will increase over time. Design skills are not learned from books like this one, or from other texts on instructional design and learning theory. Design skills are learned from designing. What is important about books on instructional design is the quality of the ideas they stimulate in the designer’s thinking which later become personal design research questions. The best hope of a work like this one is that it can plant better and more powerful ideas in the imaginations of designers. An Overview The book is divided into three sections. Section I, “Fundamentals”, introduces a theory of design layering, which provides a foundation for details for each layer introduced in the chapters in Section II, “Design in Layers”. This arrangement introduces the concept of layers early so that it can become the basis for a number of smaller design problems. This comes from a conviction that several small design problems, like basketball drills, can provide more focused practice on the details of design, stimulating more feedback than would a single large problem. Work on the small problems does not close off the possibility of a large problem carried out in parallel. Section I also contains an account of historical instructional design concepts that are important for understanding the context of design layer theory—as well as other design theories that are now emerging. This history leads naturally to a discussion of the forces at work that shape our present vision of the designer’s environment. Section I engages in a discussion of design theory to define its nature and application clearly enough to allay the uneasiness many designers feel in connection with theory. Section II examines design principles within each of the layers. In particular, it examines the design questions that pertain to that layer during design. These chapters explain how each layer is fragmented into sub-layers, each concerned with specific design questions. Section III deals with two themes: (1) the value that a trained professional designer adds to an organization, and (2) what additional value might be added in the future as a result of applying

Introduction • 15

an architectural view, specifically one that involves modularization of functions. This section describes in more detail a changing landscape of design practice: one that, if ignored, creates a risk of mid-career crisis for designers. Professional designers will subscribe to the goal of constant improvement—pushing practice forward into uncharted territory through small experiments that lead to new insights (Sims, 2011). The chapters in Section III encourage this kind of thinking.

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2

Design Layers

In most people’s vocabularies, design means veneer. It’s interior decorating. It’s the fabric of the curtains of the sofa. But to me, nothing could be further from the meaning of design. Design is the fundamental soul of a human-made creation that ends up expressing itself in successive outer layers of the product or service. —(Steve Jobs, 2000) This chapter defines the concept of instructional design layers. The use of design in layers is common practice in many design fields, including architecture, business, all areas of software design, and computer design. Instructional designers can use layers as a conceptual tool but only if they can come to “see” them; they are for the most part invisible, much like the inner skeleton of the Statue of Liberty (see Figure 2.1a and b). This 150-ton iron skeleton holds the statue upright from the inside. Without it, the copper exterior of the statue, which weighs 100 tons, would fold down into a shapeless pile. The skeleton provides the strength and the stable form that holds 350 individual copper surface plates in place. Figure 2.1a gives only a partial impression of the intricate structure and function of the skeleton. A National Park Service Web site gives this description: Four huge iron posts run from the base of the statue to the top, forming a pylon which bears the weight of the whole structure. Out of this central tower is built a maze of smaller beams, each supporting a series of outer copper sheets. Each sheet is backed by an iron strap to give it rigidity. These iron straps are fastened to the supporting framework in such a way that each section is supported independently—no plate of copper hangs from the one above it or bears upon the one below. —(National Park Service, 2000, n.p.). The outer surface of the statue represents a beautiful artistic expression; the inner skeleton represents an even more impressive feat of architectural engineering, making the outer beauty possible. The manner in which the copper skin plates are held in place independently emphasizes the intricacy of the structural design hidden inside the statue. The statue, its outer skin, and its inner structure provide a kind of metaphor for the concept of layered design—a metaphor for all design fields in general and instructional design in particular. As this chapter will demonstrate, layering in designs is a natural and inevitable response to complexity in designs in any technological field. Layers are now everywhere evident in instructional designs, design team organization, and development tools, but most designers do not “see” them and 17

18 • Fundamentals

Figure 2.1a (Left) The framework of steel that holds the Statue of Liberty upright (from Seyrig, 1887, public domain, via Wikimedia Commons). Figure 2.1b (Right) The outer surface of the statue gives no indication of the inner structures that support it. (Pearson Scott Foresman, public domain, via Wikimedia Commons.)

recognize how to use them proactively. This chapter describes how layers have been evolving quietly as a cognitive tool for instructional designers for many years. The goal of this chapter is to make the invisible layers visible so that designers can use them as a tool as they design. The Advent of the Instructional Computer The previous chapter examined the chaos caused by the rapid change from silent films to talkies as a case study of the rippling effects of technological change on a professional field. This chapter will begin by relating a similar saga that took place as the instructional media world was rocked by the low-cost and user-friendly computer. This case study of the instructional computer and the changes it brought with it provides a case study that illustrates also the phenomenon of design layers. Arrival of the Computer In the early 1960s the general-purpose computer emerged from the laboratory into commercial use. The first challenge for computer makers was to demonstrate that their product could do something interesting and useful. Early estimates predicted that there would be a need for only a handful of computers. The first computers were “mainframes”: large cabinets with spinning tape drives and panels of blinking lights. Computers were isolated behind glass and attended by trained operators. During this period a few well-funded researchers were experimenting with the educational uses

Design Layers • 19

of the computer: multi-disciplinary teams of psychologists, mathematicians, and systems analysts. These researchers created a vision for conversational, highly adaptive, and generative computerized instruction that is still not fully realized (Atkinson and Wilson, 1969b). The career field of instructional designer did not exist at the time because formalized instructional design itself was just emerging as a practice. The ideas of this first group of pioneers in computer-assisted instruction (CAI) found their way to academic, commercial, and government, organizations. An especially strong foothold was gained in computer-based simulations, including models of aircraft, electronic equipment, tactical exercises, and shipboard environments. Aircraft simulation design became a large industry as airlines and the military capitalized on the cost savings and greater training capability they provided (Caro, 1989). In most other places, however, computers for instructional purposes were hard to sell. In the mid-1960s rapid downsizing of computer size, speed, capacity, and cost and slowly improving user-friendliness of software brought the computer closer to people in everyday settings. The minicomputer did not require a protected environment, and it permitted operation by programmers, scientists, technicians, professors, and students. The minicomputer fit into a small closet, then into the pedestal of a desk, and eventually onto a desktop. Research funds increased for research on CAI. “Skunk works” research groups and individual experimenters began to develop skills in programming so they could test the possibilities and limits of the new instructional medium, which still had not reached the public eye. Impact on Instructional Design and Designers The speed of the computer’s adoption as an instructional tool was not as fast as sound in the movies, so the process of change was not as wrenching as it was for the movie industry. However, as smaller and less expensive computers began to arrive on desks in the workplace, they set off a similar pattern of changes: • CAI experimenters began preparing for new technical and professional jobs in a new field of instructional computing. • New kinds of artifact were being created that would define what the CAI “product” would be like. • New production equipment and processes were introduced in rapid succession. • Design and development processes, which had evolved independently of CAI, were modified for application in CAI development. • Public design representations became essential. Flow diagrams came into use extensively for planning the logical sequence of instructional events. The computer assimilated old media forms, and storyboards—a more complex, less sequenced design representation—became necessary. • New design and production specialties appeared as the computer assimilated the other media forms. • Formal training for CAI designers and producers began to appear, first in the form of special-interest groups and workshops at existing professional organizations, and then in the form of articles, new journals, and books. • Existing professional organizations became interested in the new practitioners of CAI, and the practitioners found themselves welcome. Over time, the subjects of interest to these organizations, especially those that were already interested in audio-visual instruction (AVI), changed to focus more on the computer. • New delivery venues for CAI began to appear. These were normally converted media labs filled with individual learning carrels.

20 • Fundamentals

• New design languages began to form around every specialty of the new CAI industry. Specialists of several kinds began to talk in terms no one else could understand. The importance of design teams became apparent, first for large projects, and then, as product sophistication continued to escalate, for most projects. • New intellectual concepts began to evolve as the structural concepts of traditional instructional designs were transferred to CAI designs. • Theories for the design of computer-assisted instruction began to appear. • New research questions emerged related to the instructional use of the computer. All of the phenomena that had occurred with the arrival of sound-on-film were repeated with the arrival of the instructional computer. The complexities of the new product design spawned new specialties, processes, tools, languages, and research questions. We shall see this pattern once more in the next section when we examine the history of a specific innovative CAI design project. Application Exercise Find articles that describe the early days of CAI, especially ones with pictures. Compare what existed then with how the computer is used for instructional purposes today. • What has changed? • What has remained the same? New Configurations and New Concepts: NSF Sponsors TICCIT and PLATO It was inevitable that at some point the mainframe and the minicomputer would face off in a comparison study. The centralized mainframe concept named PLATO (Smith and Sherwood, 1976) was paired against a smaller minicomputer system named TICCIT (Bunderson, 1975; Merrill et al., 1980). The questions explored by this research study included computer configurations and competing instructional design architectures and styles. In particular, the TICCIT system weighed in heavily on the issue of learner control—the ability for the learner to decide what happened next during instruction. The concept of giving control to learners was itself not new, but new architectural ideas that would allow learner control to be implemented on a large scale had to be devised, and that was one of the major challenges of the project. The goals became adaptivity (to learner choices) and scalability (the ability to produce product in volume without proportional increase in cost). Neither system “won” the competition, and both contributed important new concepts to the growing community of CAI designers—a group that was beginning to grow faster. A cycle began and accelerated in which designer and user expectations started to escalate rapidly, fed by new technical developments, leading to increased product complexity, followed by new technical processes, new equipment, new tools, new specialties, new job descriptions, new training, new publications, new professional organizations, and new research questions. The same forces that were shaping the world of every developing technology were at work in CAI, creating new complexity to be dealt with using more productive concepts of design architecture. TICCIT Architecture The design architecture of the experimental TICCIT system allowed it to reach the goals of adaptivity and scalability to a degree not previously attained. The key to this success was the architectural plan of the instruction: the adoption of Merrill’s component display theory (Merrill, 1983, 1994). Component display theory formalized a construct called display with specific properties that created

Design Layers • 21

a core set of primary display types. Display types combined small units of: (1) message, (2) content, and (3) representation. • Message units were of two types: expository (which presented information), and inquisitory (which asked for a response of some kind). • Content units were of two types: generality (a main idea), and instance (an example). • Representation units consisted of pre-composed graphic, video, and text material. By crossing the two message and the two content levels four basic types of display were defined: • Expository generality—this expressed a main idea. • Inquisitory generality—theoretically, this asked for the main idea to be expressed in some way by the learner, but it was not implemented. • Expository instance—this expressed an example of the main idea. • Inquisitory instance—this asked for the learner’s response (“example or non-example”, or “apply this rule”). Surrounding these basic display types, other display types were defined that provided additional context and elaboration, but the four basic display types constituted the core commitment around which everything else revolved. Application Exercise Create a set of displays to instruct the concept “house cat”. Here is the instructional objective: • Discriminate between a house cat and other kinds of cats. Use only the four display types defined above. Expect to run into a few problems. Solve them as best you can. Use your imagination. Focus on what you can learn from solving a design problem that comes with heavy constraints. Additional Functional Areas of the Design The display, in its basic and supplemented varieties, became the main structure of the TICCIT instructional architecture. As an architectural structure itself, it demanded that other kinds of architectural structures be devised to deliver it during instruction. For example, the presentation of an instance might require more than one screen’s worth of text, graphics and video. There had to be a way to administer the representation for this kind of display—something to move items off the screen to make way for new items in a paging fashion. This led to the creation of a new unit of computer logic called a base frame. A base frame was the functional link between the representation and the computer logic. It was in charge of executing individual displays, no matter how much information they contained. With just this much of the TICCIT architecture explained, it is possible to see something that was happening: the separation of the design into specialized functional units—one part content-message display units, and the other part the computer logic to execute displays on the computer screen. These functions had to be aligned, but they also had to be separated so that specialists (designers, artists, subject-matter experts, and programmers) could know how to make their contribution to the product.

22 • Fundamentals

Aenon

Exit

Repeat

Go

Skip

Back

Objecve

Map

Advice

Help

Hard

Easy

Rule

Example

Pracce

Figure 2.2 The special instructional function keypad of the TICCIT CAI system.

Other functional units had to be created as well. For example, the increased number of options allocated to learner choice created the need for a more sophisticated system of learner controls. A specialized keyboard was designed to supply this function. A set of special keys to the right-hand side of the keyboard (see Figure 2.2) aligned with the core display types and other instructional functions: • Rule = Expository generality • Example = Expository instance • Practice = Inquisitory generality and inquisitory instance. Other display types needed to augment the basic display types: • Hard = Harder version of either the generality or the instance • Easy = Easier version of either the generality or the instance • Objective = The instructional objective super-tending the display set. Keys for administrative functions such as session control: • Attention = Get the attention of the system or a monitor • Exit = Leave the current session • Repeat = Repeat the objective just completed. And navigation through topic hierarchies: • • • •

Map = List the objectives in this lesson hierarchy Go = Go into a particular objective for instruction Skip = Skip an item Back = Go back an item.

Keys were also included that gave the learner the ability to request strategic assistance: • Advice • Help.

Design Layers • 23

Application Exercise Revisit the displays you created for your “cat” concept exercise. • Are any of the problems you encountered solved by any of the special-function TICCIT keys? • If you had the choice to create additional TICCIT keys, what would they be? • If you could start fresh with no special-function keys defined, what would they be? • Would you need different keys for different kinds of instruction? • How many keys do you think learners can keep track of during instruction? • Do you think special keys are a good approach to providing learner control? • What are the alternatives? Still More Functional Areas of the Design: Unrecognized Layers The Advice key activated an Advisor in the TICCIT system that depended on two additional functional layers: a data management function to collect data on learner choice patterns, and a strategy function that could generate recommendations by comparing learner patterns with known-effective patterns. The structure of the subject-matter itself, as defined by the subject-matter expert, also had to be described, and subject-matter had to be captured. This constituted another major functional area of the design: this one related to the learnable content. These semi-independent functional areas of the design (representation, message, strategy, control, media-logic) emerged without being noticed during the TICCIT project. They shared several characteristics in common. They were determined by the need for decisions on the part of specialists who had to integrate their choices with decisions made by other specialists working on different aspects of the design. Programmers, artists, designers, writers, video producers, and content specialists had to come together with a common design language composed of compatible terms from each specialty. This resulted in the division of the overall design task into sub-problems that each specialist could work on semi-independently with the assurance that the independent work performed by each specialist would later integrate smoothly. Programmers could execute their part of the master plan, while subject-matter experts could write messages with the assurance that what they wrote would fit the operational requirements of the programs. This amounted, in retrospect, to the separation of the TICCIT design into functional “layers”. This was unintentional, but it was inevitable for such a large and ambitious project. The layering was so subtle that it was not recognized then as such by those taking part in it. It was the unexpressed by-product of a complex problem-solving process carried out by a team of specialists.

Application Exercise Imagine a design project in which you are the key player, perhaps a project director. Imagine (and perhaps it is actually true) that your limited skills in some area require that you bring in someone with more skill in some area than you possess. What skills would you look for? What areas of design competence would you like to have working with you, augmenting your own skills in that area? How would you discuss with each other the set of decisions that each of you was responsible for? How would you manage the day-to-day process of working with this specialist as a project director? How much initiative would you want the other person to take? How much final decision-making would you retain as part of your responsibility?

24 • Fundamentals

Design Layers This lengthy lead-up to layers demonstrates that layering is not just an invention. It is a natural phenomenon that occurs as a result of increasing complexity. The point is that if layering is a natural occurrence, then designers can learn about it, learn how to use it, and apply it to advantage while designing. The remaining chapters in this work will show that attention to layers produces a variety of advantages. A Definition of “Layer” A layer is an independent function within a complete design. A concrete example from the field of architecture will help explain this. Stewart Brand describes the layers in a building’s design (Brand, 1994, see Figure 2.3). The structure layer in Brand’s description consists of the parts that hold a building up: beams, columns, framing: whatever structures the designer chooses. Brand’s skin layer provides an outer protective covering that keeps out wind and rain. These two layers are independent because their designs consist of different structures arranged differently according to different principles, but they are also interdependent because they must work together, distributing forces with and supporting each other. The skin layer must be attached to the structure layer in some way. Designed properly, the skin can be removed, repaired, or replaced without destroying the structure layer. The opposite, however, is not true, so there is a shifting hierarchy of priorities among layers, which will be described later. What is a layer? Does it consist of the specific structures used to hold things up? Is it the materials used or the way things are joined together? It is none of these. A layer is a concept: the idea of an independent functional element of a design. A designer specifies components that will be used to carry out the function and how they will be connected. The layer itself is an empty container: it’s an abstract idea—“support the building”, or “provide a protective outer surface”. The designer fills this placeholder with actual components and structure that can perform the function. The layer is, in effect, a design variable waiting to be given a value by the designer. It is a functional part of a system that needs to be defined by the designer. What is considered a layer varies according to time, place, and audience expectations. In the modern world, a building is expected to carry out certain functions for users: shelter, safety, comfort, conveniences, etc. However, the functions expected of a building differ across time and place. In some societies, running water (part of the modern plumbing layer) may be expected in buildings at one point in time even though it wasn’t expected before that. The particular functions expected of any designed artifact are relative to time, place, and local expectation. This is an important idea to keep in mind, because it means that layers are not absolute; they are a concept that exists in the mind of the designer at a particular point in time.

Stuff Space Plan Services Skin Structure Site Figure 2.3 The layers of a building’s design (adapted from Brand, 1994).

Design Layers • 25

Brand gives names to other building layers. The space plan layer defines the interior surfaces— the rooms and open areas of the building and the walls that define them. The stuff layer defines the furniture, fixtures, and movable things placed inside the spaces. (Architects frequently design or carefully select the interior furnishings as part of a building’s design in order to create an overall impression.) The site layer as Brand describes it relates to how the building will be situated on the land. Sideways? Set into the hill? Set back to accentuate openness? Finally, the services layer defined by Brand provides the functions of heating, cooling, electrical supply, communication hook-ups, plumbing, and other services. This layer consists of several sub-layers, and each one of them is also a layer in its own right. Brand identifies six layers, but that’s just his particular view of building design. Donald Schön (1987) describes a different set of building design layers. His list contains twelve, and he hints that in his view there are more. How does a designer know how many layers there are in a design? Layers as Parts of a Larger System Being Designed The answer is that the designer gets to decide. Designers design systems, but the designer decides which sub-systems will receive attention during design and how important they will be with respect to each other. Everything depends on what the designer chooses to see as being important. It also depends on the conventions and expectations of the time and place. Buildings, as we know them today, are expected to have certain parts, so designers pay attention to those parts. In most societies today it is expected that there will be a structure layer, a separate and changeable skin layer on the outside, and a separate and changeable spaces layer on the inside. This, however, has only been true for a little over a hundred years. If you look at buildings constructed before 1880, you see that there is no separation between the skin layer of the building, the structure (load-supporting) layer, and the space plan (inner surface) layer. A case in point is the old house (now demolished) that used to stand on the Preston road in Idaho. Figure 2.4 shows this small but still attractive building in a state of decay. What was interesting about the house when it existed was how the outer wall was also the inner wall, because of the standards for house design and construction of the time. Figure 2.5 shows a close-up of this. Inside of the house, spaces were divided by wood framing, as can be seen in Figure 2.4 (if you look closely) through the gaping hole. Somehow the designer of the house, working according to the standard expectation of the time, didn’t see the need to put wood framing on the inner surface of the brick wall. Doing so would have made a space for electrical wiring and plumbing. But wait! There was no convention for electrical wiring and indoor plumbing when the house was built. So there was no need for inner-wall framing. There was no expectation in either the designer’s mind or the user’s that the layers would be separated. The point here is subtle yet important, so it needs to be stated as clearly as possible. The house was designed for the time and the place that it occupied. The designer’s responsibility was to meet or exceed the layer standards of the time. Designs at the time the Preston house was built did not include separate skin, structure, and inner-space layers. In those days a designer who designed inner-wall framing might even be considered extravagant and wasteful. However, today a designer who did not design with the three layers in mind might be thought incompetent. When conflicts between the layers of a design occur you get odd results: doors which hang in the air three feet above the hallway because a radiator occupies the space beneath, stairs that lead to nowhere, pipes that must be contorted around each other to avoid a collision, and skylights that leak. When layers are mismatched, buildings are hard to change over time, are hard to maintain, and are uncomfortable to be in. They may be dark inside, cold, wet, noisy, or musty. They cost more than they should, and are sometimes simply ugly. People get lost in them and can’t wait to get out.

26 • Fundamentals

Figure 2.4 Old house on Preston Road in Idaho.

Figure 2.5 Cross-section view of the Preston house outer (and inner) wall.

Design Layers • 27

Designed things are functional systems, and the systems are made up of sub-systems. The designer’s job is to be aware of and to design a complete system and all of its sub-systems in such a way that the minimum standards for layers are met and there are no conflicts between them. What the designer needs to “see” in a design depends on the layers that are currently in style as well as innovative ones that can be envisioned. Application Exercise Consider the contributions of the layers of building design to the building’s functioning as a system. What would happen in the following cases? What would be the first result of the malfunction? If the problem is left untreated, what would be the next result? How far could the consequences of this sub-system failure go? • The air conditioning system breaks down, and the temperature outside is 90˚ with 80 percent humidity. • The fire sprinkler system malfunctions when a wastebasket catches fire. • A water pipe breaks in the basement. • The building’s foundation begins to crack, and one corner of the building is slowly sinking into the ground. • A window is broken and not repaired. • The roof begins to leak. The uncomfortable or dangerous conditions you identified for the cases above are the result of some part of the building system malfunctioning. The building truly is a system, and when one part of the system breaks down, the ability of the entire system to carry out its main function is hampered or even prevented. Layer Origins Three examples given so far in this work—talking movies, computer-assisted instruction, and the TICCIT system—show the creation of new layers in response to increasing complexity. In the movie example, the new layer was the sound layer. It represented a new function added to the movie product. “Movie” itself was redefined. New functions were added to movie production and movie delivery. These new functions implied all the rest of the changes: redefined job categories, processes, tools, equipment, concepts, languages, and professional organizations. In the computer-assisted instruction example, a new function of the computer had the same effect. In that case, a whole new product concept was created, not just a redefinition of an old product. The impact, however, was the same as in the movie example. The division of labor on the TICCIT project was also a prime example of the inevitability of the layering of decision-making responsibility in the design of a complex product. What made the TICCIT project complex was the huge volume of instruction produced in a short time, the new design centered on display types, and the requirement that it be learner controlled. In all of the cases cited here, as the technical complexity of the product increased, the involvement of specialty knowledge in designs increased, which required that teams of specialists come together, each contributing to one or more expected layers of the design. Layers, then, are a natural response to increasing complexity in the designs in any technological field. New inventions such as computer-assisted instruction remain simple only for a while before user expectations begin to rise. As this happens, what used to be within the design competence of one person starts to require the help of another who possesses competence in one or more restricted areas of the design. When this happens, the phenomenon of layering begins to take place naturally, even if it is not noticed. Two designers

28 • Fundamentals

begin to share responsibility for the design, but each of them takes primary responsibility—becomes the most reliable authority, the most skilled—for one or more areas of the design. All of the parts of the design must function together, and so these designers will design their individual layers in harmony with a larger architectural concept that both designers acknowledge as their target. As product complexity continues to grow, additional competencies are required. New designers are added and take primary responsibility for their unique contributions to the overall design. The net effect of growth of this kind is to segment the larger design problem into sub-problems that can be solved more or less independently by specialists, but whose solution must become seamlessly integrated with the overall design. Those tasked with a part of the design speak design languages that may not be spoken by the other designers. They create structures within the design that correspond with their specialized language terms. And so an artist creates a “technical diagram”, and a programmer creates an “app” or a “service” that puts the technical diagram on the screen. These are their separate but essential contributions to the more complex whole. What other factors contribute to increasing complexity of designs? • New hardware and new software. Not just new versions of old things, but new concepts in these areas: larger, more powerful chips, more powerful software. • New modes of display and learner response. At one time the blackboard was considered a radical (and in some opinions unpopular) innovation. Today touch panels are back in style after a hiatus of several years of neglect. • New learning and instructional theories. Increased emphasis on social learning today creates a requirement to support new goal structures and goal negotiations. • New tastes and styles. Rapidly changing social media today create new opportunities . . . and new challenges. • New economies of production, change, and maintenance. Designs that allow layers to slide past each other gracefully and non-destructively. • New infrastructure. The Internet and the World Wide Web have made the designer’s task more complex. • New learner preferences. Preferences today for less presenting and the desire for more hands-on experience create new challenges. • New product concepts. The packaged lesson is becoming passé. The problem-solving experience, the simulation, blended learning, and the informal learning object are receiving attention. What is next? Choosing and Creating Layers There is no “given” or “true” set of layers, and “layer” is not a scientific term describing the “right” way to divide up decision-making. A “layer” is defined as a design construct that human designers can use intentionally to confer advantage on their designs: economic, practical, or theoretical. The goal may be to reach a design more quickly, or it may be to deliver products that have a longer service life. Design layer selections influence these things. The designer determines layers and layer priority. Design layering is achieved by noticing the functions of a design that can be designed more or less independently, with an eye to their seamless integration within the whole design. A designer chooses layers, and there is no “correct” set of layers. A designer who “sees” a better set of layers has an advantage because layers are chosen on the basis of their utility. This means that the ability of a designer to think in terms of layers is more important than a commitment to a particular set of layers. In Design Rules: The Power of Modularity, Baldwin and Clark (2000) report an extensive case study of the impact of layers (which they refer to as modules) on product economy. They first distinguish the physical form of the product from the conceptual functions of the product:

Design Layers • 29

The computer is a physical artifact made of plastic, glass and metal. It is also an intangible artifact, whose essence lies in meanings assigned to flickering patterns of electrical current deep within the structure of the machine . . . Where once a roomful of vacuum tubes, programmed via a switchboard, were used to calculate angles for aiming heavy artillery, today a handful of chips, using a myriad of stored programs, can calculate numbers, format text, generate pictures, compare outcomes, and make decisions. —(p. 3) The physical form of the computer has changed much more over the years than have the functions of computing. This is evidence for the need to separate the conceptual layers of a system from the specific structures that come and go over time that are used to populate the layers to create specific designs. Originally mechanical switches and relays were used to perform central processing unit (CPU) functions for computers; then the vacuum tube was invented and replaced the relays; then the transistor was invented and replaced the vacuum tube. Now thousands of transistors on VLSI (very large scale integration) chips are used to carry out the exact same computer functions. What physical means will be used tomorrow to provide the mechanism to populate the layers of CPU designs? We can only imagine, but until there is a major shift in the underlying architecture of the computer, the function will remain the same, while new devices—atoms? Quantum spin?—arise to give the function its means. Product architecture is not a mainstream topic in the instructional technology literature. That literature has tended to pay more attention to design processes. Literature describing early attempts to create intelligent tutoring systems, however, focuses almost exclusively on product architecture (Wenger, 1987), an emphasis that continues up to the present (Woolf, 2008; Nkambou et al., 2010). As its title suggests, the present work is heavily oriented toward describing the architectural features of the thing being designed as an entry point to creating designs. It taps into a rich interdisciplinary literature in which discussions of architectural structure are a major concern. This literature uses different terms to describe that same structural unit of a design: layer–sub-layer (Brand, 1994), system–sub-system (Banathy, 1968; Silvern, 1972; Romiszowski, 1981), module–sub-module (Baldwin and Clark, 2000), and domain–sub-domain (Schön, 1987). The term “layer” has been chosen as the main term of choice here, though in the discussions that follow these terms are used interchangeably. Baldwin and Clark describe three different motives for creating modules (layers): modularity for design purposes, modularity for manufacturing purposes, and modularity for use purposes. An instructional designer’s layering plans can include all three motives. • Layers may be created which represent functions carried out by an artifact (layering for design). The main layers described in the present work are of this kind. A small number of functions are identified that are inherent in virtually every kind of instructional product. A designer can use this set of layers to link design decisions to a theoretical or principled rationale. • Layers may be created that lead to economies in development (layering for manufacture). Some of the layers in this work are of this kind. Layers may be created, for example, that match specialty skills like programming or a particular type of art. • Layers may be created that lead to a particular product form for the user (layering for use). None of the main layers in this work deal directly with the form of the product seen by the user (such as a simulation or a problem or case). However, a designer who knew from the beginning of a project that there was such an expectation might create a corresponding layer. Ericsson and Erixon (1999) provide a similar list of modularization factors—which they name as module drivers. Among their module drivers they include advances in technology, organization of

30 • Fundamentals

production, maintainability. They extend Baldwin and Clark’s list by adding several other practical factors, including the need to test sub-units before final assembly and the need to recycle or reuse some part of the original product. These factors, though they were identified for industrial product layering, are directly applicable to instructional design product layering as well. A later chapter will deal in more detail with the concept of modularity. The little information given so far here shows that a designer can choose which factors are of most importance and then make layer commitments on the basis of those factors. Private and Public Layers A designer may use a set of layer definitions that is public—shared with other designers. However, the same designer will undoubtedly have privately held layers which go beyond the public ones. Public layers provide terms for a design team’s communication—or that of a profession. Public layers are defined by dialogue at conferences and in publications. They give designers a common vocabulary for describing designs and the design process. Private layers are the designer’s own and may be explicit in the designer’s mind or tacit and hard to express. That means that private layers may include things the designer can talk about and explain, but they also may include the designer’s “hunches”. Private languages are the source of a designer’s signature style of designing. Over time, a designer may be able to bring private layers to the explicit level of use. Private layers allow a designer to think about design in more detail, have new design experiences, frame new design experiments, and create new personal design insights. They are a source of constant professional development for a career designer. Application Exercise Three reasons for using layers while designing are given in the sections above: (1) layering to separate common functions of the artifact, (2) layering to separate units for manufacturing purposes, and (3) layering to separate functions for the user. In Table 2.1 below, three hypothetical design situations are presented in the right column. Match the situation with the appropriate purpose of the layering in the left column.

Table 2.1

Matching Layer Purposes with Situations

Layering in order to separate units for manufacturing purposes

A game designer realizes that new graphics capabilities will become available in the future. This leads to a decision to write the graphics programs in such a way that they can easily be changed over to the new graphics system without disrupting the rest of the program code.

Layering in order to separate functions for the user

A computer designer realizes that there are a few parts of the machine that are hard to build. The manufacturing process for these parts can easily break one of the components, causing a faulty unit. The designer decides to isolate the unit in the design so that it can be tested after it is built and before it is assembled into the computer.

Layering in order to separate common functions

An auto manufacturer designs the entertainment center in one of the car models so that the AM/FM radio can be ejected and used outside of the car at a picnic or a party. This radio has a plug-in that allows it to be connected with a separate speaker system while it is out of the car.

Design Layers • 31

Sub-layers The movie industry example that gave us insight into the way layers are created should cause us to expect that any given layer may be split at some point into sub-layers by the advance of knowledge and technology. This is exactly what happens. Another familiar example from the movie industry provides an example of this. When sound was added to movies, it included more than recorded voices. During fights in Westerns there had to be sound effects for blows, breaking tables and chairs, and gunshots. The addition of sound effects spawned a specialty much more demanding than most people realize. Frank Warner, who created sound effects for well-known movies like Spartacus, Close Encounters of the Third Kind, and the Rocky series, describes the making and selection of just the punching sounds for Rocky: Looking through my sound library books, I went through airplanes, jet sounds, I used arrows slowed down—wwwwhhhoooossssh—you’ve got a lot of that. These sounds all kind of blend . . . It was a combination of thirty to fifty sounds—in that general area. The picture is telling me what it wants all the time . . . I would just sit in there, and these sounds were working. I created the material, and it’s like putting in phrases in a music score. It’s marvelous, all of a sudden things happen all over the picture, the screen comes to life—it’s exciting. —(LoBrutto, 1994, p. 36) The sound layer itself specialized into numerous sound specialties. According to LoBrutto: The fact is, just as every visual component in a film is designed and executed by the writer, director, cinematographer, and design team, each single sound in a film is carefully conceived, chosen, recorded, edited, and mixed by an array of sound artists and technicians. —(p. xi) LoBrutto identifies the variety of specialists whose work may touch a film in the making: production sound mixers, sound editors, Foley artists, automatic dialogue replacement (ADR) editors, ADR mixers, music editors, music mixers, rerecording mixers, and sound engineers. Each of these jobs represents a specific set of skills in the use of equipment and the execution of processes. Movie-making is a mature technology, and sound is a mature technology within movie-making, therefore, a number of sub-specialties have developed. These can be considered layers of a movie’s design—in this case, layers chosen for purposes of manufacture. Many design fields have not matured to this level of specialization, and different areas of every design field mature at different rates. It would be appropriate to ask, then, whether all of the areas of instructional design are equally mature (clearly, not) and if there are any areas of instructional design that are as specialized as the area of sound in movies (clearly, yes). Common practice indicates that today there are, in fact, areas of instructional design which are highly specialized. The most common ones would be art design, video design, sound design, and computer program design. Art design provides an interesting example. A design team leader may need many different types of illustration: cartooning, technical drawing, or realistic drawing. These are seldom skills that a single person possesses, so each can be considered a specialty and a layer. Before the days of the computer and desktop publishing, another type of artist was called a “paste-up” artist. These artists were responsible for cutting out and pasting up the individual typed elements of a page containing text and art. Today this process falls to the desktop publishing specialist, who can be considered another category of art specialist. The layer still exists functionally (the layout of pages), but its skills, processes, equipment, and intellectual content have all evolved.

32 • Fundamentals

Sub-layers multiply constantly for all of the reasons named in the section above on the creation of layers. Technology advances, theories emerge, new techniques are invented, user expectations escalate, and so specialty areas are created whether we want them or not. The question for the designer, then, is not whether there is a constant ebb and flow of sub-layer specialties, but whether all of them are required for every project. Clearly this is not the case. What that means, then, is that for any given project, a designer must constantly be aware of: (1) what layers and sub-layers must be included within a given design, and (2) how each design decision potentially introduces or subtracts a new sub-layer in the project’s world. Another implication for the designer is that even though the designer will not be asked to perform each of the specialties, it will become the designer’s responsibility to manage, communicate with, and co-design with specialists. A designer must therefore be conversant with the processes, tools, languages, and skills of each specialist group or else risk making a decision whose implications are greater than the available budget of time and funds and the capabilities of the current technologies. Sub-layers in designs are discussed in more detail in several upcoming chapters. It is important to note at this point that if layers constitute the entry point of theory into designs, then sub-layers also come with either fully developed theories and standards or nascent theories and standards, so the influence of theory and standard practice still enters designs through layers, but they may be highly specialized sub-layers. The downside of this is that the practice of professional design requires more knowledge. The upside is that the quality, precision, and competitiveness of products improve, and the design of instruction becomes more like the design of a rocket—targeting can be more precise, and learners can be brought to more distant targets reliably. The Value of Layers Layering provides divisions of design responsibility, specialized architectural constructs, and a guiding set of designer questions. It focuses the attention of the designer on the nature of the thing being designed rather than on the formula of the design process itself. Layers offer value to instructional designers in many ways: • Each layer poses design questions and offers options a designer might otherwise overlook. • Layered designs are more maintainable. Maintenance and upgrade can be accomplished without destroying the entire product. • Layered design makes possible the incorporation of standard local architectures. These architectures can be reused by design teams and improved as experience with them accumulates. Over time, this could have the dual benefit of improving both team efficiency and design quality. • The economics of layered designs is favorable. For example, the economy of today’s personal computer industry is built around modularized functional units (plug-in boards, drives, cards, and chips). • Modularization made possible by design layering permits mass product customization. This manufacturing benefit is what enables many progressive organizations to build a product to individual user specifications after it is ordered. Mass customization is the key to adaptive instruction. • Layered design allows parts of an instructional artifact to be designed and manufactured by different organizations that possess the tools, techniques, and know-how. This can drive down costs and increase product quality. It allows outsourcing of manufacture to specialist producers. • Layered design holds the key to generative designs in which design decisions are withheld until the moment of use. Generativity is in turn one of the keys to creating experiences adapted in some way to the individual learner. • Layered design extends the service life of a product by making it easy to maintain and revise the product. • Layering makes possible product life-cycle planning and strategic product evolution.

Design Layers • 33

These potential benefits are described in later chapters in more detail. Many of them are economic, which allows a designer to offer a value proposition to a sponsoring organization. Probably the strongest argument for the use of layers is that they provide for more deliberate application of theoretical knowledge. How this happens is explained in a later chapter. It is an important insight for a profession that is still puzzled about how theory figures into designs (Yanchar et al., 2010; Rowland, 1992; Cox and Osguthorpe, 2003; Christensen and Osguthorpe, 2004; Kenny et al., 2004). Design team specialists bring knowledge of theories, standards, best practices, and processes to team designs. Many theories influence the construction of any design, and it is through the layer-related specialties that those contributions are made. Modularity • Is there a blender in your home that allows you to attach different tools to the base unit? Perhaps an ice crusher, perhaps a vegetable slicer? • Is there a tool in the garage, perhaps a drill, that allows you to attach a buffer or a sanding wheel or a screwdriver bit into the drill chuck? • Is there a vacuum cleaner that allows you to connect different attachments such as hoses and brushes or crevice tools to modify the use for special purposes? All of these represent modularity of the product for the purpose of making a single tool serve multiple purposes. Other kinds of modularity that are less visible to the user will be described in a later chapter. Application Exercise The previous section stated that layers are entry points into the design for theory. In the next section you will have seven generic design layers for instructional designs explained to you. You do not know what functions each one supplies, so this exercise will be a bit of a guessing game that will provide a warm-up for the layer descriptions in the next section. Listed on the left in Table 2.2 are the names of the generic layers that will be described in the next section. On the right in a jumbled order are listed kinds of theory that apply to the decisions within one of the layers. Match the theory to the layer. As you read the following sections, return to this exercise to see if you would change any of your matches.

Table 2.2

Matching Layers with Bodies of Theory

• Content layer

Theory about database structure

• Strategy layer

Theory about the use of technical illustrations

• Message layer

Theory about the structure of knowledge

• Control layer

Theory about the structure of computer programs

• Representation layer

Theory about learners in problem-solving groups

• Data management layer

Theory about how conversations are structured

• Media-logic layer

Theory about how the learner uses a response device

34 • Fundamentals

A Generic Set of Instructional Design Layers As a beginning point for independent thinking and experimentation by instructional designers, a set of seven generic design layers is described at this point. These layers are derived on functional grounds; they represent functions carried out by virtually every instructional artifact. They represent areas of a design within which a designer can make deliberate, theory-guided decisions. The layers are represented graphically in Figure 2.6 and described in the sections that follow. Each layer represents a cluster of design questions that a designer should consider answering. Any given design problem will require answers within most if not all of these layers. Design teams can use these generic layer definitions as a beginning point for negotiating their own layer and sublayer definitions using the criteria described in the section above on choosing layers. Over time, a designer or a team can develop the skill of thinking in terms of layers. The seven generic layers include: • • • • • • •

A content layer that supplies knowledge elements during instruction. A strategy layer that manages strategic interactions with the learner. A message layer that carries out strategic plans through conversational exchanges. A control layer that expresses the learner’s side of the conversation. A representation layer that provides information and meaning in sensory form. A data management layer that records, analyzes, reports, and stores learning data. A media-logic layer that executes the operations of all of the other layers.

A description of these layers could begin with the description of any one and move in any order to the others. We will begin with the most concrete layer and move toward the less concrete ones. The Representation Layer Every instructional product creates some kind of sensory experience. Conveying the experience to a learner is the job of the representation layer. The representation layer is one of the most dynamic

Content

Representaon

Strategy

Control

Message

Data Management Media -Logic Figure 2.6 A generic set of design layers for use by instructional designers based on functions common to most instructional artifacts.

Design Layers • 35

and important parts of an instructional design. Media representations are sensory signals that can be interpreted by learners and converted into symbolic meanings that have impact on both intellect and emotion. Theories give shape to representations: their forms, dimensions, textures, styles, arrangement, and timing. Even designers who do not possess formal theories express ideas and feelings using native theories that they do possess which they learn from constant exposure to media. Specialized representation theories of all kinds exist (see Mayer, 2005a). There are theories for the design of representations that give explanations (Tufte, 1997) and ones that describe how to time and synchronize representations (Mayer, 2005a, 2005b). There are theories for spatial arrangement of representations (Fleming and Levie, 1993) and theories for telling stories with media (History Shots, n.d.). There are theories for designing tactile and kinesthetic effects (Fogtmann et al., 2008; Stanney et al., 1998). There are even theories of lighting, and they are relevant in classical theater (Gillette, 2003), in architecture (Karlen and Benya, 2004; Russell, 2008) and in computer game design (Crawford, 2002). (It should be noted that the Karlen and Benya and Russell books both describe the design of lighting systems in terms of layers.) Styles of representation change constantly, and the languages of representation are always emergent; designers communicate in a vernacular in which some terms are just emerging into existence while others are moving toward obsolescence. Compare ads for any consumer product today with ads for the same product forty years ago to see this process in action. Representation styles change swiftly, and for designers this means that representation designs must be made as independent as possible of other layer structures so that as styles evolve representations can be changed without destroying slower-moving layers of the design. The representation layer is the only tangible layer of a design. All of the other layers are invisible and abstract. Understanding the other layers is therefore an exercise in imagination and abstract thinking. As designers we become design detectives trying to deconstruct other people’s designs to see the layers hidden behind the surface. Frequently new designers think only in terms of the visible, charismatic representation layer to the neglect of other layers that may be more important. Instructional designs compete for attention with the constant drone of media that surrounds our learners. We appeal to the already overtaxed attention of learners by using media judiciously to attract attention and then sustain communication through conversation. The design of professional quality representations for instructional purposes is very specialized. It is not surface features that are hard to master, since there are powerful production tools that easily create polished surfaces. It is the creation of meaningful representations and sequences of representations whose ability to communicate borders on instructional artistry (Malamed, 2009; Lima, 2011; Wurman, 1997). Even seasoned production artists from other fields gain insights when they are asked to make instructional representations. Design team specialists with particular representation skills often help the designer to fill in the details of a representation design. In addition to creating individual representations, specialists responsible for different modalities come together with the designer in the determination of a coherent style for the artifact being designed. This is a team-level decision that ultimately impacts the design of other layers. Representation specialists have their own culture, professional organizations, career paths, terminologies, processes, tools, and literature. A lead instructional designer should be thoroughly familiar with the design languages of the representation layer so that the designs produced have coherence, integrity, and unity of purpose. Application Exercise Using magazines ads to supply examples, look at a number of different ads in the same magazine. Look at the differences in quality, style, appeal, and professionalism.

36 • Fundamentals

• Are there some ads that were clearly designed by a team? Are there others that look as if an individual might have put them together? • What are the differences in the styles ads use? How well do you estimate that the ad will have appeal to its target audience (which is not necessarily the general audience)? • How much do you think each ad cost? • How much benefit do you think the ad’s sponsor stands to receive if the ad is effective? • What, then, would you estimate is the ad’s cost/benefit trade-off ? Was the advertiser’s money well spent? The Control Layer The control layer provides a way for learners to talk back to the instruction and take action. To give the learner a voice and a vocabulary, the designer defines the terms, the syntax, and the semantic of a special language: the one that the learner will use to make expressions and take action during instruction. By recognizing that they are creating control languages, designers can be more deliberate and inventive in the languages they fashion and afford the learner more expressive tools for interacting. The control layer is so named because it can give the learner control over instruction. Control systems can be very simple or very complex. More complex forms of interaction require more sophisticated principles for control system design. We use control systems everyday: at home (microwaves, alarm systems, watering timers), at work (computer desktop operations), and at play (game consoles, media devices). Probably the most concentrated collection of control systems exists in our cars. We use these systems so much that they become transparent to us; we use them without concentrating on them. This was not always true of car controls, and in early airplanes control was a major problem that led to injury and accidents before stability problems were solved (Vincenti, 1990, Chapter 3). Any pilot today is able to explain the difference in the “feel” or response of different aircraft control systems. It may be that our concepts of instructional control systems are as primitive at this time as they were in the early days of flight. The design of controls has to take into account timing, synchrony, efficiency, transparency, and contention among input sources. In his book The Art of Interactive Design, Chris Crawford (2002) describes the nouns (things acted upon) and the verbs (actions) of a control language. These represent the influence of linguistic theory on control design. A learner makes rudimentary sentences using the grammatical tools the designer supplies. Control languages have syntax and a semantic dimension as well. The more interactive the instruction, the more interesting the control design. Figuratively speaking, adjectives (qualities of things) and adverbs (qualities of nuanced actions) can be found in control languages as well. Simulation design for complex flight simulators, simulations, and virtual space navigations like Google Earth™ challenge the designer’s imagination. Languages for controlling actions in virtual worlds demand attention to advanced control design principles, as do control languages for instructing learners with handicaps and both physical and learning disabilities. The culture of control system design among the instructional designers is thin because much of current instruction emphasizes information presentation rather than real or figurative action. Information system design, where navigation is metaphorical, provides examples of more expressive control systems for exploring conceptual maps, navigating information spaces, asking questions of databases, and interrogating live three-dimensional system models. Typing questions into search engines, speaking instructions into phones, and dictating natural language into transcription software were not possible just a few years ago, but today they are passé: your phone can now joke with you. Future advances in many kinds of control technology will increase the range, value, and expressivity of control systems, so this will always be an interesting and growing layer of instructional design.

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Application Exercise Control systems are so much a part of our everyday lives that we do not notice them until they malfunction. • Reflect on the number of control systems in your car. Make a list of them. (A later chapter on control systems gives a list of fourteen such systems that exist in most cars.) • Make a list of the number of times in the last two or three weeks that you have become aware of a control system because (1) it was broken or did not work as expected, (2) it was new to you and took some getting used to, or (3) it took more effort to use than you thought it should. • Consider how you would redesign the control system that bothers you the most to use—is the most irritating, causes the most mis-operations, or usurps the most attention from a task. The Message Layer The message layer provides the structures used in instructional conversations. The message layer is one side of a two-lane highway that connects learner with instruction. The other lane is the control layer. Together, the message layer and the control layer supply the channel through which instructional conversations take place. Multiple messages are sometimes required to carry out the detailed conversational exchanges in service to the higher-level plans of the strategy layer. The strategy layer, the message layer, and the representation layer work together on the same side of the conversational highway. It is their joint effort that communicates with the learner. The strategy layer negotiates the higher-level strategic goal, the message layer determines how to turn the goal into conversational patterns, and the representation layer gives form and expression to the messages. The control layer completes the circuit by giving the learner expressive power in the opposite direction. Exchanges continue in this way until a strategic goal is accomplished. Instructional designers pay little attention to the message layer because it lies hidden in the shadow between two charismatic layers: the representation layer and the strategy layer. The message layer is difficult to “see” anyway, because message, strategy, content, and representation concerns tend to be tangled together in most designers’ thinking. In today’s parlance we talk about “content”, but what we really mean is the combination of an idea, its expression, and its representation all bundled together—something ready to display, something that can be included in a Web page, or something that is ready for inclusion into a textbook. The traditional instructional design literature on “message design” deals with the three layers combined in this way (Fleming and Levie, 1993). Van Merriënboer and Kester (2005) exemplify this view: theories for instructional message design identify multimedia principles and provide guidelines for devising multimedia messages consisting of, for instance, written text and pictures, spoken text and animations, or explanatory video with a mix of moving images with spoken and written text. —(p. 71) Because traditional message design conflates three layers into one, it emphasizes primarily layout, readability, scan patterns, salience, proportion, coloration, and other aspects of a representation. By failing to disentangle the message layer it is hard for conversation to be designed as an independent function. Combining the layers ignores two possibilities: (1) that a single message can be mediated in different ways simultaneously, and (2) that strategic goals during instruction take more than a single conversational exchange to reach, suggesting that a number of message exchanges might be

38 • Fundamentals

required to carry out strategic intentions. Separating the concerns of the message layer architecturally provides the entry point for designing conversational instructional patterns. Consider the simple case where a learner has just used a control to respond incorrectly. The feedback message may have multiple parts: Sorry. That’s not correct. The correct choice was [value]. You may have thought that [value]. You could have found the correct answer by [value] Which messages actually are used depends on the nature of the error, its seriousness, and the context of learner conversation that preceded the error. Moreover, the individual parts of this feedback message may be represented through different media channels simultaneously: a flashing light, a voice, a bit of text, a sound effect, highlighting of the error, animation of the proper response, etc. This example shows the desirability of separating the message layer from the strategy layer on the one hand and the representation layer on the other so that independent decisions can be made about the operations of each layer. This opens the possibility of adaptive instructional conversations based on hard-to-predict, non-sequential learner responses, and it also provides a place for new conversational technologies to enter designs. Though message design for instructional purposes is an underdeveloped area, examples of general-purpose conversational systems are quite common. Consider the online recommender systems that help you choose a movie suited to your tastes, the online bookstores that suggest books you might like in a display generated instantly that is based on your past purchases, and the form letters constructed from variable fields and text fragments that turn out an entire letter personalized for you. RSS feeds are also a form of tailored message system in which you leave a standing request to be notified when certain kinds of stories are reported. There are several potential sources from which a theoretical basis for message system design could be constructed: • Classroom analysis systems. Simon and Boyer (1974) review ninety-nine different analysis and categorization systems for instructional discourse. Such tools are “ ‘meta-languages’ for describing communication of various kinds” (p. 3). These systems partition verbal, emotional, and physical message types that are commonly observed in learning settings. Interest in message analysis of instructional discourse continues today as shown by Sawyer (2006), who refers to these methodologies as systems for “interaction analysis”, which he says “are designed to analyze naturally occurring conversation” (p. 188). • Conversation theory. Luppicini (2008) describes a field of “conversation design” which “focuses on the advancement of knowledge and practice about how people think, learn, and interact through conversational practices” (p. 3). Pangaro (2008) proposes that in the future all technology-centered disciplines “will incorporate constructs . . . that explore the role of conversation” (p. 37; see also Sidnell and Stivers, 2013). • Human tutoring research. Fox (1993) reports the results of a study of human tutors. Her findings demonstrate that dialogue processes possess structure that includes stages of tutorials, appropriate message types in each stage, and rules governing when to intervene and how. • Artificial intelligence and tutoring system research. Wenger (1987) gives a complete review of intelligent tutoring systems using artificial intelligence through the mid-1980s. He includes an extensive review of the mechanisms used for adaptation by different intelligent tutoring systems. He shows that there are many kinds of message and many ways for constructing messages

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tailored to the momentary need. Updates to Wenger that bring this discussion up to the present are provided by Woolf (2008) and Nkambou (Nkambou et al., 2010). The message layer will emerge in the future as an essential element of instructional designs. This is another growth area for career designers. Application Exercise Message systems complete the circuit in the conversational exchange between the learner and the instruction. Together, the message and control layers use the representation layer as a “window” through which both the learner and the instruction “see” each other. • Find some examples of technology-based instruction and notice the patterns of communication between the learner and the instruction. What messages are sent outbound to the learner through the representation? With what options can the learner respond? What are the controls in this conversation? Taking the learning goal into mind, how significant, or worthwhile for the learner, is the conversation created across these lanes of communication? • Examine an example of live instruction with the same questions in mind. What are the outbound messages? And what are the options for response? How significant and worthwhile is the conversation? • Compare the quality of the conversation in both of the observations you have made. What are the strengths of each? What are the weaknesses? How could each of the conversations be improved? Would it consist of new terms in the control language? Or new messages sent outward? • Consider what minor changes could be made that would improve the value and interest of the conversation for the learner. The Strategy Layer Instructional conversations are driven by strategic goals that lead to strategic plans. Consider the thoughts of an instructor and a learner during instruction: • INSTRUCTOR: “The goal that we need to reach next is XYZ.” • LEARNER: “What I would like to learn next is ABC.” (Thinking, not communicating) • INSTRUCTOR: “So I need to use a strategy that involves QRS.” • LEARNER: “So I need to do MNO.” (Thinking, not communicating) • INSTRUCTOR: “And I can do D and then E and then F.” • LEARNER: “So I will try I and then H and then G.” (After uncoordinated action) • INSTRUCTOR: “Why is this learner not getting it?” • LEARNER: “Why am I not getting it?”

40 • Fundamentals

This not uncommon drama illustrates two things: (1) that instructors and learners both have goals, even if they are not acknowledged by each other (which is too often the case), and (2) that goals and goal mismatches take place not just at one level but at multiple levels. Three levels of goal are depicted in Figure 2.7, and different people hold them. Since instruction is an activity that two or more people engage in purposefully, the goals—the purposes—have to be a central concern. The goals may change, but at least they are a beginning point. Then the question of time and activity becomes the next concern. These are primary design concerns of the strategy layer: goal, time, and activity. These combine into what can be termed an event. The event brings in other core strategic issues that include social organization (who takes part and what is everyone’s role?), event sequence (what happens first and then next?), and materials (information resources and actionable objects). The dynamic interaction of learner and instructor (designer) goals is a major predictor of the success of instruction. An instructional designer begins with a performance goal, and the learner also forms a performance goal, which may be either very like or very different from the designer’s. So, a good designer considers what can be done to communicate and negotiate a common goal before launching off into strategic interactions. Figure 2.7 shows that the designer derives a strategic goal that, in effect, says, “Here’s what I am going to do to help the learner reach the performance goal.” This strategic goal results in a plan in the designer’s mind outlining major moves, major steps, which will support the learner’s efforts to learn. What designers frequently ignore is that the learner also forms a goal that, in effect, says, “Here is what I am going to do to try to reach my performance goal.” Once again, there may be a mismatch between the learner’s and the designer’s goals, so this is also a point for communication and negotiation. Finally, Figure 2.7 shows that the designer forms a means goal that says, in effect, “What specific steps am I going to take to help the learner reach the current strategic goal?” Likewise, the learner asks a similar question, and, of course, there may be a mismatch that the designer and the learner need to resolve. The potential mismatch of goals and the need to communicate about both goals and means presents a challenge to some of the assumptions designers have tended to make in the past. The learner

Describes the instruconal goal the designer hopes the learner will adopt.

Describes what the learner wants the instruconal goal to be and what he/she will be sasfied with.

Strategic Goal

Describes what things the designer might do strategically to help the learner reach the current performance goal.

Describes strategically how the learner intends to go about reaching the current instruconal goal and the level of effort that will entail.

Means Goal

Describes what specific acons the designer intends to undertake to help the learner reach the current strategic goal.

Describes what specific acons the learner intends to undertake to reach the current strategic goal.

Instruconal Goal

Figure 2.7 The relationship between learner and designer goals and different levels of strategic goals.

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needs to be more involved in goal selection at all three levels in order to learn how to self-manage learning. One of the overriding goals of instruction ought to be to turn as much control as possible over to the learner—as much control as the learner shows evidence of being ready for. Figure 2.7 shows the close relationship between strategic goals and means goals—that is, between the strategy layer (what is the learning goal at any given moment) and the message layer (how will the conversation proceed for the current strategic goal?). The strategy layer has a similar close relationship with the other layers of a design because every design decision is strategic. Technically, all of the other layers represent an extension of the strategy layer, but they need to be considered separately because complexity brings in specialists to support some parts of the design. The strategy layer lies at the heart of an instructional conversation and pervades the decisions of every layer that takes part in carrying out that conversation. This does not mean that strategy decisions are always made first during design, but it does mean that no decisions can be made that put strategy and goals out of alignment. What are the influences of the strategy layer on other layers? • The knowledge elements of the content layer are strategically partitioned in a way that facilitates their gradual introduction, which strategically nurtures the gradual growth of understanding and skill. • The message elements of the message layer, as just described, carry out conversational exchanges in the service of more comprehensive strategic goals. • The elements employed in the representation layer are staged strategically for effect in maximizing the efficiency of communications and their intellectual and emotional impact. • The languages and affordances of the control layer are chosen strategically for transparency, intuitive qualities, and expressiveness. • Data selected for collection, analysis, and reporting (to multiple data users) are strategically chosen because otherwise the mass of data collected would overwhelm processing capacity. • The media-logic used for execution is strategically chosen and partitioned so that live instructors and computers can both implement the functions assigned to them. As you will see in a later chapter, during design every layer, including strategy, has an equal chance of influencing—and being influenced by—decisions within every other layer. During instruction, however, the direction of influence among the layers shifts, and the strategy layer becomes the hub of decision-making and negotiation with the learner. The other layers serve the purposes and plans agreed upon with the learner. There are multitudes of theories of instructional strategy. They range from highly structured and prescriptive systems of interaction to very loose and almost unstructured. Some systems of strategy involve the learner in decision-making and adapt to the learner, while others are rigid and fixed. Goals may be clear, firm, and determined in advance, or fuzzy, complex, and determined on the fly. Instructional settings may be formal or informal. Social roles and interactions may be precisely defined or evolutionary as instruction advances. Materials may be simple discussion objects or complexly structured products. The different, apparently opposing, views of strategy remain unreconciled in the minds of most designers. Most treatments of strategy explain only one set of strategic principles, implying that the one is correct; highly prescriptive theories are pitted against those that are more flexible and adaptive as if one position was correct and the other was wrong. This lack of unifying perspective is evidence of academic territorialism. Many kinds of theory are essential to the “compleat” designer. The later chapter on the strategy layer relates a wide variety of theories of instructional strategy and suggests that strategy itself can only be properly understood as a dynamic process

42 • Fundamentals

of shifting responsibility over time from the instructor and designer to the learner. Reconciliation of the strategy conflict has been greatly hampered by the lack of continuous measurement during instruction that would permit the assessment of the learner’s readiness to assume specific responsibilities. Figure 2.8 shows a designer’s perspective of strategy layer during ongoing instruction. Strategy appears in the form of a learning companion that takes part in the learning conversation at an appropriate level. The learner is shown interacting with dynamic cause–effect systems and noting their responses. The expert performance model represents the performance goal of the learner. The cause–effect systems are either real-world systems or a simulation. The content layer, described later, is concerned with the description and capture of the expert performance model and the cause–effect systems model. This perspective of the instructional setting, the actors, their roles, and their relationships favors learning through performance rather than learning from being told. It closely resembles the setting in which we learn naturally. In this setting the training world blends seamlessly into the real world of performance. From this perspective, instruction can be seen as an extension of the performance environment rather than as a cloistered process of information transfer. The false distinction between the instructional world and the real world as a learning setting becomes transparent. Learning can take place in the performance environment, or learning can move gradually from an artificial environment to a performance environment with continuous support from a learning companion—now called a mentor. Training becomes a performance-integrated function. This view of the instructional function helps to integrate and situate the instructional function within the day-to-day functions of an organization. No longer is it seen as an isolated area of the organization. It is part of a professional development system for valuable organization members. The designer and the organization, of course, have to accept the view that instruction is an occasion for performance and not assume from the beginning that it is an occasion for lecturing.

ENVIRONMENT

EXPERT PERFORMANCE MODEL

LEARNING COMPANION LEARNER

CAUSE–EFFECT SYSTEMS

Figure 2.8 The scenario that unites the artificial world of instruction to the real world of everyday performance.

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Application Exercise Strategy decisions are so numerous that it is hard to count them—literally. • Examine a piece of instruction. List as many decisions as you can that had to be made in the course of designing the instruction. Then set that list aside, and on another day look at the same instruction again and make a second list. The second list will be longer if you have taken the time to do some honest thinking. Why is that? The Content Layer The content layer is concerned with the nature and structure of the knowledge to be learned and its capture in a form that can be used during design and delivery of instruction. During design, types of knowledge to be learned are identified and the knowledge itself captured. Designers realize that what is captured is not really “knowledge”, but its shadow, caught in a form that is useful for a particular instructional purpose. The chapter on the content layer describes this in more detail. For now, it is enough to realize that what is captured for an expert performance model or a cause–effect system is not just a textual description of either: it is the set of rules that could be used to execute the performance and to create the cause–effect model at the level needed to conduct interactions during instruction. There is some chance of confusion in the use of the term “content”. In today’s multi-media world, content refers to a representation: content in the form of text, audio, pictures, video, or animations for presentation, usually on a Web site. Another common use of the term “content” refers to large bodies of media resources that are owned by a corporation not for direct use, but for sale to those who produce media products. Neither of these fits the meaning of the term as it is used here. Content for the purposes of instructional design within the content layer refers to abstract knowledge structures; it does not consist of any representation other than the shadow that the designer captures in cooperation with one or more subject-matter experts. Intellectual content for the purposes of the content layer does not have a physical form. It can be captured in a variety of forms: in semantic nets, in task hierarchies, in sets of production rules, in the form of a dynamic model, or in some combination of all of these. In recent years, emphasis has been placed on the emotional dimension of knowledge. Feelings, beliefs, values, and attitudes that are the targets of instruction have to be described, including intermediate states, but these are not captured in a media form. Finally, the content layer is concerned with the ability of learners to monitor their own learning using self-directed learning skills. The instructional theory called cognitive apprenticeship (Collins et al., 1989) describes learnable knowledge of four major types, only one of which is subject-matter knowledge: • Subject-matter. This consists of familiar subject-matters such as trigonometry, geography, and business law. • Problem-solving strategies. These consist of routines that are used to solve problems: the “tricks of the trade” or “rules of thumb” that we use to get ourselves out of jams. When a computer is acting squirrely, for example, and other tricks we use have not helped, we sometimes reboot the system to solve the problem. This is a problem-solving strategy, along with all of the other things we tried first. • Problem-solving heuristics. These consist of knowledge about how and when to choose a particular problem-solving strategy at any time during problem solving. • Learning to learn. This consists of skills that we use to attack new learning. Learning to learn is becoming a survival skill in a world where technical knowledge is expanding at an

44 • Fundamentals

unprecedented rate. Schools and commercial organizations place more emphasis on this kind of knowledge than they have in the past. Three of these categories allow us to think about our own thinking and form self-selected strategies to manage our own learning. They allow us to learn and solve new problems when there is no instruction. The value of this kind of knowledge will be at a premium increasingly in a world dominated by a growing knowledge economy (Kahin and Foray, 2006). Application Exercise Content types are hard for many people to see. To most people “knowledge is knowledge”. Most of us have not been made aware of the different kinds of knowledge we accumulate. Whitehead’s claim that the “seamless coat of knowledge” cannot be divided tends to make people hesitant to talk about knowledge “types”, and the statements by philosophers about the nature of knowledge make them positively frightened. Yet even the untrained person, can generally identify from personal experience kinds of knowledge that appear to be different. • Reflect on the kinds of things that you feel you know. Can you see differences between “types” of knowledge you have? What different kinds can you discriminate? The Data Management Layer A great deal of data is generated during instruction. The design for the data management layer defines how this data is gathered, remembered, analyzed, and how it is used beneficially (see Figure 2.6). Data from instruction has many uses, but traditionally the amount and kinds of data gathered and analyzed have been relatively trivial. The trend is toward the gathering and analysis of much greater amounts of data, especially on the Web, where businesses are learning to use analytic techniques for responding adaptively to customer habits and preferences (Brennan, 2010). More data capture, as well as during- and after-instruction analysis, will soon become the norm for instructional designers. Data management includes the collection, warehousing (storage and archiving), analysis, and interpretation of data from instructional events. It includes also the distribution of data analyses to layer functions, to the learner, to the sponsor or parent, and to the designer. The data management function also collects and analyzes evaluation data on the performance of individual instructional events and of the instructional system as a whole that can be used for improvement. Some data may be used at the time of instruction to determine options for negotiation. A history of the learner’s choices and performance can also be kept for later analysis. The amount and kinds of data collected and analyzed is the limiting factor on the adaptive quality of a design. Design within the data management layer produces plans for data collection, some of which may be automated by computer software, the rest being carried out by an instructor. The plans specify the data to be gathered, the instruments used to gather it, and the procedure for gathering. They also specify the rubrics for analysis, interpretation, documentation, and distribution of reports that may be provided to the learner, the system, and multiple administrator, sponsor, and designer stakeholders. During instruction, data on learner responses accumulate at an enormous rate and can include any set of variables the designer or instructor has decided in advance to collect. A key decision that a designer makes is the amount of data collected and the amount of data that can be processed within time and resource limitations. This becomes a question of how granular the data can be and how detailed the decisions are that can be supported by the data.

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Application Exercise Recall some of your own educational experiences. These are a good measuring stick for ideas on design because most people have seen during their own education so many variations on the theme of instructional method. • Consider how many different ways data that was collected during instruction (including quizzes and tests) that led to decisions of some kind. Go beyond the obvious, even thinking behind the scenes about what your instructors and your schools were doing with test scores, attendance figures, and so forth. You will be surprised at the amount of data that was collected and the many ways it was put to use. • How many lost opportunities for the use of data to improve your education can you see? • How much of the data that was collected from your instructional performance in the past still exists? A lot? A little? Is that good or not good? • What would be the consequences (good and bad) if you had more data from past learning experiences? The Media-Logic Layer All of the layers described up to this point have to be executed at the time of instruction; that is the function of the media-logic layer (see Figure 2.6) of a design. Each layer’s functions must be executed in coordination and synchrony. This is true whether there is a human instructor, a technological device, or some combination of the two. Both computers and human instructors operate according to some set of instructions—either programmed or in the form of teaching directions or teaching habits. The term “media-logic” suggests computer routines, but only when instruction is delivered by computer is this true. The functions of a live instructor have to be driven by some kind of logic as well—human teaching logic. Is it proper to refer to human teaching logic as part of the media-logic layer of the design? Yes. Instructors are constantly making decisions about what to do or say next (strategic choices), and once the choices are made there remains the decision of how to execute them. Instead of having computer logic the instructor has an internal teaching logic gained from experience and made up of elements of personal theories, habits, and personality. When a live instructor delivers the instruction, the instructor’s own media-logic supplies a lot of teaching behavior that the designer does not have to specify in detail; this part of the design is to some extent defaulted to the instructor’s instructional set. When a technological device is involved in the instruction, the designer has to give it specific directions, because as fast as they are, computers are not very imaginative. Even when an instructor is the delivery medium, some designers find it important to specify in detail specific patterns of behavior and certain ways of carrying out strategic communications. In such cases, the designer may ask instructors to modify their usual patterns of media-logic. Several examples could be cited of instructional methods that require an instructor to follow a particular pattern of behavior. Among these would be problem-based learning (Barrows and Tamblyn, 1980), reciprocal teaching (Rosenshine and Meister, 1994), and some direct instruction methods (Rosenshine, 2008). Media-logic principles involve the coordination of human and computerized instructors working together, so media-logic concerns involve the orchestration of the operations of both. As the complexity of computer involvement has grown, the architecture of computer programs and the modularization of the product have become important design considerations. These issues and others are covered in a later chapter on media-logic design.

46 • Fundamentals

Application Exercise Most media users are familiar with the concept of a playlist. Without realizing it playlist users are coming into contact with a type of media-logic. There exists a program within the player that can accept a playlist and then execute it. The playlist does not know, or care, which items it is asked to play; it simply executes the list as instructed. • Consider other media-logics that operate within your media world and describe how they separate the execution function from the event that is being executed: Video players Appointment reminders Cell phones Calculators.    

Application of Layers in Other Design Fields The principle of design architecture involving layers is common in diverse design fields, including architecture, computer design, software design, business, and engineering. John Uyemura, a noted authority in digital system design, describes the value of thinking in terms of layered design domains: The detail of interest to you at a particular time depends on the level where you are working. Sometimes you will be interested only in the overall function of a complex unit, whereas at other times you may need to understand every element that goes into making a basic unit. The power in this approach derives from the fact that the important aspects vary with the level. —(Uyemura, 1999, p. 18) By thinking of a designed product in terms of layers a designer can focus on one part of a design problem or on the whole problem, as needed. It is natural for a design team of specialists to be divided into semi-independent work groups that can attack sub-problems separately, and then integrate solutions interactively into a coherent unity. What are some of the precedents of using layer design principles from other fields? Here are a few examples: • Architecture. Donald Schön (1987) in his book Educating the Reflective Practitioner writes about an architectural design problem consisting of numerous sub-problems that exist in what he terms “domains”, each related to its own principles, standards, and design terms. Stewart Brand (1994) likewise describes the layers of a building’s design, noting that when a designer uses layering deliberately, a building’s usable lifetime is extended because some layers that have aged can be changed without destroying the whole building. • Computers. Baldwin and Clark (2000) describe how the principle of modularity (their term for design layers) is the economic factor that has made possible the modern personal computer, with its replaceable functional modules (boards, hard drives, memory modules, etc.). • Enterprise software. Fowler (2003) describes the use of layered architecture in suites of the enterprise software businesses use to conduct their computerized functions. He explains the structure of such software in terms of three main layers that can be modified independently, stating that “most nontrivial enterprise applications use a layered architecture of some form” (p. 2). (See also Evans, 2004.) • Internet protocols. The software that forms the Internet is structured in terms of layers. Different software protocols (special-function programs) carry out functions within different layers. Two competing layer models have been proposed, one with four layers (“OSI Model”, n.d.) and one with seven (“TCP/IP Model”, n.d.).

Design Layers • 47

• Web pages that work over the Internet have been described in terms of layers that represent semi-independent design problems (Garrett, 2010). • Manufacturing/business. Ulrich (1995) describes the process of matching the conceptual functions of a designed product with its physical architecture to “raise awareness of the far-reaching implications of the architecture of the product” (p. 419). To make this case, Ulrich draws upon principles from software engineering, design theory, operations management, and product development management. Ericsson and Erixon (1999) describe the concept of modular product platforms, a design principle that divides a marketable product into a family of components that can be assembled in different combinations to form different versions of the product on demand. Conclusion This chapter has introduced a new way of looking at the architecture of an instructional design in terms of layers. There are many implications of this view. The most important to the designer is the impact of layers on the order of decision-making during design. That question is the subject of the next chapter.

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3

Design Process

A designer is an emerging synthesis of artist, inventor, mechanic, objective economist and evolutionary strategist. —(Buckminster Fuller) Design is the process of making decisions. A design is nothing more than a record of decisions that have been made. No matter what design approach is used, the key question for the designer is always which decision to make next and how to make it. That is the subject of this chapter. The main idea is that decisions are made at many levels of detail, they influence each other, and there is no set order of decision-making. Viewing Design from Different Vantage Points A full description of instructional design requires that it be viewed from multiple perspectives, from high-level design within an organizational context to the design of details (see Figure 3.1). Design is not a fully rational process. This lengthy chapter examines four of the eight perspectives of design shown in Figure 3.1. It explains why a single description of design is insufficient to capture its richness and multi-dimensionality of the process. The four views from Figure 3.1 that are included in this chapter are: • • • •

Design within an organizational context Design within a design team Design according to product functions and design layers Design in the designer’s thought processes.

The descriptions of design from these perspective points differ because the objects of decisionmaking at each level differ. At the organizational level, decisions are made about launching design projects, their relation to organizational goals, and value-adding to the organization. At the team level decisions are made about how parts designed by individual specialists are integrated into a coherent whole. At the level of the artifact and its layers decisions are made about how instructional functions are assigned to different material elements of the product. At the level of the designer’s thought processes decisions are made about how abstract ideas combine to generate design architectures and then product details. 49

50 • Fundamentals ORGANIZATIONAL VIEW

SYSTEMS APPROACH VIEW

ISD PROCESS VIEW

DESIGN LANGUAGE VIEW

FUNCTIONALMODULAR VIEW

OPERATIONAL PRINCIPLE VIEW TEAM PROCESS VIEW

ARCHITECTURAL VIEW

Figure 3.1 Design viewed from many perspectives.

The multiple perspectives of Figure 3.1 imply that a comprehensive description of design requires multiple narratives. This chapter contains four of them. The four remaining perspectives from Figure 3.1—the systems approach, ISD processes, operational principles, and design languages—are described in separate chapters. The four perspectives of design in this chapter represent a continuum that begins with the big picture and zooms closer by degrees to where individual decisions are made, in the designer’s mind. The goal is to suggest how the many, diverse descriptions of instructional design in the literature relate to each other. When they seem to conflict, it may be because they are viewing design from a different angle. Since the design process depends on the object being designed, the first few sections of this chapter will dwell on the nature of what it is that designers design. What Does an Instructional Designer Design? What designers think they are designing makes a huge difference. Consider the average designer who is designing a Web site. Certain standard structural features come to mind. At the same time, certain kinds of structure don’t occur to the designer because they are not part of the “language” of Web site design. This would indicate that what the designer thinks to include into the design is strongly influenced by the kind of thing the designer thinks is being designed. This principle applies to the abstract elements of a design as well as to the tangible ones, from the biggest parts of the design down to the very smallest. Moreover, what a designer thinks is being designed changes as the designer accumulates experience and forms new concepts through observation and experiment.

AuQ1

Design Process • 51

Four Stages of Designer Thinking How instructional designers think about design tends to mature in stages. In their early work, designers tend to think in in media-centric terms. The structures that come to mind most readily are the ones related to the medium for which they are designing: pages, sites, resources, tags, and so forth. Over time, the designer’s thinking tends to gravitate toward message-centric design: design that concentrates on structures for conveying ideas—better “explanations”, “narrations”, “examples”, better use of visuals to tell a story, animation sequences, more complementary use of text and visuals, better and sequencing of key ideas. At this point the designer’s attention has shifted to something more abstract—a message and how it can be conveyed. Later, designers tend to migrate toward formal strategies and structures. Strategy-centric designers deal with “presentations”, “practice exercises”, “demonstrations”, “drills”, “models”, “problems to be solved”, or “projects to be completed”. Previously used structures are still useful, but they become secondary in importance. The explanations that were central in the previous stage become subordinated as parts of a more comprehensive strategic plan. As designers mature through different stages—and not all designers follow the same pattern—the structures from previous stages are not lost, but new abstract structures become apparent to them and are given priority, while others remain important but become subordinate to the new ones. At each stage—from media- to message- to strategy-centering—new design goals and product functions come to dominate the design, and new structural elements move to center-stage in the designer’s thinking. One additional stage which some designers reach focuses attention on dynamic content (subject-matter) structures. Simulation designers find that the central structure of a simulation includes a dynamic interactive system model whose values constantly change. Learners act upon the model, and the model values change. Special instructional services are often supplied to augment the model experience, providing explanations, giving suggestions, and summarizing principles, but the living model is the central structure of the design. This describes model-centric thinking. Thinking in Terms of Instructional “Environments” A designer’s vision of structure matures as the vision of what is being designed matures. At one point in the past, the idea took root that what was designed was a self-contained “product”: a classroom lesson plan, a media package, a Web site, a workbook chapter, or a simulation. This concept of the designed product is convenient because it defines convenient and concrete boundaries for the product and how it is used. Product designs in this tradition tend to focus on the individual user. However, the concentration on self-contained, individually consumed product is giving way to the concept of “learning environment” (Brown, 2006; Bransford et al., 2000)—a new kind of “product” that designers create. Learning environments come in many shapes and sizes, computer-based, classroom-based, and blended. There is no single definition for the concept, “learning environment”. However, environments tend to share certain characteristics: • They provide a problem-solving space, real, virtual (simulated), or in the form of augmented reality; mobile or stationary; realistic or imaginary. The problem space contains representations that provide information and controls that allow the learner to take action. • They provide a problem of some kind, which involves designing, building, navigating a problem space to a destination, diagnosing, prescribing, allocating resources, or discovering relationships, laws, or principles. • Alternatively, they may provide a challenge to explore the information space with or without a purpose for the exploration being given.

52 • Fundamentals

• They provide information, data, and other resources, sometimes physical, that can be applied to the problem-solving task. Part of the problem-solving task may be uncovering relevant information or learning about the resources and how to use them. • They usually are designed with a particular social setting in mind that may include multiple learners and/or a tutor or coach, which may be live or computerized. In group settings, problem solving usually involves learners collaborating, and collaboration skills may be among the desired learning outcomes. • They ask the learner to produce a solution to a problem. This can take many forms: (1) a correct answer and its justification (argument), (2) a multi-media product (document, presentation, etc.), (3) an artifact (construction, design), (4) a correctly managed process (performance), or (5) a criterion score (points). In the case of exploration environments, learners may be asked to articulate what they have found. • In most cases other learners or a coach or tutor will provide evaluation of solutions or products. Learning environments are normally designed with learner appeal and engagement in mind. Rieber describes learning environments in terms of their ability to support “play” within microworlds, simulations, and games (Rieber, 1996). The history of learning environments can be traced back to computerized adventure games and multi-user “dungeons” (MUDs) where exploration, wonder, and social interaction were important goals, and specific learning was secondary. Creation of dungeons by users themselves was explored in a variant called a MOO (“MUD Object-Oriented”) (Tomek et al., 1999). Over time, it became apparent that the powerful engagement of these kinds of play environments could be matched with the demands of social learning theory to generate new kinds of experiential structure for learning. Designers have come to see this kind of learning environment as a new type of instructional “product”. Designers will find it important in the future to think in terms of designing learning environments at least as much as—if not more than—insular instructional products. Preferences and patterns of media usage are rapidly changing in a world more and more connected with social media (Jenkins, 2008). The structure of learning products is evolving to take advantage of these ready-made user habits and communication channels. What instructional designers design is changing. Application Exercise Suppose that you were asked to design an innovative book for children. • Identify the structural elements of this book that you can think of right away that have to be part of the design. What are the standard structural elements of a children’s book? • What are the constraints on the design of these elements that most immediately come to mind? What are the standard parts and design considerations for children’s book design? • Now, because you have been asked to design something innovative, identify the new structures that this might imply for your design. • Look in the public library or a children’s book store for out-of-the-box, innovative children’s books. What structural elements do they possess that other children’s books don’t possess? Be prepared to see something new in terms of basic structure. Be ready to be surprised. • Now, as a last thing to do, judge the practicality of the innovative things you see in terms of marketability, durability, and continued interest from children. Which ideas do you think have the most staying power?

Design Process • 53

Dimensions of Learning Environments A designer finds it hard to construct traditional forms of instruction using a learning environment vocabulary, and vice versa. The vocabulary of traditional instructional formats includes terms like “presentation”, “demonstration”, and “practice”. These terms have the sense of one person (an instructor) doing something to or for another person (the learner) with the learner acting as a receiver and a reciter (when told to). In contrast, the terminology of learning environments employs terms describing what the learner will do: “determine problem”, “review resources”, “propose solution”, “apply solution”, “contribute”, “observe and evaluate”, and so forth. This terminology causes the designer to think in terms of designing problems, problem sequences, problem-solving resources, supports or scaffolds for the learner, and evaluation rubrics for judging solutions. The old design structures are replaced by, or at least become secondary to, new ones in a manner much like advancing through the “centrisms” described above. The composition of learning environments can vary along several dimensions. Problems of many kinds can be formed, resources of many types can be provided, and they can be made available in many direct and indirect ways. Many techniques of scaffolding can be used, and solutions to problems can be judged in many ways. How can a designer navigate a path through so many choices? Are there guidelines that point to certain effective learning environment structures? A designer wants to know about variations that count. Krippendorff (2000) shows how levels of social engagement and personal commitment can be combined to define a continuum of product types that can be applied to the design of learning environments. Krippendorff is a design theorist, and an industrial designer, but much of his work can be profitably applied in a cross-disciplinary manner to instructional design. Krippendorff names six levels of designed artifact spread along a continuum that he calls the trajectory of artificiality (Krippendorff, 2000; see Table 3.1). These artifacts identify types of “thing” that can be designed, and each type is associated with a particular structural pattern. Artifacts at the lower end of the continuum (product, goods, services, identities) do not require social interaction and personal commitment from the user. Krippendorff labels these as “products” that are manufactured for average product consumers. At the upper end of the continuum are artifacts (multiuser systems, networks, projects, discourses) that cannot perform their function without a great deal of social interaction, personal commitment, user participation, and individual contribution. The most commitment is required by projects and discourses. The names of Krippendorff ’s categories use familiar terms in new ways, so it is important to keep his definitions of the “artifact” types clearly in mind: • A product consists of the traditional self-contained, individually administered “packaged” product—a classroom lesson plan or a computerized lesson. Krippendorff ’s tone in describing this level of artifact is decidedly less enthusiastic. • Goods, services, and identities consist of brand names and branded families of instructional products. This is a relatively new kind of artifact to most instructional designers, but recent developments in the commercialization and syndication of instructional products makes this a growth category. Examples include everything from products created according to syndicated standards (e.g., AICC, SCORM) to universities (e.g., MIT, CMU, edX, Coorsera, Udacity) and private suppliers (e.g., Khan) who are branding themselves as sources of open learning by offering instruction free online. Beyond this point in Krippendorff ’s continuum the artifacts named are familiar to most instructional designers but do not exist as prominent and distinct categories in their minds. Designers recognize examples of each of these kinds of artifact, but most designers don’t think of them as things

54 • Fundamentals Table 3.1

Definitions of Krippendorff’s Artifact Categories

Artifact Category

Krippendorff Description

Instructional Design Examples

Products

Something manufactured Designed according to the producer’s best guess Used in the way it was designed

“Packaged” instructional modules Pre-designed instructional systems Unmodifiable “as is” resources

Goods, services, and identities

Goods to be traded and sold Acquires value by branding Product lines, families Symbolic qualities encourage being acquired Appeals to wide audience Creates lines of consumer goods Guarantees stability, dependable value Value is acquired by reputation, standards

Branded instruction (Khan Academy) “Open” universities (OU, MIT, CMU) Families of courses (publisher lines) Educational service providers Standards (SCORM, AICC, IPSTBI) Federations (edX, Udacity, Coursera) Remixable resources

Interfaces

Gives humans interfaces to technology functions (auto steering and pedals, OS desktop) Creates interactivity—action–response sequences Provides means for unpredictable action by user Amplifies user action (power steering) Possesses understandability, user-friendliness, transparency, configurability, adaptability, and intelligence

Personal learning environments Learning management systems Knowledge management systems Database tools and interfaces Web page controls User-configurable control settings

Multiuser systems/networks

Enables communication across space and time Coordinates human activities (traffic lights, wayfinding systems, signage systems, information systems, communication systems, telephones, computer networks) Provides a place where people connect, form, and coordinate the activities on their own choice Designer does not control how the system is used Provides platform, allows user self-organization Provides accessibility, connectivity

The Internet Social software (Facebook, Twitter) YouTube, TeacherTube E-mail Wikis, blogs Video mail Skype, meeting software Collaborative learning environments Learning communities Schools, classes, courses, programs

Projects

Forms around a desire for change Centered around a goal, a cause, a task Attracts, unites efforts of many people Purpose or goal may evolve Designer mobilizes cooperation, commitment Design involves a group, not just a single designer Controlled by stakeholders not designer Designer provides space, envisions, gives resource

Designer-made collections (iBooks) Remixing, repurposing projects Reusable object repositories Participatory design projects Professional organizations

Discourses

Organizes ways of talking, writing, and acting Resides within a community Directs attention of community members Defines “what matters” Entails tension between established forms and innovation Gives rise to new metaphors, vocabularies, discourses Involves new ways of seeing the world, categories Provides solidarity, new articulations Generates new ideas, research

All present and future “–ism” theories Movement to reconceptualize design Discourse communities in any professional field Threaded conversations at professional conferences (games, reform, design, research, etc.)

Design Process • 55

they as instructional designers might actually design. Perhaps they should, or at least they could. Naming them as designable artifacts gives them definition in a designer’s mind and introduces some new terminology into design conversations. • Interfaces consist of control systems that allow a learner to access services or functions. We readily recognize computer desktops as interfaces with the computer’s operating system functions, but we may not as readily think of Web page controls and classroom response mechanisms (including clicker systems or simply raising your hand) as serving a similar function. • Multiuser systems and networks begin to link people for instructional purposes. They provide common, usually virtual, places where learners communicate, congregate, publish, and interact with each other. Think of the Internet, e-mail, messaging, blogging, and many other communication systems we use daily. • Projects take multiuser systems and networks one step further. They provide a task within the commons area in which anyone who wishes may become a participant and contribute to a shared goal. The goal may migrate over time, because once the designer has created the task commons, what happens is more or less in the hands of the participants. Wikipedia is a good model of this type of commons, so it is evident that individual participation can take things in new, unexpected directions. It also shows that some degree of moderation of the project may be needed at different times to keep it roughly on course, but some projects are intended to find their purpose as participants reach new insights together. • Discourses represent a type of higher-level artifact that instructional designers do not normally recognize. A discourse is created by the introduction or evolution of a new set of categories and discussion terms within a common community not tied to a specific medium. A professional organization or field represents one of these. As new terms are introduced into the discourse of such a group, the fate of those terms is in the hands of the ongoing project embodied in the discourse community. Gagné (1965a, 1970, 1977, 1985) established a new set of terms for instructional designers, as did Briggs (1970). Numerous later theorists have likewise either modified the existing set of discourse terms or started new discourse communities. Krippendorff describes his continuum as “a trajectory of . . . design problems”, “each building upon and rearticulating the preceding kinds and adding new design criteria”, “extending design considerations to essentially new kinds of artifacts” (Krippendorff, 2000, p. 6, emphasis added). Design problems at the upper level of Krippendorff ’s continuum include all of the types of problems below them. This becomes apparent as you work backward through the levels: Discourses are advanced and disseminated through projects, to which many contribute. Projects require multiuser systems/networks for communication, which in turn use interfaces. These represent goods, services, and identities that use designed products. A designer moves upward through a continuum of design problems like this one to find the right kind of problem that supplies an appropriate design architecture—one that best fits the immediate context of need and purpose. Designers who think they are designing “a one-of-a-kind online lesson” use the constructs associated with such designs. Designers who design “interfaces” find that they use different conceptual structures. This principle holds for every type of artifact along the continuum. The continuum, therefore, represents one basis for grounding a design rationale for learning environments. Application Exercise Designers must learn to think of what they design in abstract terms—terms that relate their designs in terms of deep rather than the surface structure.

56 • Fundamentals

• Reflect on Krippendorff ’s categories and identify instructional products from your past experience that represent each of the categories. • Where would you place museum displays? Three-dimensional models? Aircraft simulations? Multi-player games? Flash mob events? Design Process As a designer commits to a design process, it is like placing a bet that the process will direct the team’s attention to the most important design questions in the most appropriate order. Different kinds of learning environment call for different approaches to the design process. If your design problem is to train astronauts, then one of the design processes you will certainly execute is to make an inventory of the tasks an astronaut is expected to perform—a process that falls under the heading of “task analysis”. On the other hand, if your problem is to train doctors to understand the cause–effect workings of the endocrine system, you will execute an analysis process that identifies the components of the system and their intricate cause–effect interactions. Finally, if your job is to train troubleshooters how to find and cure problems in a multi-million dollar radar system, you will probably execute both kinds of analysis: one to capture the tasks of the troubleshooting skill, and the other to capture the intricate internal cause–effect processes of the system itself. There is not just one description of how to design that works under all conditions, and simplistic descriptions of design do not equip the career designer to make decisions about how to tailor design processes to the nature of the problem. A description of design should not outline what a designer should do, but what a designer should ask; and it is not enough just to tell a designer what data to produce, it must also describe what data a designer should gather and consult and how to use it for decision-making. It is easy and tempting to think of design processes in terms of steps to be taken, but steps are a substitute for thinking deeply about the design problem. Steps can pre-define answers by subtly inferring what the completed design should look like. Professional designing draws on design approaches from many fields, blends them, and harmonizes them into flexible problem-solving processes according to need. The remainder of this chapter examines four different views of design—each from a different perspective— to see what value each one offers the designer in adapting the design process to specific design problems. The Organizational View Design is pursued within organizations that expect the time and money spent on design to produce value for the organization. Different organizations want to maximize different values: for some it is product quality, for others it is low cost. Some organizations want their instruction to send a message to the learner, such as “We value you”, or “We expect big things from you and have confidence in you.” In the past, organizations have tended to dispense the bulk of instruction in formal, sit-down, classroom training. However, that is rapidly changing. Many organizations are looking for ways to blend instruction into the workplace environment and into task performance through job aiding, mentoring, peer tutoring, and professional development communities. They are also interested in testing the ability of the new media and the Internet to reduce costs without losing personal presence. Organizations view design through the lens of value-production and value-measurement: • They are more and more realizing that training is an important part of their product offering: something they can use to enhance the value of their product to customers.

Design Process • 57

• They are realizing that there is value in training that enhances product support, making the product easier to use and taking the hassle out of the user’s experience. • They are recognizing the value of a workforce that is well trained in product skills and customer relations. • They are realizing that training can unify their workforce, focus creative energies, and increase collaboration among employees. • They are realizing that training and education help create and maintain corporate identity. • They are recognizing the function of training in creating an organizational culture. Instructional decisions support organizational goals if designers are allowed to participate in strategic planning. The first challenge is to identify problems that should be solved by instruction and discern those that are better solved through other means. Figure 3.2 shows this as a first concern. It also shows that, after the dust has settled from a design project, questions have to be answered through organization-level evaluation of whether instruction was the best way to solve the problem. Table 3.2 summarizes the goals and decisions that are typical of this level of organizational decision-making from the instructional designer’s point of view. The ADDIE process is shown at the center of Figure 3.2 to represent the instructional design process because the rational engineering model that ADDIE represents is the most popular and wellknown rational decision process within most organizations at present. This is done not to promote ADDIE but to invoke a familiar symbol as a placeholder representing all of the approaches to design described in this book. Figure 3.2 shows that the designer can be involved as an advocate for both the organization and the instructional design group. The designer must act as an honest broker for both interests. To participate at his or her level of organizational decision-making, the designer has to understand Decisions: • Problem idenficaon and link to causes • Trend/opportunity spong • Decision to use some form of training

AA

Corporate strategy management

DA

DA

AI

EA

Evaluaons: • Problem diagnosed correctly? • The right trend? • Was our ming right? • Was it the right soluon? • Is the problem solved? Figure 3.2 The first round of decisions leading to a decision to train. Designers become involved in organizational strategic planning at this level.

To determine whether training or performance support is part of the needed change

To determine where organizational change is required and possible organizational responses

To identify organizational needs, environmental trends and opportunities for performance improvement

To measure and analyze organizational performance

Performance data

To define performance goals within the organization

Organizational strategic plans

Over-the-horizon assessments of technology, economics, business climate

Internal organizational reports of needs

Professional papers, books

Competition reviews

Trends in the industry, trends in instructional design

User feedback

Productivity data

Data Gathered

Identification of elements of the solution which require training and/or performance support

Identification of opportunities which the organization is willing to exploit

Identification of trends and anticipated changes which require a response by the organization

Identification and isolation of problems that are reducing organizational productivity

Decisions

Participation of Instructional Designers at the Level of Organizational Strategic Management

Level Goals

Table 3.2

Is the problem solved?

Did the solution result in the hoped-for change in performance?

Was training and/or performance support valid elements of the solution?

Was the timing of the change correct?

Was this the right opportunity?

Was the identification of the problem correct?

Evaluation

To make data-based recommendations on possible training and performance support remedies for given needs

To support the formation and maintenance of a balanced workforce of skilled personnel for instructional design and development

To assist in carrying out analyses of costs involved in training and performance support

To assist in determining which performance needs are amenable to change through training or performance support

To spot proven trends in training and performance support which can improve performance, reduce costs, or speed the response time for improving performance

To assist in creating performance measures and in collecting organizational performance data

To help establish performance goals and valid measurement indices for the organization to monitor

Designer Participation

Design Process • 59

organizations, how they operate, their priorities, and their goals and has to feel comfortable as a participant in organizational decisions whose interests are broader than just instructional design. Figure 3.3 shows that the organizational decision process moves to a second stage. By this point a decision has been made to solve a problem with some form of instruction or performance support. The key decisions at this point pertain to what type of training and/or performance support remedy will be used and defining the scope of a project. Table 3.3 describes the goals and decisions of importance to a designer at this level of decision-making. Participation at this level means that designers can help define the environment that they or a contractor will work in to carry out the project. The organization can now begin to forge agreements with both the internal client and outside service organizations. Problem analyses that may have been initiated earlier are continued during this phase in order to: • Characterize the learner (target population analysis). • Define current training practices (current training analysis). • Identify the resources that have sustained training and training design activities in the past (resource analysis). • More clearly define the scope of the evolving project in terms of desired performance outcomes (needs analysis and performance analysis). These analyses are standard parts of the ADDIE process. They are often referred to as “front-end” analyses. Normally they begin before a project as part of the determination of whether or not there will be a project. Then, once a project is decided, the analyses advance to a greater level of detail and are normally turned over to the design team to complete.

Decisions: • Types of training/performance support to be applied • Project goals, available resources

Corporate strategy management

AA

DA

DA

AI

EA

Evaluaons: • Was training the right choice? • Right kinds of training/ performance support chosen? • Problem solved? Figure 3.3 The second round of decisions as an organization moves toward a decision to train.

60 • Fundamentals Table 3.3

Participation of Instructional Designers at the Level of Organizational Performance Management

Level Goals To continue and extend analysis of organizational needs, environment, trends, and opportunities

Data Gathered

Decisions

Target population data Project selection and and implications for broad definition design Project goals Current training system information Project client To enlist client organization(s) and organizations in Resource information service organization(s) potential projects are identified High-level To analyze in greater performance analysis, Feasibility of project detail the performance as the basis for goals to be improved specifying project Responsibility for scope performing To set specific evaluations and performance ongoing maintenance improvement goals and define measures Resource plans and to guide project agreements with formation client and service organizations To begin in-depth target population analysis and current training and resource analysis that can be applied to the project To explore training and performance support options To enumerate multiple options for training and/or performance support to meet performance improvement goals To select a combination of training and/or performance support remedies as targets for project definition To reach agreement with client organizations and support organizations on project dimensions, goals, resources, and general schedules and availability

Evaluation

Designer Participation

Was the right combination of training and performance support selected?

To participate and possibly lead analyses

Was the resourcing of the project adequate? Were the responsibilities of each client and service organization fulfilled? Did the client and service organization relationships work? Were the goals set for the design project met?

To help for the client and service organization relationships To help specify performance improvement goals for the project To help identify feasible project options through cost and other studies To help specify requirements for client organization cooperation, including resources and schedules To begin to assume responsibility for client and service organization relationships

Design Process • 61

Figure 3.4 represents the decision to launch a specific project and to assign resources to it. The goal at this point is to create specific plans, goals, and criteria for the execution of a project based on the need identified and refined at the two previous levels. The designer should be heavily involved in decisions at this level. When decisions are made without designer participation, limitations on the designer and on the effectiveness of the project are almost always a problem. Table 3.4 summarizes the goals and decisions of this round of decision-making. Decisions: • Project goals, schedules, resources, personnel, deliverables

Corporate strategy management

AA

DA

DA

AI

EA

Evaluaons: • Adequacy of schedules, resources, personnel? • Project goals met? • Performance improved? • Lessons learned? Figure 3.4 The final round of decision-making that defines and launches a design and development project. Table 3.4

Participation of Instructional Designers at the Level of Design Project Management

Level Goals

Data Gathered

Decisions

Evaluation

To define a specific plan for the execution of the design project, including role assignments to design team members, project schedules and processes, deliverable specifications, and project management goals

Deadline and deliverable expectations

Identify processes to be followed and the procedures for carrying them out

Was the project plan adequate for the design problem?

To define project communication processes To assemble and begin to train the design team for the project

Production rate and volume data Identification of specific resources for supporting team members

Identify documentation to be kept public to the team Define schedules, assignments, deliverables

Designer Participation

To define a project schedule, personnel assignments, deliverable descriptions, and project Were staffing, resources, goals and schedules realistic? To assess the skills of Were the processes design team members chosen the right ones? To arrange project Was the project needs: facilities, completed on time and communications, at desired quality? equipment, workspace How well did the To review the project communication plan with management processes meet the need? Did the designed product meet the goals set out for it?

62 • Fundamentals

Work proceeds from this point according to a specific project management plan devised at this level of decision-making. The project plan defines specific project team members and their responsibilities; it defines schedules for design processes, and it identifies specific deadlines and deliverable items, including the quality standard for deliverables. If the project is to be conducted with team members working at separate locations, one of the plans of the designer at this level is a standard for project communications. Application Exercise Data must be used to support decisions at each stage of organizational planning represented in the three previous figures. • For each of the stages, identify the kinds of data that an organization might use. The Team Design View When the organization has made its decision to launch a design project, the design team takes over. The design team views things from a different perspective because it is solving a different kind of problem. Instructional designers frequently become leaders on design teams because they tend to have more formal training than their co-workers in the design process. In this role, a designer becomes a kind of linguist. The designer becomes the interpreter and translator of the multiple specialist languages spoken by different team members. It falls to the designer to unify the often competing visions of contributing specialist designers on the team. Figure 3.5 illustrates the polyglot of specialized design languages normally represented on a small design team. Team specialists have their own languages, and designers must be able to understand them as well as speaking their own “instructional designerese”. The development of design languages by specialists is an important sign of maturing design technology. Chapter 7 discusses design languages and their value in more detail.

  ☺ ☯

  ☺

DESIGNER   ☺

SME

  



☺    

WRITER

ARTIST

PROGRAMMER

Figure 3.5 Representation of the many design languages spoken by members of a multi-disciplinary design team. A designer becomes the interpreter of these many languages.

Design Process • 63

Management of Innovation by a Skilled Team A designer coordinates the creative efforts of a team of design specialists across several phases of design activity, from team organization through design completion. What follows in several sections below is a description of team management activities that complements the activities of the design processes described in the next few chapters. Team management is described here in order to highlight separately the practicalities of organizing and managing a creative team. If instructors who are designing instruction for classroom delivery think these ideas do not apply to them, they should reconsider: it is increasingly the case that instructors require the services of specialists (e.g., artists, programmers, writers, etc.) to complete their lesson plans. In the future we can be sure that every trained designer will be leading some kind of team, and instructors should also consider their job as being that of a designer. Bucciarelli (1994) describes the value of coordinated effort and shared vision as the goal of a design team: Shared vision is the key phrase: The design is the shared vision, and the shared vision is the design—a (temporary) synthesis of the different participants’ work . . . Some of this shared vision is made explicit in documents, texts, and artifacts—in formal assembly and detail drawings, operation and service manuals, contractual disclaimers, production schedules, marketing copy, test plans, parts lists, procurement orders, mock-ups, and prototypes. But in the process of designing, the shared vision is less artifactual; each participant in the process has a personal collection of sketches, flowcharts, cost estimates, spreadsheets, models, and above all stories—stories to tell about their particular vision of the object . . .The process is necessarily social and requires the participants to negotiate their differences and construct meaning through direct, and preferably face-to-face exchange. —(p. 159) The instructional designer’s job is to manage a team toward this kind of outcome. A High-level View of Project Management The team management process can be described as a series of repeating cycles of activity aimed at: (1) the conceptual development of the design, and (2) day-to-day management of schedules, people, resources, and client relationships. Figure 3.6 illustrates this cycle of management events alternating with periods of separate specialty design. At project events two things happen: (1) team leaders share their overall concept of the product, and (2) specialists report on the progress in their individual design areas and negotiate details with other specialists. Some of the functions of the designer in this process are purely management-oriented. They are explained here because they are a practical fact of the designer’s world often omitted from texts on instructional design. These functions accomplish: • • • • •

Project direction and coordination of specialist task assignments Scheduling and resource planning Planning client briefings and sign-offs Coordination of prototype production and testing Conducting deliverable reviews.

They also accomplish: • Joint design discussions by specialists aimed at refining the design concept • Detailed specialty design reviews and sub-design integration

64 • Fundamentals

Project Conceptual and Management Events

Individual Specialty Design Work Figure 3.6 The continuous cycle of team activity viewed in terms of the day-to-day management of a team in the evolution of an innovative design concept.

• Design presentation and prototype demonstrations • Design commitments (baseline setting). Figure 3.7 depicts eight phases of a project’s conceptual development. These phases are independent of any particular description of the design process. The phases depicted in Figure 3.7 can be overlaid on whatever formal design process is used, giving the designer specific project management tasks to carry out that technical design process descriptions sometimes omit. Some of these are practical tasks (e.g., team organization) while others relate to conceptual targets in the evolution of the design. These conceptual targets are frequently mismatched with formal contractual targets, so it is important to recognize them as important steps for marking progress, along with the formal contractual targets. The phases depicted in Figure 3.7 are described in the sections that follow. Phase 1: Front-end Analysis Phase 1, front-end analysis and project definition, usually begins before an organization makes a commitment to authorize a project. As the organization works toward a decision on a project, it will sometimes begin the processes of content analysis, target population analysis, and current training and resource analysis in advance. These processes supply data to inform the decision to launch a project. Because these processes may have begun prior to formal project initiation, a designer may be asked to study the existing documentation even before beginning the project. Work on these analyses normally continues after project launch to refine and add detail. Phase 2: Team Formation and Organization The team formation and organization phase has important strategic value for the project. During this phase a designer opens communication lines with the sponsoring organization and with other stakeholders and contract service providers, who may be from outside of the sponsoring organization. Data gathering and decision-making during this phase involves a number of practical activities: • Research the client organization: To confirm the rationale for the project To confirm the need through data sources To identify stakeholders in the project, their priorities, and their goals To identify stakeholder responsibilities, investment, and authority chains    

Design Process • 65

Formal project iniaon

Producon begins at scale

Phase 1: Front-end Analysis and Project Definion

Phase 2: Team Formaon and Organizaon

Phase 3: Problem Verificaon and Framing Phase 4: Formaon of Design Strategy

Phase 5: Design Concept Creaon

Phase 6: Detailed Design Planning

Phase 7: Prototyping and Tesng

Phase 8: Producon Planning

Figure 3.7 Depiction of project phases based on the conceptual evolution of the design and day-to-day project management.

To identify approvers and sign-off authorities To confirm the deliverable and approval process To confirm project focus/scope To define success criteria in measurable detail To identify hidden agendas and potential political snags To identify real value propositions for each stakeholder To test the firmness of constraints and negotiate where possible To determine the innovation envelope To confirm review and sign-off dates and schedule times and places. • Establish relationships with service providers and subcontractors: Secure resource agreements Secure talent Finalize service/support agreements Confirm working arrangements Secure subject-matter expert (SME) time agreements Assess true SME expertise levels. • Establish the workspace for the team: Determine where to locate the team Determine the facilities, equipment, and infrastructure needed Form an off-site work plan, if needed Provision office(s) Set up security (electronic, files, location(s)) and team identifications. • Add details to project work plans, schedules and budgets: Confirm team and vendor/support work plans and confirm schedules Detail deliverable formats for vendors/support groups         

     

    

 

66 • Fundamentals

Set travel policies and guidelines Form a communications plan for/with all stakeholders Form a documentation plan Make a plan for including stakeholders in the design process. • Prepare project briefing documents and materials: Prepare a project pitch Set up a public relations plan (for reporting progress and gaining visibility) Set out reporting and communication formats. • Organize the team and begin to build a team culture: Do a team skill assessment Make team assignments Set up team communication and interaction guidelines Begin to build an atmosphere of respect and sociability Express workplace rules and expectations.    

  

    

Phase 3: Problem Verification and Framing Problem verification and framing processes are more conceptual in nature. They establish the goals of the project with respect to success criteria, innovation, and team learning. This is the phase in which the design team begins to form a common understanding of the problem by verifying that the problem has been correctly identified and described by the client. The activities identified below are carried out during this phase, for which there is usually not much time. Problem Study and Verification The design team engages in an intensive study of the design problem with the goal of expressing the project purpose, goals, and success criteria from the team’s point of view. The team members study pre-project documentation that led to the project’s establishment. They interview project stakeholders to determine the most critical performance problems, emerging needs, or other (real) reasons for the project. Different stakeholders will often identify different critical issues during this process. Projects often have multiple reasons for being. Taken together, these responses represent the “thorn in the lion’s paw” for the project—the opportunity to relieve one or more organizational pressures. A designer looks for the unspoken agendas. Study of Analysis Documents Prior to project initiation, studies may have been conducted into target population characteristics, current training practices, and training resource allocations. These studies are often incomplete in terms of the designer’s needs. The design team extends these analyses. It supplies the beginning points of innovations that can solve significant client problems. Appendix A identifies points to consider during target population analysis, and Appendix B identifies points to consider during current training and resource analysis. Problem Framing and Goal-setting A project team needs to state its goals clearly from the outset. These are not the educational goals of the learner but the design goals of the team. This statement should express the team’s aims in terms valued by the organization: product quality, innovation, time and resource expenditure, client satisfaction, learner satisfaction, and performance improvement. Assimilation of the Content and Culture of the Project An instructional designer sees inside the subject-matter and inner workings of a variety of organizations, professions, and crafts. Part of this includes learning the technical content, the working at-

Design Process • 67

mosphere, social culture, politics, commitments, loyalties, performance standards, and mutual trust relationships that typify different organizations and workplaces. Designers must learn to absorb these unquantifiable aspects of the project setting as well as the technical content quickly. This includes learning from the people who work in these environments their languages, their values, their expectations of each other, their jobs, their motives, their reward systems, their character, how they communicate, and how they obtain a sense of their own value as part of a larger enterprise. How well the designer learns these normally undocumented aspects of the project environment has enormous impact on project success. With experience a designer learns what to look for. Experienced designers learn how to communicate effectively with a wide variety of people at all levels of the organizations they work with, showing respect for the knowledge and expertise that these people represent and the investment they have made in their careers and their accomplishments. The design team cannot join this culture, but it must learn it. The design team must come to see the content to be learned with what Reigeluth (1999) calls an “epitome” view. This view comprehends the essence of what is to be learned at an abstract level of organization and structure. The designer and the design team must drive quickly to an understanding of the subject-matter that captures its details and its essence and inner structures. Chapter 11, on the content layer, describes the nuances of subject-matter structure and patterns of internal organization. Identification of Project Opportunities Every project presents unique opportunities to innovate, to try new ideas, to create an especially satisfied customer, to serve learners better, to pioneer new design and production techniques, or to learn something about instructional design by experimenting. This is one of the things a project team should consider at the outset of a project: what will be the design team’s take-away learning? Phase 4: Formation of Design Strategy The design team should strategize how it will attack the design problem through process. This will include considering design approaches suggested by all of the views described in this and other chapters, borrowing ideas from them that will enhance the team’s performance. This involves tailoring standard design process descriptions of multiple kinds to the needs of the specific project. The product of design strategizing is an outline of the design processes that will be used for the project. These will become part of the shared design language of the team. Phase 5: Design Concept Creation A design concept is not a design: it describes the broad structure within which a design will evolve (see the sections on the architectural view at the end of this chapter). A design concept represents the highest-level design decisions—the decisions that will be used as the design’s DNA to generate the secondary structures and details of the design. Subsequent decisions will come from or be tempered by this pattern. Phase 6: Detailed Design Planning Detailed design planning is the main topic of the majority of the chapters in this book. Multiple approaches are recommended to enhance the design team’s ability to adapt the design process to the needs of the project. No single approach is sufficient by itself. Whatever approaches are used, the following practical considerations should be used to test and validate each design decision: • • • •

Ability to produce the necessary volume of product within available time and budget Implementability in the existing environment and its organizational culture Maintainability Longevity, lifetime of product service

68 • Fundamentals

• • • • • •

Adaptability to multiple uses Sustainability over time (life cycle cost and ROI) Usability/ease of use/transparency of media Demo-ability Ability to attract, engage, and sustain learner interest Reusability/remixability.

Phase 7: Prototyping and Testing Prototyping is more than the piecemeal evolution of a design; it is a form of design research if it is performed in the best way. Michael Schrage, in Serious Play (Schrage, 1999) describes prototyping as it is used for innovation in the commercial world. The book’s subtitle reinforces this idea: How the World’s Best Companies Simulate to Innovate. Instructional designers are taught to prove a product under development through formative evaluations and revision until a satisfactory outcome is obtained. What is missing from this scenario is: (1) a principle-based design that possesses an explicit rationale, and (2) a set of principles that refer back to the rationale to guide revisions. Educational research is often criticized for performing shortterm studies under highly controlled, artificial conditions to find effects related to a small number of narrowly defined variables. The criticism is that tests of a very limited scope conducted under laboratory circumstances on a limited number of variables cannot produce the kind of new knowledge that can be readily applied in real-world settings. Principled prototyping—called design research or design-based research, discussed in Chapter 6—offers an alternative route to knowledge about how to make things work robustly in messy real-world settings. The solution Schrage offers is to “play” with prototypes as a means of innovation. His claim is that an organization innovates its way forward through constant “playing” with prototypes. This is how design expertise is accumulated. “Play” with “prototypes” means two things to Schrage: (1) building prototypes and testing them under different conditions to see how they work, and (2) discarding old prototypes as soon as they have been learned from and building new ones based on what was learned from the previous one. Prototyping is like a prolonged experiment that improves not only a specific product but also our knowledge about how to make products. Schrage describes the emergence of the spreadsheet as a tool of businesses to prototype future versions of the business and play with alternative versions. This principle, he says, is one that organizations (including design teams) should come to rely on increasingly. The plan for prototyping is one aspect of a project’s evaluation plan. Appendix C describes the creation of this plan. Phase 8: Production Planning and Costing Production planning and costing are management processes for which there are many existing tools and reference manuals (Kearsley, 1982; Head, 1994). Instructional designers must build manufacturability into designs. As design decisions are made, there must be “sanity” checks that ask questions about the feasibility and reasonability of choices (see Phase 6 above for a list of question topics). Application Exercise Designing within teams has become the norm. Designers require the services of specialists to create products of sufficient quality to attract and hold the attention of learners. • Make a list of the qualities in a designer that you think are required to work in a team environment. Don’t list the design competencies; list the kinds of people skills and other abilities you think would be needed in any team setting.

Design Process • 69

The Layer Design View The two previous descriptions of design in this chapter have set up a pattern of zooming-in toward the details of designing. We began by describing design processes within the context of the organization and its interests. Then we considered design within the context of the team and its interests. Now we will examine the design process in terms of the processes and concerns of the designer: the person most responsible for determining the order of design activities. Questions about the order of decision-making during design occupy a lot of the attention of any design field. Two main options available to a designer are: (1) to schedule decisions in advance based on a formal process description, or (2) to schedule decisions opportunistically based on an order that unfolds during designing. The first of these options provides the rationale for the family of systematic design procedures and models that fall under the umbrella of instructional systems design (ISD), sometimes called ADDIE (for analysis, design, development, implementation, and evaluation). ISD and ADDIE are described in detail in the next chapter, along with a history of their origins. In this section we will consider an opportunistic design process that uses the theory of design layers to provide a discipline for the order of decision-making. As a beginning point for the explanation of the layer design view, consider Figure 3.8, which depicts a simple systematic design model typical of the early days of design model building (circa 1960). This primitive model contains processes that lead up to the moment of design (the highlighted box) and processes that lead away from it, but the process of most importance to the designer—what happens as the design itself emerges—is treated as a single box in the process. Details about how to carry it out are usually sketchy. This is because design models were not really invented to describe how designs are made: they were invented to orchestrate and manage the efforts of a designer and, eventually, a multi-disciplinary design team, usually under conditions of limited time and resources. Models like this are engineering efficiency tools used for administrative planning and scheduling; they are not studies of the design process itself. Even the more detailed and complex process models that emerged over time (see Figure 3.9), some of them containing literally hundreds of process boxes, were invented for the same purpose. (See, for example, Silvern, 1965.) They too contained mystery boxes. The complex models told the designer what part of the design to produce, and how to document it, but they often did not describe to a designer how to go about designing. The layers view of design answers questions not addressed by systematic models by focusing the designer’s attention on the functional elements or sub-systems of the system being designed rather than on the administrative concerns of the design process. Design using layers assumes two things: • The designer sees the system being designed in terms of its semi-independent internal functions, its functional sub-systems. • The designer commits to an order of decision-making in which later decisions are conditioned by earlier decisions. The key question for the designer when designing using layers is which decision to make next. A designer who uses layers realizes that the order of decision-making unfolds. The order of decisions is determined by prior decisions, because any decision made firm within one layer influences future decisions within other layers. This rippling influence among decisions is what determines the order of design and causes the design to unfold in a contingent manner.

70 • Fundamentals

Define objecves

Create test items

Develop instrucon

Implement instrucon

Evaluate

Figure 3.8 A simple instructional design model, with a mystery box.

Figure 3.9 A more complex model, still with a mystery box.

Some examples will help show how reasonable this account of design is from the designer’s point of view: • A designer is assigned a project to make three videotapes. This pre-determines the medium of instruction and makes the design step for media selection unnecessary. • A designer is asked to use a particular instructional approach because it fits the style the organization has already adopted. This eliminates many design steps related to strategy planning.

Design Process • 71

• A designer has to use a certain piece of software to produce Web resources. This eliminates the selection of software tools and pre-determines the kinds of software functions available to programmers, at the same time limiting strategy, conversation, and representation options. What these examples show is that even if a designer is committed to using a systematic design model, decisions pre-made before the project become the “givens” of the project and eliminate the need for certain design sub-processes. More importantly, they almost always force the designer to rearrange the order of decision-making contrary to the order specified in a design model. It is a truism that the only projects that go according to design model order without modification are the ones completed during the designer’s schooling. Layered Design of Instructional Systems The thing being designed is always a system of some kind, and systems are “living” and “dynamic”. A system: • • • •

is capable of living and functioning harmoniously and sustainably within its environment; receives support from its environment and gives support to other systems in it; has component parts that work together flexibly and without destructive internal conflict; is able to survive changes in external conditions within some range of variability; is capable of growth and change; • is connected to its environment and to neighboring systems in multiple ways; • can serve multiple purposes within its environment. A designer designs the parts of an instructional system: sub-systems. Layers represent functional sub-systems of an integrated system. Since so many designed objects are designed in layers, it is easy to find an example of how layers represent sub-systems of a larger system. Your car is a functional system that is made up of several sub-systems: it was designed in layers. The steering system was designed by a different group of designers than the brake system. The air conditioning system required yet another design group. When your car was designed, the design team knew in advance of certain properties and dimensions it had to have: a sporty car is built to a different plan than a stretch limousine, yet both designs need a steering system and a brake system. Layers and the Order of Design From the perspective of design layers, the order of design decisions is governed by the following principles: • Any design decision in the entire design can be the first decision of the design, and design can proceed from there in any order. • Any decision can be made firm at any time, limiting the decisions remaining to be made. • Each decision made firm adds some additional new decisions to be made and eliminates the need for some others. • Once made, decisions can be unmade, leaving them and all other decisions dependent on them free to vary again. • Decisions (usually) proceed in constellations rather than individually. These ordering principles differ from those of design models in that the familiar process sequence supplied by models is missing. At the same time, the decision-making addressed with layers takes

72 • Fundamentals

place inside the process boxes of a design model, so there is not a conflict between the ordering rules of the two views of design. Most designers find that these layer-related ordering rules are intuitive because they are ones they have been using all along without realizing it, even when they were following a design model. Applying the ordering rules above, the designer enters a continuous cycle of decision-making like that illustrated in Figure 3.10. In Stage 1 of the cycle the designer determines the design space by inventorying the extent of the decisions currently unresolved. In Stage 2 of the cycle the designer chooses the next most critical decision or cluster of decisions to be considered. In Stage 3 of the cycle the designer identifies structuring principles that can be brought to bear on the targeted decisions. Sources can include theory, best practices, prior models, innovative ideas, and even flashes of inspiration. In Stage 4 the designer applies these principles to create a design hypothesis to be tested. The impact of each set of proposed decisions from Stage 4 is calculated in Stage 5. This involves determining costs, skill levels, equipment and software requirements, estimates of acceptance by users, maintainability, sustainability, and alignment with overall design goals. Potential side effects are considered. Stage 5 may be the occasion for prototyping and testing aimed to provide data to support one alternative or prove the feasibility or cost of an idea. In Stage 6 a decision takes place. This may involve reviews by stakeholders at a level proportional to the importance and potential impact of the decision. In Stage 7 a new configuration baseline is established that includes the new decision(s) that have been made. This can represent a new point of agreement within the team and potentially between stakeholders and the team. It also represents a new set of constraints on future design decisions. The key to this cycle is Stage 2—deciding what to decide next—choosing the next most critical decision(s). One or more of the following factors point to the next most critical design decision: • The decisions most constrained by the last decision made. • The decisions most constrained by external factors, such as team skills, delivery infrastructure, time and money resources, etc.

Stage 1: Define design space Stage 7: Publish configuraon baseline

Stage 6: Select configuraon

Stage 5: Determine impact for each design Figure 3.10 Stages in a continuous cycle of design decision-making.

Stage 2: Define “next most crical” decisions

Stage 3: Idenfy structural principles

Stage 4: Define alternave configuraons

Design Process • 73

• The decisions that take most advantage of an opportunity afforded by the last decision. • The decisions that leave the most options open for later decisions. • The decisions for which there is the most supporting data from the analyses of the target population and of the instructional context. • The decisions that most advance the quality of the solution. • The decisions that most directly address a major client criterion or desired feature. • The decisions that produce the most useful data for addressing future decisions. • The decisions that best lead to the satisfaction of an innovation goal. • The decisions most necessary to the implementation of a chosen theoretical position. • The decisions that respond to the latest prior decisions in other layers of the design. • The decisions that will teach the designer the most. • The decisions that will make the design most modifiable, maintainable, or efficient. • The decisions that create the most manufacturable design. This list is not exhaustive. Several factors can raise the priority level of a “next decision”, and each new decision revises the old decision queue. Though this may seem to make the design process unmanageably complicated, it is what reasserts creativity and strategic decision-making into the design of instruction: a dimension of designing that can be marginalized by over-attention to process. Clustering Design Decisions Rather than making decisions firm one at a time, a designer using the layer design approach usually advances the design across many layers at once by clustering decisions across layers. A constellation of proposed decisions is selected, and tentative decisions are made. Then the entire set is weighed together. Schön (1987) describes this method of proposing hypothetical designs and then testing them in a design “conversation”. Just as Schön’s domains of an architectural design define the loci of individual design decisions, the layers and sub-layers of an instructional design localize the attention of the instructional designer without neglecting the integrity of the whole. Schön’s domains and layers are conceptually identical. As design proceeds new priorities rise in importance as clustered decisions within different layers are made firm. A designer may advance multiple parallel design configurations—each one a kind of design hypothesis—as a way of testing the effects of different sets of priorities. In this approach, a design can grow from a few core structural commitments outward in increasing levels of detail. Details of the Design Cycle The stages of the design decision-making cycle are illustrated separately in Figure 3.10., but in everyday practice the movement of a designer’s thinking through the cycle from stage to stage is so rapid that it may seem the stages are being performed all at once. Some designers feel that the stages can’t be separated without breaking the “flow” of the design process. However, the importance or the scope of a set of decisions can have the effect of slowing down the process to where the sequence of stages becomes quite deliberate, especially when multiple members of a team are involved in the decisions. A designer must determine when decisions are sufficiently important to warrant explicit attention to individual stages in this way. It is important to note that any of the stages may raise a degree of uncertainty that requires additional data gathering to make the decision firm. This should be noted as a close connection point between design and research that is carried out by designing— sometimes called design research or design-based research, which is discussed in Chapter 6. Design advances by the progressive placement of constraints through successive rounds of decision-making until the design reaches what can be called the “default” level. This is the level of decision-making in any area of the design beyond which the designer cannot see value being added.

74 • Fundamentals

This is the point of trade-off between design effort and anticipated impact. It keeps the design process from becoming reductionist to an extreme level. It is also the way a designer tailors the design process to project resources. The cycle continues until all practical constraints have been placed and the designer judges that there are no decisions of significant value to be made. The degree of formality a designer uses at each stage of this cycle depends on several factors: the structural importance of the decisions being made, the number of decisions being made at once, and the impact of the decisions on cost, time, etc. During a design project the decision cycle is repeated literally thousands of times. In the early phases of its application the decision cycle is used for highlevel decisions that shape the architecture of the solution and set a mold for more detailed decisions. As design proceeds, the cycle is reapplied in wave after wave of decision-making, and the scope of decisions becomes increasingly focused on the details of the design. Jenn’s Table: A Practical Vehicle for Applying Layers A practical tool, called Jenn’s Table (see Figure 3.11.) helps to inject imagination and innovation into layer designs while at the same time facilitating the exploration of parallel design hypotheses. The table is named after Jenn Price, a colleague, who came up with the idea while solving a design problem involving a highly interactive simulation device. Jenn’s Table is a way of laying out the sequences of events and features of a design as they are envisioned (left column). This provides a narrative of the experience from the user’s point of view. The remaining columns represent each of the design layers. They are used to post the implications of each step in the narrative to design consequences within the layers. They pose a question to the designer: “What are the implications, for this layer, of the narrative in the first column?” The design evolving in Figure 3.11. requires that the learner put on a special sensor glove capable of monitoring heartbeat (the action listed for row 3). The machine, which has been in an idle cycle (row 1), is activated as the learner steps onto a treadmill (row 2) and dons the glove (row 3). Every

Event

Content

Strategy

Simulaon N/A idles, waing for next user

Interesng surface display draws in the curious user

User steps onto treadmill, reads direcons

N/A

User inserts right or le hand into glove

Etc.

Control

Msg.

Repr.

Med-Lo

Dat-Mgt

“There is something interesng here about your heart.”

Low audio of Idling cycle heart pulsing; display heart pulses in me; vessels pulse; so music

N/A

Simple, Glove, inving ready for direcons; a hand challenge to explore how the heart works

“Here are your direcons.” AND “How will your heart act?”

Display changes to human outline; beat increases, music up; user sees self in outline

Introductory module inializes; treadmill measures weight

Weight recorded; appropriate weight table accessed

Etc.

Etc.

Etc.

Etc.

Etc.

N/A

Etc.

Figure 3.11 Example of Jenn’s Table containing information for a particular design.

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event imagined by the designer has a potential impact on one or more layer designs: something to be designed, manufactured, implemented, and maintained. Jenn’s Table helps the designer to envision and capture fresh, innovative ideas while at the same time considering the costs for development, implementation, and maintenance. You can think of the table as a kind of sophisticated storyboard for testing out ideas and their implications in terms of layers. What are the cost, labor, process, and resource implications? Is the design doable? Is it affordable? Is it practical? Could it be built in time? Is there theory that might guide the implementation of this activity? The unfolding of events listed down the left column may not take place in a linear sequence. For this reason it takes a family of tables to represent different sequences of unfolding events. What if the learner steps off the treadmill? What if the learner fails to follow the directions on the screen? The result is more than a flowchart: it is a readable description of the learner interacting with the instruction and an account of both the surface and the underlying designs implications emerging simultaneously from an imagined experience. Benefits accrue through the use of Jenn’s Table: • The designer can focus on imagination first, and design implications second. • Broad design outlines are automatically produced for (and with) specialty designers. • The design is made public, and the design team can use the document as a common tool for teasing out details and their implications. Jenn’s Table is applied as a tool at the critical moment that a design concept is forming in the designer’s mind. The table ensures that imagination has the first say, but that trade-offs temper imagination to achieve practicality. How Layers Work During Design and During Instruction During design, the layers become filled with structure as the designer carries out the decisionmaking process described above. There is no priority order among the layers except the order imposed by design constraints that come with the problem. During instruction the structures within the layers begin to carry out their functions. At this point there is a clear priority of influence among them. The strategy layer becomes the primary layer. It negotiates instructional plans with the learner and directs other layer functions, which are delegated to artifact modules. Modularity is described in Chapter 15. The point is that during the delivery of instruction the equal influence of the layers on each other changes, and the strategy layer directs and orchestrates the operations of the other layers. This makes the strategy layer the ultimate nexus of interaction between the learner and the system. Application Exercise Make a Jenn’s Table. First, in the leftmost column describe step by step an imagined instructional experience—a simple one to begin with that involves just the presentation of information to a learner. Then in the remaining columns list the implications of each of the entries on the left column for each of the layers represented by the remaining columns. After completing this process for a simple example, take a more complicated one that involves a greater degree of interaction with the learner, such as instructing a simple procedure. • Reflect on whether and how this changes your normal pattern of decision-making during design. Does it make things easier or harder? What advantages does it offer you? What disadvantages?

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The Architectural View of Design This section of the chapter continues to zoom in on even more detailed acts of design. The discussion in this section will center on the structural implications of design decisions at the finest level of detail and at the earliest instant of decision-making. This perspective is termed the “architectural” view because, as you will see, decisions at this level of detail can determine the architecture of the entire design. Books on design often miss this most interesting part of designing. Like every other form of expertise, what happens as a designer arrives at a decision is hard to describe because much of what takes place lies within the designer’s mind below the level of verbalization, or even awareness. The description of design in this section takes place in slow motion to permit observation of designer thought processes that otherwise go unnoticed. The goal is not to describe exactly what every designer does but to describe the kinds of things a truly expert designer could or might do during design. The emphasis of this view is on how the designer brings abstract structural ideas to a design in its early stages in a way that gives underlying coherence to the design and causes it to be generated from abstract principles at the deepest level. It will be important at this point to separate in your mind the ideas of design and manufacture. Design is the making of a plan—a synthesis or bringing together of abstract forms; manufacture is carrying out the plan—the assembly of materials. This section will focus only on design, despite the fact that some of the terminology used may sound as though it is referring to manufacture. Design Coherence An expert designer leading a multi-disciplinary team is responsible for design coherence, a concept described by Brooks in The Design of Design (2010). Design coherence refers to the harmonious functioning of the different parts of a design. In a coherent design the parts work together synergistically, creating something that is more than the sum of the parts. Incoherent designs have internal conflicts, work wastefully, and sometimes act destructively. Design in Detail Systematic design process models do not describe the design process in detail because they are focused on relatively high-level, project administration concerns. However, computer architects Gerrit Blaauw and Fred Brooks deal with design at this level of detail in a book titled Computer Architecture (1997). Blaauw and Brooks have strong credentials in design theory. Together they worked on a team with several other highly credentialed computer science pioneers in the design of the IBM System 360 family of computers—a game-changing design whose evolution is documented in the book Design Rules by Baldwin and Clark (2000). The IBM 360 project required the full rationalization of a computer design: not just a single computer, but a family of computers that had to have interchangeable parts. The computer is an exacting, precise machine, and the “default” level of design, the level at which some details fall below the level of scrutiny, does not exist in a computer design. Every design decision in a computer design must have a rationale and must work in complete harmony with other decisions. Otherwise, the computer will not work. The breakthrough of the IBM 360 design team was the “layerization” and modularization of the design. This separation was necessary because all of the computers in the 360 family had to work by the same set of rules to be part-compatible in a way never before seen in the computer industry. In this way, system parts compatible with one computer in the family could also be used with other computers in the same family. One of the learnings from the project was a clear definition of the impact of architecture on computer design—its ability to make the design usable in more ways,

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and its ability to give a design a longer lifetime of usefulness. There is an important lesson here for instructional designers because so many of our products have a very short life. To make this design breakthrough, Blaauw and Brooks had to discover the differences between architecture, implementation, and realization. These are abstract ideas that lie at the heart of a design’s conception. They are terms that could enter the terminology of designers in any field. Architecture, implementation, and realization are stages of design, but not of manufacture. All three represent decisions that must be reached at some point during design, but not necessarily in any specific order. Architecture, Implementation, and Realization Blaauw and Brooks use the example of designing an analog clock (the kind that normally has hands) to illustrate these three parts of a design. • Architecture: The architecture of an analog clock consists of (1) some kind of pointer or indicator to register the current time, and (2) a set of physical positions that can be pointed to. Together these indicate the current hour and minute. These two things specify the clock’s conceptual structure and functional behavior, but nothing more. The things not mentioned in this statement of the architecture include: the size and shape of the hands, their placement, their pattern of motion, their direction of motion, their color, the material they’re made of, nor their style, the placement of the numerals, or whether there will even be numerals. The architecture describes the clock only in terms of those abstract functions essential to time telling (i.e., something pointing and something being pointed to). The description is completely free of detail. There is no mention of dimension, physical structure, nor any other physical property. The architecture, then, is a very abstract thing. It is an idea. It is the basic, unadorned idea of what is required to create a clock: pointer, and pointed-to. • Implementation: The implementation describes the mechanism of the clock. It describes how the clock works: what makes the pointer (or the positions) move. This mechanism describes how energy and information are transmitted through the clock from a source to either the pointer or the pointed-to. Blaauw and Brooks show how the architecture (pointer and pointed-to) could be made to work. The key elements of this particular implementation problem are (1) how to supply the power for moving the pointer (or the pointed-to), and (2) how to transmit that power to the pointer (or the pointed-to). This divides the implementation design problem into two sub-problems—the power mechanism, and the transmission mechanism. Notice that again there are no specific details given: no particular type of power (water, motor, magnetism, or gravity), and no type of transmission (shaft, gears, or pulleys). As with the architecture, this is abstract, not physical. • Realization: The realization finally describes all of the remaining details of the design. (Remember that this is still just the design, not the manufactured product.) Blaauw and Brooks call these the “geometries, strengths, tolerances, and finishes” of the design (p. 5), which includes the placement of individual design elements, their connections with each other, their size, shape, color, texture, material, and appearance. In designing the realization of the clock, the abstract thing called “pointer” is given specific form and dimension, as are the physical positions indicating specific times. At this point the parts of the implementation are also given specific value:

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the power supplier may be designated as an electric or a wind-up motor; the transmission system may be designated as a set of gears—not just any set of gears, but a specific set of gears with a given number of teeth, a given diameter, and a given spatial relation to each other. Blaauw and Brooks point out that if the clock is to be hand-made, the craft worker who builds the clock may make some of these realization decisions at the time the clock is being built. However, if the clock is to be mass produced, the realization of the design is completed to the minutest detail and fully documented at this point by the designer, ready to be sent to a manufacturer. Abstract ideas are powerful cognitive tools once they are mastered. Designing an architecture causes one to think in terms of the functions that an artifact will perform and the absolute minimum set of elements that will be needed to achieve those functions. Function is the essential core of the design. In contrast, the implementation causes one to think in abstract terms of the mechanism needed to carry out the architectural functions—the absolute minimum of mechanism. Together, the architecture and the implementation determine the innermost structures of the design and their manner of operation in the abstract. Together they form the core of a design concept. The architecture and the implementation are abstract. This makes them hard to understand. A novice designer does not normally feel comfortable thinking in abstract terms, but an expert designer is able to. It is, in fact, one of the indicators of an expert instructional designer to be able to see below the surface of the design into its interior—to the abstract parts of the design that actually convey forces and make it work. Over time, a designer begins to see the parts of a design differently, just as described at the beginning of the chapter. Being able to “see” new structures and to separate the essential parts of a design apart from the non-essentials is a sign of maturing design expertise. Anyone can see the outer form of a metal part that is meant to operate within a larger assembly, but the outer form is not all that the designing engineer “sees”. What is in the mind of the engineer is the manner in which the part distributes the forces that are placed upon it, how the part becomes deformed as the force is applied, and how the force applied at one point on the part is transferred outward to other points. The engineer, in effect, sees the inside of the designed object as well as its outside. The relationship between architecture and implementation on the one hand and the realization on the other are important to understand. They are the key to understanding two things: (1) the generative principle of early design decisions, and (2) the influence of design order. Generative Relationships Generativity means that a single kernel of an abstract idea is capable of generating several specific and very different designs. It means that a single architecture and implementation can generate hundreds or thousands—even millions—of different individual realizations. In the case of the clock design it means that the hands and the temporal markings can be designed in myriad specific realizations, all generated from the same architecture and implementation (and virtually all analog clocks are). Expert designers can use a few simple generative ideas to create numerous surface designs that look very different but share a common internal architecture. Without generative ideas a designer can only copy and modify the visible surface features of previous designs. Consider this: a design will always possess an internal architecture and implementation, whether or not the designer actually deliberates in these terms. There is always some mechanism by which any device works. If the designer is unaware of these things, then it does not mean that they do not exist, it just means that the designer is unaware of why a particular design works or does not work. It also means that when another design is copied and modified by a designer, it is possible that in the process of changing the original design something may be lost without the designer realizing it, and what before was a working design pattern may be broken and not work in the new application. Several historical examples could be used from instructional technology to show that this is more

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common than most designers think. For example, the decline of programmed instruction can in part be traced to low-quality programs made by copying surface features from previous programs without also copying the invisible inner architectural relationships that made the programs work (see McDonald, 2007, and McDonald and Gibbons, 2009, for examples). A novice designer who begins to design with the most familiar language terms will design from the surface of the artifact inward as far as insight allows. Over time, however, the design languages a designer “sees” become increasingly abstract, and the terms of design become increasingly abstract and generative. These more powerful languages can be used to generate designs outward from more powerful inner principles, and as a result designs take on a new coherence. Designers should examine each other’s designs, looking for new and more abstract design language terms that relate to deeper levels of designs. They should reverse engineer other designs, extracting the essences (the architecture and implementation) of the designs. As designers strive to “see” inner workings— the manner in which designs distribute energy and information through architectural features to achieve desired outcomes—they realize that dependence on copying existing designs is no longer necessary, and new levels of innovation come as a natural result. Architecture–Implementation–Realization and the Order of Design It would be a mistake to assume that architecture is always the starting place for a design and that implementation and realization must always follow in strict order. A design can begin at any place and develop in any direction—from the surface inward or from the inside outward. A designer may move back and forth between architecture, implementation, and realization until a coherence is found. Sharing a Design Concept Having a clear concept of the functions of an artifact separates the essential features from the nonessential ones. It gives the design team a common design concept to work from, which consists of the architecture and implementation. According to Brooks, talking frequently about the design concept as such vastly aids communication within a design team. Unity of concept [among the members of the team] is the goal; it is achieved only by much conversation. The conversation is much more direct if the design concept . . . rather than derivative representations or partial details is the focus. —(Brooks, 2010, p. 8) It is the job of the design team to create a realization using the original vision of the architecture and implementation that they come to share. They arrive at one or more possible realizations by defining design elements and assigning them properties, dimensions, specifications, details, operations, and surface features through a cyclical process. One of the best descriptions of this process is from Donald Schön (Schön, 1987), who describes it as a “conversation” with the problem. He narrates the activities of a designer named Quist: Quist spins a web of moves, subjecting each cluster of moves to multiple evaluations drawn from his repertoire of design domains. As he does so, he shifts from embracing freedom of choice to accepting implications, from involvement in the local units to a distanced consideration of the resulting whole, and from a stance of tentative exploration to one of commitment. He discovers in the situation’s back talk a whole new idea, which generates a system of implications for further moves. His global experiment is also a reflective conversation with the situation. —(p. 64)

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This description of design ties together many of the concepts described in this chapter: • Schön speaks of a “cluster of moves”, reminding us that multiple decisions may be under consideration at one time in tentative form. • He speaks of a “repertoire of design domains” which refers to the design layers we know well enough to use in forming a design. • He speaks of movement from consideration of local details to design wholes, reminding us of the architecture-to-realization continuum. • He speaks of tentative decisions becoming firm only after evaluation of their fit, bringing to mind the notions of design coherence and design concept. • He speaks of the impact of local decisions on global ones. The notion of a “reflective conversation” between the designer and the problem is a powerful description of how designs emerge. A reflective designer follows a trail of possibilities through what Schön refers to as “a web of [possible] moves”. Many spinnings of this web may be necessary before the best fit between hypothesized moves and existing constraints is found. In some cases early moves must be unraveled when blind alleys block the way forward. Eventually by moving backwards and forwards the details of the design emerge. The Relation of Design Languages to Layers Designers can choose their moves in a strategic way: “Quist makes his moves in a language of designing . . . Quist uses words to name elements of design . . . to describe consequences and implications of moves, and to reappreciate the situation” (p. 58, emphasis added). The mind of the experienced designer is not a blank slate. Before design begins, the designer possesses a catalogue of design terms. These terms are the designer’s own possession, though other designers may share some of the same terms in their thinking as well. Schön points out that the “language of designing” known to the designer suggests possible moves the designer can make. If the designer is aware of a term, he or she can use it in a proposed configuration; if the designer does not have a design language term, it will not be included in a move by the designer. Schön’s notion of design languages is applicable across design disciplines. In every field of design designers have specialized terms—to describe the elements of their designs, the process of designing, and the common standards, practices, artifacts, and structuring principles of their profession. If you were to overhear two designers speaking in their specialized language, you would recognize the grammatical nature of what they were saying, but you might miss the meaning completely. This is illustrated by the title of a book about the design languages of movie-makers: Strike the Baby and Kill the Blonde (Knox, 2005). The author is referring to two kinds of set lighting fixture (baby, and blonde) and to two actions (strike, which means take down, and kill, which means turn off ). What sounds violent in everyday terms turns out to be innocent in design language terms. Designers have specialized, compartmental languages, and Schön shows how all of the languages are related. “Elements of the language of designing”, he says, “can be grouped into clusters” (p. 58). This he does using the principle of design domains: These design domains contain the names of elements, features, relations, and actions and of norms used to evaluate problems, consequences, and implications. As he designs, Quist [the designer] draws on a repertoire of design domains to fulfill a variety of constructive, descriptive, and normative functions. —(Schön, 1987, pp. 59–60)

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These domains are the layers described in the previous two chapters. Design languages are described in more detail in Chapter 7. Design languages and layer domains supply the designer cognitive tools for dividing and conquering design problems and assigning parts of the problem for solution by specialists. A designer’s personal professional development should include constantly noticing the terms used by other designers and design theorists and the manner in which they are used. The Primary Generator: The Initial Architectural Commitment of a Design One final concept needs to be considered in a discussion of design origins—what can be called the primary generator of the design. As an architecture and implementation are produced, there must be a bridge between the real-world purpose of the design and the design abstractions. How does a designer relate Simon’s (1999) inner world and outer world? Jane Darke (1979) studied the design practices of large-project architects to understand how they attack complex design problems. She discovered the concept of a “primary generator” which answers these questions. A primary generator is “a concept or object that generates a solution” (p. 38). It is the first value-laden commitment a designer makes that has the ability to govern the rest of the design. Brooks would describe this as the source of the design’s coherence. A primary generator can serve as a hub of meaning around which everything else in the design takes form. Darke explains that a primary generator is not the product of rational analysis: “either the visual concept springs to mind before the rational justifications for such a form or the analysis does not dictate this particular concept rather than others” (p. 38, emphasis in the original). As described in Chapter 1, Darke used the redesign of war-ruined Coventry Cathedral to exemplify how a primary generator can serve as the central idea around which a complete design begins to unfold. The generator for the redesign included two main elements—one of them an object, and one of them a mythical motif. The object was the intact altar of the cathedral and the half-destroyed walls around it. The mythical motif was the story of the Phoenix firebird resurrecting from the ashes. Together these formed the central narrative—the coherence-producing idea—for the entire redesign. When this narrative was selected, the first firm design decision had been made. Other decisions would follow, but they would all be conditioned to fit and complement that initial commitment, so long as it remained unchanged. A primary generator has the effect of “narrowing down the range of solutions” (p. 38). Darke describes the generator as “usually more of an article of faith . . . a designer-imposed constraint” (p. 38; see also Stokes, 2005). Any major design goal may give rise to a primary generator: the nature of the environment, the desire to fit into the environment, the desire to support particular social patterns, the purposes of the design, and so forth. Darke describes how the design language repertoire of the designer may limit the design at this point: “A frequent problem in a school of architecture [read: instructional design] is the student who has a limited stock of generating ideas which he [sic] attempts to apply to every problem without consideration of whether it is appropriate” (p. 38). Designing from a primary generator goes beyond a strictly rationalized design process in which analysis strictly precedes synthesis or in which a defined process is applied. It resembles more closely part of Schön’s “conversation” between the designer and a design problem: a conversation in which the designer proposes a set of choices and then teases out the implications of each choice for all prior and remaining choices in order to determine the impact on the integrity of the evolving design, but all in the service of an overarching vision that distills at an early point in the design. Darke is not trying to describe the “right” way to design, nor is she trying to describe the whole process of design. She observes that “different methods are appropriate at different levels of complexity. Individuals might differ in their approach to design” (pp. 37–38). Rather than a single decision-making sequence, what emerges is a sequence that is contingent upon the problem, its

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constraints, and the primary generator. This creates a design process narrative in which the process unfolds as the design matures, in a non-specific order. Application Exercise Building architects are famous for using primary generators. • Find a description by a designer of how the design unfolded in stages from a preliminary generative idea or concept. Consider what Frank Lloyd Wright was trying to accomplish in the Falling Water house design. Determine what concepts Rem Koolhaas and Joshua Prince-Ramus were trying to apply in the design of the Seattle Central Library building. Identify the primary generators used by ecology-friendly architects in their designs. 

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Conclusion Design looks different when viewed from these four different perspectives, yet there emerges from them a more complete and rich concept of design—from the decision-making chambers of the organization to the chambers of the designer’s private musings. The concept of design layers fits with these views. It becomes clear that design takes place at several levels and that a simple description of design process is not adequate to capture its most important aspects. Most importantly, instructional design can be seen as a part of the family of design disciplines, and this realization unites instructional design with the larger design world and all that can be learned from it about how designs emerge from a few simple ideas. Though simplified descriptions of design provide reassurance for novice designers and confer design capability and efficiency at a basic level, they can also leave the designer with the unjustified impression that design is highly rational and mostly sequential. This can discourage career designers, including commercial designers and live instructors alike, from giving rein to curiosity about the deeper aspects of design. It can foreclose further interest in the development of design skills and leaves the user with pat answers rather than questions. Rather, new designers should look upon their entry into design as the beginning of a lifetime of growing skill and appreciation for great complexity and nuance and yet the impressive simplicity of elegant designs.

4

Systems in Design

The motorcycle is a system. A real system . . . There’s so much talk about the system. And so little understanding. That’s all a motorcycle is, a system of concepts worked out in steel. There’s no part in it, no shape in it that is not in someone’s mind. I’ve noticed that people who have never worked with steel have trouble seeing this—that the motorcycle is primarily a mental phenomenon. —(Robert Pirsig, 1974) The practice of instructional design is intimately associated at every level with the concept of a system. This chapter deals with two of the views of instructional design shown in Figure 3.1 of Chapter 3: the systems approach view, and the ISD model view. It concentrates on three themes: (1) the early association of instructional design with systems concepts, (2) how simplified engineering design models have become the primary tool for explaining design practice to new instructional designers, and (3) how the renewal of interest in systems thinking supplies new insights into instructional design practice. Theme #1: The Impact of the Systems Approach and General Systems Theory on Instructional Design The systems approach was a process for solving problems more complex than any before engineered. It evolved out of the shadowy world of operations research during World War II and the Cold War (Hughes and Hughes, 2000): In general it can be said that the systems approaches had their origins in the military realm in the period 1939–1960. After 1960, proponents of the systems approach increasingly emphasized its possible applications in the civil realm. While physicists, mathematicians, and engineers were its early practitioners, social scientists, including management specialists, started adopting systems techniques after World War II. —(p. 2) The systems approach emerged because of: (1) the rapid, almost explosive, growth in the complexity of technological systems such as radar and the computer, and (2) the scramble to develop large and complex systems that integrated these and other technologies within a limited time frame. The systems approach is not a single procedure but a set of problem-solving tools and techniques applied by multi-disciplinary teams in an unpredictable order of processes dictated by the nature 83

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of the problem itself. A feeling for the difficulty of problems attacked by the systems approach is conveyed by this description by Ramo and St. Claire (1998) of a situation faced in the planning of a hospital/medical center: The systems engineer is faced initially, even in trying to state what the problem is, with the same inevitable “which came first, the chicken or the egg” problem. The “facts” of the matter are not simple, clear, absolutely determinable quantities. They are statistical in nature and they can only be described by stating a range of possibilities. Beds per year, X-rays required, heart patients expected—these are all only expressible as probabilities associated with the possibilities, ranging from an indefinite minimum to an indeterminate maximum. How do you design for situations in which the basic parameters and the data describing the performance requirements to be met constitute a whole spectrum of possibilities? —(pp. 63–64) The systems approach is challenging to describe because it is a toolbox of methods rather than a set program of processes. Ramage and Shipp (2009) name thirty leading contributors to the systems approach, each of whom had a slightly different perspective, leading to different applications in practice. Reynolds and Holwell (2010), in their book, Systems Approaches to Managing Change: A Practical Guide, describe five varieties of systems approach, each suited to a particular situation and set of analyst goals. Several key characteristics shared by systems approaches are evident in Ramo’s hospital-planning example and are listed below. Additional items derived from the Ramage and Shipp and Reynolds and Holwell reviews have been added to this list: • The systems approach involves solving a complex problem or a family of related complex problems. • The problem is viewed in terms of multiple complex, interacting systems. • Problems consist of multiple sub-problems, which have to be identified and attacked separately. • Analysis of the problem and its context are normally followed by synthesis (the design) of one or more solutions in a cycle that is repeated multiple times. • Quantification of variables is desired, if at all possible. • A multi-disciplinary team of scientists and engineers is formed to work out solutions. • Problem study and verification are a usual first step. • Heavy reliance is placed on data gathering and data analysis. • Decisions are made using the best data obtainable. • Many different mathematical and statistical research methods may be employed. • Methods are selected according to problem requirements, not by following a set sequence of problem-solving steps. • Multiple alternate solutions are explored. • Alternatives are evaluated on the basis of multiple factors and the perspectives of multiple stakeholders. Stakeholders are involved in decision-making. • System modeling and simulation is often used to test proposed solutions. • Factors of interest include cost, maintainability, sustainability, and user acceptance. • The solution almost always involves some degree of innovation. • The best scientific and technological knowledge available is applied. • When science is not available to guide, the best rational approach is chosen. • Life cycle costs and planning are included in calculations. • Human factors principles are used to fit the solution to the user.

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The systems approach evolved for the purpose of solving problems much more complex than the hospital problem. During the wars, engineers began integrating the computer with emerging electronic and mechanical systems—radars, avionics, and communications. These problems presented unknowns and uncertainties that made each problem unique. Churchman (1968) notes how the key to solving the problem of stopping submarines during World War II required that “the scientists kept asking stupid questions” (p. viii). He continues: The scientists noted that the depth charges dropped from aircraft were set so that the charge did not go off until at least 35 feet below the surface. The scientists asked the stupid question: Why not try to set the charges so they go off at a shallower depth? Once you’ve asked a stupid question, then you have to defend your right to ask it, and the scientists pointed out some weaknesses in the assumptions that were made by the military in the manner in which the aircraft was approaching its target. Eventually some experiments were run, and sure enough, the submarine kill went up significantly as a result of setting the charges at a shallower depth. —(p. ix) Churchman describes some of the unique strengths the systems approach acquired as it matured during the 1950s and 1960s from a brainy idea into a toolbox of problem-solving techniques: As the scientist’s perspective widened, he [sic] began to think of his approach as the “systems approach”. He saw that what he was chiefly interested in was characterizing the nature of the system in such a way that the decision making could take place in a logical and coherent fashion and that none of the fallacies of narrow-minded thinking would occur. Furthermore, using his scientific knowledge he expected to be able to develop measures which would give as adequate information as possible about the performance of the system. —(p. x) The project of placing an astronaut on the moon puts the complexity of the systems approach in perspective. The project was divided into three major steps, each the name of a major project by itself: Mercury, Gemini, and Apollo. • The Mercury project consisted of twenty rocket launches carrying no humans, two launches that carried humans without orbiting, and four launches that orbited humans. It demonstrated that humans could be sent into space and safely recovered. • The Gemini project consisted of ten launches, each carrying two astronauts. These missions increased in length, sent astronauts outside the spacecraft to perform extra-vehicular activities, docked with other spacecraft, and returned safely to earth. These missions prepared the way for moon missions. • The Apollo project consisted of seventeen launches, each carrying three astronauts. The Apollo 11 mission landed astronauts on the moon, as did all of the remaining missions except Apollo 13. After 1970, the systems approach became less visible in the public eye. Enthusiasm for a systems approach peaked during the early Lyndon Johnson administration (1963–1969), after which the trajectory of advocacy moved downward in step with the reverses of the Vietnam War and the rise of a counterculture. The counterculture associated large systems with the military/industry/university complex and with the Vietnam quagmire. —(Hughes and Hughes, 2000, p. 1)

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Given the Hughes account, one might believe that the systems approach would continue to die out, but that was not the case. The future of the systems approach and general systems theory, with which it ran in parallel, has been determined by: (1) absorption of the systems approach into the mainstream thinking of engineers and social problem solvers, where it tended to lose its identity through familiarity and simplification, and (2) absorption of systems thinking into the very counterculture that the Hughes and Hughes account says rejected it (Turner, 2006). Application Exercise Churchman asked of the systems approach “Does this type of thinking about the whole system help very much in terms of our attitude toward the problems of the world today?” (1968, p. 9). • Identify your own list of the top ten problems that could be submitted to solution using a systems approach. List problems in which incoherent and illogical thinking are currently being applied without producing progress. • Who would apply the systems approach in addressing the problems on your list? Would the solution be multinational? Who would manage the process of problem solving? • Whose interests would be affected by a solution to the problem? Who would lose income, power, or influence, and who would gain? Who would benefit from having ideologies satisfied? • Does this analysis give you insight into why many problems go unsolved? The Growth and Then Decline of the Systems Approach Reynolds and Holwell (2010) document the growth of the systems approach as it moved beyond technical engineering problems and was applied increasingly to social, political, and economic problems. Ramo’s hospital example represents this later evolution; the full range of problems addressed using systems approaches included problems of planning of city growth, social services, information systems, business strategy, and a great variety more. Chapters in Hughes and Hughes provide a historical panorama of case studies of the application of the systems approach to a variety of major problems beginning in 1940: • Mindell (2000) describes the integration of humans with machines into a single control system following the development of radar. • Rau (2000) recounts the resistance of the (then) new U.S. research establishment to the importation of operations research methods (later, the systems approach) which originated in Britain. • Johnson (2000) describes the transformation by the U.S. military of the systems approach into a cost control tool in the form of “phased planning”. • Bugos (2000) describes the Development of the Bay Area Rapid Transit System of San Francisco as an example of the increasing effect of the systems approach on civilian projects. • Hecht (2000) portrays the rise of the “technocrat”, replacing some of the functions of the politician in the decision-making process. • MacKenzie (2000) explains the halting acceptance of computers as a systems approach tool in safety-critical applications, even by computer system architects. • Akera (2000) describes the appropriation of governmental computer information processing systems for accounting purposes, eclipsing their engineering uses. • Edwards (2000) details the use of the new computer technology in support of the study of systems through modeling.

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• Hounshell (2000) details the rise of quasi-governmental research and development corporations during the Cold War—the foundation of the military–industrial complex—and the wake-up call delivered by Sputnik in 1957. • Jardini (2000) gives an account of the adoption of the systems approach by the U.S. government as a major tool in the engineering of military and social programs. • Dyer (2000) describes the bumpy transition of now-large defense contractors into civil systems engineers in a period of reduced government funding. Many projects, it turned out, proved to be inappropriate for the systems approach. Noting these case studies provides a cross-sectional view of the evolution and aging of the systems approach over time from being pre-eminently a process for solving scientific and technological problems—carried out for high stakes, under survival conditions, and with relatively few resources—to being a tool used for governmental policy and corporate R&D for the ends of security, an economic status quo, and social programs. In the early days highly motivated engineers and scientists worked with the military toward crashing deadlines using few materials to create prodigies of innovation. This gave way to a corporate life style much richer in some ways, not as hungry, and not nearly as committed to a cause. In fact, near the end of the transitional period described above, it was hard to tell what the goals worth working for might be. The stakes were no longer survival, and the engineer worried about the size of a new house was hardly as engaged as when home and homeland were hanging in the balance. Application Exercise Examine today’s headlines and identify projects (military, civilian, social, industrial, commercial) where you think there is evidence that the systems approach is being used. • What factors from the profiling list given above make you think that the systems approach is being used? Convergence with General Systems Theory The systems approach converged during the 1950s and 1960s with another new idea called general systems theory (GST). The systems approach was a method for solving complex technological problems. General systems theory was a whole new scientific world view that grew from very different roots. The story of GST involves a student named Ludwig von Bertalanffy in pre-war Austria. He first expressed general systems theory in a doctoral dissertation. Von Bertalanffy’s theory saw the whole world of living things in terms of their interconnectedness and interdependencies—as “systems” that were capable of adaptive change and growth. Later he wrote: The chief task of biology must be to discover the laws of biological systems (at all levels of organization). We believe that the attempts to find a foundation for theoretical biology point at a fundamental change in the world picture. This view, considered as a method of investigation, we shall call “organismic biology” and, as an attempt at an explanation, “the system theory of the organism”. —(von Bertalanffy, 1972, p. 410) Von Bertalanffy felt that he was introducing a new way for scientists to “see” the inner workings of the systems of all living things. He knew his idea was revolutionary and that it would change the most basic views not just of biological science but of all science, including social science. In 1972 he

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wrote, “If the term ‘organism’ in the above statements is replaced by other ‘organized entities’, such as social groups, personality, or technological devices, this is the program of systems theory” (p. 410). For years von Bertalanffy shared his idea with only a limited audience: “The notion of general systems theory was first formulated . . . orally [in German] in the 1930s and in various publications after World War II” (p. 411). Being a scientist in an occupied country during wartime and being a military officer made it hard for von Bertalanffy to disseminate his ideas to an international audience, but other scientists from a variety of disciplines were working on the same general concept of organic systems, preparing for a major shift in the scientific world view. Even so, GST when it was available more widely came as a surprise to the scientific establishment: At first the project was considered to be fantastic. A well-known ecologist, for example, was ‘hushed into awed silence’ by the preposterous claim that general systems theory constituted a new realm of science, not foreseeing that it would become a legitimate field of the subject of university instruction within some 15 years. —(p. 413) After the war it was a different story. Von Bertalanffy published significant works in English beginning in 1949 and 1951. Thereafter the volume of his work in an accessible form increased greatly. By 1956 the Society for the Advancement of General Systems Theory had begun publishing a journal, and work motivated by GST was being initiated in fields as diverse as neurobiology, economics, psychiatry, weapons development, and computer technology. Von Bertalanffy was careful to note that: It is incorrect to describe modern systems theory as “springing out of the last war effort”—in fact, it had roots quite different from military hardware and related technological developments—cybernetics and related approaches were independent developments which showed many parallelisms with general system theory. —(p. 414) One of these “independent developments” was the systems approach, which did have its roots in the expediencies of war. The systems approach and general systems theory both became influential worldwide during the late 1940s and the 1950s. As they did, they exerted a mutual influence. GST added the concept of dynamic systems to the discourse of the systems approach, and the systems approach lent problem-solving techniques to research and development of systems of all kinds. Many other new concepts were emerging at this time that also found compatibility with GST and the systems approach. According to von Bertalanffy, these included: “cybernetics, theory of automata, control theory, information theory, set, graph, and network theory, relational mathematics, game and decision theory, computerization and simulation” (p. 416). These same ideas influenced the practice of the systems approach as well and added to an arsenal of concepts and methods that were eventually found to be attractive to early instructional design theorists, who adopted both the systems approach and GST in principle, if not necessarily in practice. Application Exercise The notion of systems and their complex interrelations pervades our thinking today to an extent that many of us do not realize. We need to become aware of the subtle influence of systems concepts like dynamic balance, ecology, self-regulation, and feedback.

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• Identify a list of ten areas of school subject knowledge where systems are described. Use a university course catalog to give you some ideas. The Application of the Systems Approach and General Systems Theory in Instructional Design The systems approach and general systems theory found early application in instructional design. During the war, as complex electronic and mechanical systems worked their way through the design process, the systems approach not only organized the design of the systems themselves, but provided the basis for a parallel effort to design and develop training systems for the massive workforce that would operate, manage, and maintain them. This was the question of the “human” element in what came to be known as “man-machine” system engineering (Gagné, 1965b). Though many find it hard to accept the human as a component of a technological system containing also computers and servomechanisms, it is an important way of looking at design problems to ensure the integration of human and machine functions so that the system fits the human as much as the human fits the system. This concern for the human operator improved the design of everything from airplane cockpits to radar scopes during World War II, and it continues to be the basis for the design of quiet, functional, comfortable, safe automobile interiors to this day. Ironically, it is appropriate today to think of crash-test dummies—simulated humans—as contemporary tools of man–machine systems design. Likewise, general systems theory has influenced instructional design. Designers became aware that they engage in the design of systems, not just media products. Two personalities stand out in the history of the adoption of the systems approach and general systems theory in instructional design practice: Robert M. Gagné, and Bela Banathy. The attractions and tensions caused by the convergence of these two bodies of thought can be seen in a comparison of their work. Robert M. Gagné: Man–Machine Systems Development Robert Gagné, an experimental psychologist, was given the task during the war of ensuring that new complex equipment systems were met on their arrival in the field of operations by a trained force of operators, maintainers, and managers. After the war, Gagné described the processes originated during the war for engineering the human side of human–machine systems. He paid specific attention to the design of the training function, which because of the complexity of the systems themselves tended to be a complex design as well. Gibbons et al. (2013) give an account of this period. Gagné’s contribution began in a work titled Psychological Principles in System Development (1965b). According to Gibbons et al. (2013): “To the members of the instructional design community it represented a monolithic statement about the systems design process whose influence even today silently dominates the discourse of instructional design practice” (n.p.). Tracing the influence of this “silently” dominant idea on current instructional design practice is one of the main purposes of this chapter, for without an acknowledgement and critical appraisal of the roots of current practice, it is hard to evaluate future trends aimed at improving that practice. It is especially important to assess the reasoning used to justify the practice in the first place and to examine the values it held to most tightly at the beginning. This will be especially important with respect to Psychological Principles because, as will be shown later, Gagné’s work, and that of his collaborators, set the mold of instructional design process for the next fifty years. The systems approach and general systems theory were just emerging in many fields. Gagné’s work shows their early interaction (see, for example, Finan, 1965). Gagné structured Psychological Principles in terms of the stages of a systems engineering process. He describes this pattern in the book’s introductory chapter, including a diagram of a generic man–machine system development model.

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An important feature of the engineering process model Gagné proposed was that it departed from the robust unpredictability and innovative problem-solving flavor of the systems approach described at the beginning of this chapter. Gagné’s process diagram is a sequenced and rationalized design management process. This aspect of Gagné’s treatment of the systems approach is echoed in many of the later works of his collaborators and the work of Gagné himself. In this model, Gagné makes a number of simplifying assumptions about specific processes carried out during system design and development. Some of the assumptions limit the application of the process to only certain kinds of training problem: assumptions about the nature of the knowledge to be learned (task analysis), about the context and purpose of the instruction (job description), about elements of the solution (job aids), and about the social setting of applying the learning outcomes (team training). That is, Gagné’s staged model was not generic to all purposes. It contained embedded biases that are characteristic of the specific type of instruction that Gagné would have been designing in the military. This is important to notice because this bias in Gagné’s original process model is retained in most instructional design process models that fashioned themselves after it, making it hard to talk about design theory and instructional theory separately. (See Chapter 6 to see why this is a problem.) If unspoken theoretical or practical assumptions are built into the design model, then the model is not generic, and some designers will abandon some or all of the model when it does not meet their perceived needs or when it conflicts with their theoretical world view (see Bednar et al., 1991). According to Gibbons et al. (2013), the systems approach made no such assumptions: The systems approach was atheoretic, meaning that it did not entail theories about the inner working mechanisms of the artifacts designed (domain theories). These theories were brought to the problem by the individual problem solver. This meant that a systems approach could be used equally well [for problem solving] by any designer regardless of theoretical bias. —(n.p.) Saettler (1968), writing three years after the publication of Psychological Principles, included an enthusiastic prospective chapter in A History of Instructional Technology, proposing that: “the systems approach to instruction offers a conceptual framework which, hopefully, can provide a model for the achievement of this ideal [of a truly scientific technology of instruction]” (p. 268). On the one hand, Saettler describes the use by the military of systems approaches for the improvement of their internal operations and organization; on the other, he describes “the organismic concept of systems” and refers to the work of von Bertalanffy: The basis of [von Bertalanffy’s] concept is that a living organism is not a collection of separate elements but a definite system possessing organization and wholeness. An organism is an open system which maintains a constant state while matter and energy which enter it keep changing (so called dynamic equilibrium). A central feature of the organismic outlook is its emphasis on the dynamic mutual interaction of subsystems operating as functional processes. That is, in biological terms, a total organism is a system whose behavior is influenced by a still larger system—the organism-in-its-environment. Life is purposive in that it maintains itself in steady states, is self-regulating, and actively explores and manipulates its environment. Life is interactive rather than reactive, and organisms exchange energy and information with their environment. Such a description of an organismic system adequately fits the instructional setting. —(pp. 271–272)

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The importance of Gagné’s work in Psychological Principles is summarized in its Foreword by Arthur Melton: This book has the distinction of being the first of its kind, and a very significant first indeed. It marks the coming of age of a systematic conception of the application of psychological principles to the invention, development, and use of a theory of psychotechnology—and a very broad based theory, at that, when one considers the wide range of basic psychological knowledge that it focuses on the many issues that relate to the human component or components in a system. As a theory of the psychotechnology of man–machine systems, it achieves integration of what has heretofore been variously called “human engineering”, “human factors engineering”, or “engineering psychology” on the one hand and “personnel psychology” or “personnel and training research” on the other hand. —(p. v, emphasis in the original) Hinting at the future trend of merging the ideas of the systems approach with those of general systems theory, Melton adds: “This union comes easily and naturally once the concept of system is examined and once the full implications of the concept of the human being as a component of a man–machine system are recognized” (p. v, emphasis in the original). Bela Banathy: Instructional Systems One instructional design theorist most strongly influenced by general systems theory was Bela Banathy, who writes in his book Instructional Systems: In building [World War II] aircraft, designers realized that they could not simply take an existing airplane and add weapons, bomb and fuel storage space, communication and detection equipment, and protective armor. Adding such equipment at random restricted the airplane’s carrying capacity, speed, maneuverability, range of flight, and other vital functions. What emerged from this realization was a new method of planning and development in which designers learned that they first had to identify the purpose and performance expectations of the whole system before they could develop all the parts that made up the system as a whole. —(Banathy, 1968, p. 2). Banathy gives special emphasis to the system concept inspired by general systems theory: It is the system as a whole—and not its parts separately—that must be planned, designed, developed, installed, and managed. What is significant is not how the individual components function separately, but the way they interact and are integrated into the system for the purpose of achieving the goal of the system. —(p. 2) Though Banathy’s purpose is to promote systems thinking among instructional designers, his description of the systems approach in his early writing has a process flavor, just as does Gagné’s: “The systems approach to the development of systems offers a decision-making structure and a set of decision-making strategies. It makes available to the designer a self-correcting, logical process for the planning, development, and implementation of man-made entities” (p. 14), Banathy’s definition of the systems approach describes it in sequential terms: “Component strategies of this methodology include the formulation of performance objectives, the analysis of functions and components, the distribution of functions among components, then scheduling, the training and testing of the system, installation, and quality control” (p. 91).

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As the systems approach and general systems theory concepts came together, the systems approach was beginning to win a greater share of attention. This trend continued because the systems approach as described by Gagné and Banathy offered something the systems concept could not—a convenient and relatively concrete path of action toward producing results. In the work of these two influential authors, the systems approach began to imply a logical, sequential process. In Banathy’s book, just as in Gagné’s, a process was used to provide a framework for the discussion of systems concepts and theories, and in both works a graphical representation of the systems approach provided in the form of a flow diagram reinforces this impression. Not only was the systems approach winning a greater share of attention, the meaning of the term itself was shifting away from referring to an open-ended problem-solving process and toward referring to a sequenced set of processes. But what was the alternative to a process approach? A clue is hidden in Banathy’s definition of the systems approach: he names as processes “the analysis of functions and components”, and then “the distribution of functions among components”. Banathy defines “components” in its traditional sense: The term components analysis refers to who or what should be employed to carry out the specific functions identified as the outcome of functions analysis. Educators have rather firmly set ways of thinking about the employment of educational resources—men [sic], media, and other material resources. —(p. 63, emphasis in the original) Banathy gives the new definition of “components” under systems thinking: More specifically, the value system expressed by and inherent in the term teaching aids has completely changed. We no longer talk about the teacher and his instructional aids, but about the components of a system that are considered and used on the basis of their ability to accomplish specific educational functions. This last statement is the central concept of component analysis. —(p. 64, emphasis in the original) Then the principle of function identification is given primacy: One of the rules of component analysis is that the component should fit the function and not the function fit the component. The idea of function fitting the component, or the nonsystems way of thinking, is reflected in the widespread practice of assigning instructional functions to the teacher simply because he is in the classroom anyway. It is to overcome the temptation inherent in this habit that we insist on the order of identifying functions first and components next. —(p. 64) Banathy is arguing against the traditional practice of thinking of components as set media categories with set patterns of use. Instead, he argues that how the function will be carried out should be decided by the designer, then media and material means for carrying out the “how” should be selected, possibly in ways and combinations contrasting with traditional media usage. The emphasis is on envisioning the desired instructional function first, then assigning means to the function. Banathy is trying to break down traditional media categories. And yet, he eventually appeals to them: The human component will include the learner and the teacher, as well as personnel engaged in a variety of educational support and service functions. The material components will include both software and hardware, such as textbooks, programmed instructional materials, tapes, films, teaching machines, and other media —(p. 65)

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Application Exercise Both Gagné and Banathy took part in a trend of making the systems approach more useful to the ordinary instructional designer. • Consider from your own perspective as an instructional designer whether this was a good trend or not. What may have been lost as this trend deepened? What may have been gained? Commentary on Gagné and Banathy What Banathy tried to accomplish was to reverse the traditional order of asking first what a medium could do and then forming media (component) decisions around that capability (p. 66). Banathy was trying to assert the primacy of strategic (functional) thinking as the principle of design decisions: he was trying to bring designers around to thinking more abstractly about the functions of instruction rather than rushing immediately to existing media forms as a source of designable structures. Banathy recognized that design required a functional analysis of the nascent system and of the media and then a matching of functions to vehicles that could carry them out—sometimes in innovative forms. This would imply that the designer would need to inventory the functions to be performed by the designed artifact—a functional decomposition of the artifact-to-be. Both Banathy and Gagné show the tendency toward this type of analysis. According to Gagné: The various parts of a system can no longer be thought of as tools for the extension of man’s capacities but instead must be designed in such a way as to integrate their functions with other parts of the system in the accomplishment of system purposes. —(Gagné, 1965b, p. 2, emphasis added) Despite these good intentions on the part of both Gagné and Banathy, what happened historically was the decomposition of design processes instead. Rather than exploring the functional nature of the artifact being designed, design model builders concentrated on defining the processes to be followed. The functional nature of the artifact was neglected. This was a point of decision at the diverging road in a wood, so to speak, and by taking the process turn, design theorists began to ignore the value of functional decomposition—a major principle of layer design. This choice led to the proliferation of engineering process models as described in the next section. Theme #2: The Evolution of Simplified Engineering Models of Instructional Design The systems approach in its pristine form came at a high cost in money and talent, and it was highly unpredictable. It was a problem-solving process whose direction was dictated by the unsolved parts of a problem. Meeting deadlines was problematic because the steps to a solution were unknown, and the nature of the solution was problematic because the problem itself was constantly in the process of being discovered. This is why highway projects have time and cost overruns, town center renovation projects stall, and national budgets don’t get passed. The systems approach was normally applied by interdisciplinary teams of scientists and engineers working together. Solutions often involved the application of mathematical and statistical methods and the discovery or invention of new principles and new technologies. The systems approach in its most robust form was beyond the abilities, needs, and budgets of the average system designer, but the idea of the systems approach had great appeal and weightiness, and it was suggestive of solving design problems authoritatively, so teams using less comprehensive methods sometimes claimed the term “systems approach” to give their projects heft.

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In the field of instructional design after 1960, this led to the rapid proliferation of “systems approach” models of instructional design (Gustafson and Branch, 1997a, 1997b). A very large number of such models began to appear in the literature. Though it would be impossible to review all of the models created, a handful of the more significant models are reviewed by Twelker et al. (1972), Stamas (1973), Andrews and Goodson (1980), Gustafson (1981), Gustafson and Powell (1991), and Gustafson and Branch (1997a, 1997b, 2002). A detailed review of all of the models in the instructional design literature would reveal their sameness and the fact that most of them are rearrangements of very similar elements. In contrast, Dubberly (2005) has collected examples of design models from many fields which show how this is not a necessary outcome of model proliferation. His review provides an interesting perspective on the variety of fields that have employed models and the variety of approaches that have been taken to forming them. During the 1970s and 1980s the trend was toward the simplification of instructional design models for use by less sophisticated novice audiences. This trend was not counterbalanced by critical reviews of models and their use for advanced audiences, so until recently the discourse on instructional design has centered on increasingly simple models. This trend was noted in Smith and Boling (2009) and Boling and Smith (2011). Gibbons and Yanchar (2010) identify multiple dimensions of design description that have been lost through this process. What is an Instructional Design Model? An instructional design model is a description of design processes at a high level of abstraction. The ultimate aim of process models is to provide a general template of design processes that designers can tailor to individual projects. The most widely known instructional design models go by the name ADDIE (analysis, design, development, implementation, and evaluation) and ISD (instructional systems design or development). Early design models tended to claim grounding in the “systems approach”, but as time passed, the resemblance to the original systems approach became increasingly hard to identify, and ISD models became more like general engineering process sequences for designers to follow. Some design models claim a strong association with science, but the processes themselves are not scientifically derived. What is more likely is that general systems theory and information theory (often both linked with ADDIE and ISD) and other theories such as cybernetics are mistaken for scientific foundations when they are more correctly seen as companion theories and principles that enlarge our understanding of design but do not generate models. The origins of instructional design models can more easily be traced to systems engineering. Design models were beginning to appear in the late 1950s. By the mid-1960s they were proliferating. Though the systems approach was breaking over the whole Western society at the time, models were not the only expression of design approaches. The Design Methods Group was forming at U.C. Berkeley in 1967, Herbert Simon was publishing The Sciences of the Artificial (Simon, 1968), Alexander was completing The Timeless Way of Building and A Pattern Language (Alexander, 1977; Alexander, 1979), and Jones was writing Design Methods (Jones, 1970; Margolin, 2010). These design theorists did not rationalize design on the basis of process models. Each of them chose a different approach to design problem solving. Alexander and Jones later changed their positions, but neither moved in the direction of process models. Design process models grow by subdividing high-level processes reductively into smaller ones, as Taylor and Doughty (1988) describe. The subdivision of processes leads to hierarchical levels of process, so design models can become very detailed. The difficulty caused by level-to-level process breakdown is that at some level of detail the model stops being generic and has to reflect the particular constraints of a specific design problem. This is, in fact, the way a design process model is applied to a particular project. The decomposition of processes into sub-processes in this way could lead to

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an infinite number of detailed models, depending on the circumstances of the project. No amount of step detailing, however, leads to a better understanding of the design process, which is a theme discussed later in this chapter. The rise of instructional design models and how they came to dominate the culture of instructional design is best described with reference to the work of two influential proponents: Leonard C. Silvern and Leslie Briggs. Leonard C. Silvern: Systems Engineering of Education Leonard C. Silvern was one of the most prolific writers on instructional design models during the early 1960s and 1970s. His production includes nearly twenty books on instructional design and many journal articles. Silvern was a strong proponent of the engineering aspect of the systems approach, but in his writing the shift from a full-featured systems approach to an engineering design model is evident. His writings addressed both educational institutions and corporations. Though Silvern maintained ties with academic institutions, his work had a strongly applied bent, and though he cultivated the sense that theory was fundamental in his work, it was largely the applied aspects of cybernetic theory that allowed him to discuss the issues of feedback, feedforward, and self-corrective system control in practical applications. Silvern’s long-term goal was to create a concept of instructional system design based on an engineering process model that incorporated mathematical expressions, which would allow systems to be simulated and tested before full implementation. He created a graphical language for capturing the elements of a designed system and its functions. The value of the graphical language was in theory to provide a computable base for testing system designs in order to refine the relationships between functional elements of the system. Some of Silvern’s model diagrams are large. One diagram created for an industrial client (1965, p. 99) occupies a seven-foot-long foldout. Prepared in small print, it contains over a hundred process steps. Interfunction relationships on the diagram, which are shown with lines and arrows, are so numerous that the diagram looks more like an electronic circuit diagram than a design model. Despite the size and complexity of the model, it represents a complexity that requires further decomposition for application to a specific project, which, if it were fully detailed, would require a small book. We no longer draw diagrams for any but the largest projects. We are more concerned today with instructional impact brought about by sound instructional theory than by the nuances of the design process. However, the multiplicity of concerns addressed in Silvern’s concept of design and the complexities of design decision-making are increasingly important in a design world that is changing rapidly. The major concerns today are not the individual lesson as much as how lesson leads on to lesson in a larger curriculum plan, how it fits into a corporate training plan, or how it compounds the instructional investment made in previous lessons. The concern today is how truly multi-media instructional artifacts can be designed that meet the escalating sophistication and expectations of the new multi-media user. Several things about Silvern’s contribution, therefore, make him an example worth study by the serious career designer: • His work bridged the time period from the emergence of the systems approach during and after World War II to its implementation in commercial corporate settings. He provides a window into that period of maturation in a newly forming field. • His work exemplifies the imperfect struggle to make sense of the colliding worlds of engineering principles, technical systems principles, general systems theory, and the systems approach, a struggle that led many during the same time period to blur together terms and definitions from each area, confusing communication among theorists and designers.

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• His work exemplifies the attempt to apply what he saw then as systems thinking to instructional design, regardless of its corporate or educational venue. He clearly shows a transition from the early systems approach to today’s instructional design models. • His work shows an attempt to describe how the systems approach dealt with a wide spectrum of practical design issues, from the design of individual elements of instruction to the planning of physical, temporal, logistical, and information processing systems. Few writers who came after him approached the practicalities of design with such a broad functional view. • His work raised issues at the most minute level of design detail and the most inclusive. He was concerned with the design of lessons and courses but also curricula and entire school systems. Writers after him tended to restrict their attention to one or the other of these concerns. • His work exemplifies the energy and hopeful vigor of the times when instructional design was emerging as a practice, when funding was more liberal, when there was much yet to discover, and when new ideas were encouraged. Silvern lived on a wild frontier. Leslie Briggs: Instructional Design Models Leslie Briggs lived on the same frontier, and he was exposed to the same wildly changing environment, but his impact on the field of instructional design was much different. Briggs was a protégé, co-worker, and publishing associate of Robert Gagné. After ten years of military and industry experience in instructional design, Briggs joined Gagné on the faculty at Florida State University. Briggs observed that “our early attempts to deal with problems in training for specific jobs provided a practical background for the development of generic concepts which are now in the vocabulary of modern instructional designers” (Briggs, 1980, p. 45). Briggs was a trained psychologist, but his writings and his ideas were clearly derived from these experiential sources rather than theory. The persistent theme throughout his publication history was the instructional design model. Briggs did perhaps more than anyone else to establish the design process model in the minds of what became a new class of workers initially called educational specialists. This group was recruited for the most part from teachers, audio-visual practitioners, and the military because of their familiarity with media production. Briggs defined a new career path for them. Research on teaching machines and programmed instruction left Briggs with questions about how to blend the use of programs with teacher-led instruction. Programs kept learners’ attention focused on the delivery medium, and programs did not encourage—or even seem to leave the opportunity for—social interaction with the instructor or with other learners. This redirected Briggs’ interest from device-centered thinking to abstract structural thinking about design processes and strategy structures. Once Gagné had published The Conditions of Learning (Gagné, 1965a; see also Gagné, 1970, 1977, and 1985), Briggs joined him in several textbook publications that combined Gagné’s strategic ideas with Briggs’ own design process ideas. Because of his practical bias, some kinds of questions occurred to Briggs sooner than they would have occurred to a theorist. He relates that: One day while writing a programmed instruction booklet to be used for research . . . I dropped my pencil and asked myself, “But why am I teaching this objective by programmed instruction?” This question led me to the more general question of how to select media to match the objective and the learner. —(Briggs, 1980, p. 48)

Systems in Design • 97

In 1967 Briggs published Instructional Media: A Procedure for the Design of Multi-media Instruction, a Critical Review of Research, and Suggestions for Future Research (Briggs, 1967). In it he explored a new concept of “multi-media” instruction, meaning (in those days) a blend of programmed instruction with instruction delivered by a teacher using the new non-programmed media such as film and educational television. Briggs’ goal was to establish the instructional objectives as the basis for selecting instructional media. He felt that media requirements could be determined systematically and procedurally using newly invented taxonomies of instructional objectives like the one that had been recently published by Gagné (1965a) and a previous one by Bloom (1956). He stated: The purpose of the [media selection] project is to outline a procedure by which teams of educational specialists would conduct analysis of course objectives for the purpose of identifying the media predicted to be the most effective for each educational objective. —(Briggs, 1967, pp. 8–9) Briggs expressed the concern that media research had not provided a sufficient basis for matching media with objectives, but he felt strongly that decisions about media use should be made on whatever systematic basis could be defended rather than according to the claims of equipment and instructional material manufacturers. This was a radical position in its day. In Instructional Media Briggs introduced several ideas that were to shape the future direction of instructional design concepts and practice. • First, he clearly indicated that he thought the future lay along the path of design process. Even though it is a book ostensibly about media selection, Instructional Media is a description for the process of selecting media. • Second, Briggs clearly identified a shift in the responsibility for design decisions away from the teacher and toward a new category of worker that he called “educational specialist”. This began to affirm the instructional designer as a professional category apart from the teacher, and it revealed a not-so-subtle philosophy that the designer should in many cases be given greater authority in design decisions, presumably because the designer had access to superior systematic design knowledge. • Third, he introduced the idea of “multi-media” instruction administered by a teacher, which countered the radical position held by some that schools could be made mostly teacher-less. Briggs’ proposal had the effect of drawing the teacher back into a central position in the instructional process, even if it was in service to a pre-set design. • Fourth, in order to give definition to media usage and align it with stakeholder logistics, Briggs introduced the idea of the “package” of instruction: “The resulting packages of instruction, accompanied by a teacher’s guide prepared by educational specialists, would be available for adoption by teachers” (p. 9). By inventing the “package” Briggs brought into alignment the objective, the medium of instruction, the teacher’s activity (to administer the package), and a unit the publisher could profitably produce as a product. It was a formula that seemed to work to everyone’s benefit. Many of the ideas introduced by Briggs became key terms in the design language of the new and rapidly growing instructional design field’s discourse on design practice, and they proved to have real sticking power. In Instructional Media Briggs proposed “a solution . . . based on a rather sweeping overhaul in the way educational materials are prepared and used” (p. 1). Three years later, Briggs published Handbook of Procedures for the Design of Instruction (Briggs, 1970). This completed the shift in Brigg’s perspective toward process models. According to him:

98 • Fundamentals

This monograph presents a set of procedures for the design of instruction. The procedures may be designated as a “model”. In present context, the word model means three things: (a) The process of instructional design described in an orderly series of steps, (b) based on research findings when possible, psychological theory, or upon common reasoning, and (c) dependent on empirical tryout to be judged satisfactory. —(p. vii) Briggs concluded this opening statement saying, “This model may be said to employ ‘the systems approach’ ” (p. vii). Here was the adoption of a term separated from its original meaning. The use of the term continued, but the spirit of the original systems approach concept was lost. Silvern (1965) was writing about system approach process models for design for corporate and higher education as early as 1963. Ofeish (1963/2008, referenced in Silvern 1965) was doing similar work for the Air Force, and it is almost certain that the military had been nurturing instructional design process models well before that. The importance of the model in Briggs’ Handbook is that it made a definitive statement about itself, stating that it was a model and defining what that meant. This made a “model” a thing in the minds of Briggs’ readers—and a new term in the educational specialist’s discourse. Things would never be the same for instructional designers, especially for those entering design practice as novices. Whereas Silvern addressed a largely industrial and public education audience, he was talking to it from the outside and as a consultant and a lecturer. Briggs, as a newly minted full-time professor, would write several textbooks, most of which went into multiple editions, that would be used as basic design texts for generations of graduate students, military personnel, and public school teachers. Moreover, his colleagues at the same university, Gagné, Dick, Wager, and others, would publish similar textbooks with similar distributions supporting the same basic ideas. Briggs set the mold for publications directed at designers in all of these audiences. Though his desire was to elaborate the design process in book-length detail, others less careful and more eager to publish showed less caution, introducing design models in relatively brief articles. This and other factors set off a process of simplification—some would say over-simplification—that continued throughout the remainder of the twentieth century. Not only did new authors continue to simplify their models, but they began to target them to focused, niche audiences: business, public school teachers, and military designers. Many models took the form of simplified descriptions of instructional design for novices and potential clients, and as business offerings in proposals. The number of models tailored to special audiences grew rapidly, and in superficiality. Few authors took the time to elaborate their models in any detail except textbook publishers and specialized military training doctrines. Before long, every military service had its own design process guide. A joint service guide was also created (see, for example, Branson et al., 1975). Application Exercise From the 1960s into the 1980s was a time of great ferment in the newly forming field of educational technology. There existed feelings of great expectation and enthusiasm. This led to speculation in ideas that were in many cases ill-informed. • Examine the literature on design from that period and capture the sense of excitement. Identify statements in the literature that convey the speculative spirit of the time. The Aging of Design Models As mentioned previously, several reviews were written surveying instructional design models. Early reviews by Twelker et al. (1972), Stamas (1973), and Andrews and Goodson (1980) included a

Systems in Design • 99

variety of models from a wide range of literature sources. These were written for technical designers who typically would be working on high-consequence projects, usually larger ones. The series of reviews by Gustafson (and later Gustafson and Branch) between 1981 and 2002 restricted their scope to the literature reported and available at the ERIC Clearinghouse and became focused almost exclusively on models related to educational applications. These reviews were intended for a non-technical and mostly novice audience. These reviews limited the scope and depth of the discourse on design models considerably. The term “ADDIE” became generally associated with design models of this type. The origins of the ADDIE term are uncertain (Molenda, 2003), which is symptomatic of an increasingly disorganized and unsystematic literature on design models. By 2000, there were relatively few new ideas being advanced in the literature on instructional design models. Throughout these reviews of design models, several trends may be discerned. The first is that over this period models tended to lose the energy and robustness of the systems approach and the hopeful, energetic, expectant tone that was evident in the work of Gagné, Banathy, Silvern and Briggs. In some cases detailed and complex models lost sight of the systems approach altogether. Some became heavily procedural, bureaucratic, and even linear (Braden, 1996). Accompanying this complexifying trend was the notion that designers need have only “a half-dozen really different models in his/her tool bag and know how to modify them for each new situation” (Gustafson, 1981, p. 4). This points to the growth of the idea that there should be classes of design models and that models could be selected using guidelines and be tailored to individual projects. Guidelines for selecting and tailoring, however, were never well articulated (Smith and Boling, 2009). In the second trend, detailed models of sub-processes, such as objectives analysis and media selection, and specialized processes, such as computer logic design, appeared in greater numbers and became more common, adding to the procedural flavor of the models. Sub-process models were left out of most reviews, and though highly detailed models have since fallen out of vogue, they pointed to an increased interest at the time in specifying design processes at a fine-grained level, which deepened the focus on procedural aspects of the process rather than on the structural and functional aspects of the thing being designed. The third trend revealed in these reviews is the growing sameness of the models. The models included in the reviews were similar enough that over time they could be compared using a tabular format on the basis of comparable processes. In the Gustafson and Branch reviews (1997a, 1997b, 2002) somewhat deeper analyses were introduced. Gustafson and Branch lamented the lack of progression across generations of models and the lack of knowledge or design improvements flowing from them, despite their proliferation, but there was little evidence at this time that any view of design or development other than that of process-oriented models was being seriously explored. An exception to this trend was a handful of designers from other fields who, looking in from the outside, were able to collect design models from many disciplines to contrast their similarities and differences (see Dubberly, 2005). Instructional design was becoming more invested in fewer, less-innovative models. These were found mostly in textbooks tailored to the needs of a novice designer audience: public school teachers, and graduate students in instructional design. At the same time, in the design research community, design theorists were pursuing second-generation design methods (Rith and Dubberly, 2006; Rittel, 1973). Schön (1987) was conducting empirical studies of design leading to robust descriptions of design “conversations” between a designer and a design problem. Journals focused solely on design research were being initiated (Margolin, 2010). The almost exclusive focus on elaboration of instructional design models through revision and rearrangement of the same set of basic process elements isolated the instructional design practitioner from outside views of design, which could have enriched the discourse of instructional designers (Smith and Boling, 2009). Emphasis on the visual representation of flowcharts and diagrams of design models contributed to the impression that design was sequential.

100 • Fundamentals

Popular misconceptions about the origin and evolution of design models obscured their true nature (Gibbons and Yanchar, 2010). There were few histories of model sources; authors retained terms like “systems approach”, “scientific”, and “systematic” even though the meaning of the terms became blurred. By neglecting the theory-agnostic property of systems approaches, authors added to design models domain-specific baggage that added assumptions to the models, restricting model application to a narrower range of problems. One assumption, for example, is the assumption that task analysis is the main form of content analysis when in fact many subject-matters do not yield meaningful results to task analysis. By incorporating specific philosophical and theoretical commitments to domain theories, such as behaviorism, models entered a dead end in which the only reasonable ways forward were either: (a) continuing to rearrange and reword existing models, or (b) viewing models with suspicion and advocating their marginalization. Many practicing designers and theorists took the latter course. Meanwhile, the literature from other design fields reminds us that other possibilities exist (Brooks, 2010; Cross, 2007; Goel and Pirolli, 1992; Kruger and Cross, 2006; Lawson, 2005; Lawson and Dorst, 2009; Rowe, 1987). The Instructional Design Model Today A typical instructional design model today consists of five major processes: analysis, design, development, implementation, and evaluation (see Figure 4.1). To this core of essential processes are often added processes for: • analyzing data to determine the need for and the needs of a design project; • managing the design process itself; • planning the details of ongoing (after-project) implementation, evaluation, and instructional management. Analysis: • Connue target populaon, current training, and resource analysis • Analyze content, performance

Development: • Prototype, test, evaluate • Revise, recycle • Integrate and test

Design: • Describe instruconal events, media use, event sequences, strategic plans, lookfeel, prove costs, and plan development

Implementaon Front-end Analysis

Evaluaon

D D I

Implementaon: • Train personnel • Prepare environment • Roll product out into regular use • Monitor, collect data

E

Life-cycle maintenance

Evaluaon: • Analyze usage data • Determine effecveness • Prescribe revisions • Determine costs • Monitor trends

Figure 4.1 The context in which most instructional design models are carried out, showing the relationship of the model core (ADDIE) to front-end analysis and ongoing implementation, evaluation and life-cycle maintenance processes which begin during and continue after a project’s completion.

Systems in Design • 101

Table 4.1 lists the process names from a number of prominent instructional design models to provide a sense of their diverse terminology, while at the same time showing their high degree of similarity. The main intent of Table 4.1 is to portray the variety of terms exemplified by the models, so no care has been taken to list the most current version of each model. Model builders cluster design processes differently while still drawing from the same basic pool of processes. Groupings in the table are not definitive, but suggestive. Clearly the basic terminology of instructional design is far from settled. Partitions in the table suggest clusters of processes. The small size of some clusters compared to the larger size of others provides a kind of verbal histogram that reveals the core of processes most authors felt were critical.

Table 4.1

Analysis and Comparison of Design Processes Included in Prominent Current Instructional Design Models

Process

Model

Organize project management

Hamreus (1968)

Analyze job

Branson et al. (1975)

Construct job performance measures

Branson et al. (1975)

Select tasks/functions

Branson et al. (1975)

Situational analysis

Gustafson and Branch (1997a, 1997b)

Analyze setting

Hamreus (1968)

Analyze learning contexts

Smith and Ragan (1999)

Analyze existing courses

Branson et al. (1975)

Instructional problems

Kemp et al. (1994)

Analyze the problem

Leshin et al. (1992)

Problem analysis

Seels and Glasgow (1990)

Identify problem

Hamreus (1968)

Analyze learners

Smith and Ragan (1999)

Learner characteristics

Kemp et al. (1994)

Analyze learners

Heinich et al. (1996)

Describe entry behavior

Branson et al. (1975)

Identify entry behaviors

Dick et al. (2009)

Assess needs and analyze content

Tripp and Bichelmeyer (1990)

Assessment of entering behaviors

Gerlach and Ely (1980)

Identify instructional goals

Dick et al. (2009)

Identify instructional goals

Reiser and Dick (1996)

Instructional goals

Gustafson and Branch (1997a, 1997b)

Analyzing domains

Leshin et al. (1992)

Conduct instructional analysis

Dick et al. (2009)

Instructional analysis

Gustafson and Branch (1997a, 1997b) (Continued)

102 • Fundamentals Table 4.1 (Continued) Process

Model

Specification of content

Gerlach and Ely (1980)

Task analysis

Kemp et al. (1994)

Analyze and sequence tasks

Leshin et al. (1992)

Task and instructional analysis

Seels and Glasgow (1990)

Develop tasks

Branson et al. (1975)

Analyze learning tasks

Smith and Ragan (1999)

Identify objectives

Reiser and Dick (1996)

State objectives

Heinich et al. (1996)

Performance objectives

Gustafson and Branch (1997a, 1997b)

Set objectives

Tripp and Bichelmeyer (1990)

Specification of objective

Gerlach and Ely (1980)

Instructional objectives

Kemp et al. (1994)

Write performance objectives

Dick et al. (2009)

Determine objectives

Diamond (1997)

Develop objectives

Branson et al. (1975)

Identify objectives

Hamreus (1968)

Objectives and tests

Seels and Glasgow (1990)

Write test items

Smith and Ragan (1999)

Develop assessment tools

Reiser and Dick (1996)

Develop criterion-referenced tests

Dick et al. (2009)

Develop instructional strategies

Dick et al. (2009)

Instructional strategies

Gustafson and Branch (1997a, 1997b)

Instructional strategies

Kemp et al. (1994)

Plan instructional activities

Reiser and Dick (1996)

Analyze and sequence supporting content

Leshin et al. (1992)

Content sequencing

Kemp et al. (1994)

Determine delivery strategies

Smith and Ragan (1999)

Select instructional formats

Diamond (1997)

Perform interactive message design

Leshin et al. (1992)

Instructional strategy

Seels and Glasgow (1990)

Specify learning events/activities

Branson et al. (1975)

Specify learning events and activities

Leshin et al. (1992)

Specify methods

Hamreus (1968)

Select instructional setting

Branson et al. (1975)

Determine sequence and structure

Branson et al. (1975)

Determine organizational sequence

Smith and Ragan (1999)

Choose instructional media

Reiser and Dick (1996)

Systems in Design • 103 Table 4.1 (Continued) Process

Model

Media selection

Gustafson and Branch (1997a)

Review/select existing courses

Branson et al. (1975)

Select media and material

Heinich et al. (1996)

Instructional resources

Kemp et al. (1994)

Evaluate and select existing materials

Diamond (1997)

Media decisions

Seels and Glasgow (1990)

Construct prototype

Tripp and Bichelmeyer (1990)

Write and produce instruction

Smith and Ragan (1999)

Develop instructional materials

Dick et al. (2009)

Materials development

Seels and Glasgow (1990)

Construct prototypes

Hamreus (1968)

Specify instructional management plan and delivery system Branson et al. (1975) Determine management strategies

Smith and Ragan (1999)

Design and conduct formative evaluation

Dick et al. (2009)

Design evaluation instruments and procedures

Diamond (1997)

Evaluation instruments

Kemp et al. (1994)

Implement instructional management plan

Branson (1975)

Test prototypes

Hamreus (1968)

Produce and test new and available materials

Diamond (1997)

Implement instruction

Reiser and Dick (1996)

Pilot test

Gustafson and Branch (1997a)

Utilize prototype

Tripp and Bichelmeyer (1990)

Conduct instruction

Branson et al. (1975)

Implement, evaluate, and revise

Diamond (1997)

Instructional delivery

Kemp et al. (1994)

Utilize material

Heinich et al. (1996)

Conduct formative evaluation

Smith and Ragan (1999)

Conduct internal evaluation

Branson et al. (1975)

Evaluate instruction

Leshin et al. (1992)

Validate instruction

Branson et al. (1975)

Evaluation/review

Heinich et al. (1996)

Analyze results

Hamreus (1968)

Revise instruction

Reiser and Dick (1996)

Revise system

Branson et al. (1975)

Revise instruction

Dick et al. (2009)

(Continued)

104 • Fundamentals Table 4.1 (Continued) Process

Model

Revise instruction

Smith and Ragan (1999)

Coordinate logistics for implementation

Diamond (1997)

Implementation

Seels and Glasgow (1990)

Implement/recycle

Hamreus (1968)

Install and maintain system

Tripp and Bichelmeyer (1990)

Maintenance

Seels and Glasgow (1990)

Summative evaluation

Seels and Glasgow (1990)

Summative evaluation

Gustafson and Branch (1997a)

Conduct external evaluation

Branson et al. (1975)

Design and conduct summative evaluation

Dick et al. (2009)

Dissemination/diffusion

Seels and Glasgow (1990)

Application Exercise Every new version of design model had to be justified by its author on some grounds. That is, some claim had to be made to support its legitimacy. • Study one of the design models referenced in Table 4.1. Identify the justification used by the author for its validity. Why did the author express the model? Why did the author think it was different or better than previous models? Summarizing ADDIE/ISD Some generalizations can be made about ADDIE/ISD design models: • ADDIE/ISD is a special case of a generic engineering design model that has been adapted for application to instructional design. The incorporation of the engineering model began with Gagné’s publication of Psychological Principles and was energetically promoted by Leonard C. Silvern to both corporate and public education audiences. This shows that ADDIE/ISD shares a common ancestry with problem-solving models evolved in another discipline. The history and use of design models in other fields can thus be studied profitably. • Design models described at a low level of detail appear to be applicable to a wide range of problems. However, as a model is broken down into processes and sub-processes, its generic qualities disappear, and at the lowest, most detailed level it begins to represent the work plan for a specific project. • There is no process box in ADDIE/ISD models labeled “insert theory and best practices here”. Research shows that designers tend to be confused and frustrated by models in which it is not clear how theory enters designs (Yanchar et al., 2010). • ADDIE/ISD models use the terminology of the systems approach to problem solving, but adapting the model for simplicity requires simplifying assumptions that restrict and channel the problem-solving process. The result is less and less a systems approach and more and more a process model. The more specific the model is made through process detailing, the less it

Systems in Design • 105

resembles a true systems approach. This difference is very important, because a systems approach is much more variable and unpredictable in the order of its problem-solving steps, and everything, including the problem itself, can change. Design Models and Design Layers The central principle of design layers is different in a very basic way from that of instructional design models, and yet the two are compatible. Together they increase the set of design tools available to the instructional designer. Thinking in terms of design layers leads to a different order of decisionmaking and to a deeper level of attention to product architecture than does ADDIE/ISD. Briefly stated, while the ADDIE/ISD process is a systematic approach to design, design layers cause the designer to come to grips with the artifact being designed as if it were a true system with interacting and mutually interdependent parts. Design layer thinking begins with the nature of the artifact and its functions and proceeds to decompose the design problem, not in terms of processes, but in terms of the functions of the system being designed. To use design layers a designer does not have to abandon the engineering design model, since the steps of a model contain a useful built-in imperative. Certain major processes tend to come first, and others follow naturally. You cannot implement and evaluate until you have produced; you cannot produce until you have designed; and you cannot design until you have made some kind of analysis of the elements of the problem. This is true even in the absence of a formal design model. And yet a designer cannot allow a process model to be the central concern of designing. The structures of most importance during design are those of the product, not the process. Design layer theory represents coming at design from a different angle. Layers do not replace design models, but they can radically modify the order of design decision-making away from a prescribed model order. With design layers, the process begins with the “givens” of the project. By taking this approach to design—an approach based on the functions of the evolving artifact—the design unfolds in an order that at the detailed level is unique to the problem. The benefits of the layer approach are: (1) the ability to tailor the order of design decision-making to the givens and constraints of the specific design problem, (2) a greater emphasis on the role of imagination in design (see the discussion of Jenn’s Table in Chapter 3), (3) a greater emphasis on the structural elements incorporated into the design and their interrelations, (4) a more internally integrated and coherent design, and (5) a design into which the theory and research can be integrated much more directly and at a finer level of detail. The last three of these benefits and the means of obtaining them are the subject of later chapters. Theme #3: The Renewed Influence of Systems Thinking in Instructional Design The message of this chapter has been that designers design systems and that they have choices when they plan how to attack a system design problem. In some cases the ready affordances and simplicity of an existing design model may be just what is needed. At the same time, other projects may require a good deal of problem solving, research, and uncertainty on the way to a design solution. In those cases, something with more of the flavor of a true systems approach may be necessary, and multiple cycles of prototyping may become more valuable. In either case, the architectural concept of layers is also a useful tool and harmonizes with both the set approach of a model and the more uncertain problem solving of a systems approach. Layers are a useful tool in either case because they represent a functional way of looking at the artifact being designed rather than the process used to create the design. A layer describes the

106 • Fundamentals

functioning of a part of a system. Layers allow the designer to enter within the boxes of a process model and decompose the design problem according to product’s system architecture. The layered approach as described in Chapter 3 frees the designer from the order constraints of process models and provides structure for less sequenced problem-solving approaches like the systems approach. One does not have to give up either approach with layers, because layers are a characteristic of the designed thing. Systems Thinking There is more to this argument than describing the choice between a set engineering model and a systems approach. The real challenge for new and maturing designers is acquiring systems thinking. What is systems thinking? For instructional designers it is the ability to see instructional artifacts as systems and to see that what they design must live and function harmoniously within the context of other systems, such as existing infrastructure systems, logistic systems, and other instructional systems. Reynolds and Holwell (2010) explain that: Systems thinking is precisely about changing the focus of attention to the forest, so that you can see the trees in their context. Understanding the forest gives new and powerful insights about the trees . . . If one considers the situation as a whole, rather than focusing on its component parts, then there are properties which can be observed which cannot be found simply from the properties of the component parts. —(p. 8) Banathy (1968) explained that: It is the system as a whole—and not its parts separately—that must be planned, designed, developed, installed, and managed. What is really significant is not how the individual components function separately, but the way they interact and are integrated into the system for the purpose of achieving the goal of the system. —(p. 2) Banathy referred to systems as “deliberately designed synthetic organisms composed of interrelated and interacting components” (p. 3, emphasis added). The time once was when instructional designers could think of their work in terms of isolated products designed for use by individuals in selfstudy mode. Learning theory was an account of the mental processes of the individual, knowledge was considered a possession of an individual, and competent performance was viewed as individual capability. Instructional products were like books on a shelf in the library: independent, and self-contained. These views have changed in a world that today views learning as a social phenomenon and performance in terms of the contributions of team members to a shared problem-solving task. Lave and Wenger (1991) describe learning that takes place naturally through a process of “legitimate peripheral practice” where learners are tutored, mentored, and apprenticed into competent practice through engagement in the activities of a community of practice—first on the periphery of the community and eventually moving toward its center—on tasks of increasing complexity and importance—to the point where an individual is capable of functioning as both a learner and a teacher of others. In this view, a community is where a person learns and where a person participates with others in the activities of the community, learning relevant tasks through participation that is legitimized by the community. Communities themselves are a type of learning organism—a

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system—within which learners both give and receive the community knowledge that they have been given or that they discover. What is the value of describing the situation of learning within a social context? It not only places traditional classroom instruction into perspective as one variety of a learning community (albeit, one with very formal social rules), but it gives new meaning to learning as it occurs in other formal and informal settings that are less structured. This view of learning supplies an underlying continuity to what we sometimes think of as separate activities—the processes of formal classroom learning, on-the-job learning, self-directed learning, after-school learning, and occasional learning that takes place at the moment of need. This view recognizes that learning is inherently social, that it is carried out in a variety of social settings, and that this is accomplished through both formal and informal social interactions. The classroom and the water cooler are, in this view, places of learning within a participatory system. The systems view leads a designer to step outside of traditional categories and consider what is being designed in terms of its properties as a system: how it functions as a system, and how it fits within the context of other systems. Learning Systems Thinking Within Communities of Designers Learning to be a designer may require stepping outside of traditional categories. Banathy (1996) describes design in terms of a participatory system where learning takes place: In my view, design is a creative, disciplined, and decision-oriented inquiry, carried out in iterative cycles. During the cycles we develop the design solution by repeatedly exploring organized knowledge as well as testing alternative solutions. We constantly integrate information, knowledge, insights gained, and the findings of testing into emerging design solutions. —(p. 17) He describes how learning occurs within a design team: Systems design carried out in the third-generation [of design thinking] mode is not directed by an expert but it emerges from the intensive, creative and dynamic interaction of members of design teams. Rowland’s (1995a, 1995b) musical metaphors—the orchestra and the jazz ensemble—well represent the contrasting design modes of the user-designer and the expert. The orchestra is the metaphor for the expert-driven design. The conductor makes all the decisions, which the large number of musicians follow. In contrast, in the jazz ensemble the small group decides what to play and how to play it. They improvise around a basic plan, react to each other as they play, and challenge each other with new ideas. They explore opportunities and design new patterns. The play involves a high degree of interaction and cooperation. These are the kinds of behavior that creative and interactive design implies. If we engage a user community in design, then we [had] better learn to play design in the mode that the jazz ensemble uses. —(p. 237) The jazz metaphor does not mean that design is uncontrolled or that it lacks direction. It means that members of a design team are trusted to follow a “basic plan” that has been worked out jointly, with input from the whole team, each adding their specialized expertise to creating a coherent and unified design that blends the contributions of all into a focused, functional—and in the case of jazz—interesting system. This trend in design is not unique to instructional design. Lawson and Dorst (2009) describe what they term “the myth of the single mind”:

108 • Fundamentals

Design on a substantial scale is essentially a collaborative effort. This is where design historians . . . sometimes find themselves in trouble in their research. They tend implicitly and automatically to look for the single elusive author of a design or a design idea, in an effort to describe the history of a design as a series of geniuses that single-handedly change the world. However, the ideas in a design firm often emerge from a collaborative creative process, rather than from a single contribution. —(p. 187) Designers design within design systems, and they design systems. A designer’s concept of what is being designed evolves over the course of experience in designing. One of the conceptions a designer has to arrive at eventually is seeing the artifact as a system rather than just a media “thing”. Perhaps therefore it has to be seen as a media thing that has to work harmoniously with other media things and an experience that has to blend in some way with the experiences that precede and follow it. Its functions have to work harmoniously and sustainably, both internally and within the larger context. Finally, it has to be designed for survivability and economy of development and use. A design that employs layers as a thought tool makes this goal much easier to attain. The challenge for instructional designers is not just to become aware of how the problem-solving spirit of the systems approach can be captured and kept alive but to realize that in solving problems the systems of the classroom, the learning environment, the community, the world, and the lives of people are impacted in some way. It is not just good advice for a designer to learn to think in systems terms, but it is an ethical responsibility. Tim Brown, CEO of IDEO design firm, expresses the designer’s responsibility this way: Designers can’t prevent people from doing what they want with the products they own, but that does not excuse them from ignoring the larger system. Often in our enthusiasm for solving the problem in front of us, we fail to see the problems that we create. Designers, and people who aspire to think like designers, are in a position to make important decisions about what resources society uses and where they end up. —(Brown, 2009, p. 194) Banathy describes the designer’s responsibility in terms of stewardship: Stewardship asks each one of us to be responsible and accountable for the outcomes of the system we design. Stewardship is shared accountability, which is fueled by a shared commitment to service . . . The kind of stewardship described here is the only viable governance of designing communities: stewardship transcends leadership. —(Banathy, 1996, p. 236) Design as Research Designers learn from designing, not because they design in order to learn, but because designing inevitably leads to learning. Design is largely a process of answering questions. During the design process, when a question is asked for which there is not a ready answer, some means is used to find or make up an answer. The answer is tested along with all of the other answers that may have been asked and answered, and the results impact what the designer knows. Many see this as a form of research. Collins et al. (2004) describe a new tradition of research bent on bring research on learning and instruction out of the laboratory and into everyday settings in a way that resembles the way a designer learns from designing. Design research, also called design-based research, studies learning and instruction in messy real-world contexts. Scientific researchers use existing theory and re-

Systems in Design • 109

search as the launch pad for excursions to discover unknown scientific principles. Proponents of design-based research claim that a similar process exists for design research that uses design theory and domain theory as starting points (see Chapter 6). Instead of trying to infer causes from observed effects, design-based research pursues knowledge by studying how presumed causal factors can be manipulated to produce desired outcomes. George Klir (1969), a general systems theorist, explains why this is an important claim. According to Klir, scientists study systems in an attempt to explain “why” they behave the way they do. Says Klir, what engineers—who are designers—study is also systems, but they do it in an attempt to explain how systems can be made to behave in a certain desired way. The point is that both scientists and engineers study systems, but from a different angle and for different purposes. The knowledge they create is of a different kind (see Vincenti, 1990). In each case, new knowledge is produced. Collins et al. (2004) draw a contrast between traditional educational research, which is based in a scientific mindset, and design research, which is based in a design mindset (see Table 4.2). Design research brings research out of the laboratory and into everyday settings that range from formal classrooms to informal settings, to on-the-job. It studies a number of variables at once, and it does that by revising and retesting working systems multiple times. The revision that follows each testing is connected to a theoretical proposition so that the effects of change are linked to theory and can be interpreted in a way that builds theory. Since many variables are involved in design research, the researcher must describe the research situation, for each new iteration, in as much detail as possible, in the spirit of keeping a lab notebook. Whereas traditional research methods are fixed and specified in detail beforehand in order to impose control on the experiment, everyday settings and their sometime unpredictability require the ability to adjust to momentary needs in a flexible way. This sometimes leads to unexpected results and serendipitous findings that can lead to further experimentation. Design research searches for patterns of causability—patterns of variables that work in a desired way—which leads researchers to describe or profile the conditions that are set up in each new test. Finally, the design researcher participates with others in an interactive way. As previous results are interpreted, causability patterns are interpreted, and new revisions are planned. This includes input from all of the stakeholders in the research: instructors, design team members, and even participants, through a process of co-design (Churchman, 1968; Schön, 1987). Bannan-Ritland (2003) proposes that design leading to educational interventions should “move past isolated, individual efforts of design research” and undertake research “that considers both field studies and experimental research methodologies” (p. 21) in programmatic rather than piecemeal studies. What this means to the instructional designer is that every design is an opportunity to learn

Table 4.2 The Contrast between Traditional Educational Research (which is Based in a Scientific Mindset) and Design Research (which is Based in a Design Theory Mindset) Traditional Educational Research

Design Research

Conducted in a laboratory setting

Conducted in a messy, everyday setting

Single dependent variable

Multiple dependent variables

Methodology of controlled variables

Methodology of characterizing (describing) the setting

Fixed procedures

Flexible design revision

Social isolation of subjects

Social interaction of participants

Testing a hypothesis

Developing a profile

Experimenter stance

Co-participant design and analysis

110 • Fundamentals

something from having designed and that chained design efforts over time can be used to create new understandings, new knowledge, about instruction and about design. The original principle of the systems approach was to learn from a data-intensive combination of designing and research that transcended the single project or the single research study. The systems approach originally consisted of design and research conducted together in pursuit of usable solutions, and also reusable knowledge. The tendency to reduce the systems approach in instructional design to a process or a model can be balanced by considering each new project and each new design problem as a type of research and an opportunity to learn about designing. What has been learned from past projects can be chained with what is learned from the present project. This is the mode of thinking that designers engage in anyway: each new project is an occasion to try something new: some new element of a design, or some new twist in the design process. Our mistake in the past has been in feeling guilty for doing this—for allowing ourselves to think that following an approved process was more important than experimentation in design. Bannan-Ritland (2003) proposes a positive alternative for instructional designers that “draws from traditions of instructional design . . . product design . . . usage-centered design . . . and diffusion of innovations . . . as well as established educational research methodologies” (p. 21). She compares the traditional instructional design model with the models used for innovation in these other design-related fields. This restores a larger perspective that is lost when the point of reference is applying procedures in the conduct of a single project. She therefore concludes: It is important to note that [this process] is not intended to be a description of a single study in which an intervention is designed in a relatively short space of time and then tested and disseminated. Rather, it is meant to provide a program-level perspective. —(p. 21) Application Exercise Design-based research makes every designer into a researcher only because every designer is a researcher. • Find an article that describes the application of design-based research. • Compare the design-based research approach described in the first article with the research approach used in a more traditional empirical laboratory research study. • Identify the key differences you can detect. Conclusion Instructional designers, as designers of systems, profit by taking the larger systems view that moves beyond process approaches without abandoning them. Whether designers are applying a systems approach in a long-term programmatic way in pursuit of design knowledge, or tailoring one of the existing instructional design models to the needs of a particular project, systems thinking will help them to maintain their orientation to the creation of products which function internally as coherent systems and which function within their context harmoniously and sustainably with other systems.

5

The New Contexts of Instructional Design Instruction, Learning, Technology, and Design

I have come to believe that a great teacher is a great artist and that there are as few as there are any other great artists. Teaching might even be the greatest of the arts since the medium is the human mind and spirit. —(John Steinbeck) This chapter defines four basic terms in ways probably somewhat different than you expect. The terms are: • • • •

Instruction Learning Technology Design.

These concepts are basic to this book, but the meaning of these terms has shifted over the past two decades in important ways, and the meaning of each one influences the meanings of the others. As a foundation for the remaining chapters, this chapter considers in more detail how these terms might be seen today. What is Instruction? The best way to start defining the term “instruction” is to find out what you think it is to begin with. Start with this application exercise: Application Exercise What do you think instruction is? • Stop reading for a moment, and on a sheet of scratch paper list as many words as you can in three minutes which describe to you acts that occur during instruction. Then compare your list with the list in Table 5.1. • As you compared your list with Table 5.1, what percentage of overlap did you find? How many terms in Table 5.1 did you miss? How many terms did you write down that were not listed in Table 5.1?  

111

112 • Fundamentals Table 5.1

A List of Acts that Occur During Instruction

Conditioning

Disciplining

Educating

Emancipating

Empowering

Enlightening

Facilitating

Guiding

Indoctrinating

Inducting

Lecturing

Managing

Mentoring

Modeling

Nurturing

Pointing

Structuring

Telling

Training

Drilling

Preparing

Finishing

Tutoring

Schooling

Responding

Helping

Improving

Advising

Revealing

Assigning

Posing

Occasioning

Fostering

Inculcating

Remediating

Mediating

Liberating

Improvising

Framing

Participating

Minding

Listening

Ordering

Directing

Supporting

Scaffolding

Warning

Enculturating

Ministering

Advocating

Apprenticing

Initiating

Explaining

Coaching

Parenting

Testing

Measuring

Controlling

Punishing

Rewarding

Asking

Enticing

Disrupting

Examining

Conversing

This exercise is intended as a reminder that instruction comprises more acts than we sometimes bring to mind. Instruction is an activity as complex and nuanced as any other human activity. At the same time, it is one of the most common and frequent occurrences—as common as conversation. Consider this definition of instruction: instruction is a conversational process engaged in by mutual consent by two or more agents for the purpose of promoting learning by one or both of the agents. This definition of instruction underlies all that follows. Where and when do instructional conversations happen? Anywhere and at any time. Probably the best time is when the learner is receptive and occasion and need have set the stage for learning. A Model of Instruction A simple model can be used to describe the context and the dynamic of instructional conversations. During instruction we create artificial worlds that can be considered environments. These environments resemble to some degree conditions that exist in the real world where learning is used. Instruction always takes place in some kind of environment—no matter how real or how artificial. The real world itself is an environment in which we are daily instructed by our own actions and the reaction of things around us to these actions. Even in a natural setting, instruction is a process that involves interaction with systems. The learner is a complex human system; and the surrounding environment and the things within it are systems also. When we act and then observe an effect, we try to connect the effect with a precursor cause. This is how we make sense of the world. This kind of learning is natural, but it has shortcomings: • It can be dangerous. Some actions can lead to bad outcomes if no one is there to stop us. • It can lead to incomplete learning. We may not think of every action that will lead to useful learning. • It can be inefficient. It would take more than a lifetime to learn the knowledge we need through experiments. • It can be misleading. We don’t always interpret experience accurately.

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We employ the processes of instruction to learn safely, more completely, and more efficiently. As designers we learn how to attract and influence the natural forces of learning for instructional purposes. Figure 5.1 presents a model of how learning takes place in the real world when the learner is acting with outside help from social sources. The learner is shown interacting within an environment with one or more cause–effect systems, either naturally occurring or human-made, that are placed within the environment. Whatever learners within the environment are doing, their action results in some reaction from the system: water flows from the faucet, the temperature in the room drops, there is an explosion, or the lights go off. Cause–effect systems follow their natural laws to produce an outcome that can be observed by the learner. Then the learner begins to construct an explanation. Whatever the learner concludes from the experience—whether it is valid or not—is what can be learned. In this model of the learning situation, which also doubles as a model for instruction, a question forms in the mind of the learner, and the learner acts in order to answer it. The learner is assumed to be an active rather than passive agent. Over time, the acts of the learner accumulate in the form of an expert performance model. According to this model, the strategic acts of a designer or an instructor include: • • • •

providing the environment, whether realistic or not; populating the environment with things to interact with; forming an expert performance model as a target; providing the services of a learning companion to assist the learner in reaching performance targets (theirs or the designer’s).

ENVIRONMENT

EXPERT PERFORMANCE MODEL

LEARNING COMPANION LEARNER

CAUSE–EFFECT SYSTEMS

Figure 5.1 This illustrates the situation of learning within an environment that depends upon acting, noticing, and concluding on the part of the learner. Note that the learning companion can be any person or thing in the environment arranged for the purpose of averting danger, assuring completeness of learning, increasing efficiency of learning, and/or supporting accurate interpretations of experience.

114 • Fundamentals

Who or what is the learning companion? And what services does the learning companion offer? These are the strategic questions faced by the designer and the instructor whose answers constitute the bulk of the content of this book. It is important to avoid stereotyped assumptions about the identity, role, and function of the companion and to begin to see the elements of Figure 5.1 as variables that can take on an infinite number of values in many combinations. Providing instructional support through designs that use these combinations is the whole study of instructional design. Though the companion is shown as a tiny human figure, it really represents a companion function that can be supplied by media, by a computer program, by a teacher, a tutor, or members of a peer group. The discussion of design layers in later chapters centers on the many identities and functions of the learning companion, the kinds of cause–effect system models provided by the companion, and the environment the designer creates within which acting and responding by the learner can take place. The importance of the model presented in Figure 5.1 is that it emphasizes the learner as an actor and a decision-maker during instruction, not just a receiver. This model stresses the role of the learner as an active agent. Instruction as Conversation Conversation is the most comprehensive and powerful metaphor available to instructional designers for generating active-learner designs. The model in Figure 5.1 embodies this metaphor, and it describes an underlying principle for virtually all instructional designs. The model provides a principle for generating more powerful designs that are structured as conversations. Reconsider the definition of instruction offered earlier: instruction is a conversational process engaged in by mutual consent by two or more agents for the purpose of promoting learning by one or both of the agents. The key assumptions of this definition are: • Instruction is a specialized form of conversation. • Engagement is the intentional act of two or more agents. • The conversation has a purpose. Most would agree that instruction involves communication between two or more persons, but how can a person carry out a conversation with technology? And how can self-instruction be considered a conversation? Or a lecture? Answering these questions requires a more inclusive definition of conversation. Conversation We usually think of conversation as a verbal or symbolic (e.g., sign language, visual language, action language, etc.) exchange between two or more people who are in each other’s presence, with communication taking place in short bursts called “turns”: Hi, Tom. Hi, Sue. How’s Grandma? She’s fine. Got a little cold. I’ll call her. Call soon. She’s going on vacation. OK. Got to go. Bye. Bye.

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This is a conversation by almost any definition. But as we read it, we make certain assumptions that might not be apparent to us. These assumptions become clear if we probe the dimensions of a conversation. For example: • What if these people were located in different rooms in a house, moving about, carrying out different tasks as the conversation takes place? Is it still a conversation if it is not direct and face-to-face? • What if these people are talking on the phone (or any medium)? Is it still a conversation? • What if the exchange is carried out surreptitiously by texting while one of the people is in a meeting? And what if one of the participants can’t answer right away every time (time lapse)? • What if the exchange takes place over the period of a week using notes posted on an office bulletin board (medium and time lapse)? • What if only pictures are used (symbolic forms, no words)? As we continue to suppose situations in which the exchange takes place over increasing distance and lengths of time, we see that the idea of conversation may be more flexible than we originally thought. Let’s change another dimension: length of utterance: • What if the exchange were to take place with one person making an utterance that was three written pages long (a letter) or an hour of speaking (a lecture)? Can this be said to compress the conversation into one turn? And can the reply take place at a different time and place using a different medium and still be part of a conversation? This kind of conversation might have many separate threads (topics, goals, concerns) open at the same time. For present purposes, all of these examples and others that could be devised will be classified as conversations, because they share key defining characteristics of a conversation: • • • •

Information is exchanged. There is intention of all agents to engage. There is a willingness to listen and think before responding. There is a shared purpose to the exchange.

These essentials describe many kinds of conversation that are not the simple face-to-face kind that the term commonly evokes: • Conversations in which there are tens, hundreds, or thousands participating at once (think MOOC—massive open online course—for example). • Conversations in which one of the turns is so long and the numbers of people sufficiently large that responses of participants have to be delayed, have to be given in a more formal way (for instance a written response), or may be impractical to give at all. • Continuing conversations among scholars or problem solvers that take place over a period of years in conference papers and through journals and books. • Conversations whose turns are distributed across multiple media forms: person-to-person, speeches, letters, video clips, and messages through third parties. • Conversations in which each participant may be separated by lifetimes but in which one writer or speaker responds to what an earlier one has written—sometimes only once (see Bloom, 2003, Introduction, for example).

116 • Fundamentals

The variety of conversational forms continues to grow in this way because it is the core of essential characteristics—information exchange, mutual intention, listening and thinking followed by responding, and shared purpose—and not the outward and visible parts of the communication that define a conversation. All patterns of human interaction that share these core characteristics may be considered conversations, and it will be assumed that these can also be considered instruction. This broadens the concept of instruction considerably. It encompasses formal and informal encounters and those in which the explicit goal of conversing may not be acknowledged as “instruction”. There are several dimensions across which conversations can vary, among them: • • • • • • • • • • • • • • • • • •

Placement and sharing of initiative Perceived roles of participants Specific purpose Number of participants Location of participants Time between turns Length of turns Shared standards of evidence and argumentation Degree of civility or mutual regard Presence of a mediator Number of shared symbolic meanings Medium of communication Degree of willingness to participate Motive for participation Ulterior motives Specific topic of conversation Respect for turn-taking Negotiability of conversation rules.

Conversations are the fabric of social relations. We learn (or are instructed) by these conversations, whether formal or informal. For example, when we can’t get a word processing program to do what we want it to, we may go down the hall and strike up a conversation with someone who knows the answer that will help us figure out the problem. Failing that, we may seek an online forum where someone has solved the problem before us. We seldom read the manual, unless there is no choice. By doing this we are consulting a learning companion by means of a conversation. It is important to emphasize that the expressions in a conversation do not need to be words: they can be acts, gestures, facial expressions, symbols, or media resources. Conversations are the means by which the social interactions in our otherwise separate worlds take place. They are the activities that allow us to “calibrate” what we know with what others know, and they are the bridge that connects us with our culture, our society, and the beliefs we choose. Conversations include meetings, rituals, celebrations, courses, and all kinds of events both formal and informal. We learn from them all. Application Exercise Suppose you had to justify to a new instructional designer the proposition that instruction was a form of conversation. How would you defend your position against the following objections?

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• What about lectures? Nobody is conversing in a lecture. • What about a museum display? There is nobody to converse with. • What about homework assignments? Where’s the conversation in those? Instructional Conversations Designers usually have in their mind’s eye some image of what they think it is that they are designing. For some it is an instructional “product”; for some it is a self-contained instructional “package”; for others it is an “environment” within which learning can be supported and nurtured; for some it is an “experience” and not a media product. In fact, all of these are valid, and different designers over the years have used one or more of these images as a kind of abstract target to tell them when they have reached the design goal. Why, then, would we choose conversation as the most useful instructional metaphor? Because the conversation metaphor requires something that the others do not: that there will be two active agents during instruction, and that they will be listening to each other, interpreting, reflecting, and then responding to each other. Too often when another metaphor is used, it is easy for a designer to create a one-way conversation in which the learner becomes a receptive and mostly silent (non-) participant. In many cases, we have come to accept this as a standard of adequate instruction: the presentation of information without relevant responding and engagement by the learner. Informing is adequate to some needs, but simply to inform is insufficient in most learning situations, even when it is conceptual knowledge and not skill that is the instructional goal. We cannot just inform people and assume that they will know how to use the information in daily situations where it is to be applied. The definition of conversation above has several traditional instructional applications in mind: • • • • • • • •

Instruction over a distance. Instruction using many different forms of media. Instruction involving large groups. Instruction that takes place over long periods of time. Instruction involving many different role and initiative patterns. Instruction based in technology. Instruction delivered by human instructors. Instruction that blends the human and technology.

Many more forms of conversation might have been described, all of them useful for instructional purposes under the right circumstances. For example, games have instructional value. Salen and Zimmerman (2004) describe computer games as a formal conversation with specialized rules for turn taking and a restricted language of player actions choices. Play in general can be described as a kind of conversation. Bruner (1983a) observed that, “a game, in its way, is a little protoconversation” (p. 47). Rogoff (1990) describes guided practice, which generally includes a conversation of actions and words, as a means of instructing children to function within their culture and context. Even formal schooling can be seen in conversational terms. Tharp and Gallimore (1988) note that, “the task of schooling can be seen as one of creating and supporting instructional conversations, among students, teachers, administrators, program developers, and researchers” (p. 111). Designers should consider envisioning what they design in the abstract terms of instructional conversations. Significantly, all of the acts of instruction listed in Table 5.1 (and many more that are not listed) are typical of and common within the conversational metaphor. It may be worthwhile to return to Table 5.1 to confirm this for yourself. Conversation is a metaphor that is congenial with and

118 • Fundamentals

accommodates the full range of these actions. What are some of the values of looking at instruction through this metaphoric lens? • It encourages us to place the learner more prominently in the instructional equation as an involved, active, and more responsible participant. • It makes it possible for the designer to take advantage of newer instructional theories and approaches in both formal and informal learning environments. • It means that designers will pay more attention to the nature and frequency of learner activity during instruction and less to mechanical design formulas and processes. • It increases the possibility that designers will be led to create instruction that can adapt to the learner during instructional conversations. • It supplies design constructs (turns, exchanges, passages) that facilitate structuring a design at both the highest and lowest levels of architectural detail. What about self-instruction? Can it be seen as a form of conversation? An innovation in instructional technology shows that perhaps it can. In reading instruction Brown and Palincsar (1989) used a technique where each of the students in a reading group modeled a single part of the reading comprehension process. Together, the efforts of the group created a model of the complete process. Brown and Palincsar describe how this instructional procedure: renders . . . internal attempts at understanding external. Reciprocal teaching provides social support during the inchoate stages of development of internal dialogues. In the course of repeated practice such meaning-extending activities, first practiced socially, are gradually adopted as part of the learner’s personal repertoire of learning strategies. —(p. 415) The external instructional process of conversation becomes assimilated, to become an internal cognitive process that can be used by the learner: one in which the learner finally performs all of the conversational parts. Viewed in this light, self-instruction can be seen as an inward extension of outward conversational processes that learners have experienced in the past. An instructional designer designs and thereby becomes a participant in the formation of the conversation. What about the inability to practice, apply, and test what has been learned? Some instructional theorists define instruction in terms of the ability to test what a person has learned: they say that if there’s no test, it’s not instruction. This would seem to disqualify conversations as instruction, because we seldom give tests on the outcomes of conversations. Perhaps this argument can best be dealt with by reverse logic: certainly it does not mean that people fail to learn if testing is omitted. Since it is clear that learning does occur without testing, we should take a practical view and acknowledge that learning can and does result from our interactions with our social and physical environments. In a conversation, since each new exchange is contingent upon the previous turn, it can be said that testing takes place at every step of a conversation. This view unites the concepts of assessment and instruction, which have too long been separated in our literature. The conversational metaphor focuses the attention of the instructional designer on a broad range of methods, media, and variables that should appropriately be considered instructional. All social experience can be viewed in terms of its instructional potential: camps, parades, concerts, public meetings, political campaigns, amusement and theme parks, displays on city streets, signs on buses, rallies, sports events, service projects, museums and zoos, online forums, clubs, ritual observances, ceremonies, public demonstrations, professional organizations, even visits to the dentist. Viewed in conversational terms, the list of potential instructional venues becomes astonishingly large.

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Implications for Design The conversational metaphor has important implications for instructional designers. It supplies a different underlying structural basis to designs, and it gives it a high priority in making designs. Designers of technology-based instruction should explore the use of conversational structures and move conversation into a central position in their design thinking. This will lead them to confront new challenges, but it will also open new options for centering the instruction on the learner and on significant interactions. With this metaphor the designer comes to think early in the design process in terms of what the learner will be able to say or do, focusing the designer on the activities of the learner more than the activities of the teacher from the outset. This is desirable for several reasons: • It makes it more likely that the learner will engage in performance, rather than just receiving information. The design will provide environments for the learner’s performance activities first and foremost and treat the presentation of information as a supporting or scaffolding function for the performance. • It makes it more likely that the designer’s problem solving will focus more on the match between activities during instruction and activity in everyday settings where the knowledge is used. • It makes it more likely that the architectural and mechanical structures of the design will match performance-supporting functions. This will result in designs where information-giving instructional functions are seen as add-ons to a basically performance-centered environment. The sections below will point out some of the important implications of the conversational metaphor for instructional designers. Four will be considered: • • • •

The dimensions of a conversational design Greater emphasis on negotiation during instruction The issue of intentional learning The idea of a conversational object.

The Dimensions of a Conversational Design A designer of conversations gives certain kinds of decisions a high priority: • The length of a single turn: How long (in terms of time and substance) will the average turn be in a conversation? Very long (a lecture)? Very short (closer to a true conversation)? • Initiative and roles: Who is responsible for moving the conversation ahead? Does one agent control? Is the initiative negotiated? Does the learner have an assigned role? Does the initiative shift over time from one agent to another? When multiple learners take part, who has initiative and how does one exercise it? • Object of engagement: What object or event is used as the starting point of the conversation and around which the conversation will be centered? A question? An unsolved problem? An incident or event? An object? Something chosen by the learner? A project? A topic? • High-level structural patterns: What general pattern will interactions during the conversation be expected to follow? Is there a beginning event, a middle part, and an ending event? What parts of the interaction will be structured in terms of expected actions and what parts will permit actions executed in an unplanned order? Will there be one or more judgment points at which performance will be assessed? Will there be gates between parts of the conversation through which the conversation must pass?

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• Mediation: What media channels will be used for expressions from the learner and to the learner? Will multiple media channels be used? • Decision-making and interpretation: How will responses to learner communications be processed before the instructor responds? • Negotiation: Which elements of the conversation will be negotiable by the learner during the conversation, and which will be fixed? • Engagement plan: What provisions will there be for enlisting learner engagement and maintaining it throughout the conversation? How is the learner kept in the conversation until it achieves the agreed purpose? A design produced from the conversational metaphor can look like traditional instruction because all forms of instruction can be seen as variations on the conversational theme, but by attending to the structures of a conversation rather than just, say, “interactions” a designer is opening new dimensions and a new design semantic that will in many cases place the learner in a more prominent role in the design. Interactions, which surely must be part of a conversation, will take place within a broader framework and context of the learner’s needs rather than the designer’s. Application Exercise Revisit your responses to the previous application exercise. • Did any of your perceptions of instruction as a conversation change after reading this section? Emphasis on Negotiation during Instruction Conversational instruction involves a greater degree of negotiation with the individual learner. What is there to negotiate? An analysis of almost any conversation shows that unless one person dominates the conversation completely, there is a dynamic negotiation of: • Initiative—Who defines what comes next in the conversation? Who determines the path the conversation will take? Normally, initiative shifts several times during a conversation. One clue that a shift is taking place is when one person who has been answering questions begins to ask questions. In instructional terms, this would be considered a very positive shift. • Roles—What are the responsibilities of those involved in the conversation? What is the instructor’s range of activities? What are the responsibilities of the learner? • Goals—What is the current matter of learning? And what is the target performance that will determine when instruction has had its desired effects? How broad is the goal? Is there a progression of goals to serve as intermediary points? What are they? How definite are the goals? Do they need to be worked out and refined through conversation? • Means—What kinds of resources and activities will be used to stimulate and further the conversational goals? In much of technology-based instruction, negotiation of these things is handled by defaulting to traditional non-conversational patterns. Most classroom instruction assumes that when students want to ask a question or make a comment, they will raise their hands. We take this symbolic action—a conversational act—for granted. However, this is simply a tradition, and there are many alternative patterns of classroom conversation. Implementing the conversational ideal—an ideal that is completely reached only in human tutorial conversation—can appear complex given the current state of the art in (1) tools and (2) design concepts. However, giving priority to this principle will result

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in a change toward increased negotiation, more measurement integrated within instruction, and adaptivity based on momentary data. The trend toward adaptive instruction is producing tools and concepts that the everyday designer will be able to use before long (Nkambou et al., 2010; Woolf, 2008; Luckin et al., 2007). It is important for a designer to prepare now conceptually for changes which are sure to accelerate in the future. Intentional Learning The purpose of instruction is to support the learner in having a personal, intentional experience with the raw materials out of which they can build knowledge. It is easy to describe a situation where this does not happen. We are all familiar with the grade school assignment, “Read the chapter and answer the questions at the end of the chapter”. Perhaps it is the hope of the teacher that learners will actually read the chapter and find a reason to engage with the ideas in it. But the more likely response is that learners turn directly to the questions and find keywords with which to enter the chapter body, hoping to find something nearby that will be accepted as a defensible answer. The learner short-circuits the learning process in order to comply with an activity that does not require them to use the mental processes that constitute a real performance (see Kahneman, 2011). Researchers Carl Bereiter and Marlene Scardamalia (1989) call this the problem of intentional learning. In the intentional learning view, the learner learns something by pursuing the knowledge: We know that we can be actively pursuing learning goals while listening to a lecture or doing assigned problems, just as surely as we can engage in the same overt behavior without any active effort at learning. Indeed . . . we might characterize the serious student as one who maintains pursuit of learning goals under external conditions that can be satisfied without doing so. —(pp. 362–363) The serious student—the intentional learner—is one who not only goes through the motions of a learning activity, but who also feels a responsibility to monitor whether the intended learning— which is in the student’s intention as well as the instructor’s—has really occurred. Here, something is implied beyond efforts involved in getting good grades. The word serious seems to refer to a special relationship between the student and the subject matter. But what kind of relationship is it? Suffice it to say, at this point, that the relationship does not seem to be adequately represented by available scientific terms. —(p. 362) This kind of learning involves self-direction, self-monitoring, deliberation—intention—on the learner’s part. In this learning regime a learner takes some degree of responsibility for selecting learning goals, forming questions, seeking and using information-bearing resources, planning actions that will lead to learning, monitoring progress toward the goals, and adjusting personal learning plans based on the assessment. What is the responsibility of the instructional designer? To support these processes—and if the processes don’t exist, to create situations where the processes can be established and become habitual to the learner. Conversational Objects Conversations always have a motive and an occasion. In daily practice, we consider a conversation over when one of the participants decides there is no more to talk about. Designers stimulate conversations by attracting learners to engage in what looks like being an interesting and worthwhile conversation. Often the best tool for doing this is a conversational object. The following is a partial list of things that can be used for this function:

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• • • • • • • • • • • • • •

Interesting new information A thematic topic A visual A problem or puzzle A question A physical object An arguable or controversial proposition An anomaly or asymmetry A direct experience A model of an action or process A place A story, a narrative A personal need A task or project.

Conversational instructional methods use one or more conversational objects to provide the occasion for conversation. This is an important design insight: a designer can deliberately pick or design the object. Some instructional methods carry the name of the conversational object they use in their name: problem-based learning, thematic instruction, project-centered instruction, modelcentered instruction, goal-based scenarios, and so forth. As designers collect personal catalogs of conversational object types, they are adding to a category of design structures. What is important to the present discussion is not the particular type of object that they choose but that they are able to think architecturally in terms of the abstract structures that make up their designs. A Final Point about Conversation and Instruction Conversation should be at the architectural core of strategic thinking by designers. Other elements of strategy—events, moves, sequences—can be seen as structures involved in carrying out conversations. The effect of a conversation is to involve the whole person, not just the cognitive part, but emotions, motives, and personal values as well. People learn from conversations things that are not explicit in conversational exchanges. Conversations have influence beyond conveying new information and building performance capability. Good conversations leave us with increased desire, confidence, vision, hope, goals, emotion, and energy. Most importantly, what we learn from conversations is how to converse. What is Learning? Whatever image the designer has of the learning process, it will strongly influence the designer’s instructional intervention plans. A designer’s understanding of learning is fundamental, yet many instructional designers are unaware of new currents in learning theory that offer new design options. Application Exercise What do you think learning is? In the previous section there was an exercise in calling words to mind that describe actions associated with instruction. Now consider the other side of the coin. • Make a list of all of the words—like “observing”, “analyzing”, and “connecting”—that describe actions a learner might engage in during learning. • When you have made your list, compare it with the list in Table 5.2. • As you compared your list with Table 5.2, what percentage of overlap did you find?

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• How many terms in Table 5.2 corresponded? • How many terms did you write down that were not listed in Table 5.2? As in the previous exercise, the purpose was to remind you of the rich network of associations you possess with the concept of learning, perhaps without realizing it. Did you identify the “present– listen” pair? How about “pose–solve” pair? Making this comparison leads you to consider how the actions of an instructor or a designer complement the learner’s actions. Whatever pairs you detected, they represent only a small sampling of the ways conversational processes are involved in instructing and learning. A designer should have a model of these processes in mind as the basis for effect–cause reasoning that is essential to designing. Effect–cause reasoning? When a designer is designing there is a calculated effect—a goal—in mind. A designer’s reasoning is a search for some cause that can influence that effect for the learner. A designer begins with the effect in mind and works backward to identify suitable probabilistic causes. Maybe “cause” is not the right term. Maybe the phrase ought to be “effect–influence”, because the designer’s work doesn’t really cause learning: it simply influences natural learning processes which are continuously underway and which the learner ultimately controls. Even if a particular learning conversation does not involve the work of a professional designer, there are still plans that unfold to influence the course of the conversation calculated to produce a desired learning outcome, and those plans ultimately should be centered around what the learner needs and should support the learner’s efforts to learn. Propositions about What is Learned Rather than approaching an understanding of learning through studying different specific learning theories—which is the more traditional approach—the discussion of learning will represent ways of looking at learning that hold implications for instructional designers. This represents a change that has taken place with respect to theorizing about learning. A new trend has found traction in which Table 5.2 A List of Acts that Occur During Learning

Attending

Noticing

Discriminating

Questioning

Requesting

Requesting

Organizing

Connecting

Deciding

Exploring

Observing

Articulating

Reviewing

Sensing

Wondering

Appreciating

Comparing

Evaluating

Using

Acting

Memorizing

Ignoring

Expressing

Conversing

Remembering

Testing

Anticipating

Agreeing

Disagreeing

Judging

Desiring

Selecting

Listening

Enduring

Complying

Exerting

Proportioning

Structuring

Feeling

Responding

Sensing

Accepting

Believing

Classifying

Naming

Synthesizing

Abstracting

Assessing

Caring

Participating

Reverencing

Allocating

Managing

Building

Making

Posing

Reflecting

Controlling

Disciplining

Obeying

Experimenting

Inferring

Reasoning

Analogizing

Deducing

Deriving

Trying

Educing

Consulting

Conferring

Targeting

Seeking

Asking

Knocking

Analyzing

Transforming

Translating

Persevering

Concentrating

Filtering

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the cooperative work of many researchers and theorists comes together in the form of many local theories which together provide a richer view of the learning process—a view not dominated by a single personality or world view. By following this approach we can establish a narrative of learning that has more practical application from a designer’s point of view. This chapter will examine what is learned. What is Learned Can Be Viewed in Explicit/Implicit Terms One point on which there is little disagreement is that a great deal of what we learn and what we know is not explicit: that is, it is knowledge that we can’t express. The opposite of explicit knowledge is implicit knowledge. The boundary between the two kinds of knowledge is not well defined, but it is clear that some of the knowledge we possess is not accessible to recall, verbalization, and control, whereas some of it is. We should be careful as we use the terms explicit learning and implicit learning. To use these terms would be to imply that different learning mechanisms were at work. The body and the mind learn, adapt, and preserve themselves from the moment of birth, and knowledge accumulates in great quantities even before the higher-order reasoning areas of the brain develop. This implies that much knowledge is acquired and used well before reasoning processes are functional. Most of this learning never becomes available to conscious control or recall, and we are totally unaware of most of what we possess. As powers of reasoning develop through the maturation of the brain and the accumulation of experience, the system that regulated learning in the early years is still at work at the center of learning and memory processes. Research evidence indicates that the learning mechanisms built early in life—which are regulated through emotions—are fundamental to the newly developing reasoning and problem-solving capabilities and continue functioning as an active factor in learning. Antonio Damasio (1994) describes this interface: In short, there appears to be a collection of systems in the human brain consistently dedicated to the goal-oriented thinking process we call reasoning, and to the response selection we call decision making, with a special emphasis on the personal and social domain. This same collection of systems is also involved in emotion and feeling, and is partly dedicated to processing body signals . . . I would like to propose that there is a particular region in the human brain where the systems concerned with emotion/feeling, attention, and working memory interact so intimately that they constitute the source of energy of both external action (movement) and internal action (thought animation, reasoning). —(pp. 70–71) In this view, learning is firmly rooted in a system that governs and regulates both emotions and higher-order mental functions. This relationship has implications for the instructional designer. It means that emotions and feelings are an integral part of the learning process and that instructional experience must appeal to the emotional as well as the cognitive processes of the learner. Involving the emotions in the structure of instructional designs is of great importance. Designers should be aware of ways to make instructional designs attractive, personally appealing, emotionally engaging, and intellectually interesting—not just in terms of surface features, but at the very epicenter of the design. Can some of a learner’s implicit knowledge be made explicit? Can knowledge at one point inaccessible to the learner be made accessible to deliberation and reasoning? It is arguable that doing so is one of the most important goals of instruction.

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What is Learned can be Viewed in Terms of Thinking about Thinking Metacognition is thinking about thinking. It is a state of awareness on the part of learners that makes it possible for them to reason about their own reasoning. We all have the ability to reflect and to think metacognitively. It is the thinking that allows us to take some degree of deliberate control over our own thinking and problem-solving processes. Most of us have had moments when we were solving a problem and the lights went on and we had the approach to a solution. Metacognition is not when you say, “I could have had a V-8!™” but rather when we say, “If I want, I can make a list of juices and then choose one!”. V-8 itself is a solution, but the listing of juices is an approach to a solution—it is a moment where you think about your thinking and how to improve it. Much of instructional design is concerned not just with teaching people facts and figures but with teaching problem-solving and reasoning skills within certain contexts: the shop floor, the laboratory, the professional office, the pilot’s seat, and the business call. Instruction that teaches and exercises metacognitive abilities can make the difference between a learner who can adapt to new and unexpected situations and one who can only execute memorized solutions. In a rapidly changing world, the premium is on the individual’s adaptability—their problem-solving and self-directed learning skills—and this depends on the individual’s ability to think about thinking. What is Learned can be Viewed in Terms of Schemata and Mental Models Learning theorists have puzzled for years over the organization and representation of knowledge in the mind. What are the atoms and molecules of thought? One idea that has been popularized under different names is that knowledge is organized in clusters of meaning called schemata (singular: schema), semantic nets, scripts, frames, or mental models. These are usually thought of as closely interrelated semantic units in the mind in which the activation of one part of the unit increases the likelihood of activation of the others that are related to it. In the case of mental models, it has been thought that they might represent the mind’s own miniature models of aspects of the real world. The variety of names for schemata and mental models is a clue about the vagueness of the concept itself. The idea of cohesive semantic units within our knowledge structures has a long history. Learning psychologists have tried to explain the use of these units in reasoning, performing, prediction, and explanation forming. Such units are generally described as non-verbal—being expressed in the languages of thought—but useful in analogical thinking (thinking by analogy). The greatest interest in schemata and mental models was shown during a time when the preoccupation of learning psychologists was with the representation of knowledge in memory. Was it represented as individual facts? Pictures? A movie-like projection? Or a dense network of associations? What is Learned can be Viewed as Procedural Rules, Interacting with Semantic Units Anderson (1993) proposed that what is learned is a combination of semantic units plus procedural rules that operate upon them during reasoning and problem solving. Procedural rules are “If . . . Then . . .” statements. Anderson’s rules operate on semantic units he calls working memory elements (WMEs). Anderson’s claim is that when the “if ” portion of a rule matches something in working memory, the rule becomes a candidate for execution of the “then” portion of the rule. For example, “If < the equation is in standard quadratic form > then < attempt factoring to find a solution >”. Execution of this rule would create a goal to be placed into the working memory pool with other WMEs. The goal might be: “attempt factoring to find a solution”. Then if there existed another rule in the rule set that said “If < there is a goal named ‘attempt factoring to find a solution’ > then ”. It’s clear that solving a quadratic equation using rules and WMEs in this fashion would take a lot of rules, and a lot of WMEs would come into and go out of existence as new goals were formed and then satisfied.

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What is the relevance of Anderson’s system to instructional designers? Anderson and his research team have used the system as the basis for a family of intelligent tutoring systems that have been applied to highly structured subject-matters like computer programming (LISP) and mathematics (algebra, geometry) as well as less-structured subject-matters. Tutors based on this system have as a goal teaching students the rules of a discipline and when to apply them during problem solving. What is Learned can be Viewed in Terms of Skilled Performance Jeroen van Merriënboer (1997; see also van Merriënboer et al., 2002) has answered the “what is learned” question in terms of skilled performance. Van Merriënboer does not claim that all of what is learned is skill, but he gives a thorough description of an approach to teaching skill. Skilled performance constitutes a great deal of what is instructed—in public education, in commercial, government, and military instruction. What is skill? It is a combination of well-practiced performance routines embedded in a matrix of decision-making. A skilled worker performs an action, evaluates the effect of the action, and then selects the next action—in a continuous cycle of do–evaluate– select. What parts of our daily actions can be considered skilled performance? Almost everything we do during a day from rising to when we retire is part of a skill: grooming, cooking, driving, work tasks, social tasks, and recreational tasks. Skills are not fixed procedures. Decision points occur during skilled performance at which the course of the performance may change, producing a whole new direction of activity. Skilled performances require decision-making and judgment, and as judgment improves, skill improves in the direction of expertise. The difficult part of instructing skill is that the boundaries of skills are not well defined. With the many course corrections that are characteristic during skilled performance, it can be difficult to teach and exhaustively test the learner’s ability. For this reason, performance in skills is often taught in a systematic manner in which a comprehensive performance is broken down into its constituent parts. The parts are learned and practiced to a high degree of performance individually, and then they are practiced together under varying conditions in combinations of increasing size, difficulty, and variety. Skill instruction is often carried out in the context of mentoring or apprenticeship. Gradually, as the learner participates as an assistant in minor skill tasks, more and more of the performance and decision-making is turned over to the learner, until the moment when the learner performs the whole task alone except for the supervision of a competent performer. Shelton (1989) gives an example of this process in the training of specialists in a type of brain surgery: Generally, the attending surgeon will watch the resident’s work . . . until the resident reaches the limit of his experience; then they switch places, and the attending surgeon operates while the resident watches and learns. The process is accretive; no resident suddenly performs an operation from start to finish, but rather does a little bit more each time. ”I remember vividly the first time I did a case all the way through,” says Mark Dias, a senior resident on the neurological surgical service. “I was going right along, just concentrating in what I was going to do next, and I realized that I was doing it without anyone telling me what to do. The attending was just sitting there very quietly at the side microscope, not saying anything, and I thought, ‘That was it; I’m there.’ It’s an eerie feeling the first time to recognize that you’ve just done an entire operation without any help.” —(pp. 61–62) Because the development of skill requires observation of others performing as well as staged approximations toward full performance, learning skill may take place best in an atmosphere that can be

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termed “community of practice” through a process of “legitimate peripheral participation” (see Lave and Wenger, 1991). These terms are described in more detail in a later section of this chapter. Learning can be Viewed in Terms of Growing Expertise In an important book titled How People Learn, John Bransford and a committee of collaborators (Bransford et al., 2000) describe the difference between novice and expert performance. In doing so, some important insights are given about what is to be learned: We consider several key principles of experts’ knowledge and their potential implications for learning and instruction: 1. 2. 3.

4. 5. 6.

Experts notice features and meaningful patterns of information that are not noticed by novices. Experts have acquired a great deal of content knowledge that is organized in ways that reflect deep understanding of their subject matter. Experts’ knowledge cannot be reduced to sets of isolated facts or propositions but, instead, reflects contexts of applicability: that is, the knowledge is “conditionalized” on a set of circumstances. Experts are able to flexibly retrieve important aspects of their knowledge with little attentional effort. Though experts know their disciplines thoroughly, this does not guarantee that they are able to teach others. Experts have varying levels of flexibility in their approach to new situations. —(p. 31)

Through instruction we hope to assist learners in reaching some level of expertise in an area. We also hope we can help them build a foundation of knowledge that allows them to continue to progress toward greater expertise even after our instruction is finished. Understanding the difference between novice and expert-level performance can supply the instructional designer with ideas for promoting movement toward expertise. Bransford explains: “an understanding of the structure of knowledge provides guidelines for ways to assist learners acquire a knowledge base effectively and efficiently” (p. 237). Then several factors are given that “affect the development of expertise and competent performance”. Among them are: • Learners can be helped to organize their knowledge in ways that support recall. • Learners sometimes must be helped to see the transferability of knowledge they possess to new situations. • Learners can be taught to use their knowledge to go beyond what is explicitly given through reasoning. • Learners can learn to work with the problem representation, manipulating it so that solutions become more apparent. • Learners can be helped to see where their knowledge does and doesn’t apply. • Learners can understand that different subject-matters are organized differently and can learn to use this knowledge to see more deeply into knowledge structures. • Learners can learn to use the resources and help available in everyday problem-solving settings to solve otherwise impossible problems. Learning can be Viewed in Terms of the Different Types of Knowledge to be Learned A contemporary instructional theory called cognitive apprenticeship divides knowledge into four general classes. This typology of knowledge does not depend on the specific performance, but instead describes levels at which the learner is likely to use the knowledge.

128 • Fundamentals

The literature on cognitive apprenticeship lists the categories of knowledge under the heading of “content”, meaning the content of learning, or what is being referred to here as the “what” of learning. Chapter 6 describes the content structures identified by cognitive apprenticeship in more detail Learning can be Viewed in Terms of Dynamic Models We live within an ever-changing environment of cause-and-effect systems: natural systems and human-made. We learn to operate within this environment by noticing what things change as we act. Dynamic interaction with the environment teaches us to notice the stable cause–effect relationships that define what we can know. We learn more than memorized facts about the world: we learn to use its dynamic properties for our own purposes. As we gain this understanding, there are three types of knowledge involved: • Knowledge of the cause–effect systems that exist in nature. • Knowledge of expert performances that operate upon those systems. • Knowledge of the environment that envelops both the actor and the systems. As we interact within the environment to produce these kinds of knowledge, we are constructing mental representations of systems—perhaps they are “mental” models as described in a previous section—that have dynamic properties. The importance of modeling to our growth in personal knowledge is very important. Designers must learn to think in terms of models of systems with which the learner can interact. The value of model interactions is that they can temporarily mask portions of the world’s complexity so the learner can concentrate on a particular subset of expert performance. As the learner’s ability to perform improves, the masking can gradually be removed, and the learner performs in increasingly complex environments. This is described in a paper playfully titled “Skiing as a model of instruction” by Burton et al. (1984): Learning environments can be examined in terms of a paradigm called “increasingly complex microworlds” (ICM). In this paradigm, the student is exposed to a sequence of environments (microworlds) in which his tasks become increasingly complex. This allows the student to focus on and master one aspect of the skill in a context that requires related subskills. As a result, the student learns when to use the skill as well as how to use it. The purpose of the sequence is to evolve the simplified skills toward the goal skill. The ICM framework focuses both on what is learned in any particular microworld and on how to choose the next microworld in the sequence. —(p. 139) Designers should learn to see subject-matter and performance requirements in terms of the knowledge and performance models that lie within them. They should become experts in building the kinds of microworlds described by Burton and his associates and exercises that bring learners through intermediate stages toward expertise. Learning can be Viewed in Terms of Stories and Narratives Instructional designers should be aware of the relationship of stories to learning. Schank and Berman assert that: When we consider the stories of others, or when we have new experiences, our existing thoughts and beliefs are sometimes challenged. These expectation failures lead us to examine

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our beliefs and sometimes build on them or change them. This is how we learn, and this is why we believe that knowledge is largely constructed of stories. —(Schank and Berman, 2002, p. 294) Jerome Bruner (2002) also notices the effect of expectation failure: “Stories reassert a kind of conventional wisdom about what can be expected, even (or especially) what can be expected to go wrong and what might be done to restore or cope with the situation” (p. 31). Bruner links stories with ageold methods of instruction: Skill in storytelling is recognized and honored even in the simplest societies. And the skill has a formal structure that goes beyond mere expressiveness. Folklore studies offer ample evidence that stories told by the teller of tales were composed of strings of module-like hunks that could be re-strung to generate different tales for different occasions. —(p. 98) He states elsewhere that “it is our narrative gift that gives us the power to make sense of things when they don’t” (p. 28). Stories are immensely important in answering the “what” question about learning: What is learned? Schank extends this theme. When we listen to stories we attempt to find evidence that confirms what we already believe. Changing our beliefs requires expectation failures powerful enough to convince us there is actually something wrong with our existing beliefs, our representations of the domain. —(Schank and Berman, 2002, p. 309) Stories are more than just accounts of people and events: they are saturated with positive and negative emotions and feelings. When we come into contact with stories, we come into contact with the emotions and feelings within which they are situated as well as the intellectual subject-matter. We react to and learn from these emotional colorings as much as we do to the facts of the account. This realization creates a bridge between knowledge as a cognitive structure and knowledge as a value structure. This bridge is what gives stories their impact and is probably one of the things that make stories the currency by which we transmit our histories, our culture, our deepest beliefs, and our sense of self to the world. That stories go so deeply into our sense of self is clearly demonstrated in this account by Bruner: A neurological disorder called dysnarrativia, a severe impairment in the ability to tell and understand stories, is associated with neuropathies like Korsakov’s syndrome and Alzheimer’s disease. It is more than an impairment of memory about the past, which is itself highly disruptive of one’s sense of self . . . In Korsakov’s syndrome particularly, where affect as well as memory is severely impaired, selfhood virtually vanishes. [Oliver] Sacks describes one of his severe patients as “scooped out, de-souled”. One characteristic symptom in such cases is an almost complete loss of the ability to read other minds, to tell what others might have been thinking, feeling, even seeing. Sufferers seem to have lost not only a sense of self but also a sense of other. —(Bruner, 2002, p. 86) Narratives encode invisible force and information transfers that are the key to understanding. There is an economy of expression and communication in story-like tellings of information. Instructional designers explore the ways for taking advantage of this economy. Learning can be Viewed in Terms of Categorized Behaviors One of the major principles introduced by Robert Gagné and others was the notion that instructional goals could be categorized and that conditions could be prescribed most likely to bring about

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the desired learning. In service of this type of instructional theory, Gagné and the other theorists devised multiple category systems for instructional goals. These systems became widely used and characterize what is called a “taxonomic” approach to knowledge structure. The long-lasting influence of these systems is due to their simplicity of application across a wide variety of subject-matter areas. The two most influential of these category systems include: • Robert Gagné—Ultimately a system of five domains, one of which, intellectual skills, was subdivided into several sub-categories (Gagné, 1965a, 1970, 1977, 1985). • Benjamin Bloom—A system of three domains, all of which were eventually subdivided by Bloom and his successors into sub-categories (Bloom, 1956; Anderson et al., 2001). Chapter 11 describes taxonomies in more detail. Learning can be Viewed in Terms of Belief The recent research on how students learn science, mathematics, and other school subjects has brought to light an unexpected dimension of learning, specifically that the ability to acquire new knowledge is heavily weighted by a learner’s prior beliefs. Bransford and his associates (Bransford et al., 2000) explain that: “In the most general sense, the contemporary view of learning is that people construct new knowledge and understandings based on what they already know and believe” (p. 10). This would imply that new knowledge and understandings are also likely to be a form of belief. It may appear strange to associate the notion of belief with learning and knowledge, which have been treated for so long as essentially intellectual phenomena. But research shows that when students learn scientific concepts well enough to pass mastery tests, they often revert following the test to prior naïve beliefs about how the world works: Consider the challenge of working with children who believe that the earth is flat and attempting to help them understand that it is spherical. When told it is round, children picture the earth as a pancake rather than as a sphere . . . If they are then told that it is round like a sphere, they interpret the new information about a spherical earth within their flat-earth view by picturing a pancake-like flat surface inside or on top of a sphere, with humans standing on top of the pancake. The children’s construction of their new understandings has been guided by a model of the earth that helped them explain how they could stand or walk upon its surface, and a spherical earth did not fit their mental model. —(p. 10). Instructional designers may feel that their subject-matter is beyond interference from prior beliefs, but even if a salesperson is trained in a particular approach to people, it is only reasonable to expect that eventually the learner’s prior beliefs, based on personal experience of some kind, will reassert themselves. According to Scardamalia and Bereiter (2006): “It is impossible to function in a society without taking large amounts of information on authority. Even when it comes to challenging authoritative pronouncements, doing so effectively normally depends on bringing in other authoritative information as evidence” (p. 103). It is worthwhile to consider whether learning from instruction is an act of believing—faith in the instructor, faith in the instructional materials used, and inherent faith in the learner’s own ability to take in and comprehend new knowledge. If instruction is expected to supplant prior beliefs with new knowledge, then it is reasonable to interpret the new knowledge itself as being a form of belief as well.

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What is Learned: Summary This section has discussed new concepts of learning and what is learned. Over the course of a career, designers will find each of these views of “learning” useful at different times. In the current narrative of learning a designer or teacher disassembles what they know into their elements and creates externalized representations to convey their essence(s). The learner, upon encountering the representations, interprets them and attempts to make sense of them. In the process, new constructions result within the learner’s mind that may to some extent correspond with the original intention. Rather than referring to this process as a “transfer” of knowledge, it may be more accurate to describe it as the result of a communication process in which the learner must participate (Wenger, 1987). Application Exercise Why is it so hard for humans to think about what they know? As an instructional designer you will learn other people’s subject-matter. Each new project will be a challenge in capturing a new body of knowledge. What similarities do you see in the bodies of knowledge used by the following pairs of performers? Consider all of the kinds of knowledge they use. • • • •

A pass receiver in football and a fighter pilot? A fighter pilot and a laboratory chemist? A laboratory chemist and a computer programmer? A computer programmer and an instructional designer?

What is Technology? As a designer you are a technologist. What is a technologist? And what is technology? This section defines technology in a way you may find new. A Metaphor for Technology The essence of technology is illustrated by likening it to an irrigation system. Rawlins (1997), a computer scientist, describes it in those terms: An electrical current is really a river of electrons, and each one of the millions of tiny decision-making boxes inside a computer chip is like a sluice gate controlling whether electrons will flow through it. So a computer chip is a giant electron irrigation project laid out on a nearly flat plane, with microscopic hydroelectric plants, wells, water tanks, and pumps, and millions of canals and sluice gates—enormous complexity working at enormous speeds and tucked into an enormously small space. —(p. 28) Irrigation systems assume that gravity will cause water to flow downhill. An irrigation technologist diverts this natural energy by creating pathways in the soil that lead to where the water is needed. As Rawlins describes, an analogous process takes place inside of a computer. Electrons are motivated by electrical “gravity” called voltage. They flow through pathways that deliver them to where they are needed to perform work. When humans undertake to arrange, divert, or otherwise employ naturally occurring forces to achieve their goals, they are practicing technology. The technologist does not make the forces work, he or she just taps into existing energy and information to harness it for a specific purpose. The study of technology is the study of how to do this. The study of instructional technology is the study of how to work with and influence the naturally occurring forces of learning.

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A scientist learns how light comes from the sun, how it acts, how it affects materials when it strikes them, how it bends, reflects, refracts, and so forth. A technologist uses this scientific knowledge, but only after converting it into a form that makes the knowledge usable in guiding design processes and artifacts that capture, store, transform, or otherwise harness the energies of sunlight in a particular way and for a particular purpose. Studying the properties and behavior of sunlight is the domain of a scientist; studying how to harness and use the energies of sunlight for human purposes is the domain of the technologist. Many of the great accomplishments described in the name of science are in fact accomplishments of technology. Once we understand that sunlight striking a particular type of material causes the material to give off electrons, the means of collecting, controlling, and using those electrons in everyday applications may take decades of further research and the generation of much additional theoretical knowledge. The research that accomplishes this is technological research or design research, and the knowledge obtained will be in categories the scientist does not seek. Walter Vincenti (1990), an aeronautical engineer, explains: “What engineers do . . . depends on what they know, and my career as a research engineer and teacher has been spent producing and organizing knowledge that scientists for the most part do not address” (p. 3). He continues: “Technology appears, not as derivative of science, but as an autonomous body of knowledge, identifiably different from the scientific knowledge with which it interacts” (pp. 3–4). On this point Vincenti quotes an unnamed British aeronautical engineer, who makes the same point more emphatically: Aeroplanes are not designed by science, but by art in spite of some pretence and humbug to the contrary. I do not mean to suggest for one moment that engineering can do without science, on the contrary, it stands on scientific foundations, but there is a big gap between scientific research and the engineering product, which has to be bridged by the art of the engineer. —(p. 4) Even if we know something about how learning processes work, technology has to add to that research that converts scientific knowledge into knowledge about how to influence the learning process. In the absence of complete scientific knowledge, a design must still be produced, and in many cases the technologist must bridge the gaps in our scientific knowledge with best estimates, guesses, or by imitating existing models. This highlights the importance of the science–technology relationship. Since science uses technologies to detect, observe, and measure, it cannot do without technology, and since technology relies on science to supply as much knowledge as possible about natural systems, technology has to fall back on best guesses without it. The relationship is symbiotic (see Gibbons and Bunderson, 2005), but the technologist must be able to see this difference in order to think and act as a technologist, not a scientist. What is Instructional Technology? Instructional technology is the diversion of natural forces and information flows for the purpose of supporting learning. The definition of technology does not name specific means that the instructional technologist uses to harness, enlist, and transform information and forces to support learning. Most importantly, it does not specify that equipment or media of any kind must be employed in the instructional process. Instructional technology has been strongly associated with media devices for many years. However, this does not necessarily imply that the use of machines and communications media is essential. The words and acts of an instructor are technological because they influence the course of

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learning. If instruction is a form of conversation, then conversations can be carried out without the need for equipment. Though many conversations are carried out using social media and worldwide connectivity, it is not the medium that makes effective conversation. The technology of instruction employs hardware and software when needed in ways that are appropriate. More often a blending of media with instructor actions is the best solution. The important perspectives are that: (1) instruction is a technological enterprise, (2) the technology of instruction can be carried out without hardware or software intervention, and (3) this view best typifies the phrase “instructional technology”. The Value-added of Hardware and Software Technologies When we do use technological devices to support instructional conversation, it is as a vehicle for delivering or mediating the conversation. Placed in proper perspective, the hardware and software technologies can be seen as powerful amplifiers of: • Sight, time, and space by making the invisible, visible. • Individualization because technological devices can support decision-making based on the history of student choices. • Practice opportunities by allowing learners to do tasks again and again, thereby gaining fluency. • Opportunities for receiving feedback thereby learning self-direction over time. • Realism by “situating” learning in realistic settings. • Reach, allowing geographically or economically stranded learners to access the best teaching. • Social interaction by facilitating communication and the formation of community. • Resources by making multi-media documents available anytime to any connected place. A Model of Technological Intervention Figure 5.2 illustrates how technological artifacts are both created and used. The importance of this model is that in addition it supplies a basis for categorizing and generating research questions. As the figure shows, natural energies and information flow forward in nature unaided (depicted by the line entering from upper left). Undisturbed, this flow continues along a natural course. The sun warms the atmosphere; warm air rises; it meets cool moist air; clouds form. The energy of these processes proceeds along a course that scientists study: the natural unfolding of events. Humans can intervene within this natural flow, diverting energies and information toward a specific purpose. This constitutes technological activity. Human technologies collect, store, and transform these energies and information. They devise ways to release and focus the energies when and where they can have a desired effect. This requires the design and development of artifacts—human-made tools. Artifacts range from physical things to non-material processes to conceptual structures. For example, humans invent processes called algorithms used to structure the programs that lie at the heart of computer operations. These artifacts have great impact despite their non-materiality. Instructional designers create environmental artifacts and resources that support and promote experiences, but it is not the artifact that creates the experience: it is the impact of the artifact on the individual. The artifact creates conditions where experiences can happen. The action of artifacts can be direct or indirect, and multiple artifacts may be necessary to create a desired effect. Artifacts are applied when measurements show that conditions support an intervention. We feel a child’s forehead to see if she has a temperature. We may decide to intervene with aspirin—an artifact that places information in chemical form into the body system—or we may obtain a more precise temperature measurement. This, of course, requires a heat-measuring artifact.

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Artifact

Design and Development

Measured intervention point rn tte pa on nti ve ter In

Measured termination point Figure 5.2 A model of technology capable of generating research questions (from Bichelmeyer et al., 2006). Copyright, 2006 by Libraries Unlimited. Reproduced with permission of ABC-CLIO, LLC.

Measurement is a constant need once artifacts are applied. After the first temperature-taking, a concerned parent continues periodically until the temperature falls to normal. The series of descending boxes in Figure 5.2 represents multiple measurement points, the last of which is the one that indicates that the goal of the intervention has been reached. This (over)simplified sequence of measurements and continued intervention represents an intervention pattern. At any point, the pattern may be discontinued, or a new intervention may be started. Moreover, many artifacts may be applied. After further testing, aspirin may be joined by another medication, surgery, or other treatments. When instruction is the intervention, the most desired outcome is that learners will eventually become responsible for their own interventions. The historical pattern of technology use has tended to neglect this aspect of instructional interventions, which differ in that respect from medical and other forms of intervention. To develop habits of self-direction, learners must come to understand the dynamic of Figure 5.2. In Figure 5.2, everything to the right of the measured intervention point lies in the realm of technological activity. This includes the design and development of plans and artifacts used in interventions. Instructional design, development, implementation, and evaluation all lie in this region. The fact that they are technological processes does not mean that they are not influenced by scientific knowledge, but it does mean that these activities are not fundamentally scientific in nature. This is an important distinction because many instructional designers have been taught that they are applying scientific principles. Such a claim is comparable to the claim that if one understands Bernoulli’s Law they are capable of designing an airplane wing. As has been already pointed out, scientific findings have to be combined and transformed through design research into results that are directly applicable to designs. Activities in the technological region of Figure 5.2 hold implications for the kinds of knowledge a designer should cultivate through research: • Knowledge of how and what to measure • Knowledge of how and when to intervene

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• Knowledge of the principles behind intervention patterns • Knowledge of the structural aspects of artifacts, including the materials of which they consist and their effective properties • Knowledge of different processes involved in the design and development of artifacts. These kinds of knowledge constitute topics for study by technological researchers; this research produces knowledge useful in the design, manufacture, and use of artifacts. It also produces theories— theories about the design, manufacture, and use of artifacts. A later chapter will discuss in some detail the kinds of knowledge and theory that are related to technological activity. Application Exercise Pick a professional field other than instructional design. What evidence can you see that this field seeks and uses the kinds of knowledge in the bullet list directly above? • How about a pharmacist? • How about a lawyer? • How about an emergency-room physician? What is Design? Design is a manifestation of technological thinking. Some science writers have advanced the opinion that humans are natural-born scientists, but that is strictly true only when we need information in order to make a design decision. We are better classified as natural-born designers. We design our way through the day: we design our wardrobe every morning, we design an exercise plan, we design a breakfast, and from there we continue designing our day as we go. We make short-term designs and long-term designs, both of which we call plans; we make simple decisions and fashion complex strategies; we make some decisions on the fly and others we consider carefully in view of improving future options. Deliberate design has taken on increasing importance in an increasingly complex and technological world. This section describes how much we have to learn about this constant and most essential activity. It outlines several different ways that design theorists have tried to describe what design is. We have a great deal to learn about designing and how designs are made. Much of the interest in design processes came with the introduction of the computer into design fields. In order to create instructions for the computer, humans had to discover how they designed. This experience has revealed a story of design that is more interesting and multifaceted than any of the standard rationalized models of design that most design books describe. Design is practiced professionally in many fields—even in some fields we don’t normally consider to be design fields. Simon (1999) includes a number of professions as design professions that many people have not considered in this category: your doctor is a designer (designs individualized treatment plans); your lawyer is a designer (designs cases and how to argue them strategically); your accountant is a designer (designs tax plans, retirement plans, financial plans); and most pertinent to this book, your teachers and instructional designers are designers. Through design studies in many areas of professional practice, the principles of technical, high-performance design are being discovered little by little. Instructional design is just one design field among many, but progress in design within this field has been agonizingly slow. In general, instructional design is pursued today much as it was fifty years ago, while design practice in most other fields has matured more rapidly. (Although there are those in each of those fields who feel progress in their area of design has been

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similarly slow.) Today, for example, computers carry out roughly 95 percent of the work on most computer chip designs. In order to teach computers how to design, humans had to introspect about how they made designs. We might ask why, if humans design all day every day, was it so hard to see inside of the design process? The answer is probably that so much of design takes place on the border between rational decision-making and intuitive, creative thought. This is a mostly uncharted territory of implicit, unconscious knowledge where so much of our being and behavior resides and yet where we have so little deliberate control. Design is a process that is hard to discover because so much of it is carried out by logics that involve cloudy rationality. Design, as we are learning, has a logic of its own. This chapter will describe some of that logic, with the intent of introducing different views of design from different fields. How Designs Happen We have many narratives of the design process, reminiscent of the story of the blind men and the elephant. Each narrative described below sheds light on a different facet of the complex design processes we take so much for granted. Design as a Reflective Conversation Donald Schön (1987) studied designers in many fields and described what he saw as a kind of conversation. In this conversation, a designer studies a problem and then decides on a “move”—decides to make a decision that will affect other, later, decisions. Before making the decision final, the designer “listens” to the ramifications of the decision: how the decision fits with prior decisions, how it will influence future decisions, how hard the design would be to build, how it would influence the experience of the user, how much it would add to the cost, and many other considerations. Schön calls this a “reflective conversation” with the problem because the designer pauses to consider the effects of each commitment, especially the early ones, which set the mold of the design for those that follow. Schön describes the effects of the earliest decisions as “imposing a discipline” that becomes an anchor for the solution. If designing proceeds and the discipline—the anchor—results in unacceptable implications down the line, the designer can “break the discipline” and choose a new one, which of course has the effect of undoing the decisions that were made after the original discipline was imposed. Schön’s description of design recognizes that there are groupings or clusters of design decisions. Sometimes a design decision will influence decisions that are within its own cluster, but often a decision will influence decisions from other clusters. He calls these clusters of decisions the “domains” of the problem. Each domain poses a local design problem. Associated with each domain, Schön says, there are design terms—primitives—that the designer can use to conceive and then express solutions. What Schön calls the domains of an architectural design correspond exactly in principle with the concept of instructional design layers. Schön describes domains that are appropriate to architectural designs. The domains or layers described in this book are appropriate to instructional designs. It is important to note that in both cases it is not the exact domains named that is as important as the concept of domains or layers themselves. For example, Both Schön and Brand (1994) identify domains (Schön’s term) and layers (Brand’s term) appropriate for architectural designs. The layerdomains identified by the two are different. Is one correct and the other not? Or is it more correct to say that Schön found one set of layers suitable to his purposes and Brand found another more suitable to his. Likewise, the layers defined in this book are not absolute. They are the layers considered to be of most central and universal for application to instructional designs. Layers represent functional divisions of an artifact and are

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relative to the purposes of the designer, the characteristics of the artifact, and the circumstances of the design problem. More layers could and probably will be named in the future by others. Later chapters describe in more detail how layer definitions can evolve during the solution of the problem. Design as the Progressive Placement of Constraint The “progressive placement of constraints” refers to the fact that each decision made at any point during design places a constraint on the choices in other parts of the design. Moreover, with each successive decision, the number of remaining decisions changes, because the space of the design problem has changed. If, for instance, an instructional designer selects video as an instructional medium, certain decisions, such as the video file format to use, become immediately relevant, while other decisions, such as workbook page format, become immediately irrelevant. In this way, the design space changes with each decision. This view into design, described by Mark Gross and his associates (1987), shows that at the beginning of a design process there is no way to know how many decisions there are to be made, or which ones will take priority. The requirements of the design unfold with each decision and can be greatly increased by certain kinds of decision. The useful idea here has to do with the impact of constraints on the designer. By making a decision the designer imposes requirements on a solution. Equally important, every design problem arrives on the designer’s desk with certain constraints already imposed. If the client asks for “three half-hour videos”, then the process of deciding about which medium to use is irrelevant, and many decisions related to the design of the videos have been added to the project. Design by constraint relieves a problem typical of design models: the tailoring of the model to the immediate problem. As described in an earlier chapter, a designer posts the constraints that arrive with the problem to the layers they represent. Then design proceeds in an order dictated by the next most critical (or advantageous) decision(s). Another point regarding design as constraint placement is raised by Stokes (2005) in a book with the ironic title Creativity from Constraints. Stokes describes several examples of creative “geniuses” who experimented with their designs by imposing what seemed to be impossible constraints on themselves and then designing within those limits. Instructional designers often find themselves operating within restrictions of time, skill, or resources. Stokes’ book shows how constraints such as these can stimulate the creative process, sometimes producing solutions more interesting than if there were more room to work in. Design as Search Herbert Simon (1999) described design as a search for a bridge between a current state of affairs and a desired goal state. Seen this way, what you are looking for in design is a chain of linked ends and means that reach across a gulf of uncertainty to a desired end. If you face the dilemma: “How am I going to get to the airport?” to find an answer you may consider several options: Take a shuttle van? Hitch a ride with a co-worker? Take my own car? Have someone drop me off ? Ride the commuter rail? Each of these options represents a means for getting to the airport. Each of them has advantages and disadvantages, so the process of means searching has a lot to do with finding the means that best matches your resources, your circumstance, and your outcome goal: the commuter rail might be cheaper, but a personal car might allow you more freedom to come and go at odd hours. For different trips under different circumstances and goals, different choices might be best. This view of design sees a solution as a chain of means–end transitions, implying that the outcome of one transition supplies the input for another. Of course, this also implies that in between the starting point and the final goal state, many intermediate states may exist. The designs of a chemical engineer may illustrate this. Suppose the engineer’s job is to design a process for the large-scale

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production of a chemical substance—aspirin. Beginning with raw materials that may bear no resemblance to the final product, the first step may be to produce a slurry of water and corn starch. This represents one transition state. Acetylsalicylic acid (the active ingredient) and other ingredients may be added to this slurry and blended in a large mechanical mixer to create a second intermediate. These may be subjected to further addition of ingredients that lubricate the mix and give it the proper consistency for pressing into pill form. Of course, aspirin is available in non-pill forms also. For them different sequences of transition states may be necessary, beginning with or omitting the slurry as their initial state. In another example, a biochemist may be interested in designing the most effective yet economical sequence of intermediate steps (means–end connections); for synthesizing a complex organic molecule, such as an enzyme, there may be hundreds of steps to such a process and therefore hundreds of intermediate points. Means–end chaining can apply to the design of sequential manufacturing processes, and it can supply the central structural discipline for the design of an artifact as well. When you buy hand tools today, you have the option of buying a single power unit (an electric motor of some kind) that can be hooked to several power-using attachments—a drill, a sander, a small saw, and so forth. This modular structure of the artifact breaks the functions of the tool (motor, drill, sander, etc.) into physical functional sub-units. One of the functions—the motor that provides rotary power—can be used with the other sub-units. Several configurations of power unit and attachment can be considered. Which one the market will want becomes part of the decision formula, making the search for a “satisficing” (adequate but not necessarily optimal, see Simon, 1999) solution more difficult. Design as the Application of Patterns Christopher Alexander, an architect and design theorist, describes how the expression of an architectural design can result from the assembly of different terms from an abstract pattern language (Alexander, 1977; see also Alexander, 1979). The terms of this language are not words, but abstract patterns. Inserting a “transition place” pattern into a house design might lead you to consider a connected “entryway” pattern or a “porch” pattern. Moreover, the connection points between these areas of the design might cause you to consider either a “doorway” pattern or an “open” pattern. When designers speak to each other in strange dialects intelligible to other members of their design community, these terms of the design language they use often name patterns the designers are used to employing in their designs. Though Alexander later moved on from his concept of pattern languages, his pattern language ideas have important implications for instructional designers. First, his principle of bringing patterns together in a design assumes that the patterns must be brought together in a harmonious way—or more correctly, in a coherent way. Alexander is as concerned with the impression of the design on the user and its natural functionality for the user as any other factor. He refers to the overall quality of the final design and the kind of unity it possesses. For example, a cathedral is expected to leave a different impression than a patio. Second, Alexander is interested in the harmony of the patterns at several levels of organization. If a house has an essential unity for a person, then, Alexander says, so should the neighborhood where the house is located have a certain quality. Of course, in Alexander’s view, this means the town and county also must have the proper qualities. Alexander’s emphasis on organic unity at all levels no doubt grows out of his concern that the solution to a design problem should fit the context for which the solution was created. This is a concept he expressed in Notes on the Synthesis of Form (1964). Third, Alexander’s use of the language metaphor is important. Alexander’s patterns are all named abstractions. In many cases they represent an arrangement of elements (a porch, or an entryway, for example) without specifying the exact dimensions and specific placement of the things that compose

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them. Alexander’s pattern languages constitute a kind of design language of abstract ideas that a designer can use in the critical but invisible stages of design where inchoate ideas are combining and recombining and coalescing into the structural formations of a design. Alexander’s emphasis is on the manner in which structurings form an impression as a whole and not as much on the specific details of structures. He provides a way for designers to think in terms of combining structural fragments into coherent, unified wholes. Later in this book (Chapter 7) the concept of patterns as represented by design languages will resurface. Design as a Social Process Bucciarelli (1994) describes a social process of design that resides within a group of minds rather than a single mind. Rather than focusing on the physical, visible output of the design process (blueprints, specifications, etc.) Bucciarelli describes a design that in reality exists only in complete form in the minds of multiple designers: Shared vision is the key phrase: The design is the shared vision, and the shared vision is the design . . . Some of this shared vision is made explicit in documents, texts, and artifacts—in formal assembly and detail drawings, operation and service manuals, contractual disclaimers, production schedules, marketing copy, test plans, parts lists, procurement orders, mock-ups, and prototypes. But in the process of designing, the shared vision is less artifactual; each participant in the process has a personal collection of sketches, flowcharts, cost estimates, spreadsheets, models, and above all stories—stories to tell about their particular vision of the object . . . The process is necessarily social and requires the participants to negotiate their differences and construct meaning through direct, and preferably face-to-face exchange. —(p. 159) Design within a team involves defining common terms, forming goals, and reaching consensus. According to Bucciarelli, it also means that even after design there may be local differences in interpretation concerning what the design actually contains. Most instructional designers come to realize this in their first few designs. Even after the designer has approved the many sub-designs that are part of the larger instructional design (the content design, the visual design, the software design if there is one, the message design, etc.), there are many details left unexpressed in the minds of the art director, the program designer, and the writer. This only becomes apparent as the design is being manufactured and the missing details are called for. Bucciarelli’s notion of design as a social process is especially relevant to instructional designers because the days are gone when a single person had the time, energy, and skill to design and create a complete instructional product or system single-handedly. What used to be considered a revolutionary idea—used at one time as a major selling point for early authoring tools for computer-based instruction—has become impractical in today’s much more complex designs. Interfaces that create a virtual design studio for team collaborative design are becoming more common. Design as Engineering Walter Vincenti (1990) writes about design as an aeronautical engineer and researcher. Over a long career he created aeronautical designs, but he also studied how the designs were made, the kinds of problems designers had to solve, and the kinds of knowledge designers needed for the process. In particular, he studied in the larger context how certain design standards that came to be used by an entire professional field (of aviation design) evolved over time. Vincenti found that underlying the design process there were categories of knowledge that were essential to design making, regardless of the design problem. His ideas are so generalizable that his book What Engineers Know and How They Know It can be studied profitably by designers in any field, including instructional design.

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One major finding of Vincenti’s design studies was that the kinds of knowledge required for designing were not what could be called scientific knowledge. In his words (quoted earlier): “What engineers do . . . depends on what they know, and my career as a research engineer and teacher has been spent producing and organizing knowledge that scientists for the most part do not address.” The impact of Vincenti’s research is to focus the designer on the several categories of design knowledge, which Vincenti names. Peter Kroes (1992), a historian of technology, comes to the same conclusion in his study of the history of the steam engine, which in the late 1700s and early 1800s was a major power source. Kroes describes the work of Guyonneau, Comte de Pambour, who was not as interested in deriving scientific theories of heat and thermodynamics as he was interested in creating a theory that would enable him to predict the power output of a particular configuration of steam engine before it was built, from the proposed dimensions of an engine. It was a question of great importance to the buyers and sellers of steam engines, and though this may seem to be a science-related problem, it actually deals with a set of terms that a scientist would not include in a scientific theory. Pambour wrote about “foot-pounds per minute”, “bushels of coal”, “boiler”, “cylinder”, “cylinder pressure”, “piston”, “crank”, “condenser”, and “horsepower”. These terms are too specific and dimensioned to appear in a scientific theory, but not a technological one. Design viewed as an engineering pursuit involves assigning specific materials and dimensions to abstract structures, using data about structural properties to determine in advance the performance of the assemblage on such issues as power output, life expectancy, and resource consumption. Design as Prototyping and Iteration Michael Schrage (1999) describes the evolution of designs through a process that involves a cycle of prototyping, testing, and revision, through multiple iterations. An important implication of this view of design is that the problem itself comes into focus during the process of iteration—a reminder that designers very often solve the wrong problem by leaving out problem clarification and verification at the outset of the design process. Through repeated cycles of design, trial, and revision, the problem and the solution become clear. Schrage offers the spreadsheet as an example of a paradigm-breaking prototyping tool. Before the spreadsheet, businesses had no easy way to model different business plans. With the spreadsheet, multiple plans can be tried out in a short period of time based on different assumptions about trends, available resources, and other changeable conditions. Today, corporations are forced to use the spreadsheet as an economic prototyping tool in order to stay competitive. In programming, there is no such thing as a spreadsheet, but many programmers have adopted techniques with names like “agile” and “extreme” in which they rapidly cycle and test versions of evolving software products. This involves refining the solution and the problem at the same time revising the core as the problem comes into clearer focus. Design teams can use the same principle to arrive at instructional designs and working prototypes. Design as Model Copying or Templating An interesting development of the Internet is the ability to borrow working html code from an existing page. Although borrowing is more complicated today because of client–server relationships and the extensive use of databases, it illustrates a principle of design-borrowing that is time-honored in virtually all media forms—templating. Normally, building a template begins with selecting a model design to be used as a base. The designer strips out portions of the design content, leaving behind a reusable shell structure that can be repopulated with new content. Styles in templating can be classified based on the particular configuration of structures removed and those left behind after the depopulation. In educational

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software design, this might mean keeping certain control or strategic functions in place but taking out specific content or representations. In instructional design the idea of templating has meaning at several levels of the design’s architecture. Layer theory gives a designer a detailed way of specifying template dimensions. Design as a Process Design wisdom can be captured in the form of a process. In the early days of virtually all design and manufacturing fields, a general design process is created that for a time guides the activities of most designers. It is usually based on a general engineering model that includes stages such as requirements specification, design, development, testing, and maintenance. The growth of models for instructional design in this way was described in Chapter 3. As fields mature, several factors combine to make early process models less central. These factors are usually related to: (1) the complexity and detail that the process model accumulates over time, (2) the inflexibility of the early process model to variations in design problems, (3) a better understanding of the nature of design problems in the solver’s domain, which leads to more nuanced design approaches, and (4) the use of the computer in design processes, which foregrounds functional elements of the artifact in preference to design process steps. Instructional design practice today is largely dominated by variations on a general systems engineering model, of which there are a multitude of specific instances. However, the instructional design field seems to be undergoing a change in this orientation to accommodate and assimilate new views of design like the ones described in this part of the chapter. One of the motivations for this book is to suggest concepts of how instructional designs can evolve—not with the purpose of invalidating process models, but to give designers additional new perspectives that combine with process models to open additional avenues for attacking design problems. Design as Creative Thinking Creativity has for a long time been advertised as a key to successful design solutions, but creative approaches often become superficial and concentrate on gimmicks that overlay the processes of a design group. One new approach to applying creative thinking to design is exemplified by companies like IDEO (Kelley, 2001, 2005), a design consultancy which practices “radical collaboration”—a relationship between a client and a highly skilled, highly diverse design team that practices energetic, data-driven design. Although IDEO is the most recognizable practitioner of this approach, it is being used by a growing number of commercial organizations, and universities, many of whom act as creative SWAT teams. This type of creative design depends on breaking with some design traditions of the past. It is pursued in an energetic, iconoclastic, highly social, and often playful manner by a multi-disciplinary team, whose skills are often outside the topic area of the design (Kelley, 2005). This approach to design concentrates heavily on user needs and perceptions. It relies on extensive studies of the user and the context in which the design solution will be used. Research in advance of the actual designing often focuses on previous solutions and why they don’t work and significant observation of users of alternative solutions. This leads to reconceiving the existing view of the problem, usually discovering that what is needed is what Simon (1999) would call a different representation of the problem. Design teams of this type often try to redefine the paradigm of solutions in a given area. If they are given a problem related to personal or portable computing, they may decide that the best approach to the solution is to see the computer as a person’s personal communication device, a life organization tool, or a family of devices that support both functions (for example, Apple’s line of computing devices). Creative design teams usually place great reliance on rapid prototyping with cycles of user testing, often internally to the team before presenting more polished prototypes to the client. Continued

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close collaboration with client during solution roll-out ensures that ideas stick in the client organization, since a common response to innovations is rejection, even of ideas that have already proven workable. An important style of creative design teams is intensity, and one of the main challenges within such a team is the leadership that keeps up the forward momentum of the team. A Final Point about Design This section has explored several views of the nature of the design “elephant” in hopes that one or more of the “gurus” described will add new direction to your personal study of design. Instruction has multiple goals. The goal of becoming an independent, life-long learner, capable of continuing to learn without direct instruction, is one of the most important in a rapidly changing workplace. A short-term view of instruction that focuses only on immediate goals, and a fragmented view of the design profession has encouraged this narrow view. A more responsible view of instruction expands the responsibility of the designer. Inouye and his associates (2005) discussed this fundamental concern: “The central concern [of instructional designers] should be to improve learning by providing help to learners and teachers. IDT [instructional design and technology] is essentially a helping profession whose mission is to foster the growth of individuals” (n.p.). Designers cannot simply nod in the direction of the importance of the individual: the perspective of help must exist at the center of the designer’s concerns. Inouye connects this issue with the designer’s professional identity and ethics: “Our perceived identity, our reason for being, should be to help learners learn. Like doctors, lawyers, and psychotherapists, we should see ourselves as belonging to a helping profession with an ultimately ethical central concern” (n.p.). It is common for books on instructional design to take on an intellectual technological tone. It is easy for a reader to interpret from this that the focus of instructional design is the technology and not the individual. This may or may not be the intention of book authors, but technology decisions demand a lot of the designer’s attention, and it is easy for important perspectives to get lost in details about processes and products. Unfortunately, the relative invisibility of the ends of our profession has caused us to focus instead on its more readily visible means. Consequently, the curricula we use to train IDT professionals, the literature we have them read, and the specializations they enter upon graduation are heavily weighted in favor of the means of our field, i.e., the theories, techniques and technology which we use instrumentally to help learners to learn. Little training is offered concerning IDT’s ethical ends, and the prudent, practically wise considerations that members of a helping profession must be schooled in to help and safeguard those they serve. —(Ibid., n.p.). Underlying the technical issues of instructional design there must be the understanding that instructional technology has its roots in human relationships and human interactions. The emphasis being placed on the issue in this book is in proportion to the strong tendency of instructional technologists to obey first the imperatives of the technical part of their craft, while allowing the gap between good precepts and necessity-driven practices to widen. We need not see ourselves as technologists any more than doctors should see themselves as technologists merely because they use computers, electronic instruments, and pharmaceuticals. Just as doctors see themselves primarily as healers; so should we also see ourselves primarily as instructors and teachers. —(Ibid., n.p.) Inouye implies that there may need to be changes in the priorities of designers, even in the identity they claim:

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With the adoption of an ethics-centered paradigm, or world-view, IDT practitioners will continue to do many of the same things they are doing, using many of the same skills they now possess, but the meaning of what they do will be enhanced. We can now see our activities under the general rubric of helping, rather than just researching, evaluating, measuring, designing, developing, or delivering. Our ultimate ends can justify, and even hallow, these means. —(Ibid., n.p.) The concept of conversational instruction is in harmony with the idea of instruction as help. Gibbons et al. (2008) propose: Instead of something that the instructor or designer does to the learner, the helping view recognizes that participation in instruction represents an expression of the learner’s will to learn and the instructor or designer’s will to assist in the process of learning. Instruction becomes a process of mutual adaptation for those involved. The instructor or designer supports an ongoing learning process in a variety of ways, for which the learner comes to have ultimate responsibility. —(p. 130) Application Exercise Which of the ways of thinking of design described above makes the most sense to you? Which one makes the least sense? • Suppose you were trying to explain to a friend what you do as a designer. Which of the approaches above would you be most likely to describe to your friend? • What if the friend was a designer in another field? Would you use a different approach to explaining what instructional designers do?

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6

Instructional Design and Theory

Data is only available about the past . . . With data or without it, every time managers [and designers] take action, and every time they look into the future, they use a theory to guide their plans and actions—because a theory is a statement of what causes what, and why. —(Clayton M. Christensen et al., 2004) He who loves practice without theory is like the sailor who boards ship without a rudder and compass and never knows where he may cast. —(Leonardo da Vinci) It’s commonly assumed that the design of a new airplane will incorporate the best available theoretical knowledge. An airplane designer who neglects theory has no solid grounding on which to base designs and no guidance to supply effective ideas along the way. The theory most useful to an airplane designer is not likely to be scientific theory. This chapter describes the kinds of theory that are most useful to designers. Then it describes how this leads to the incorporation of theory into instructional designs. The concept of design layers plays a key role in making this relationship. Technological Theory vs. Scientific Theory: What’s the Difference? This chapter makes a distinction between two categories of theory: scientific theory and technological theory. One kind of theory—scientific theory—is analytic, used to construct an understanding of the forces that drive natural and human-made phenomena. The other kind of theory—technological theory—is used for synthesis of designs. Herbert Simon (1999) makes the distinction between these two categories of theory: “As soon as we introduce ‘synthesis’ . . . we enter the realm of engineering. For ‘synthetic’ is often used in the broader sense of ‘designed’ or ‘composed’. We speak of engineering as concerned with ‘synthesis’ while science is concerned with ‘analysis’ ” (p. 7). Simon’s book The Sciences of the Artificial (1999) is a description and definition of technological theory and its application. Simon notes that the applied professions have neglected to recognize and develop a robust concept of technological theory separate from scientific theory: “The professional schools will reassume their professional responsibilities just to the degree that they can discover a science of design, a body of intellectually tough, analytic, partly formalizable, partly empirical, teachable doctrine about the design process” (pp. 131–132). 145

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The professional schools to which Simon refers include those that may draw upon scientific principles but that should have also created a separate body of synthetic principles which can be used to design, to plan, to prescribe, to devise, to invent, to create, and to otherwise channel natural forces for accomplishment of human purposes. Vincenti (1990) argues that “technology appears, not as derivative from science, but as an autonomous body of knowledge, identifiably different from scientific knowledge with which it interacts” (pp. 3–4). He proposes that there is more to the notion of theory than what science alone supplies. There is a clue to this difference in the description given by Klir (1969) describing both science and technology in terms of their common grounding in general systems theory. According to Klir: In our experimental investigation of objects, we concentrate on the observation of a distinct set of quantities at a given resolution level, on the search for a simple expression of the time-invariant relation between these quantities, and on the search for the properties (as far as we want to discover them). —(p. 39) In this technical-sounding language, Klir is saying that scientific research stabilizes almost all variables in order to observe the relationships between a few. He describes how experimental conditions are set up to enable study of these variable values: We say that we define a system on the given object from a distinct point of view. The set of quantities, the resolution level, the time-invariant relations between the quantities, and the properties that determine these relations are the fundamental traits of systems studied by experimental branches of science. —(p. 39, emphasis in the original) Thus scientists impose controls on many variables in order to study the outcome values and interactions of a few variables. From the outcome, cause–effect relationships are inferred. The goal of this research is to produce explanations for observed outcomes—to answer the question, “Why?” Next Klir describes engineering research, which involves a different approach to experimentation: In the engineering branches, the system has the same traits as in the experimental sciences. As a rule, however, the problems involved are different. The relations between its quantities are usually prescribed, and we are to find a suitable manner of implementing them with the aid of the technical resources available, or, conversely, a distinct realization is given, and we are to find the relations between certain quantities. —(p. 40, emphasis added) Klir is stating that engineers—who are designers—also study systems to create knowledge, but their experiments begin with the outcome in mind. Instead of waiting to see what the outcome will be, the designer deliberately arranges and adjusts many variable values in order to reach a particular outcome that is generally known from the beginning. For the designer it is not the outcome that is the mystery: it is the arrangement of variables necessary to produce the known outcome. Why is the distinction between scientific theory (produced after much scientific research) and technological theory (produced after much technological or design research) important? It is important because both scientists and technologists study systems—the same systems—but they study them with different knowledge goals in mind. In one case (science) they are trying to understand

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how and why things happen, and in the other (technology, design) they are trying to discover how to influence things to happen. In instructional technology, ignorance of this clear difference leads many technologist-designers into an identity crisis. They are taught the processes of science and how scientists produce knowledge, but they are not taught as clearly and thoroughly the processes of technology and design and how design can also produce knowledge, but of a different sort. There is some disagreement about the science–technology distinction. By the time of World War II science had become an institution, but technology had not. This has become institutionalized, and it is one of the reasons for Simon’s The Sciences of the Artificial (1999). World War II escalated technological development, but many forces, including economic and political ones, wished to preserve the distinction between science and technology. Following the war, retrospective studies of the science– technology relationship multiplied (summarized in Carlisle, 1997). A historical review by Mayr (1976) describes how the science–technology barrier is disappearing in practice: Nowadays, practitioners seem to be more clearly identified by an academic degree or a job title, but, if we look at their actual work, the labels turn out to be arbitrary. Many, perhaps most, of present-day “scientists” turn out to work for technological goals, whereas academic engineers occasionally are occupied with research that has no technological applications in mind at all. —(p. 667) However, he asks: “Are we . . . to abandon the problem of the science–technology relationship as unprofitable and inappropriate? Yes and no: the problem requires redefinition” (p. 669). His answer is to consider science and technology in terms of: “Interactions and exchanges between what can . . . be labeled ‘theoretical’ and ‘practical’ activities, that is between man’s investigation of the laws of nature and his actions and constructions aimed at solving life’s material problems” (p. 669). His conclusion is that though science and technology cannot be distinguished in semantic terms, they can be distinguished in terms of the activities engaged in and their purpose. Why is the Difference Important? The main pursuit of science is to achieve explanations through theory building. The main pursuit of technology is to create useful artifacts using theory. But the kinds of theory used in each instance are different. In the past, the almost complete dominance of scientific reasoning as taught in school makes it hard for designers in general, including instructional designers, to give a clear explanation of what instructional theory is and how it is applied to designs. This makes it hard for designers to engage in synthetic modes of thought. This is why it is so important for designers to understand the alternative pattern of thinking offered by the concept of technological theory. The advent of the computer and its employment in making designs has forced technologists to confront the issue of technological theory. This was Simon’s message in The Sciences of the Artificial. Simon identifies not just one but several categories of technological theory generated in ways different from those used to generate scientific theory. Likewise, Vincenti confronts the problem of technological theory in What Engineers Know and How They Know It (Vincenti, 1990). As Vincenti shows, a designer who tries to generate designs by thinking only like a scientist is sure to be frustrated: Engineering knowledge, though pursued at great effort and expense in schools of engineering, receives little attention from scholars in other disciplines. Most such people, when they pay heed to engineering at all, tend to think of it as applied science. Modern engineers are seen

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as taking over their knowledge from scientists, and by some occasionally dramatic but intellectually uninteresting process, using this knowledge to fashion material artifacts. From this point of view, studying the epistemology of science should automatically subsume the knowledge content of engineering. Engineers know from experience that this view is untrue, and in recent decades historians of technology have produced narrative and analytical evidence in the same direction. Since engineers tend not to be introspective, however, and philosophers and historians (with certain exceptions) have been limited in their technical expertise, the character of engineering knowledge as an epistemological species is only now being examined in detail . . . What engineers do, however, depends on what they know, and my career as a research engineer and teacher has been spent producing and organizing knowledge that scientists for the most part do not address. —(p. 3) Ann Brown (1992), a noted researcher in learning and instruction, describes her intellectual journey from theoretical learning research to theoretical instructional research: “In the classroom and in the laboratory, I attempt to engineer interventions that not only work by recognizable standards but are also based on theoretical descriptions that delineate why they work, and thus render them reliable and repeatable” (p. 143, emphasis in the original). Much of Brown’s late work is based on theory of a technological type—instructional theory. Her writing describes her realization that strictly scientific research and only scientific theory were inadequate in designing more effective instructional artifacts. Brown describes how she came to understand the need for bridging scientific and design theories: My change in focus was a gradual evolution rather than an unpremeditated leap into instruction . . . Even though the research setting has changed dramatically, my goal remains the same: to work toward a model of learning and instruction rooted in a firm empirical base. I regard classroom work as just as basic as my laboratory endeavors, although the situated nature of the research lends itself most readily to practical application. —(p. 143)

Two Kinds of Technological Theory: Design Theory and Domain Theory Within the category of technological theory there are multiple sub-types of theory relevant to the work of instructional designers. Simon (1999) defines several classes of technological theory used by designers. These include theories for representation of design problems, solution search, generation of alternative solutions, evaluation of solutions, expression of formal design logics, and the architectural structure of inner design organization. What he describes is the need for technological theory and not a “retreat to the methods of the cookbook that originally put design into disrepute and drove it from the engineering curriculum” (p. 135). Vincenti likewise describes multiple bodies of technological theory. On practical grounds, I propose a distinction between two types of technological theory: design theory and domain theory. • Design theory is theory about how designs are made. It is not restricted to uses for instructional design. Design theory cuts across all design-related disciplines. A computer designer would be as interested in design theory as an instructional designer. A multi-disciplinary field

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of design research concerns itself in uncovering theories of design that can be applied in any field. • Domain theory, in contrast, is specific to a particular domain or field of design. The domain of interest to instructional designers is instructional theory. A computer scientist would not find constructs in instructional theory easy to incorporate into computer designs. (However, an instructional designer can borrow certain technological theory ideas from the computer scientist.) The two categories of technological theory proposed here are included in Figure 6.1, which summarizes all of the categories of theory that have been named in this chapter and shows how they can be related. The practical grounds for separating two varieties of technological theory are that one can be used in a cross-disciplinary manner, and the other is restricted to a limited field of specialized users. One describes frameworks for how designs can be made, and the other supplies design content to fill the positions in the frameworks. Design Theory Design theory deals with how designs are created and their architecture. Design layer theory, as it is described in this book, is a design theory. It does not identify the structures that go into a design. Rather it deals with the architectural framework of designs, regardless of what domain theories are applied within the framework. Design theory can be applied across all design fields. The theory of design layering has been applied in many design fields, which is described later. Design theory is portable. It allows instructional designers to speak about the processes of design with engineers, architects, and designers in other professional fields. Simon addresses his remarks on design to all professions involved in design: Engineers are not the only professional designers. Everyone designs who devises courses of action aimed at changing existing situations into preferred ones. The intellectual activity that produces material artifacts is no different fundamentally from the one that prescribes

Explains how things work naturally

Scienfic Theory

Technological Theory

Design Theory Describes how designs are constructed and their architecture

Describes how things can be made to work

Domain Theory

Describes the kinds of structures that can be included into a design and their arrangement

Figure 6.1 The distinction between scientific and technological theory and the types of technological theory of most interest to instructional designers.

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remedies for a sick patient or one that devises a new sales plan for a company or a social welfare policy for a state. Design, so construed, is the core of all professional training; it is the principal mark that distinguishes the professions from the sciences. Schools of engineering, as well as schools of architecture, business, education, law, and medicine, are all centrally concerned with the process of design. —(Simon, 1999, p. 111, emphasis added) Significantly, Simon includes education as a designing profession, even though most educational researchers consider themselves to be carrying out scientific experiments. These researchers engage the rhetorical style of scientists, devise their studies according to the best social scientific research principles, and report their findings using scientific formats and styles. Design research within the field of education has had a difficult passage to acceptance, and many educational researchers still question design research methods and conclusions (see Reeves, 2011). This work proposes that if instructional designers are more aware that they are engaged in a non-scientific but still theoretical enterprise, and if they are aware of how to use the specialized theories and design principles that pertain to all designers, they will be better able to organize their designs efficiently and will be able to prepare more interesting, sophisticated, and effective designs. Designers in instructional design should therefore begin to draw profit from design theory advances from other design fields. Domain or Instructional Theory The second type of technological theory proposed here is domain theory. Every design field possesses theories that pertain to designs within that field. Domain theories supply structuring concepts that fit into design theory frameworks and give specific substance to a field’s designs. The domain theories from one field may overlap with the interests of other fields (see Figure 6.2). For this reason, a domain theory from the creative arts can provide useful domain theories to an instructional

Domain theory from Computer Science Domain theory from the Creave Arts Domain theory from Instruconal Design

Domain theory from Technical Communicaons Domain theory from Economics

Domain theory from Business

Domain theory from Anthropology

Figure 6.2 The contribution of domain theory by several related design fields to what can be referred to as a body of “instructional” domain theory. Note that there also exists a body of domain theory that is uniquely related to the design of instructional experiences.

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designer. Likewise, domain theories from computer science, business, general systems, economics, technical communication, anthropology, and many other fields may be useful. Even fields that do not appear to be directly related to technology or design contribute. Sociology and psychology also contribute technological theories to instructional design. Instructional design as a field draws upon and combines many other-field domain theories. However, all of the domain theories from art combined would not be sufficient to inform a complete instructional design. Likewise, all of the domain theories from computer science or business would be insufficient to guide a design. However, there is a body of domain theory that is unique to the interests and design problems of the instructional designer. As the body of practice in any technological field matures, it produces theory. Though such a field borrows theory freely from other fields, a core of domain theories unique to the interests of the field will exist. This is also shown in Figure 6.2. As instructional designers employ layer theory (a design theory), domain theory structures from many different design domains (computer science, art, business) may inform different layers of the design. However, the essential core of field-unique theory will also contribute. Instructional theory, therefore, refers to that unique combination of borrowed and field-specific theories that, taken together, can inform an instructional design. There is no single, unified and well-delineated body of theory that exists independently that can be called “instructional theory”, and a creative instructional designer is well advised to read outside of the instructional design literature as well as within it in search of theoretical ideas that may improve designs. Identifying new and useful domain theories from other fields is one of the skills of a mature and innovative designer. One aim of research within the field of instructional design should be to test domain theories originating in other design fields for their applicability and force in instructional designs. If there is no single instructional theory that can supply a sufficient theory base for an entire design, then it is apparent that a single design must draw upon numerous domain theories, despite the fact that it can place some theories in a more central position within the design. In the past the field of instructional design has been limited in four theory-related ways: • It has failed to recognize the number and diversity of the theories it employs. • It has failed to recognize the connection between its own design knowledge and that of other fields. • It has failed to move beyond process-centered thinking to consider the variety of abstract structures that can be employed in instructional designs. • It has failed to develop a robust theoretical vocabulary for discussing designs and the acts of designing. The terms “instructional theory” and “domain theory” will be used from this point on interchangeably. This will be especially useful in connecting with the literature from outside the domain of instructional design. Application Exercise Domain theories in instructional design are called instructional theories. Pick an instructional theory and find two different sources that describe it. • • • •

How much difference was there between the descriptions? Is the difference major or minor? If there were big differences, why did two writers see the theory in different ways? What does this tell you about the nature of theory and how we understand them?

152 • Fundamentals

More Deliberate and Nuanced Design Seeing the different value propositions offered by design theory and domain theory, and seeing them as being distinct from scientific theory, is a key to understanding the operations of theory in any design field. Christensen et al. (2004) address the role of theory in the constant redesign necessary to keep businesses market-relevant. In Seeing What’s Next: Using Theories of Innovation to Predict Industry Change, Christensen explains an insight gained while wrestling with the idea “that decisions should be grounded in solid analysis of data” (p. vii), which is a distinctly scientific view: Data is only available about the past. I then realized that with data or without it, every time managers take action, and every time they look into the future, they use a theory to guide their plans and actions—because a theory is a statement of what causes what, and why. —(p. vii, emphasis added) Christensen’s book is about what he calls “theories of innovation” that can be used to predict outcomes probabilistically, even when complete information is not available from scientific research. This is the condition that exists every time a designer creates a design: the designer does not know with certainty the impact the design will have. Everything included into a design is based on a prediction about what the designer believes will have the desired effect. Christensen’s book is significant because he gives specific examples of business theories, which are domain-specific because they are limited to application to the design of commercial organizations. By studying these theories, we can gain a better insight into one type of technological theory (domain theory) and how it can be applied. As examples of game-changers based on a theory, Christensen names the mini-sized steel mills that drove large integrated mills out of business, Walmart’s discount store configuration, and Dell’s online ordering business model which allows the customer to purchase a customized product directly from the manufacturer instead of a standard stock item from a store. Christensen concludes that: “the only way to look into the future is to use these sorts of theories” (p. xx). If we were to restate this principle in terms of instructional design, it might read: “the only way to look into the future [effectiveness of an instructional design] is to use these sorts of theories”. An example of the discovery and then application of domain theory in computer science is provided in an article describing the presentation of the Turing Award to Jim Gray of Microsoft Research (Association for Computing Machinery, 1999). The Turing Award is the computer science equivalent of the Nobel Prize. Gray and his design team made “seminal contributions to database and transactional processing” according to the article. A transaction is “the fundamental abstraction underlying database system concurrency control and failure recovery”. In an interview, Gray gave more detail on the accomplishment and how it came about. Gray first described how numerous other design teams were working on database programming problems at the same time: There was quite a lot of ferment in this area. People were building systems that actually worked. But there wasn’t much discussion about what the underlying theory was for, why the systems worked and whether there were better ways of doing things. At IBM Research in San Jose, there was a group of people, including myself, who owe their intellectual heritage to another Turing Award winner, Ted Codd. We were fairly academic in background and more interested in studying systems than actually building them. What I mean by that was we were in research and were particularly interested in making computer systems that were extremely easy to use. We believed that if a fairly formal theory was the basis of the system, then the system would have much simpler behavior than one with an ad hoc design. I think the success of the relational database has vindicated that approach. —(p. 13–14).

Instructional Design and Theory • 153

As Gray described the theory in the interview, it became clear that it was a distinct domain theory—a theory of means and architectures—expressed in a form that made it useful for structuring design solutions. Gray was asked: Q: What were the fundamental tenets of the theory? A: One was that all of the data be represented in a relational form. At the time, this was a pretty radical approach. —(p. 14, emphasis added) Gray was describing the creation of a new architectural construct—the relational form of database record structure—that interacted with the concept of a database “transaction” that has since then been one factor in immense gains in computing power, economy, and insurance against data loss for virtually all of today’s database users. The invention of this new design theory also stimulated research into distributed databases and data mining. Gray’s team discovered a new domain theory for the structuring of databases. This example illustrates the invention of a new abstraction in the realm of uniquely technological knowledge—an abstraction that has been applied in different ways to not just one but a multitude of design problems. Gray was not trying to describe a universal law. His discovery was about a way things could be made to happen: a useful way of structuring things to achieve an intended outcome. Once an instructional designer becomes aware of the application of theory in designing, the examples that can be found in other design fields multiply, and it becomes apparent that the power of designs is not in surface features and tactics but in architectures and architecturally deep abstractions. The value of instructional designers will increase proportionally with the extent to which they can become fluent in this more abstract language of designing offered by design and domain theories, and their products should be expected to be more precise and predictable in their impact on learning. Instructional Theories Instructional theories supply the structures that populate design layers. In this way, design theory and domain theory work together to produce designs. An instructional theory names structural elements and the manner in which they can be effectively related. This means that the first clue to decoding a theory is to look at the language in which it is expressed—what Christensen calls “every word to which we ascribe unique meaning” (p. xxxii). Structures named by a theory may be physical things, events, acts, processes, or other abstractions. They also consist of arrangements, patterns, or relationships among these things thought to have impact. They can consist of ways in which something is done or qualities of the way in which it is done. A significant observation by Reigeluth (1999a) describes instructional theories as “dealing with cause-and-effect relationships or flows of events in natural processes, keeping in mind that those effects or events are almost always probabilistic . . . rather than deterministic” (p. 7). Therefore, an instructional theory says “should” or “could” or “might” or “can” or “ought”, but it should probably not say “must” or “always”. Every instructional design employs many, many theories. These may be formal theories published by a theorist or private theories held only by the designer. Some form of public or privately held theory underlies each decision; otherwise design decisions are random. A later section shows how instructional theories relate to design layers in detail. A formal instructional theory may describe several things about its structural elements, including: • Descriptions and definitions • How elements are joined into structure suites

154 • Fundamentals

• • • • • • • •

How elements work How elements work with other elements What functions are carried out by elements Why elements work When elements should/can be used How elements are applied in practice Common combinations, arrangements, or sequences of elements Connections to scientific theory.

Not every theory is expressed completely in all of these areas, nor does every instructional theory  make statements that pertain to every design layer. Reigeluth has created the most comprehensive recent compilations of instructional theories (Reigeluth, 1999a; Reigeluth and Carr-Chellman, 2009) in his edited volumes, Instructional-Design Theories and Models, Volume II, and Volume III. Theorists and Design Layers Instructional theorists propose theories to answer burning questions about how things can be made to happen. A theorist might ask, “How can I increase the probability that the learner will learn how to do ‘X’?” or “How can I reduce the time that it takes to learn ‘X’?” or “How can I help the learning of ‘X’ to be more memorable?”. A key to understanding different theories is to understand what question or questions the theorist was trying to answer. These questions are also the questions the designer asks. For example, the representation layer is concerned with questions like, “How shall message ‘X’ be presented to the learner’s senses?” The message layer asks questions like, “What shall be done next to continue the forward momentum of the instructional conversation?”. Every layer of a design asks the designer to answer questions. By matching the theorist’s question with the layer’s questions, a designer sees how a theory might apply to a particular design. Table 6.1 shows how a sample of important instructional design theories aligns with different design layers. The theorists include John R. Anderson (Anderson et al., 1995), Robert Gagné (1985), and Collins, Brown, and Newman’s “Cognitive apprenticeship”, originally published in Resnick (1989). You may wish to learn more about these theorists and their theories through independent study. The present interest is in noting that in each case the theorists addressed some layers but not others. All of them had something to say about the content layer and the strategy layer, but after that they had less and less to say about the other layers, despite their importance. The theorists clearly had questions in mind that pertained to what they considered to be key layers and no questions about some of the other layers. If you are seeking theoretical guidance related to the other layers, you have to consult a different theorist and theory. This expanded view of the correspondence between theories and layers is presented in Table 6.2. For each layer a representative (but by no means exhaustive) group of theorists is shown. The theorists listed did not attempt to describe how to answer all of the questions of every layer. Each theorist had specific questions in mind pertaining to a particular layer, and in some cases a very specialized sub-layer. Harris (1999), for instance, is only concerned with principles for representing data in graphical form. This idea about the relation of theories to layers is common sense. If you were an architect you would not use theories about electrical systems to design the foundations of the building, nor would you use theories about airflow through spaces to try to design the supporting framework.

Instructional Design and Theory • 155 Table 6.1 Analysis of Some Well-known Instructional Theories to Show the Relationship of Instructional Theories to Design Layers (From Gibbons & Rogers, 2009, p. 320) Theory

Anderson

Cog. App.

Gagné

Content layer

Content subdivided into two types: “production rules” and semantic units called “working memory elements”

Four content types: Domain knowledge Problem-solving strategies and heuristics Control strategies Learning strategies

Taxonomy divides knowledge into five main types; one type, intellectual skills, is subdivided into several sub-categories

Strategy layer

Production rules learned in prerequisite order Learning by practice and error correction

Six methods: Modeling Coaching Scaffolding Reflection Articulation Exploration

Conditions to support learning are determined by the type of knowledge to be learned; nine events of instruction provide occasions for those conditions to be expressed

Five social strategies: Situated learning Culture of expert practice Intrinsic motivation Exploit competition Exploit cooperation Three sequencing strategies: Increasing complexity Increasing diversity Global before local

Control layer

Control resides in the system; student responds to problems presented

Implied in apprentice interpersonal relationships, but not enumerated

Implied instructor control; student responds to instruction

Message layer

No formalization of message structuring guidelines

No formalization of message structuring guidelines

Types of message used in illustrations, but no formalization of messaging guidelines

Representation layer

No formalization of representation terms or guidelines

No formalization of representation terms or guidelines

Types of representation used in illustrations, but no formalization of representation terms or guidelines

Media-logic layer

No formalization of media-logic guidelines

No formalization of medialogic guidelines

No formalization of media-logic guidelines

Data management layer

Data management specified as use of data from previous responses to influence future selections of the system regarding problems to present

No formalization of data management guidelines

No formalization of data management guidelines

Source: From Gibbons and Rogers, 2009, used by permission

Application Exercise Return to the previous application exercise and consider the instructional theory you chose to read about in the literature. • Which layer or layers does the theory say something about? Which not?

156 • Fundamentals Table 6.2 Sampling of Work by Theorists or Research Reviewers Attempting to Identify Layer-specific Principles (From Gibbons & Rogers, 2009, p. 323) Layer

Theorist/Author

Principles

Control

Crawford (2002)

Conversational interaction and the design of interfaces to support rich user communication and conversation with the system

Gibbons and Fairweather (1998)

Varieties of human–machine communication (learner to system) during instruction and the computer’s ability to implement them

Merrill (1994)

Categorization of message elements that make up an instructional strategy; texturing principles that prioritize certain messages and foreground certain information

Horn (1997)

Categorization and logical grouping of information tableaus; emphasis on underlying relationships within message groupings rather than on their display

Simon and Boyer (1974)

Compendium of analysis methods for describing student– teacher communications and interpretable actions during classroom instruction

Mayer (2005a, 2005b, 2005c, 2009)

Principles for the use of synchronized multi-media channels to convey instructional information in a manner that supports learner formation of appropriate mental models

Tufte (1990, 1997)

Principles for the use of graphical representations to present complex and dynamic bodies of information

Wurman (1997)

Visual designers explain and illustrate their principles for explaining using visual and textual structure

Harris (1999)

Varieties of presentation of data in graphical form and principles for constructing data representations

Fleming and Levie (1993)

Message design principles, concentrating on the representation of information

Gibbons et al. (2001)

Principles of merging media structures with other design structures

Seels et al. (1996)

Principles related to the design of instruction involving the television medium; extensive glossary of terms, many of which are the terms of a specialized design language

Hannafin et al. (1996)

Principles related to the design of computer-based instruction as a medium

Romiszowski and Mason (1996)

Principles related to the design of computer-mediated communication

Message

Representation

Media-logic

Data management

Stanney (2002)

Principles related to the design of virtual environments

Wenger (1987)

Summary of intelligent computer-based instruction design principles, including use of data to create adaptive instruction

Stolurow (1969)

Early conception of the principles for the use of data from instructional interactions to determine the future path of instructional events; dated by reference to programmed instruction but relevant in principle

Source: From Gibbons and Rogers, 2009, used by permission

Sub-layers and Theories Layer design theory is an alternative way of breaking design problems into solvable sub-problems. The breakdown method used by most instructional designers at present is to break the problem down by design sub-process. This is the principle behind ADDIE and ISD process models. In

Instructional Design and Theory • 157

contrast, layer theory breaks a problem down in terms of the functions that the designed artifact will carry out. Once a designer sees that a design can be subdivided by functional layer, it also becomes evident that the decomposition doesn’t end there. This leads to a cascading and reductive breakdown process similar to the one that is encountered when processes are used as the decomposition principle. Layers decompose into sub-layers, and those in turn decompose into additional, smaller sub-layers. In both the functional and process breakdowns, the fragments from decomposition keep getting smaller and smaller, until one wonders whether the effort of arriving at all of this detail is worth the trouble. In the case of functional breakdown, the effort is worth the cost, because when it is artifact functions that are being broken down the alignment of sub-layers with theory continues to work in the designer’s favor. Each successive functional breakdown is matched in most instances by a further breakdown of theory as well. This is, in fact, a validation of the layer theory. Process models cannot match theory with process steps at lower levels of decomposition. In the case of layer theory, it is often the fact that new layers are the result of advances in a technological area, and so layer theory anticipates that new theory will create new sub-layers. An example of this is found in the representation layer. Since it is one of the more mature layers, it has more detailed theory associated with it and therefore more sub-layers and design specialties. There are representation theories dealing with representation form, composition, and type of representation (chart, text, schematic, animation, etc.). As this breakdown of functions occurs, there are corresponding theories to inform decisions at the lower level—chart-making theory, theories of text representation, schematic drawing theories, and animation theories (see Agrawala et al., 2011). When is the value of creating a new theory or layer high enough? It happens when the computer begins to participate in making design choices. In the case of the 3-D histogram, enough theory has been developed that it can be embedded in the most sophisticated spreadsheet software, driving the dynamic design of 3-D histograms. Since there is no single “right” way to design a 3-D histogram, what the spreadsheets do is provide you with a way to manipulate what are deemed by the software company to be the most useful variables to adjust. When you use your mouse to select a style of histogram and then pull the histogram around into different perspectives and assign colors, labels, and other features, you are co-designing with the computer according to a limited domain theory that has been embodied in the software. A competitor software company might provide you with alternatives. When the payoff to you is sufficient for you to switch programs, you will make the change to a different, perhaps more sophisticated embedded theory. Decomposition of layers into sub-layers is a possibility for any of the design layers described in this book. Since these layers are not the only possible ones (and most definitely not the only ones), there exists the possibility that other layer configurations might hold the key to discovering and exposing to view new theoretical considerations for instructional designers. In the final analysis, layers and theories are to some extent co-existent and drive each other. A theorist who expresses a new domain theory opens the possibility that designers will find a new layer useful in their designs. Likewise, a designer who finds a new layer useful may be opening the door to new domain theory just waiting to be discovered and articulated. When the payoff is high enough, that happens. .

The Origins of Design and Domain Theory Books on classical scientific theory building in education are plentiful. Books on the origins of technological theories—both design theories and domain theories—are becoming more numerous due to the emergence of a body of literature on design research, also referred to as design-based research (Collins et al., 2004; Kelly et al., 2008; van den Akker et al., 2006; Educational Researcher, 2003;

158 • Fundamentals

Journal of the Learning Sciences, 2004; Educational Psychologist, 2004; Educational Technology, 2005; Reeves et al., 2005). Edelson (2002) contrasts the traditional educational research paradigm with that of design research: Design research explicitly exploits the design process as an opportunity to advance the researcher’s understanding of teaching, learning, and educational systems. Design research may still incorporate the same types of outcome-based evaluation that characterize traditional theory testing, however, it recognizes design as an important approach to research in its own right. —(p. 107) Edelson describes the application of design research to a wide variety of education-related questions, including the design of curriculum, software, professional development, school organizations, and school–community collaborations. Other researchers report knowledge and theory building in detailed instructional strategies that has produced a new family of instructional approaches centered on modeling. Gibbons and Fairweather (2000) review a number of these approaches, which are typified by instruction that teaches problem solving, metacognition, collaborative learning processes, and design—skills and knowledge that prepare learners to take part in a knowledge economy (Kahin and Foray, 2006). Collins et al. (2004) describe design research as an alternative to controlled laboratory research and an opportunity to carry research into real-world settings under real-world conditions. They explain that: Design experiments were developed as a way to carry out formative research to test and refine educational designs based on theoretical principles derived from prior research. This approach of progressive refinement in design involves putting a first version of a design into the world to see how it works. Then, the design is constantly revised based on experience until all of the bugs are worked out. —(p. 18) Although the try-out and revision cycle has long been a standard doctrine related to instructional design practice, the reality has been that the demands of the workplace and limited resources severely limit its application. However, try-out and revision is only one aspect of design-based research. Edelson explains: “In this theory development approach the design researchers begin with a set of hypotheses and principles that they use to guide a design process” (Edelson, 2006, p. 106). As results of try-outs are interpreted: Through a parallel and retrospective process of reflections upon the design and its outcomes, the design researchers elaborate upon their initial hypotheses and principles, refining, adding, and discarding—gradually knitting together a coherent theory that reflects their understanding of the design experience. —(p. 106) Edelson identifies three categories of theory that can emerge from this process: The goal of ordinary design is to use the lessons embodied in a design procedure, problem analysis, and design solution to create a successful design product. Design research retains that goal but adds an additional one, the goal of developing useful, generalizable theory. The opportunity that design offers for theory development is the possibility of using the lessons

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learned in constructing design procedures, problem analyses, and design solutions to develop useful theories. For each of these three elements of design, there is a corresponding type of theory that design research can develop. I call these three types of theories domain theories, design frameworks, and design methodologies. —(pp. 112–113, emphasis in the original) Edelson proposes that a domain theory is “the generalization of some portion of a problem analysis . . . It is a theory about the world, not a theory about design per se” (p. 113). This definition of domain corresponds with Schön’s. For example, Schön describes one domain of building design called “organization of space”. In this he includes constructs that represent not where things are but where things aren’t—spaces and their arrangement—that create “a general pass through” or a space that “[carries] the gallery through [to] look down here, which is nice”. In Schön’s mind such a domain exists because he and other designers found examples of it and found that taking the constructs of this domain into account during design created better, more innovative designs. The domain relates to a “theory about the world” because it relates to how the world can be arranged. Edelson’s second type of theory—design frameworks—consists of “generalized design solution[s]” (2006, p. 114). As examples of design frameworks, Edelson names anchored instruction (CTGV, 1990, 1993) and goal-based scenarios (Schank et al., 1999). These represent the equivalent of meta-domain theories that exist when several domain theories have come together into a larger pattern that can guide a design. Edelson’s third category of theory resulting from design research is called design methodologies, defined as “a general design procedure”. The responsibility of a design methodology is “for ensuring that the design process addresses all the essential issues”. Three major design methodology approaches described in this book—the classical systems approach, instructional design models, and design by constraint placement which design layer theory provides—all fall under this heading of design methodologies theory. Theory development by design-based research takes place in many design-related fields. It is essentially the same as the commercial research and development process used by the industrialized world for decades to bring ideas from their early conceptual stages through successive stages of research and integration into a commercial market (Jolly, 1997). This process takes place in all design-related fields and at all levels of detail, and advances in this kind of theory are not restricted to traditional disciplinary silos, because this kind of research is inherently interdisciplinary. For example, an emerging technological theory described by Agrawala et al. (2011) growing out of research in computer graphics relates to the design of graphic representations of only particular types. The work identified a set of design principles derived through design research for computer-based visual communication of spatial information. The research took place in three stages: • Stage 1: Identify design principles—“We identify domain-specific design principles by analyzing the best hand-designed visualizations within a particular information domain” (p. 63). (Note here the narrow specialization of this research within the larger field of visual design practice. This is the genesis of very local theory.) • Stage 2: Instantiate design principles—“We encode the design principles into algorithms and interfaces for creating visualization” (p. 63). (Note the differentiation of functions required not only to create but to display the visualizations: algorithmic analysis, representation analysis. This local proto-theory will bridge two layers—representation and media-logic.)

160 • Fundamentals

• Stage 3: Evaluate design principles—“We measure improvements in information processing, communication, and decision making that results from our visualizations” (p. 63). (Note that knowledge production here involves designing specific instances and then testing and revising their effects in a repeating, cyclic manner. This is the pattern of design research.) Agrawala and Berthouzoz found that a different set of rules had to be derived for each type of visual they attempted to automate: exploded views, “how things work” illustrations, cutaways, street diagrams, and navigation diagrams. Even at this level of detail at which most of us would consider all of the visualizations to be generated from the same basic set of rules, when the goal was to be explicit about design decisions (so that a computer could generate representations), the design technology had to become specialized into a different set of rules for each type of visual representation in order to provide the requisite amount of guidance. This is an example of the relentless subdivision of layers and theories within the already mature area of visual design. In many other less mature layers of design the subdivision of layers and corresponding design theories is in a very early stage. Application Exercise Suppose someone asked you about your personal instructional theories. • How would you describe your theories for forming representations? • How would you describe your theories about instructional strategy? • How would you describe your theories about data management? Relating Design Layers with Design Models ADDIE/ISD is considered here to be an expression of a design theory based on decomposition of process rather than artifact function. Layer design theory is compatible with ADDIE/ISD models. The two approaches to design supplement each other in useful ways. Using both together adds a new dimension of innovation to a project. Design models supply: • • • •

A useful framework of administrative functions A general high-level order of decision-making based on common sense in engineering A strong literature base An existing body of experienced practitioners.

Design layer theory supplies: • • • • •

Greater detail with respect to lower-level design decisions A means of associating theories with design decisions at every level of detail A method for tailoring the decision-making order to the specific project A way to give higher priority to the creative impulse within the design process. Each approach to design supplies something the other approach lacks, and each has its drawbacks.

ADDIE/ISD has its drawbacks: • It commits you to hidden domain theory assumptions (e.g., task analysis in some models). • It leads to the wrong kind of decisions (process decisions) at lower levels of decomposition.

Instructional Design and Theory • 161

• • • •

It does not lead easily to theory application at lower levels. It can become bureaucratic, process for the sake of process. It fails to lead to project-specific decision order at lower levels. It centers on process, not artifact.

Layer theory has its drawbacks as well: • It requires a different pattern of thinking: artifact function rather than process function. • It denies the designer the familiar and comfortable sequence of design activities. • It lacks a robust literature within the ID field (though such a literature exists in other fields). Defining a Generic ADDIE/ISD Model Aligned with Layers As it has evolved, ADDIE/ISD is a design theory. To quote an earlier section in this chapter, it “is [a] theory about how designs are made”. It represents a type of engineering model used in other fields with a few modifications. In contrast, ADDIE/ISD is not a domain theory. It does not—in its pure form—describe structures that will populate the design, but rather a framework that guides the designer in selecting those structures, whatever domain theoretical position the designer favors. This section analyzes a generic ADDIE/ISD model and gives a commentary on it from a layers perspective. Through this analysis, something can be learned about the relationship between design models and layers. Differences in the terminology of this generic design model from the standard model terminology used in the literature are due to the desire to show a closer fit between design models and design layers. This will broaden parts of the traditional design model, particularly in the area of content analysis, that currently limit model application and may confuse designers who find domain theory intruding into a design theory. (This occurs, for instance, in models that specify task analysis. Task analysis represents only one form of content analysis, and stipulating it as part of the model limits the application of the model by imposing a specific theory of content within a design model.) It may be beneficial to compare the model description below with Table 3.1 in Chapter 3 to note the differences introduced in this model to make it more compatible with the principles of design layer theory. Those differences consist mainly of modifying elements of the traditional model that in some way limit its applicability. This model description below considers just two of the five ADDIE/ISD stages—analysis and design. It does not detail the activities involved in development, implementation, and evaluation. Detailed plans for these phases are created during design, and the planning process for development, implementation, and evaluation is described in this commentary. The design (described here) and development (not described here) stages are interdependent in two ways. First, rapid prototyping as a design approach (Tripp and Bichelmeyer, 1990) alternates between design and development in quick cycles. This means that design in early prototyping cycles should place strong emphasis on the essential architectural core of the design, adding features and details in later cycles. Second, when design and development are separated, the analysis of the content must be more thorough in order to avoid the inadvertent omission of content, which would be discovered later in the project (at some expense). On the other hand, emphasizing analysis and design at the expense of development creates a useful contrast with what can be called “keyboard design”. The sophisticated production tools available today encourage designers to jump directly into production without adequate planning. The consequences of this approach are the same as if a person were to begin building a house without a plan. The results of this approach are the same as they would be for a house: costs for product maintenance

162 • Fundamentals

Content Analysis Survey

Catalog

Goal Analysis

Job/Task Analysis

Content Domain Analysis

Semanc Model An.

Analysis Selecon

Select Entering Behavior Analysis

Assess Criterion-seng Scoring

Goal Priorizaon Producon Rule An. Metacognion An.

Instrumentaon Selecon to Instruconal Mode

Taxonomic System Analysis

Environment Seng Assessment Plan

Fault Analysis

Target Populaon Analysis

Current Training and Resource Analysis Current Training Environment Analysis Training Resource Analysis Figure 6.3 Processes of the analysis stage of the generic instructional design model.

will be greater, product quality will be uneven, and the lack of a coherent architectural plan will make later additions and modifications difficult. The Analysis Stage of ADDIE/ISD The analysis stage of instructional design is a period for gathering and examination of data that have design implications. The analysis stage takes advantage of data from studies by the organization leading up to project initiation. The information and logic in these analyses is proven, extended, and deepened. Then additional processes are applied to create more detailed data through content analysis, target population analysis, and current training and resource analysis. A generic analysis phase is illustrated in Figure 6.3. Content Analysis During content analysis a design team describes in detail the body of performance that falls within the scope of the instructional project. Designers do not have a terminology sufficiently mature to describe what is gathered during this process. The nature of learnable human performance is so varied and complex that we do not have a coherent language for talking about it publicly, though many have tried to contribute terms. Bereiter (2002) grapples with what it means to “understand”; van Merriënboer et al. (2002) and van Merriënboer (1997) try to define skilled performance. During content analysis designers do not become subject-matter experts, but they work with subject-matter experts to create an externalized representation of an expert’s performances, knowledge models, values, and conative states.

Instructional Design and Theory • 163

The term “content analysis” is used deliberately in Figure 6.3 (instead of “task analysis”). This is a deliberate choice to avoid the use of a term that incorporates unacknowledged assumptions into the analysis of the learnables. As the name suggests, task analysis assumes that the subject-matter can be represented in the form of tasks. Discussions in prior chapters have tried to widen the concept of what is or can be learned and instructed. One problem this creates is that the term “content analysis” has different meanings in fields as diverse as communications, the humanities, the social sciences, and recently in Web design and analytics. To many current Web designers, for example, “content” consists of “media resources”. Content in that context is created, owned, and purchased: it is a commodity, an article of merchandise. For example, a major topic in instructional design is open “content” (Caswell et al., 2008) and the Creative Commons (2012) is an attempt to distinguish copyrighted from reusable “content”. To others, including some instructional designers, “content” refers to written, graphic, or implied messages that the designer wishes to have conveyed: a text or a subtext. In this sense content is abstract. A third, very different, meaning of the term “content” is used here. It describes the structure of what there is to be known and performed. Whereas in the past instructional objectives were couched in terms of concepts, procedures, or bodies of verbal information, more recently instructional goals refer to higher-order performances, metacognitive abilities, attitudes, dispositions, habits, and skilled performances as well. All of these—things that are “learnable” and subject to being instructed—constitute what is referred to here as content. And whereas in the past for some designers one content analysis method may have been considered sufficient to complete a design project, that can no longer be accepted as a standard. Content analysis as depicted in Figure 6.3 consists of four main processes that do not assume that any particular form of subject-matter dominates the analysis. The processes are: (1) survey the extent of the subject-matter, (2) catalog the subject-matter, (3) select the subject-matter to be instructed, and (4) create assessment criteria and guidelines. Figure 6.3 indicates that the capture of learnable subject-matter must include consideration of many kinds of subject-matter represented at the second level of the diagram: motor and cognitive tasks, bodies of semantically related knowledge, bodies of production rules, metacognitive abilities, and skills, as well as traditional taxonomically categorized performance types. Analyses of content must not only include a positive expression of what is knowable but also must reveal common performance errors (Burton and Brown, 1979; Tatsuoka, 1990), to fortify learners against them and instruct learners how to back out of errors through problem solving. This assumes the ability to self-monitor and self-correct performance. Survey Design team members survey the kinds of learning that are within project scope and use this to determine the extent of the learnable knowledge. What different kinds of things does this body of subject-matter contain? Which kinds are of the most importance to stakeholders and to competence for the learner? How well must the learner be able to perform? How much is there to be learned? What are the dispositions and values of a competent performer? How new and unfamiliar will this be to the learner? The content analysis that reveals these things will require a constellation of processes (see Chapters 4 and 11). Which and how many of these will be of most importance to the project? The designer helps the subject-matter expert discover the nature and extent of content structures relevant to a project. Often this survey and the subsequent content capture process leads to new insights for both the designer and the expert. Catalog Cataloging is the process of capturing specific elements of subject-matter of the types identified in the survey. One of the problems arising from incomplete cataloging is that pockets of subject-matter

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discovered late in a project can cause budget problems. One safeguard against this is the experience of the designer, who, like a detective, ferrets out areas of content that logically should exist, even when subject-matter experts are unaware of them because of over-familiarity with their own expertise. (See Means and Gott, 1988; Tenney and Kurland, 1988; Clancey, 1988.) Confirming the expert’s deficit may require consulting a number of sources, including manuals, prior training, other subject-matter experts, and everyday performers. Techniques for information gathering may include interviews, observations, and informal talks with subject-matter experts to listen to their stories. One special challenge of cataloging is how to represent the subject-matter as it is being identified so that an entire team can inspect it and use it. Select Selection of subject-matter identifies what content lies outside of the scope of the project and helps choose an appropriate instructional venue for these parts of the content. Learners may already possess some of the performance ability identified during analysis, so it is tagged as prerequisite knowledge. Also, some of it may be too advanced for the target population, and is also tagged for another venue. Some of the content may be simple enough that it can be learned on the job or through the use of a performance support system. Selection is the process of making these initial distinctions. This sets the stage for deciding where, when, and how to train: On the job? In formal instructional sessions? In informal settings? Through a job aid? All of these? The selection process gives an initial answer to these questions. Performances selected for instruction within the bounds of the project may ultimately rely on different instructional vehicles such as simulation systems, apprenticeship and mentoring systems, job-aiding, formal training courses, workshops, instruction at a distance, instruction through social interaction, informal learning, and self-directed study. Additional selection factors include questions like: • • • • •

Which performances are absolutely essential for performance in everyday work? Which performances are of most consequence (danger, cost, central to competence)? Which performances are most difficult for new learners? Which performances require time and practice to become skilled? Which performances are prerequisite to other critical performances?

Assess Designs should be built around assessments. Assessment that is constant and continuous is one of the keys to more adaptive instruction. The number, types, and placement of assessments is, therefore, an early design decision. During analysis data is gathered that leads to assessment designs and criteria. Traditionally, as part of content analysis, information needed to support assessment design is gathered in the form of performance criteria and conditions attached to each content element identified. In the generic model in Figure 6.3, assessment planning is pushed forward to begin in the analysis stage, so that assessment plans begin to be outlined as early as possible. As Figure 6.3 shows, this means that there should be an associated measurable criterion insofar as possible for each content element. Moreover, in cases where the new learning is likely to be difficult, gradations of improving proficiency should be identified as intermediate, measurable waypoints in performance improvement. Target Population Analysis Target population analysis consists of a study of the characteristics of the learner. The implications of those characteristics are later traced forward to potential design features. The designer skill most important to the success of target population analysis is the ability to select which of the multitude

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of possible learner characteristics to study and how to connect findings about the learner to design characteristics. This is an area where the designer’s understanding of people becomes a competitive edge. Appendix A provides an outline of target population analysis issues and suggests some aspects of learner differences that may make a difference to the designer. Current Training and Resource Analysis Current training and resource analysis consists of two relatively independent analyses: training environment analysis and training resource analysis. The goal of this analysis is to identify implications for the design—special opportunities to help the design fit within a competitive organizational environment and survive. A designer studies every relevant aspect of the organization, its performance problem, its current training practices, its financial, skill, and other resources, its organizational goals, and how the current project fits within and has opportunity to advance those goals. This analysis is where the designer determines how value can be added to the organization as well as to the learner. Appendix B provides an outline of the current training and resource analysis issues. Management Concerns: Analysis Stage Some of the managerial concerns of the analysis stage that do not belong to any of the above processes should be reiterated from previous chapters, including: • • • • • • •

Forming the design team, into a dynamic problem-solving group Becoming familiar with the extent of the skills and abilities of team members Establishing a common design language among team members Defining team member roles Establishing communication patterns among team members and the client Establishing relations with subject-matter experts and setting schedules Establishing relations and finalizing agreements with vendors, service organizations.

These processes, which usually don’t appear in design models, are critical to the success of a design project. They suggest that a designer should be well versed in areas other than design technique. Preparation of designers should include some study in organizational behavior and dynamics, business processes, team leadership, team membership, and project management. These things increase the value of the designer in the strategic planning of an organization’s instructional services. Application Exercise Consider the uses of the target population analysis and the current training and resource analysis. • What if you found that all of your learners were over six feet tall? How would that influence any of your instructional decisions? • What if you found they all spoke different languages? How would that influence your design? • What if you found that some parts of the training took place in very dangerous surroundings? How would that affect your design? The Design Stage of ADDIE/ISD The goals of the design stage are to synthesize experience structures. Instructional design deals with invisible structures of time, space, goals, resources, information, and activity. These become concrete and visible only in media artifacts capable of supporting learning experiences within learning environments. However, much of the effective part of a design is invisible.

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Strategic Planning Specify Detailed Instruconal Goals/ Objecves

Prototyping and Producon Planning Specify Event MacroStrategy Sequences

Prototype, Perform Producon Cosng, Test, and Revise

Specify Events

Specify Delivery Media and Produce Media Elements List

Document a Coherent Instruconal System Design

Design Assessments And Update the Assessment Plan

Specify Event MicroStrategies

Specify Work Model Groupings

Plan Producon

Sustainment Planning Plan Evaluaon

Plan System Management Plan Implementaon

Figure 6.4 Processes of the design stage of the generic instructional design model.

The process of creating structure follows a pattern that moves the abstract to the detailed and concrete. As a designer designs, structures are imagined and integrated with other structures for the purpose of performing coordinated instructional functions. Major structures appear first, followed by the addition of more detailed structures closer to the surface. Design never follows a strict linear flow, and the boxes in the diagram of a design model overlap a great deal in practice. Four major concerns dominate the design stage (see Figure 6.4): • • • •

Strategic instructional planning System design documentation Prototyping and production planning Sustainment planning.

Conceptually what is achieved in the design stage is the planning of: (1) the environment of instruction, (2) the cause–effect systems with which the learner will interact, and (3) the functions of the learning companion, which consist of all provisions for augmenting the learner’s interaction with the cause–effect systems (see Figure 2.8 in Chapter 2). The data gathered in the analysis phase, and particularly in the target population analysis and current training and resource analysis, are used to inform design decisions. The questions of the design stage are primarily those of the strategy layer and its subordinate layers: message, control, representation, and data management. Content concerns are dealt with in the analysis stage, and media-logic layer concerns are (or should be) secondary to strategy layer concerns.

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Strategic Planning Strategic planning is the part of design stage in which the alignment of instructional goals, strategies, and assessments takes place. Around this core is built a set of structures that carry out the functions of instruction. The essential decisions include the alignment of: • • • • • • • • •

Structures of time Structures of sequence Structures of place and space Structures of goal Structures of delivery Structures of content Structures of social roles and responsibilities Structures of activity Structures of information and information resource.

During the design stage the form and substance of media artifacts are woven together into a unified system capable of producing instructional experiences. In building design, elements of electrical, plumbing, structural, and inner and outer surface systems come together in a similar manner. During instructional design, ideas emerge as abstract structures, which then take on dimensions, properties, and surface features until something concrete is produced. The processes of strategy planning in this generic instructional design model are described in the sections that follow. Specify Detailed Instructional Goals/Objectives During front-end analysis and then subsequently during the analysis stage, an inventory of real-world performances was produced, and then selected for instruction in different settings, or modes as described earlier. The goal definition process culminates with the specification of instructional objectives. “Instructional objective” has many definitions. Moreover, the literature on instructional design contains a confusing array of terms which incorporate the term “objective”, including “terminal objective”, enabling objective”, “performance objective”, “learning objective”, and others. Objectives emerge as a designer chooses how to link content elements during the analysis stage with specific and measurable performance goals. In some designs the designer chooses fixed goals for the learner, and they are sequenced into a set order. In other cases, the designer prepares a field of goals from which the learner may select the ones that are of interest. In yet other cases, the designer builds into an experience the possibility of a number of goals in hopes the learner will choose one of them. In these different cases, one can see three styles of objective use: the fixed order of instruction in a typical classroom, the learner-chosen order of a self-directed learning environment, and the unordered experience one might have at a museum display. Goals perform a practical function. During design they give the designer a performance target for which to plan instructional experiences and assessments. During instruction and assessment they provide the standard against which progress is measured. Whatever form they take in the designer’s mind, the following will probably be true: • They operate momentarily. The scope of instruction changes dynamically from objective to objective; this has the effect of scoping and focusing the instructional landscape for the learner for a few moments at a reasonable level of learning expectation. • They are performance goals, not topics, and not activities. They are not topics because phrasing them as topics subtly influences the designer to instruct “about” and not “how to”. There is no measurable performance implied by a topic. They are not activities because activities are the means by which a performance goal is reached; activities are not a learning goal.

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• They work within a context of other objectives and may create progressions of performance expectations. Learners should be able to approach higher levels of performance capability in steps sized to fit, but stretch, their learning ability. • They may be broad and inclusive or specific and exact. There are many styles of objective. Some learning outcomes, especially for learning in informal settings, are deliberately nonspecific and general. Others, in highly directed instruction, are very specific. • They combine cognitive and physical performance goals with goals for attaining attitudes and values that are critical to skilled performance. Instructional goals integrate across intellectual, physical, emotional, and dispositional dimensions. Specify Work Model Groupings When objectives are identified, they lie along a continuum from fragmented bits to integrated performances. If instruction is based only on fragmented objectives, there will be no integrative pattern to the instruction, and if only integrated objectives are represented there will be no way for the learner to learn and practice the constituent elements of a performance. This situation can be corrected by defining work models that provide for instruction and practice at intermediate levels of integration (Bunderson et al., 1981; Gibbons et al., 1995). A work model is a cluster or grouping of one or more objectives. A work model can consist of one objective, or it may consist of many. The term indicates that a work model is an expression of some amount of work or performance. The work model is a tool for creating progressions of increasingly complex performances that lead to fully competent performance, when that is desired. The learner may be asked to accomplish individual objectives first, or some grouping of objectives for the purpose of providing practice at the level of challenge needed. Work models can isolate complicated performances for practice purposes or bring together different combinations of performance that represent difficult integrations. Artificially structured sports drills are often constructed for this purpose (see also Burton et al., 1984). Work model synthesis allows the designer to specify the practice and instruction of any combination of objectives deemed beneficial. Specify Events Work models, which have no temporal dimension, become associated with instructional events, which do. Events are instructional occasions that occur in time and within a space. They can be placed in a schedule once or more than once to create repeated practice occasions for a particular grouping of objectives. Events are not equivalent to class periods: they are practice and/or instruction occasions that can be of any length. They may be random experiences in which a museumgoer wanders past a designed display. When a designer offers an experience and a learner decides to engage, that is an occasion for an event to take place. Events are architectural structures of time and place. The event list becomes the basis for the definition of larger structural units, including macro-strategy sequences (see the next section). A course is an event comprised of events: class meetings, readings, online resources, homework assignments, quizzes, and tests. Each class session in a course is in turn an event made up of a number of events: a short lecture, a quiz, a demonstration, a discussion, and a case study. Each of those types of event is likewise made up of events: information, visualizations, etc. The evident pattern is that events at every level are, in turn, made up of smaller events at every level. How small can events be? Every turn in a conversational exchange is an event, and every expression and visualization used in a turn can be considered events as well. An instructor will sometimes hesitate, searching for just the right word—just the right event—while making an important explanation. Individual media elements can be considered instructional events at whatever level the designer chooses to deal with.

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Specify Event Macro-strategy Sequences The specification of macro-strategy sequences is what many designers refer to as syllabus construction. Van Patten et al. (1986) used the term “macro-strategy” to differentiate sequencing decisions at the between-event level from decisions at the within-event level. Macro sequencing involves designating the order in which a learner can or will encounter instructional events. With an assessment plan in hand, one of the first things a designer can define is the succession of practice environments that prepares the learner for the final assessment. Performance during practice can become an assessment, and test-like data can be gathered during instruction. When the practice environment is designed first, instruction can be seen as an adjunct activity to practice. A greater portion of the designer’s effort can be focused on giving the learner the role of actor rather than receiver of information. One good reason for emphasizing assessment, and giving it design priority, is that it converts instruction into a conversation, and decisions about the status of the conversation can be made more frequently. The very nature of conversational instruction implies the importance of frequent assessments of the learner’s performance level. This continues to be a central principle of intelligent tutoring (Wenger, 1987; Nkambou et al., 2010). What can be sequenced into an instructional conversation, of course, depends on the size and composition of the work models that have been created. Any designer can control the level of the instructional conversation by controlling the granularity of work models and the structure of assessments. Fully adaptive instruction relies on continuous assessment. Specify Event Micro-strategies Micro-strategy is a term used to describe the selection and sequencing of the activities (the smaller events) that constitute an event—whatever size the designer has chosen to deal with (Van Patten et al., 1986). Micro-strategy includes many detailed decisions about setting, siting, social roles, strategy, sequence, interaction, and representation related to a single event. These structural dimensions of instructional strategy are discussed in detail in Chapter 12. Specify Delivery Media and Produce Media Elements List Specifying a media elements list is the point at which the designer chooses the modular plan of the design: the plan for mapping elements of the instructional strategy to media elements. Functions that to this point have existed in the abstract are assigned to a medium—live or technology-based—for execution. Media elements a designer wants to use may already exist, or new designed items may go onto a list of media elements to be created during development. The computer has assimilated many separate media forms, yet questions of instructional media choice are still valid. Media decisions are based on anticipated instructional value but also on practical concerns about life cycle cost, availability and copyright, capacity of the existing infrastructure, and the ability to rapidly scale numbers of users. One medium that has not been assimilated by the computer is the human instructor—still in many ways the most flexible and intelligent instructional medium. The plan for blending the actions of a human instructor with those of the delivery technology is one of the most important questions during media design. Document a Coherent Instructional System Design Instructional designers work with teams, so a design has to take form in the minds of several people at once. The design team must be able to talk about design decisions, the new concepts they represent, their degree of firmness, and their stage of completeness. There must be something for the design team to work with that is analogous to an orchestral score—an authoritative representation of the design itself from which each specialist can get the latest version of his or her part of the design,

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analogous to creating a musical score. What is unusual about the documentation of an instructional design is that the members of the orchestra in this case are also the composers. This is captured in design documentation, whatever form the designer decides that should take. A design document must at any point during design be sufficiently clear to give direction to the design team itself. It is a communication of the present state of the design. Having the design in a documented form for the client is also important and can provide commonly agreed progress and quality benchmarks as design moves toward development. Prototyping and Production Planning Prototyping is important for many purposes: for cost determination, for effectiveness testing, as a concept proof for the client, as a test case for the design and production teams, and as a concrete example of the product’s evolving style, look, and feel. There are reasons for prototyping designs on almost every project, but they may be different for different projects. The extent of prototyping also differs from project to project. Prototyping can be used as an exercise in simulation for the purpose of innovation (Schrage, 1999). The most important reason for prototyping on some projects is for rapid iteration and testing of alternative forms. The final prototype becomes a model for production breakdown and planning. Sustainment Planning Sustainment means: (1) the ability for persons other than the designer to operate the instructional system; (2) the ability to know how well the system is working, compared with its goals; (3) the ability to maintain and replenish an instructional system over time as it uses up material, breaks, and needs repair; (4) the ability to finance and resource the system over time at a sustainable level; and (5) the ability to modify the product design when major changes take place, without destroying the entire product. Key elements of sustainment planning include: • Evaluation planning • Management planning • Implementation planning. Evaluation Planning: The need for continuous ongoing evaluation of system operations and effectiveness is an essential part of a design. Engineers, doctors, businesses, professions of virtually all kinds, have learned that conducting quality control is an indispensable part of doing business. During design, the designer makes plans for an ongoing evaluation of the instructional system: (1) while it is still being designed and developed, (2) during its first implementation in the field, and (3) once it is in regular use. An organization is interested in carrying out constant, ongoing evaluation of its instructional systems to protect its investment in creating them. Evaluation planned is described in Appendix C. Management Planning: A designer will not be present when the instruction is used. Those people who will be given responsibility for setting up, maintaining, and operating the instruction must have directions that tell them how to manage the day-to-day operations of instruction, including when to administer evaluations. A learning management system (LMS), if one is to be used, is only one aspect of a management plan. Management planning is described in Appendix D. Implementation Planning: The first time instruction is used in a new location there are one-time processes that must be carried out to prepare the instructional site for use. This is true whether the site is a classroom, a museum display, or a mobile hand-held device. A special version of the management plan called the implementation plan describes these one-time preparations that must be made. Instructors must be trained, facilities must be in some cases upgraded with additional technological

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infrastructure, hard copy materials must be duplicated, and software must be installed. Appendix E describes this planning in more detail. Application Exercise Consider the processes of the design stage. • Which of them seem more theoretical in nature? • Do any of them seem more administrative in nature? Generic Model Summary This section of the chapter has described a generic instructional design model, showing its compatibility with design layers. Appendices describe some of the model processes in greater detail, particularly in terms of the range of decisions made during design. The model was described for multiple reasons: (1) so that generic ISD model terminology could be placed in perspective with the use of design layers, (2) to broaden the definition of some design processes, particularly in the area of content analysis, and (3) to show that there is not a conflict between ISD and an architectural approach to design that uses layers as a design tool. Conclusion This chapter has defined multiple classes of theory to show the variety of theories an instructional designer uses. The emphasis on theory is important because even when a designer does not intentionally apply a formal theory, personal theories are applied. It is important that designers be aware of the kinds of theory they deal with so they use them with deliberation rather than by default. A second motive of the chapter was to show that design layer theory—a design theory—opens the way for the application of domain theory within individual layers. Each layer possesses a related body of theory that can be used by a designer. The layers a designer chooses can be used for theoretical development as well as for practical purposes. A final purpose of the chapter was to show that design layer theory and ADDIE/ISD are compatible, despite the fact that they are based on different approaches to decomposing the design problem. Design layers expand the designer’s ability to incorporate domain theories into the different layers of designs—not just today’s theories and today’s layers, but ones yet to be expressed.

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7

Operational Principles and Design Languages

You uncover what is when you get rid of what isn’t. —(R. Buckminster Fuller) Right after my father died, I would come up here a lot. I’d imagine the whole world was one big machine. Machines never come with any extra parts, you know. They always come with the exact amount they need. —(Hugo, in the movie by that title) An architect designs structures that stand up, but in the mind’s eye the architect doesn’t just see the visible outside surface of the structure. There are forces that work invisibly inside the structure to keep it upright in winds and earthquakes. It is the knowledge of these forces and the ability to manage them with precision that constitutes the expertise of an architect and an engineer. Designed artifacts—including instructional experiences—work according to invisible forces in balance with each other. A building exerts force on the ground that it sits on, and the ground exerts an equal force back on the building (see Salvadori, 2002, Chapter 3). Forces in balance that can accomplish work constitute what can be called operational principles (Polanyi, 1974). Operational principles are abstract expressions that describe the invisible forces that make things work. Therefore, they also describe how things can be made to work. Instructional designers can apply operational principles to harness natural forces and obtain desired outcomes. Over time, architects give names to common structures that result from the application of operational principles. “Arch” and “column” are examples. Names make it possible for architects to communicate about designs in a way separated from specific designs. The process of naming things creates a verbal element in the design languages of a field, but many “things” that go into a design have no formal name. Nonetheless, they influence the operation of the design. This chapter explores the concepts of operational principles and design languages and explains why they constitute important cognitive tools for instructional designers. Operational Principles Rube Goldberg machines are back in style. You see them in retrospective articles, books, Web sites, and on t-shirts. Popular music videos and automobile ads enact elaborate Goldberg contraptions, some large enough to fill a warehouse. Interestingly, you also see serious R&D and educational Web sites for science, technology, engineering, and math where Goldberg 173

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contraptions are used for teaching design, problem solving, and some of the most basic principles of physics. In a Goldberg contraption a trigger event occurs that sets off a chain reaction of subsequent events. A boot kicks a latch, which comes undone, freeing a spring to open a trap door, which releases a canary, which flies to a perch, which is bent down, striking a match, and so on and on. The average Rube Goldberg invention is an illustration of operational principles combined into a machine that carries energy and a signal from one point and delivers it to another point at which some desirable outcome is achieved: a dish gets washed, an egg gets cooked, a shoe gets polished. Though Goldberg’s machines involve concrete things like wood, metal, and animals, they are more than collections of parts. They are concrete examples of something more abstract and invisible: the conveyance of energy and information through a chain of events to a final destination event where something is accomplished. In physics terms, these machines deal with potential and kinetic energy and the transfer of information through energy. At each point in the event chain energy is supplied at a point from which it is transferred or transformed and passed further along the chain. What you see in a Goldberg illustration is the physical embodiment of things, but what you don’t see is the transfer of energy and information that takes place as trap doors open and canaries land on perches. But it is these invisible transfers of energy in Goldberg machines that do the work. Similar transfer operations, when viewed in abstract terms, appear in designs again and again. A Goldberg machine can use basic principles like “lever”, “spring”, and “inclined plane” in multiple places in the same contraption, but in one place it may look like a trap door, and in another it may look like a barn door. These abstract principles can be referred to as operational principles. Operational principles are invisible to the eye, but they are the real mechanisms that designers use to direct, conduct, and focus energy and information to do work. Just as they can do physical work like polishing a cartoon shoe, they can do the work of supporting learning by conveying energy and information to a learner’s senses and sensibilities. Just as operational principles describe the invisible forces that make things happen in a Goldberg machine, operational principles also describe the invisible but very real forces that support learning. For that reason, they are intimately related to the moment-by-moment goals and actions of both the learner and the designer. Therefore, operational principles applied intelligently supply the power of instructional designs. The Generative Power of Operational Principles An operational principle is an abstract germ of an idea capable of generating a hundred or a thousand different surface designs, all based on the same underlying principle of operation. The flight of airplanes today, in all their variety, is based on a single underlying operational principle for flight identified by George Cayley in the early 1800s. Cayley’s competitors created the most interesting contraptions with flapping devices, rotating-screws, and glider wings, each of which was based on a different essential idea about how to raise a machine off the ground. George Cayley, departing from surface explanations of flight, refined the problem into a single solvable problem statement that contained the key to its own solution: “to make a surface support a given weight by the application of power to the resistance of air” (Vincenti, 1990, p. 208). Cayley’s principle did not have to specify very much: just the core principle of the solution. Cayley was saying that he was sure that the right combination of “surface” and “power”, pushing against the “resistance of air” could lift a machine and the person in it—the “weight”—off the ground. The size, the shape, the material, the relative dimensions and proportions of the surface were unspecified by Cayley, as was the nature and size of the power source. Cayley stated an operational principle; designers and their imaginations did the rest. What Cayley devised was not the design for a single airplane but the essence and pattern for a million airplane designs—a basic pattern of the distribution and balance of forces from which an

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endless number of specific designs could be generated. When the Wright brothers flew successfully, they credited Cayley’s idea, which they incorporated into all of their machines. When Curtiss improved the concept of flight controls, it was on a plane designed according to Cayley’s operational principle for flight. As the variety of specific flyable designs multiplied, and new waves of technology matured to supply the functions of propulsion and wing stiffening, aircraft designs still incorporated Cayley’s operational principle. Today, thousands and thousands of specific airplane designs exist, all based on Cayley’s principle, from the smallest experimental craft to the largest passenger liner. The details of these thousands of designs differ, but there is a common architectural structuring principle underlying them all: a surface that is powered forward through the resistance of air. It is not the technology of the day that determines whether an artifact works, it is the unchanging operational principle of winged flight incorporated into the design. What kinds of things can vary in such a design? The placement of the engine (forward- or backward-facing, centered or distributed on the wings), the placement of the wing surface (above the body, below the body, forward, aft), the shape of the wing (flat, thick, tapered), the type of power used (reciprocating, turbine, jet), the means of propulsion (propeller, jet exhaust), and so forth. Everything is free to vary that does not nullify the operational principle. Why Worry about Operational Principles? Rube Goldberg machines and airplane designs are relevant to a discussion of instructional design because every human-made artifact incorporates one or more operational principles. To expand on the irrigation metaphor from Chapter 5, operational principles represent all of the forces in play that move the water to the desired spot: gravity pulling the water downward, the slope of the terrain that determines its direction, the channels that make it easy for the water to follow particular paths, and the gates that stop the water’s forward flow and turn it aside to where it is needed. Likewise, in the example of the analog clock design from Chapter 3, there were essentials: dimensionless pointers, locationless spatial positions, a power source, and a connection between the source and the pointers (or between the source and the spatial positions, since either one can move). These things taken together describe the operational principle for an analog clock without describing the details of any particular clock. They constitute an operational principle for that type of clock that is capable of generating literally millions of unique designs. In both of these examples, the design elements resulting from the application of operational principles act together to transfer, transform, and channel energy and information invisibly to bring about a result. In the description of the clock design, just as in the description of the airplane design, the generative value of operational principles is apparent. From a single operational principle (or a combination of a few operational principles) the mold of a design can be set, and what remains is to work out the details closer to the surface. Every designed thing, unless the design is random, incorporates one or more operational principles, even if the designer is not aware of what one is. The thesis of this chapter is that a designer can gain greater leverage and design with greater precision and insight if he or she is aware of operational principles that are being incorporated into a design, or if the design is deliberately centered on a particular combination of operational principles. Application Exercise Suppose you were tasked with designing an automobile. • List the absolute minimum number of design elements you would have to include for the car to be functional. • Identify elements of modern auto design that you could omit without harming functionality.

176 • Fundamentals

Operational Principles: The Active Ingredient Richard E. Clark (2009), a noted educational researcher, describes the concept of an operational principle, calling it by the name “active ingredient”. Clark describes a systematic, four-stage research and development cycle that can be used to isolate “active ingredients” through experiments and then apply them in real-world settings: “Active ingredient analysis . . . yields a recipe for constructing [a new] intervention that reflects the critical elements of the [laboratory] intervention that worked under controlled conditions” (p. 17, emphasis added). Clark says that caution “must be exercised so that we do not simply group the treatments that share the same name” (p. 13). He warns against using common labels of things that resemble each other on the surface. What we should learn to see, he says is “both novel and critical” and “we must look more deeply” (p. 13). Effective intervention design requires identifying the “active ingredients” or the key structural elements of the interventions or research treatments that have been found in stage two experiments to influence our chosen outcomes . . . There are no rules yet for conducting this kind of analysis, but it is clear that we must look beyond the labels researchers give to their treatments in published articles and analyze the operations they implemented and their presumed impact on people and organizations . . . The active ingredients we need as the core of a new technology are the causal agents in the experiments that were surveyed in [research] stage two. We have evidence that these ingredients influence the problems we want to solve at the deepest structural level and so they must be the centerpieces in a solution. —(pp. 13–14, emphasis added) Most designers work at the edge of their art, incorporating deep patterns that they have no way of describing. There is value in bringing these patterns to the attention of the designer and giving them names. How does one identify an operational principle for the design of instruction? Certainly Clark’s four-stage research method is a sound approach, and it should be adopted more widely by academics, but most on-duty designers aren’t paid to refine ideas through formal research cycles in this way. There is a rough and ready approach that is more closely aligned with the day-to-day job requirements of the average designer. It is a method of subtraction in which a design that works is whittled down in successive trials until it breaks and no longer works properly. At the point of breakage, something essential has been lost and has to be restored. Then trials can continue, dissecting out other features until the machinery breaks down. This method works in a practical setting—usually over the span of multiple projects—because trials of evolving products take place as a natural part of the design-development cycle. The research is hidden, so to speak, within the fabric of daily work. In this way, the professional skill of the designer grows as a natural part of doing work. Applying Operational Principles to Instructional Designs Applying operational principles in a design first requires that the designer be clear about what is being designed. This book proposes that instruction is best seen as a conversation. Conversation is an operational principle from which instructional designs can be generated in great number and variety, and all of the forces normally at work during an everyday conversation are at work during an instructional conversation. A designer must reckon with these forces and plan for their effective use. Just as airplanes fly according to a balance of accelerating and resisting forces, a conversation has accelerating and resisting forces. Table 7.1 shows a few examples of what these might be. Imagine a conversation that takes place as two friends pass each other in the hall.

Operational Principles, Design Languages • 177 Table 7.1

Attracting and Resisting Forces of an Everyday Encounter of Friends in the Hall that Influence Conversation

Attraction May Be Strengthened By:

Resistance May Be Strengthened By:

• Having plenty of time • Not having seen the person for a while • Seeing the person as someone who can help you • Seeing the person as someone you can help • Feeling a strong personal attraction to the other person • Finding value in being seen with the other person • Having a question for the other person • Having some important information for the other person • Wanting to discharge some feelings to the other person • Finding the company of the other person enjoyable • Sharing a goal or project with the other person

• Being late for a meeting • Being embarrassed about a forgotten promise • Being uncomfortable because others are present • Feeling momentarily ill • Needing to hide an emotion from the other person • Not having anything interesting to stop and chat about • Feeling inadequate in the other person’s presence • Not being a close friend to the person • Seeing another more interesting person down the hall • Having a big zit on your nose • Having no shared goals or projects with the other person

A conversation holds together when there is more value (attraction) than lack of value (resistance) for both parties. Table 7.2 contains a more detailed list of things a designer can choose to do during instruction that influence the course of an instructional conversation. Instruction is a dynamic process because levels of attraction and repulsion on both sides of the conversation vary from moment to moment, depending on the subject of the conversation and how useful it is to each party. Participants in a conversation are constantly assessing the state and progress of the conversation in terms of their own momentary goals and weighing the value of continuing the conversation against the priority of other activities. The designer (or instructor) activities listed in Table 7.2 influence the dynamic of the conversation, and since each successive action has an impact, a conversation is like a balancing act. The goal is to maintain and enhance the value of the conversation for both participants. This tension between the attraction to participate and the resistance that would break things off poses a challenge for the learner. Will the learner stay in the conversation even when it becomes difficult, confusing, or ego-threatening? Will the learner continue to engage through discouragement, disappointment, and the call of more enjoyable activities? The same questions apply to the instructor and influence the designs of the designer as well. Will the instructor persevere with a difficult learner? And will the designer anticipate the difficulties to be faced by the learner and provide remedies, encouragements, and success occasions to draw the learner forward? These questions make it plain that starting and continuing an instructional conversation is as much an emotional challenge as it is an intellectual one, and one that has at its base forming and fulfilling a constant stream of value-weighted goals for each participant. Willingness to engage depends on maintaining the value proposition of the conversation. Application Exercise Recall the last few conversations you had before reading this, no matter how lengthy or how short they were. Analyze them in terms of the dynamic changes moment-to-moment in the value they held for you. • • • •

Can you remember moments when you couldn’t wait for the conversation to end? Can you remember other moments when you were happy for the conversation to go on longer? Did these feelings occur in the same conversation? What were the forces that you felt in each case? Why did you want to stay or go?

178 • Fundamentals Table 7.2 Actions that Create Operational Principle Forces that May Influence Learning Each action represents something a designer or a live instructor might do. Place the word “to” in front of each one. Consider situations where you might have used any of these in your own teaching or designing. abandon abbreviate abdicate abide abridge absent absorb abstain abstract accelerate accent accept acclaim accommodate accompany accomplish accuse achieve acknowledge acquaint act activate adapt address adhere adjourn adjust administer admire admit adopt advance advertise advise advocate affect affirm aggravate agitate agonize agree aid aim alert align allay alleviate allot allow allude allure alter amaze amend amplify amuse

analyze anchor anger animate announce annoy answer antagonize anticipate apologize appeal applaud appoint appreciate approach approve arbitrate argue arouse arrange articulate ascertain ask aspire assemble assent assert assess assign assist associate assure astonish astound attain attempt attend attract augment authorize avert await awaken award bargain barter be calm bear beckon befriend beg begin believe belong bend beseech

bet bid bind bless bluff boost boss bother brag brave bribe broaden build cajole calculate call calm captivate capture care carry catch cause caution cease celebrate censure certify challenge champion chance change channel characterize charge charm chase chasten chat check cheer cherish choose chronicle cite civilize claim clap classify clinch close coax coerce cohere collaborate collide

comfort command commence comment commiserate communicate compare complain complement complete complicate compliment comprehend compute conceive concur condemn condense conduct confer confide confirm conform confront confuse conjure connect consent conserve consider console consolidate conspire construct consult contact contend contest continue contract contribute control convene converse convert convict convince cooperate cope correct correspond counsel counter counteract court create

credential credit critique culminate cultivate curb cushion customize dare daydream deal debate decide decipher declare decline decree deduce defend defer define deflect delay delegate deliberate deliver demand demonstrate deny depend deprive derive describe design desire detain detect deter develop devise devote diagram dialogue dictate differ digest digress dilute diminish direct disagree disapprove disarm disbelieve discern discipline

disclose discomfit discourse discover discriminate discuss dispense display disprove dispute disregard distinguish divert dole dominate doubt dramatize draw dream drill drop earn edit educate elaborate elevate elicit embody emote emphasize enable encourage end endear endorse endure enforce engage enjoy enlarge enlighten enliven ensure entertain enthuse entice entitle entreat envision equip esteem evoke evolve examine exasperate exchange

excite excuse execute exemplify exercise exhibit expect experience experiment explain explore extend extract fade fascinate fault favor feature feed finalize find finish fix focus forgive forgo form formulate fortify foster frame free frustrate furnish gather gauge generate gesture giggle give glamorize glory gloss govern grade grant gratify group grow guarantee guide habituate heal hear hearten heed

(Continued )

Operational Principles, Design Languages • 179 Table 7.2 (Continued ) help herd hesitate hint honor hope humble humor idealize identify ignite ignore illustrate imagine imitate immerse imply importune impress improve improvise incentivize incite incline include indicate individualize induce indulge infect infer inflame influence inform inhibit initiate innovate insist inspect inspire instigate instill institute instruct intensify interest interpret introduce invent invest investigate

invite involve issue jest join judge justify kindle labor laugh lead learn lecture level liberate light like limit listen logic love magnify maintain make make believe manage maneuver manifest map mark master maximize measure mediate meditate memorize mend mention mimic minimize minister mirror mitigate mobilize model moderate moralize motivate move muster nag

name narrate navigate negotiate neutralize normalize note notice notify nourish nudge obey object oblige observe obtain occasion occupy offer omit open operate opine oppose order orient originate outwit overcome overlook pacify pardon parry pass patch pause peace-make penalize perceive perform permit perpetuate persist persuade perturb pester petition philosophize pick pioneer pitch

pity plan play plead please pledge power practice praise preach precipitate preclude predict prefer prepare prescribe present preside pressure pretend prevent proceed proclaim procure produce profess prohibit prolong promise promote propel proportion propose protect protest prove provide provoke punish puzzle qualify quantify quest question quiet raise rally rate plot point popularize

portray position postpone ratify ration rationalize reason reassure rebel rebound rebuke recall reciprocate recite recognize recommend reconcile reconsider record recruit reduce referee reflect refrain refuse regulate rehearse reinforce reject rejoice relate relate to relax release relieve rely remark remedy remember remind reminisce renew reorganize repeat reply report represent reprieve reprimand repudiate request

require rescue resolve respect respond rest restrain restrict retract reveal revere reverence review revise revive reward rouse sacrifice safeguard salve sanction satisfy save say schedule school scrutinize sense serve shock show slant socialize solicit solve soothe speak specify spur start startle state stimulate stipulate stop strengthen stress stripe structure struggle study

stump submit suggest summarize supplement supply support surprise survey sustain sway sympathize synthesize talk tantalize task teach tease tell tempt terminate test think theorize thrill tolerate train transmit treat trust try understand unify unite uplift validate value verify visualize wake warn welcome wish withhold wonder yield

Instructional Conversation and Instructional Goals The use of instructional goals is closely related to the application of operational principles during instruction. The traditional view of instructional objectives sees instructional goals as monolithic, relatively high-level structures used to select instructional strategies. The objectives also define the

180 • Fundamentals

assessment task. The tacit traditional assumption is that objectives place the instructor or designer in a position of authority to enforce attention and good performance. In many minds, the instructor or designer is expected to set the goals, and the learner is expected to reach them. That description is only one end of a continuum of possibilities. At the other end, the initiatives are reversed, and the learner makes goal and strategic choices. There are many reasonable positions between these extremes. If we are to move toward instruction that is more conversational and adaptive to individuals, our ideas of instructional goals and how they operate during the formation and execution of instruction must expand. We will have to learn how to monitor the moment-by-moment formation and fulfillment of goals at a much more detailed level, and do that in cooperation with the learner. Then we must understand how operational principles bring to bear forces and information that help their accomplishment. The formation of goals implies the making of commitments; their fulfillment implies satisfying results, which supply energy that sustains conversation. Anyone who has watched a live instructor at work knows that the goals of both the learner and the designer-instructor are in constant churn, being created and fulfilled moment by moment as instruction progresses. As was described in Chapter 2, the goal-making and fulfillment process takes place at multiple levels (see Figure 7.1). Performance Goals Both the learner and the designer have performance goals. Bereiter and Scardamalia (1993), prominent Canadian educational researchers, describe three kinds of performance goal a learner might choose. They indicate that “students who are trying hard to be good students may nevertheless be pursuing quite different goals—different notions of what it is to be a good student” (p. 160). These goals may be different from what the instructor intended. The learner’s performance goals can include: • Task accomplishment goals—The goal to comply with whatever the authority figure requires. • Instructional goals—The stated goals that are the performance targets. • Knowledge-building goals—The learner’s personal goals for self-directed learning, independent of the instructional goals and often more inclusive.

Describes the instruconal goal the designer hopes the learner will adopt.

Describes what the learner wants the instruconal goal to be and what he/she will be sasfied with.

Strategic Goal

Describes what things the designer might do strategically to help the learner reach the current performance goal.

Describes strategically how the learner intends to go about reaching the current instruconal goal and the level of effort that will entail.

Means Goal

Describes what specific acons the designer intends to undertake to help the learner reach the current strategic goal.

Describes what specific acons the learner intends to undertake to reach the current strategic goal.

Instruconal Goal

Figure 7.1 The interaction of designer and learner goals during instruction.

Operational Principles, Design Languages • 181

“These goals . . . may at times compete with one another”, according to Bereiter and Scardamalia (p. 161): “In more comfortable circumstances, however, the three types are nested one inside another: Pursuing one’s private knowledge-building agenda will often entail pursuing established instructional goals, which in turn may entail accomplishing the assigned tasks” (p. 161). This, of course, would be the designer’s dream. The designer’s dilemma is detecting which level of performance goal the learner is working from, and the challenge is to help the learner to become a self-directed, self-motivated learner over time: one who will continue to learn even after the formal conversation has ended. Strategic Goals Strategic goals are formed by the designer and the learner in the service of performance goals. They create paths to the performance goal. It is as if the designer said, “Now that I have formed this performance target, what major kinds of activity can I use to help the learner reach it?”, and as if the learner said, “Now that I have formed this performance target, what major kinds of activity can I use to help myself reach it?” The answer to these questions results in the creation of strategic goals by both the designer and the learner. The designer goals might include things like “I will provide a demonstration”, “I will provide a model for the learner to interact with”, or “I will explain something”. What the designer has to offer depends on the theoretical and structural concepts embodied within the design. The learner’s strategic goals may or may not match what the designer offers. Though there is a plentiful literature on what the designer might offer, there is less research on what the learner might desire. This suggests what might become a fruitful area of research, especially at a time when social media are rapidly reshaping both resource structures and learner preferences. Strategic goals represent high-level intentions, not plans for specific moves. They represent large blocks of intention that require numerous individual moves to carry out. It is possible to negotiate with the learner the pattern of strategy and who will be in control of which strategic choices. If the learner is to be included in the choice, then part of the instructional conversation must be devoted to working out—negotiating—an agreement as to what strategic goals might be. If we wish to cultivate active learners able to take charge of their own learning, the learner must have experience with making strategic choices and experiencing the outcome. Means Goals Means goals are fine-grained goals formed in service of carrying out blocks of strategic goals. Means goals are the paths to accomplishing the strategic goals. Means are not simply media resources: they are interactions—conversational exchanges with humans and/or devices that may include the use of media resources. The difference between strategic goals and means goals is important. Strategic goals are high-level goals; they describe a plan made up of blocks that require the execution of multiple individual interactions. A demonstration, for example, may take ten minutes and may include multiple individual messages to be conveyed in different media forms, as well as messages coming from the learner in the form of questions or acts of participation. Means goals divide the large strategic goal into smaller goals that are fine-grained enough that they can support the planning of conversational exchanges between the learner and the instruction. Past attempts to individualize instruction, particularly those undertaken by the research in intelligent tutoring, have handled means goals and strategic goals through software engines capable of generating the details of designer–learner interactions using conversational algorithms. Another approach to dealing with means goals has been to specify guided and patterned social interactions among learners and designers-instructors using assigned roles and standard interaction forms, or patterns. Both the intelligent tutor research and the social learning research recognizes in this way

182 • Fundamentals

that strategic and means goals are formed and fulfilled in a constant cyclic process that a designer is aware of and provides for. A first step in this direction is for the designer to recognize the existence of strategic and means goals and their relation to each other and to performance goals. Together these three types of goal provide a way for an average designer to envision a path toward increased conversationality in everyday designs. Without them, conversationality will remain at its present level in the form of artificial and impersonal “interactions”. Dealing with means goals brings into focus the reason for isolating the message layer as a separate area of design decision-making. Not every design requires the sophistication of a messaging plan, but many do, and the design questions of the message layer draw the designer’s attention on this often overlooked area of design. The main implication of recognizing both designer and learner goals is that they raise the issue of negotiation, which can take place at the beginning, middle, and end of an instructional conversation. This requires that the designer be sensitive to the attractive and resistive forces at work during the conversation. This brings the discussion back to the operational principles that can be used in the design of instructional conversations. Application Exercise Consider your most recent conversations again with some new questions in mind. • As you entered (assented to) the conversations, can you recall the goals you had in mind for each? What was the agenda you had for each conversation? • Can you recall strategizing, as the conversation proceeded, how to fill that agenda? • As you arrived at strategies at different points in the conversations, can you recall another step in the process of carrying out the strategy? Things like, “How can I tell him that I’m not a Democrat?”, or “How can I get across how happy I am to be invited to the demolition derby?”

Conversations: Beginning, Middle, End, and Dramatic Structure Instructional conversations have a beginning, a middle, and an end. They are held together by a balance of attractive and resistive forces—different forces during different stages of the conversation. The conversation is maintained through a constant process of negotiation and assessment. As Parrish (2008) points out, instructional conversations—like everyday conversations—have dramatic structure: “Learning experiences are always much more than the cognitive processing of well planned subject matter and structured learning activities. They also encompass how the learner feels about, values, and, ultimately, establishes a level of engagement with the instructional environment” (p. 121, emphasis added). Conversation is a thing that has to be secured, maintained, renewed, moderated, and successfully terminated. Renewing a conversation’s energy and interest is important in maintaining engagement, especially when the attraction seems to be wearing thin. By exploring how the emotional and intellectual forces related to conversation work, designers can learn how to balance them in managed instructional conversations. Parrish explains: “Beyond being a cognitive activity, learning experience . . . is also political, ethical, emotional, and, perhaps most important in consideration of engagement, aesthetic in nature” (p. 121). Once a conversation is initially established by attraction, it has to be sustained and strengthened by commitment: Learner engagement is likely the most critical factor in any learning experience . . . [Learning] will occur only when a learner desires the change or is shown the necessity of embracing it.

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Engagement describes a relationship to an instructional situation in which the learner willingly makes a contribution that is active and constitutive. Beyond task persistence, it involves investment of effort and emotion, willingness to risk, and concern about both outcomes and means. While IDs work to tame instruction into a manageable, replicable process that begins by predetermining outcomes to be measured through properly aligned assessments, engagement describes that wild aspect of the process in which the learner is as much or more in control of the activities and outcomes as the [instructional designer]. Natural learning in everyday situations occurs as people willingly invest themselves in tasks, either alone or with others, with immediately meaningful goals. —(p. 121) The dramatic element of instruction arises from the constant creation and fulfillment of interesting instructional goals by both the learner and the designer—not casual goals, but goals to which the learner is committed in an emotional as well as an intellectual way. A primary goal of the instructional designer and instructor should be to secure and maintain real commitment from the learner. The designer can suggest goals and even try to enforce them, but if instructional conversation is to begin being centered on the designer’s goal, then at least it must end being centered on the learner’s. Designers need to research the processes by which a learner joins a conversation, how a conversation is maintained, how it is sustained, and how it is successfully terminated. They need to understand the forces that allow this to happen and how the designer can call the necessary attractive forces into play. Moreover, they need to understand the principles of dramatic engagement during the conversation in order to help manage the level of commitment using emotional and intellectual forces like those represented in Tables 7.1 and 7.2. Involving the Learner’s Agency Our understanding of the need for learner involvement and willing, even eager, cooperation in the learning process is growing. In particular, it has become apparent that learners should be active agents during the learning process and that the variable of agency has to be added to designs for learning and instruction. Instruction from this agency-centered point of view gives the learner initiatives for decisionmaking during learning. Initiative can be shared in a number of ways and to a number of degrees, with the balance of control shifting over time in the direction of the learner. This brings us to view the instructional process in terms of constant negotiation, and assessment—assessment being essential because it is the “eyes” which provide feedback data for use in decision-making as instruction proceeds, and negotiation because that is how the learner arrives at a state of commitment and engagement. How does a designer open a conversation when the learner is under no obligation to accept? How does a designer carry out the process of instruction in a way that involves the learner’s questions, initiatives, and explorations? How does the designer secure and renew ongoing commitment from the learner? How does the designer participate in a conversation which teaches not only subject-matter but “how to learn” at the same time? How does the designer leave the learner with appreciation for the instructional experience and the desire to engage further? Conversational instruction does not fit the traditional mold of instruction dominated by an instructor or a technological device. Conversational instruction is “helpful” instruction (Gibbons et al., 2008). It assumes that: • What is helpful at any point during instruction changes and is situational. That is, it is not the specific instructional act that has inherent value, but the specific act in the context of what has gone before.

184 • Fundamentals

• Determining what is helpful at a given moment depends upon measurement. In a conversation, both or all participants are constantly alert for signals that help them “read” how to respond— and whether to even stay in the conversation. • Some measures that would be useful are practically impossible to obtain, putting limits on the designer’s ability to be helpful. Help has to be administered at some practical level of granularity. • Help, even intensive help, may be given during early learning, but the object of instruction is ultimately to withdraw the help, leaving an independent learner. Helpful conversational instruction also assumes the existence of the continuous cycle shown in Figure 7.2. Is this cycle really possible to design? • If every detail of instruction has to be negotiated with the learner, then it seems there is no time or energy left for instructional exchanges related to subject-matter. • Development tools and technologies do not seem to exist that a designer can use efficiently to create conversational exchanges. • If a designer has to pay attention to the contingencies related to so many decision points, the design becomes incredibly, impossibly complex. These are reasonable concerns. Conversational designs have been less numerous than fixed designs for these and other reasons. What has become evident as we face this dilemma is that we have little explicit knowledge about how to conduct instructional conversations using technology. This must

Iniave Negoaon

Evaluaon and Adjustment

Role Negoaon

Measurement/ Assessment

Goal Negoaon

Means Execuon

Strategy Negoaon Means Negoaon

Figure 7.2 The continuous cycle of conversation activities which keeps the conversation engaged and moving ahead.

Operational Principles, Design Languages • 185

change, and for that to happen designers must begin to think about this practical question. One step in this direction might be the realization that a designer can be selective about what dimensions are made conversational. By managing the variables intelligently, designers can create designs that adapt along the dimensions that are most significant. Barbara Fox, in a study of human tutoring dialog (Fox, 1993, p. ix), quotes the view of Galdes (1990) in this regard: We should not spend our time looking for results which say, “Intelligent computer systems will never be as good as humans because humans do X and computer systems can’t do X.” Instead, we need to think in terms of what we can mimic in the human’s behavior that makes the interaction flow more smoothly. —(p. 373, emphasis added) Significantly powerful tools for creative adaptation have come into existence and are finding increasing use: • An active industry has developed rapidly over the last few years around Web analytics, and there are varieties of analytics that can be adapted for everyday use. • There are techniques that can be used by a live instructor to gather data at key points that allow even group presentations to be more adaptive. These include simple non-technological techniques as well as group sampling systems such as clickers. • The designer can tap into the tools of recommender systems that are becoming more prominent in business applications. Moreover, research in intelligent tutoring (Nkambou et al., 2010; Woolf, 2008; Luckin et al., 2007) is making conversational design variables more explicit and approachable by designers in general. The next five to ten years are certain to see major progress in this area. Designers preparing today must therefore begin to become aware of and seek out practical answers to questions of conversationality. The trend in the future will be toward increasingly available and powerful means for creating adaptive, conversational instruction. This trend will be accelerated by the expectations of the user, which are rapidly being conditioned by the connectivity and new communication habits of social media. At some point in the future, adaptive instruction will become the rule rather than the exception, so the present is the time to study and absorb the fundamental concepts of conversational design. Application Exercise One of the challenges of conversational design is to begin to recognize its basic building blocks. • Observe your favorite instructor, asking yourself, “How many different conversational patterns does this instructor really have?” • Consider your favorite computer game. Do you consider it to be conversational in nature? If so, what are the basic building blocks of its conversation?

Operational Principles and Dramatic Structure This excursion into the nature of instructional goals and the functions they serve for both learners and designers was a necessary preface to bringing together a discussion of how operational principles are and can be employed at a detailed level by instructional designers.

186 • Fundamentals

Since attraction is emotional as well as intellectual in nature, it can be described in the dramatic terms used by Parrish (2008), which involve forces such as anticipation, expectation, interest, commitment, and enjoyment. Parrish describes the narrative pattern of an aesthetic experience: Like a narrative, effective learning situations will have well-established beginnings, middles, and endings that follow the pattern of aesthetic experience and contain the narrative components described above, revealing a necessary struggle to resolve a problematic situation that leads to learning. Whether the problematic situation is a true problem, a stimulating question or issue, or merely puzzlement or new experience that throws current knowledge into doubt, it is a call to seek out the information that allows one to test possible answers. Any of these situations initiates a sequence of events similar to the dramatic arc found in nearly all narratives, but which also comprise aesthetic experiences of whatever kind. —(p. 95) The learning situation has a beginning . . . Engagement curves may show an initial rise in engagement if the instruction is designed to achieve it or if learners possess native interest. —(p. 100) . . . a middle: The middle is more likely to be relatively steady, but only if we are sufficiently clever to introduce activities that sustain or reinvigorate interest, or lucky enough to have learners with perseverance. Otherwise, the middle will likely see declines in engagement, as the initial novelty wears off and the arduous work of learning begins to test learners. —(p. 100) . . . and an end: Endings, with the potential consummation of unifying activities like final reports and projects and their promise of impending relief, may reveal a sharp rise in engagement corresponding to a flurry of closing activities. —(p. 100) What Parrish describes as applying to course-long experiences applies as well as to hour-long or minutes-long events. Operational Principles for the Beginning of an Instructional Conversation The left-hand column of Table 7.3 identifies functions typical of the initiation of a conversation. Consider these as means goals at the finest level of detail. The right-hand column (taken from Table 7.2) matches these with instructional actions that bring forces and information to bear such that goals can be pursued. These functions (left-hand column) that initiate a conversation happen so quickly and so often in everyday life that we don’t normally notice them. The advantage of paying attention to operational principles at this level of conversation is that it allows the designer to thoughtfully notice the things that are most important to building attraction, heightening expectation, boosting hope for success, and establishing vision at this early moment of securing engagement.

Operational Principles, Design Languages • 187 Table 7.3

Functions and Associated Operational Principle Forces for Initiating an Instructional Conversation

Conversational Functions

Ways to Apply Operational Principle Forces

Attract attention

Allure, amaze, amuse, arouse, astonish, astound, attract, awaken, cajole, captivate, coax, confront, dare, describe, disagree, disarm, dramatize, dream, emote, entertain, enthuse, entice, excite, fascinate, feature, glamorize, humor, importune, impress, incite, inspire, interest, jest, laugh, listen, motivate, nag, persuade, perturb, pester, petition, play, plead, pose, promise, promote, puzzle, rouse, shock, startle, stimulate, surprise, sway, tantalize, tease, tempt, thrill, uplift, visualize, wake

Offer conversation

Address, approach, ask, beckon, challenge, connect, converse, cultivate, dialogue, elicit, engage, entreat, extend, gesture, incline, include, induce, initiate, instigate, invite, involve, join, lead, negotiate, offer, permit, question, recognize, recruit, relate to, request, respond, socialize, solicit, speak, start, suggest, talk, welcome

Propose dimensions of engagement

Advertise, advocate, confer, define, describe, design, devise, elaborate, explore, frame, identify, imagine, improvise, indicate, measure, propose, quantify, rationalize, recommend, schedule, scrutinize, stipulate, structure, survey, verify

Negotiate performance goals

Accept, accommodate, adapt, adjust, advance, affirm, agree, align, allot, allow, amend, analyze, appeal, approve, arbitrate, arrange, articulate, assemble, assign, augment, authorize, bargain, barter, bend, bribe, build, calculate, caution, certify, change, check, choose, collaborate, communicate, concur, confer, confirm, consent, consider, contract, convince, cooperate, counsel, counter, counteract, customize, deal, decide, decline, deflect, deliberate, dialogue, differ, direct, disapprove, discuss, edit, encourage, endorse, enforce, entitle, expect, finalize, fix, formulate, generate, grant, guide, help, incentivize, individualize, influence, insist, judge, justify, liberate, limit, manage, maneuver, map, maximize, mediate, moderate, negotiate, nudge, object, permit, plan, plot, position, prefer, present, prevent, ratify, reason, reassure, reciprocate, reconcile, reconsider, referee, reflect, request, require, resolve, restrict, revise, sanction, stipulate, structure, suggest, support, synthesize, task, yield

Use learner history to form instructional recommendations Negotiate strategic goals Negotiate means goals and means Negotiate roles

Establish mutual confidence

Acknowledge, acquaint, admire, advocate, allay, applaud, appreciate, approve, assure, befriend, believe, calm, care, celebrate, certify, champion, charm, comfort, compliment, confide, court, credential, cultivate, disarm, disclose, earn, endear, endorse, ensure, esteem, exemplify, honor, identify, impress, inspire, kindle, lead, like, love, manifest, minister, nourish, open, persuade, pledge, praise, protect, reassure, reciprocate, relax, rescue, respect, reveal, sacrifice, safeguard, salve, serve, support, sustain, trust, validate, value, wait

Establish criteria for success

Acquaint, advise, agree, announce, anticipate, articulate, assert, confirm, decree, demand, dictate, discipline, disclose, enforce, establish, expect, formulate, frame, grade, inform, insist, inspect, issue, measure, prescribe, proportion, quantify, rate, report, reward, specify, stipulate, test, transmit

Establish productivity expectations

Remind relevant content Remind of prior learning experience

Ascertain, assess, cite, compare, conjure, consult, digest, disclose, discover, discuss, drill, evaluate, examine, focus, fortify, identify, inspect, interpret, judge, manifest, map, mark, mention, name, note, orient, portray, position, recall, recite, rehearse, reinforce, relate, reminisce, review, summarize

The initiation of an instructional conversation involves a sometimes silent and sometimes verbal negotiation of the terms of agreement for willing mutual participation. Agreements may be formal or informal. They may consist of assumed and unspoken standards (such as universal expectations of class members), or they may be very formal and include explicit, high-stakes agreements (such as agreeing to serve as a dissertation chair, including signed forms).

188 • Fundamentals Table 7.4

The Cycle of Functions during the Body of the Instructional Conversation

Figure 7.2 Function

Ways to Apply Operational Principle Forces

Means execution

The list of actions in Table 7.2 describes ways operational principles are applied during ongoing conversation. It is not the single action that makes a difference as much as the sequencing of actions. The goal of the sequence is to: (1) sustain engagement and forward momentum while (2) progressing toward the performance goal. Individual instructional events employ Table 7.2 actions.

Measurement/assessment

Measurement is possible with each control input from a learner. Data management functions record control uses. Repeated control uses provide sufficient data to spot patterns of responding. This allows assessment of progress toward agreed-upon goals.

Evaluation and adjustment

Evaluation in this case refers to evaluation of effectiveness and continued engagement. Adjustment means the initiation of the next cycle of the process shown in Figure 7.2, including a revisitation of the original decision to converse, possible modifications to goals, and possible modification of means.

These initial functions are more than formalities or bureaucratic process. During the initiation of an instructional conversation a spark is lit, curiosity is aroused, goals are set, relationships are either begun or strengthened, energy is allocated by the learner to be used in the learning activities, and personal commitment to learning takes place on both sides of the conversation. The conversation is launched like a surfer on a wave. It is up to both parties to the instruction to keep from pearling (falling forward off the wave), or losing the energy of the wave and settling back into the froth. Operational Principles for the Body of an Instructional Conversation During the body of the instruction, the forces of operational principles can be used to sustain initial momentum. Table 7.4 identifies the events of the conversational cycle that take over once the initial negotiations are complete. These involve the last three steps on the left side of Figure 7.2. Means execution begins, and the rest of the cycle steps follow: measurement of performance, assessment of performance, evaluation of progress, and adjustment as needed to keep up momentum. The Figure 7.2 cycle continues as long as the body of the conversation continues. One of the important features of this cycle is its use of the cybernetic feedback loop, which can provide very fine-grained adjustment points where engagement can be renewed, goals can be reconsidered, and alternate means can be selected. This is a key to increased adaptivity. This cycle is an important architectural feature for instructional designers: it incorporates periodic assessment and negotiation functions into the body of instruction. How are these functions provided? This can be visualized by assigning specific values to the relationships in Figure 7.3. This illustration, which appeared first in Chapter 2, can be seen as a general pattern for instruction that embodies the conversation operational principle. The functional variables in this pattern that can be assigned specific values and given detail during design are: “environment”, “learner”, “expert performance model”, “cause–effect systems”, and “learning companion”. All of the discussions in this chapter about instructional conversations, their form, and how they are conducted relate back to the operational principle of “conversation” represented in this figure. What is not visible in this figure is the invisible forces at work among its different parts. Those can’t be represented in a static two-dimensional graphical model because they are constantly changing. A designer creates spaces for instructional conversation by assigning identity and value to each of these elements and by defining relationships and dynamics between them that are capable of exerting influence in a desired direction. The sections that follow discuss these relationships in detail.

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ENVIRONMENT

EXPERT PERFORMANCE MODEL

LEARNING COMPANION LEARNER

CAUSE–EFFECT SYSTEMS

Figure 7.3 The variables of the conversational instruction operational principle represented graphically.

Key Questions As the designer uses this pattern to give its variables specific values, certain key questions become apparent: • What transfers of energy and information will take place among the participants in the conversation, and how will that be accomplished? • Will the learner be a large group of learners? A small group? Just one person? • What functions will be assigned to the learning companion? • How will the expert performance model grow to become the learner(s)? • Will cause–effect models supply a responsive, dynamic system? These questions and others can be summed up in one question: What functions will be carried out by each of the elements in Figure 7.3 during instruction, and how will they exert influence on each other? Functions of the Environment The environment provides the space for instructional conversations (see Figure 7.3). It is the stage on which instruction is acted out (Laurel, 1991). It does this by: • Simulating a performance environment • Instructing in a real-world environment • Instructing in a desktop or classroom setting. The environment influences the learner by providing the conditions under which performance takes place. The environment also influences the cause–effect systems contained within it by setting bounds on their conditions. The learning companion, in turn, controls the environment because it is the stage on which the conversation takes place. The learning companion also has access to the

190 • Fundamentals

expert performance model and so is able to provide information, direction, coaching, and feedback at the right moment. This is important to note. The assumption here is that instruction takes place within a performance environment, and performance takes place within the instructional environment supplied by the learning companion. This is a non-traditional instructional design philosophy. This assumes that the learner will act and that learning will take place through coached and scaffolded performance. This is an architectural design principle because it places the first priority on the design of the practice and assessment environments, followed by the design of what has been traditionally considered “instruction”, which often consists of presenting information to a receptive learner. This reversal places emphasis on the creation of an action-oriented learning environment in contrast to the opposite assumption, which places emphasis on the delivery of a message. If a designer places highest priority on creating action environments, there is less likelihood that traditional presentation-based forms of instruction will be the default. Instead, performance environments will be the default, and a designer will yield ground to message-centric instruction only to the degree that constraints make necessary. This difference has enormous structural implications. Whereas current development tools offer easy creation of products that provide static presentations and simple logic structures, the provision of learning environments requires more careful planning. This discussion points up the fact that conversational instruction does not mean that the learner and the instructor chat back and forth, but rather that the learner is actively responding and performing in some way—to a system, to a problem, or in a discussion—arranged by the designer. Our earliest conversations (with parents, before we can speak) are non-verbal and based on actions, not words (Rogoff, 1990; Siegel, 1999). Action-based conversation has the automatic consequence that learners receive feedback on the value of their responses: a conversation that proceeds in terms of actions and reactions. Conversation in this sense consists of chatting only if chatting is part of what the learner is learning. Because of this, the designer attends first to the creation of an action or work environment for practice and assessment. Other functions of instruction are designed and built around this central action engine. The learning companion is an influencer of the environment, because one of the companion’s functions is to provide a learning environment suitable for the instructional goal. The learning companion, being the designer’s agent in the conversation, manages the practice and assessment environment that matches the instructional plans negotiated by the learner. The learning companion participates in deciding how far the bar will be raised (or lowered) to maintain a healthy tension between ability and challenge level that can lead to increased engagement. Functions of the Cause–Effect Systems The cause-effect systems within the learning environment provide a vehicle for assessments, practice, and demonstrations for the learner. The learner acts upon the cause–effect systems, and they respond realistically. The learning companion can augment these interactions with a number of functions, which may include problem posing, scaffolding performance, providing hints, providing models of performance, and providing feedback either within or at the end of performances. The learning companion provides performance challenges that are suited to the learner’s ability level, taking advantage of the principle of the zone of proximal development (Vygotsky, 1978). Presentations are minimal, used as augmentations as needed and just-in-time, which can be just prior to or just after a part of the performance. The environment provides the conditions under which the performance is carried out. Cause–effect systems provide the occasion for the learner to observe and exercise expert performance models. Cause–effect models respond to learner actions according to their own inner rules, showing normal reactions to the learner’s performance. Multiple performance sessions allow

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the learner to tune and gradually improve personal performance models. The cause–effect systems provide the companion with reports of current values, allowing the learning companion to interpret the learner’s performance and diagnose areas for improvement. Functions of the Expert Performance Model The expert performance model provides the standard for learner performance. It is made visible through the operations of the cause–effect systems and through the demonstrations, commentary, and feedback supplied by the learning companion. Expert performance is achieved through practice, whether the subject-matter is a skill, a body of conceptual knowledge, or the ability for selfdirected learning. Expert performance models are dynamic. Rather than “reaching” mastery, a learner reaches a series of escalating performance criteria, which at first may be set by the learning companion, but eventually must be controlled by the learner. Expert performance research shows that the need for a learning companion never goes away. Sports professionals have multiple coaches, for each major element of performance (driving, putting, strategic play, etc.). But at some point the learner must select the learning goals (even after reaching the level of “expert”) and learn when to listen to the “coach” and when not to. The learning companion demonstrates competence with the help of the expert performance model. It also collaborates to determine appropriate routes to improvement through gradual criterion-raising, by changing the task to be performed, and by modifying environmental conditions. This coordination becomes an important part of the learning companion’s goal, strategy, and means negotiations with the learner. Functions of the Learning Companion The learning companion may be an instructor or a computer program. It may be highly skilled or somewhat inexpert and inflexible. The learning companion’s roles in the learning conversation are: • Support the learner until the learner can take charge of self-directed learning. • Lead learning plan goal negotiations using the expert performance model as a roadmap. • Place values into the environment and cause–effect systems for demonstration, practice, and assessment events. • Use the expert performance model to provide demonstrations. • Carry out negotiated plans under agreed-upon conditions of role, initiative-taking, goals, strategy, and means. • Monitor the progress of the instructional conversation. • Assess performance. • Interpret assessments. • Recommend goals, strategies, and means during negotiation. • Observe levels of learner engagement continually. • Maintain conversational tone and energy. The terms on Table 7.2 pertain to the plans of all learning companions. Instructional designers should see themselves as designers of learning companions. Instructors should see themselves as learning companions. Operational Principles for Concluding an Instructional Conversation Table 7.5 suggests how operational-principle forces are involved in concluding an instructional conversation. Compare the entries in the right-hand column below with right-hand column entries in Table 7.3.

192 • Fundamentals Table 7.5

Functions and Associated Operational Principles for Concluding an Instructional Conversation

Expanded Functions

Ways to Apply Operational Principle Forces

Review and reflect on learning Articulate learning

Express new learnings Describe new performance ability Re-teach to another

Make connections with past learning

Recall prior learning/performance Consider value of new learning Connect past with new learning

Envision future possible learnings

Envision future learning tasks Envision future performance Envision future goals

Evaluate the conversation

Identify difficult moments Identify productive moments Consider possible improvements

Evaluate personal performance

Evaluate effort expended Evaluate commitment level Evaluate personal engagement

Negotiate future performance goals

Envision future interests Envision possible future goals Assess commitment to goals

Evaluate strategic choices

Evaluate best/worst choices Discuss performance goal alignment Identify best choice patterns

Evaluate means choices

Evaluate best/worst means choices Discuss strategic goal alignment Identify best choice patterns

Negotiate interim expectations/tasks

Recommend useful tasks/practice Evaluate choices Plan interim tasks/practice

These steps of concluding a conversation should be considered important because they focus attention backward on what happened during instruction. This is a reflective process described by Collins et al. (1989). Reflection on the success of past choices improves the learner’s ability to make deliberate instructional choices, and provides important feedback to the designer on what worked and what didn’t. This is an important factor in the education of the learner as a learner, which is often neglected. In order to increase expertise, a chess master reflects on past games, and a sports champion reviews choices that made a difference in the outcome. This principle applies as well to learners and the skill of learning. Application Exercise In the light of this section, refer back to Table 7.2. • How do the individual terms in Table 7.2 represent ways of applying forces and information during instruction? • As you reflect on how you may have applied each of the terms in your own instruction, which ones are more representative of your own instructional style? • Consider how terms applied in a sequence shift the dynamic of the forces. What sequences do you think would be most useful? Which ones work together the best? Which ones do not work well together? Postscript: Operational Principles Operational principles represent the operation of unseen forces. Though our focus is often on the visible means used during instruction, we speculate that it is these invisible currents stirred by the instructional experience that have the real impact. William James, the original American psychol-

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ogist, wrote about emotions analytically in terms of stimuli and physical responses that eventuated in feelings of passion. His work, Principles of Psychology (1890), was tremendously important in its time. James’ interpretation of emotions and their effects was influenced later in his life through an experience with students from Radcliffe College that he had taught and who gifted him on Easter of 1896 with a potted azalea plant. His response to them in a letter showed that after the experience he saw emotions and their influence on human feelings as being much closer to the center of human psychology than he had previously thought: Dear young Ladies I am deeply touched by your remembrance. It is the first time anyone treated me so kindly, so you may well believe that the impression on the heart of the lonely sufferer will be even more durable than the impression on your minds of all the teachings of Philosophy 2a. I now perceive one immense omission in my Psychology—the deepest principle of Human Nature is the craving to be appreciated, and I left it out altogether from the book, because I had never had it gratified until now. I fear you have let loose a demon in me, and that all my actions will now be for the sake of such rewards. —(James and James, 1920, p. 33, emphasis in the original)

Design Languages Operational principles are only part of the designer’s cognitive toolbox. Over time, the same underlying force and information patterns are seen at work in different surface forms. A designer has the need to communicate about them with other designers. Consequently, the operational principles should start to have names like “conversation”, “dramatic arc”, “model”, and “feedback”. Each term tells a story about how agents interact during instruction, and slowly a set of terms in an instructional design language begins to accumulate. This naming of elements is common to every design field. Consider the following examples: • Scene One: A computer begins to run a program and the stage curtains open, revealing a robotic figure gesturing and posing synchronously with the soundtrack of a well-known patriotic speech. The audience is driven to wonder at the fluid motion of the arms, hands, and fingers. The facial expressions of the figure are so life-like! Eyebrows, cheeks, jaws, eyes, and eyelids move simultaneously to create expressions of concern, absorption, serenity, and animation. The effect of the words, the music, the lighting, and the precise timing persuades the audience to enter into a suspension of disbelief: for a moment the robot seems almost alive. Its message is very impressive and believable. How is it done? That is, the robot—how is it made to work? The answer to this question brings up the issue of design languages. • Scene Two: From what appears to be a flat plaza a jet of water appears suddenly, rising fifteen feet straight up, and just as suddenly collapses in a giant splash. Then a second column rises and a third and eventually ten jets align to form a wall that slowly recedes and disappears as the jets subside. Children run onto the plaza, and the water begins to play with them as walls form, disappear, re-form in another place, then back again, teasing them. Curtains of water begin to rotate and move like a chorus line. Waves of pulses form from nothing to create an

194 • Fundamentals

asymmetric curve that animates a pulse from one end to the other. The children are mystified, trying to guess the next eruption and thrilled when it splashes them with water. Then suddenly the show ends, and once more there is just a plaza. How is it done? An interactive, emotional event is created with nothing more than pipes, valves, pumps, and spurts of water. Again, the answer must be couched in terms of design languages. The observer who maintains detachment realizes that the robot performance consists of individual joint articulations, each of which has only a few positional states. The fountain, likewise, is made up of perhaps 300 identical water jets, each of which has only about ten distinct spurt patterns. The observer realizes that the moving walls of water were simply the coordinated action of patterns of jets which have been timed previously, precisely with respect to each other and a piece of emotionally moving music. Likewise, the robots seemingly human postures and movements are synchronous, timed sequences of relatively uncomplicated joint motions. Both of these performances depend on emergent phenomena made up of individual elements that can be named. These examples provide an insight into one aspect of design languages: designers join together relatively simple primitive elements into sequences that have a greater influence together than any of the parts individually. They are interpreted as a group, and together they are able to convey information, produce emotion, and inspire inquiry. At one end of the spectrum are languages of abstraction that define grand effects: “walls” of water, moving “shapes”, playful “randomness” and awe-producing “order”. At the other end are individual robotic gestures, individual part movements, and timed pauses, all calculated to produce an emotional reaction. The analytic viewer recognizes these named symbols at both ends of the spectrum as terms the designer can use to convey a message and evoke an emotion at the same time. The designer has a concept and perhaps a name for each effect, for each emotion, and for each independent element: the crooking of a finger, the lifting of an eyebrow, and the rotation of the neck joint. The creation of the grand effects from small means involves the conscious use of design language abstractions—terms given names so that a team of designers can express and talk about an evolving design—to produce individual mechanical acts which combine to produce a coherent experience. What is a Design Language? The purpose of this part of the chapter is to describe the concept of a design language. A design language is a set of conceptual building blocks for describing designs and making designs. The vocabulary of a design language exists in two senses: (a) as mental structures in the mind of an individual, and (b) as named entities that have verbal or symbolic identifiers that make them public. Every designer possesses and uses a large number of design languages, each having numerous terms. One measure of the maturity of a design field is the precision with which designers can discuss their activities and designs in shared design language and unambiguously understand each other. Design languages allow professionals to communicate generally about their work. They also allow a designer to think about designs without verbalizing at all, because not all of the concepts in a design language have verbal names. The emergence of a verbal design language term becomes evident as designers converse about a design concept and begin to use a hyphenated phrase: “thatthing-we-did-on-the-last-project”. Many language terms born as hyphenated phrases are at some point given a name, and the concept becomes public. Design teams use public design languages and add to them by inventing additional new terms shared at first only by the team. Sometimes design languages are retained within a closed circle to describe trade secrets that constitute a source of advantage. To the extent that any designer is familiar with the many languages of instructional design, it constitutes an advantage, and if one designer knows more than another, chances are that

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the more knowledgeable designer will be able to function productively and innovatively in more situations. Design languages borrow the syntax of a native language, substituting design language terms— which are nouns, verbs, and modifiers—into standard sentence patterns. When practitioners converse it can be difficult to understand by one who does not have the terminology and even harder to understand by one who does not have the concepts. How Do New Design Languages Originate and Grow? When Thomas Edison began inventing, it is likely he had no idea of the multitude of design languages he would eventually have to deal with, all of them specialized languages of professionals: Technologists [like Edison] are tied into less obvious meaning systems [professional worlds] for the development, appreciation, production, funding, operation, maintenance, social control, evaluation, and distribution . . . Paper must be filed with financial backers, government regulators, technical R&D departments, sales forces, material suppliers, production machinery producers, and shop floor designers. —(Bazerman, 2002, pp. 336–337) Invention in Edison’s shop required technical terms that allowed the workers to communicate: “bulb”, “filament”, “base”, “contacts”, and so forth. These terms found their way into the documents and discourse of people who had nothing to do with the shop. Bazerman names some of these: “financial backers, government regulators, technical R&D departments, sales forces, material suppliers, production machinery producers”. The financial backers and the others adopted just enough of Edison’s new terminology as was necessary to do their job, and Edison had to learn just enough of their terms to do his job. Specialists from different fields had to converse about their common enterprise, so they mingled their languages. Design team specialists in every field find this necessary, and instructional design teams are no different. As the lighting technology continued to develop, additional terms were created because new parts of the invention had to be named as they came into being: “socket”, “lead”, “terminal”, “connector”, “switch”, and so forth. After many years Edison had invented an entire electrical generation and distribution system, along with a host of additional new design language terms. Interestingly, though the new terms were technical, not all technicians shared the same set of terms. Makers of light bulbs did not have to have in-depth knowledge of dynamos and electrical trunk lines, and those who worked with those things did not have to know much about leads and filaments. Design languages are for and by specialists, but they are also the common ground that allows different specialists to converse without having to become deeply knowledgeable in each other’s specialties. Each has to know just enough to understand the other’s concerns and how to interface their different areas of a design. How do design languages come into being? Certainly they do not spring full-blown into existence. Consider the following ways that new terms can appear gradually as technologies mature: • A split occurs between a mature field of practice and a new sub-field that has grown rapidly in sophistication. Today, Web development is a specialized form of computer-based asset development. • The introduction of a new instructional theory brings with it a whole family of new terms. “Reinforcement” is not as current as it once was, whereas “scaffolding” is very current. • New specialties appear. Projects used to need “compositor artists”, “paste-up artists”, and “illustrators”. Now there are “database managers”, “PHP programmers”, and “computer graphic artists”.

196 • Fundamentals

• A team member comes up with a new technique that saves the time of more expensive personnel by farming repetitive tasks out. Today we do “tweening”, and film credits list “tweeners”. • A new piece of equipment requires a new set of design language terms. “Touch panels” were once popular, went out of style for nearly three decades with the introduction of the “mouse” and now “touch screens” have come back into fashion. Who would have guessed? • New software tools introduce new design language terms or modify old ones. Authoring systems introduced the “frame”: a block of computer code and a display. Later, “frame” referred to a Web page structure for laying out screen space. Today some multi-media authoring tools use the “frame” as a division of a timeline (that contains logic and displayable resources), but that is also changing. • A new process introduces new design language terms. “Agile” design is still a relatively new idea to instructional designers. • A new kind of product introduces new design language terms. Your inbox probably contains an invitation to a “webinar”. You may still keep a “blog”, but it is more likely you “tweet”, “message”, or “Facebook” (but not for long). • A new professional organization can introduce a new name. NSPI (National Society for Programmed Instruction) becomes NSPI (National Society for Performance and Instruction) when programmed instruction loses popularity. • Publications become a focal point for language development in a field of practice. New online publications and journals are introduced almost monthly, defining new perspectives on interdisciplinary research. • Popular books add design language terms. Peter Senge (1990) popularized the “learning organization”. • Theorists’ names become design language terms. We refer to “Gagné” taxonomies, and “Vygotskian” theories: “zone of proximal development” is a recent term in general usage. • A leading practitioner’s name can become a design language term. A design team may decide to do a project “like IDEO” or “David Hon”, one of the early and incredibly inventive simulation designers. • Academic cultures with a particular world view become design language terms: “behaviorists”, “cognitivists”, and “constructivists”. • Trends in thought supply design language terms. Today we speak of “blended” learning, “online” learning, “open” learning, and “mobile” learning. • New product forms produce new names: “interface”, “multi-user network”, “project”, and “discourse” (Krippendorff, 2000). There is much more, but this suffices to show the vast range of design language terms and ways in which they enter our professional discourse. We become initiated into our design languages as novices. We do not realize that we are using them until the puzzled looks on other people’s faces tell us we have begun speaking in a code. Novice languages allow us to absorb the new culture of instructional design, but if an experienced designer continues to use novice languages, it is a sign of stagnation, either personally or as a profession. Design Languages and Design Layers Design languages are closely related to layers. They are the terms designers use while designing within a layer. In building design the designer of the structure layer (a structural engineer) has certain terms that refer to structural concepts and how structural members are assembled. Terms like beam, foundation, riser, header, supporting wall, upright, and rafter will pertain to that layer of the design. In the services layer, on the other hand, the electrical system designer is interested in a different set

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of terms: mains, circuit breakers, junction boxes, leads, switches, and outlets. Differences in the terms used by specialists should be expected, since they are each, in effect, architects designing with particular layer structures to perform layer functions. Instructional design layers are similar. The design languages of the representation layer are concerned with the structure of the visual and audio resources—things that impinge on the senses. Other layers are associated with other functions. Content layer design languages deal with elements of knowledge and performance—concepts, semantic units, and production rules. Control layer design is concerned with how learners act—buttons, sliders, text entries, joystick manipulations. Issues of event structure—sequences, goals, settings, and activity—occupy the strategy layer. Each layer has its design questions and architectural structures that can be used to answer them. Within each layer, all of the kinds of terminology listed in the previous section are present: terms from theory, common practice, technical specialties, equipment, processes, publications, professional cultures, product types, and techniques. Specialists ply their trades using their unique but somewhat overlapping languages. An instructional designer’s attention is occupied with all of these, plus the designer’s own specialized languages. As shown earlier, a design team trying to communicate looks like Figure 7.4. The designer is the translator of all of the languages and so must be familiar with the terms in all of them and understand the basic intellectual and practical concepts in each one. Design languages are also involved in the interface between layer designs, and this implies relating design concepts between layers. As an instructional designer makes strategy decisions, certain terms pertaining to the strategy layer influence the artist, who is interested in the representation layer. The designer will try to merge the terms and design principles of each layer into a coherent unity in which both the strategy and the art become transparent to the user. Later chapters on individual layers describe these interlayer relationships in more detail, but to illustrate the problems involved, we can refer back to the robotic example given earlier. The mechanical acts of the robot do not create the desired effect when they are performed randomly. Only when they are part of a larger pattern do they come to have impact. In order to achieve this impact, the

  ☺ ☯

  ☺

DESIGNER   ☺

SME

  



☺    

PROGRAMMER

Figure 7.4 The specialized design languages of different team members.

WRITER

ARTIST

198 • Fundamentals

designer must translate the terms of a grand effect—the sweeping gestures, the expressions—into individual robotic motion acts, and sequences of acts. In the end, the robot has no idea of the experience it produces for the user, but it faithfully performs its individual acts, and the suite of acts supports the designer’s desired effect. In a similar way, the artist may not understand why a designer wants a graphic created in a particular way. The designer tries to explain why. The artist, sometimes in exasperation, tries to explain back to the designer why that is not a good idea. Each team member speaks using terms of a specialized design language. In many cases the best answer lies in neither specialty, but in a new space between them. This is one source of innovation. Design languages, then, work within layers and translate between layers. Translation, a mapping process, maps an element of the content from the content design layer, translating it, and matching it with one or more bite-sized messages within the message design layer. The larger purposes (strategic objectives) of the strategy layer are translated or mapped into the more local purposes (means objectives) of the message layer. The message layer is, therefore, a translation from two contributing design languages into its own language of message structures. Message units from a message layer are in turn translated into parts of representations that the learner can sense. This translation is strictly analogous to the robot and fountain examples. In order to create an emotional impression a designer translates grand effects into small actions that can be executed individually. These individual acts create the emotional response, and, in the fountain, children express their acceptance of this by dancing with the water. Application Exercise Professional specialties all possess their own “lingo”, or specialized vocabulary. Pick a profession from the list below and find a sample of its technical language that is hard to understand because of its use of technical terms (by listening to users converse). Which terms refer to “things”? Which to “processes”? Which to “qualities” of the product? What other categories of design language can you discern? • Movie-making, culinary arts, dramatic performance, medicine, law, engineering, business. Private Languages and Emerging Design Expertise A designer’s increasing sensitivity to design language is an important factor in the improvement of a body of skill. Expert performers in any field (singers, actors, doctors) are designers; they practice performance until it becomes routine. A professional voice coach once described to the author levels of design language awareness associated with vocal performance. A singer is a performance designer, and a serious singer’s professional progress depends on constantly refining an understanding of the detailed qualities of a vocal performance. When these qualities are given names, a singer and a voice coach can talk together about ways to craft a particular performance. This voice coach described categories that she had become aware of in her own teaching and in her own journey toward expertise: • Native modeled performance language—Terms abstracted from popular or respected models without the learner’s awareness. Design language terms are abstracted through imitation and have no technical terms: “She changed her tone like this . . . ”, “She changed the volume suddenly like this . . .”, or “She did this neat thing with her voice as she sang this word.” • Standard public performance language—Basic terms taught to a learner by virtually all voice coaches: diction, breathing, volume, vibrato, etc. These basic terms are given to novices in basic music instruction textbooks and do-it-yourself books.

Operational Principles, Design Languages • 199

• Premium public teaching language and notation—Terms possessed by performance-quality singers who sing to a critical audience and are able to perform experiments with their skills in a constant quest for improvement. Performers who reach this level of language need to have moved past the other levels and need to have made the basic design languages of performance their own, having modified some meanings through discovery and having added some of their own unique terms—usually style-specific. For these people, design language has gone beyond the standard terms, which have been previously mastered, to a level of consciousness of the elements of performance that is more detailed, disciplined, and internally consistent with a particular style. This level of design language is usually accompanied by a personalized system of dynamics and diction notations in the form of a private code that captures for the singer the stylistic design features of a performance. Notation becomes related to the performer’s specific style, and during the design of a performance for a specific piece, the singer may mark the music with personalized markings. Since this level of design language is deliberate and conscious, it can be made public and taught. • Private performance language and notation—Terms that are half-explicit and half-intuitive and therefore not amenable to being made public, because they are concepts emerging in the performer’s own understanding. These are the source of the performer’s private experiments and his or her improvisational language. Star-level performers with signature styles that set the performer apart possess this level of language. When the star rolls out something new, it thrills those who know or have sensed the performer’s design language. Because this level of design language is partly intuitive, it is only partly formalizable, and it is at this level that the performer’s own innovations emerge. Jazz listeners are acutely tuned to nuances in their favorite performers’ designs, and they can define periods of development in each performer’s unique style. Design Languages and Design Design languages have been an important factor in advances in our understanding of design. Herbert Simon (1999) makes the point that the great interest and progress in the field of design studies over the last sixty years must in part be attributed to the computer and particularly the desire to use the computer to assist in the design process. Simon’s thesis is that in order to bring the computer into design processes, the processes themselves have to be examined more closely, and designers have to begin to question in more detail what it is exactly that they do as they design. This process of the closer examination of design practice began early in the history of computer chip design. It was found that some highly repetitive process chunks could be assigned to the computer and that this relieved designers of some of the more tedious tasks, such as drawing diagrams and computing routine electrical values. This meant that the human designer could concentrate on solving more complex problems that the computer could not help with. It required that the processes be expressed in language terms both the computer and the human could understand. This was the first tentative step into the world of design languages. Success in the automation of easy design tasks emboldened computer engineers to find other tasks that they could turn over to the computer. This involved the invention of still more design language terms. Over time the computer has shown itself capable of assuming many tasks if they could be described in the proper terms. The end result of this evolution of automated design has been that, today, the great majority of computer chip design decisions are made by computer programs that use special design languages that have meaning to both humans and computers. Humans express a problem to the computer using the specialized terms, and the computer does its part and communicates back a solution, sometimes in the form of a computer program capable of driving production machinery. Elements of the design left to humans are high level and strategic. The net result of the

200 • Fundamentals

involvement of computers in automated design has been much faster chip design and greater complexity of designs. Today, it would be impractical to design a chip without computer support. What does this portend for instructional design for the involvement of computers in administering adaptive instruction that generates experiences in real time based on learner choices and learner performance? Certainly it is not possible for instructional designers to design by hand all of the computerized instruction that is needed, and even if that were undertaken the result would be mass-produced mediocrity. Previous chapters, and this one, have described ideas that provide a conceptual foundation for gradually involving the computer and the human in a greater number of negotiations, goal settings and instructional conversations. The important concepts in achieving this goal include the idea of conversation as the basic pattern of instruction, the concept of dramatic structure in instruction, the concept of moment-by-moment goal negotiation and fulfillment, the idea of maintaining challenge level, the idea of design languages as translation tools, and the concept of modularization leading to mass customization (see Chapter 15). Just as computer chip designers had to come to understand the design problem more thoroughly before they could frame it in terms the computer could help with, instructional designers will have to come to better understand the terms of instructional conversation, operational principles, and the design languages of instruction. Conclusion Operational principles and design languages are universal concepts that cross design field boundaries. Design languages are key in the continued learning and improvement for any practice. An important factor in the future progress of instructional design will be how well designers can gain control in their use of design language terms—those that are easy to express, and those that require deeper understanding.

II

Design in Layers

The chapters in this section describe seven instructional design layers in detail. These are proposed as a hypothetical set of architectural layers of a design, but not as a taxonomical system. Chapter 8: Design Within the Message Layer This chapter describes the most difficult layer to understand, but also the layer that is the most essential to designing conversational instruction. The message layer lies at the heart of the adaptivity, generativity, and scalability of instructional designs. Chapter 9: Design Within the Control Layer The control layer is the layer through which the learner makes expressions in response to instruction. It is the second lane of a two-way highway across which the instructional conversation is carried out (the message and representation layers together constituting the other two). This chapter describes the linguistic nature of the control layer and how with each new design a designer adopts or creates elements of a language that the learner uses to form expressions. Chapter 10: Design Within the Representation Layer The representation layer is the visible layer, the most charismatic, and therefore the one that receives the most attention. It is described after the message and control layers to avoid diverting attention away from those more abstract and invisible, yet important, layers. Representation is technologically the most mature layer in terms of conceptual and technical development. This chapter describes principles of representation at a level beyond the concerns of specific media types. Chapter 11: Design Within the Content Layer This chapter describes the content layer and its surprisingly rich design languages. Appreciation of these languages brings the designer to regard what can be learned in a new, more varied and more nuanced way.

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Chapter 12: Design Within the Strategy Layer This chapter makes the assertion that every decision a designer makes is strategic, but that certain classes of decisions represent the most central issues of strategy. The strategy layer orchestrates conversations with the learner, making the structure of this layer the basis for social and emotional as well as intellectual experiences. Chapter 13: Design Within the Data Management Layer This chapter describes the concerns of data management and reporting. It describes the central role data plays in adaptive instruction and how data that is now often discarded or ignored by designers is essential to conversational experiences tailored to individuals. Chapter 14: Design Within the Media-Logic Layer This chapter describes the layer within which the concerns of the live instructor and the technological device meet and blend. Media-logic is also shown to be the interface point between the design of conversations and the organization’s IT infrastructure at several levels. The concerns of the media-logic layer pull instruction and learning issues into the heart of the education and training of organizations and their learning management systems.

8

Design Within the Message Layer

The computer, with its attendant peripherals and networks, is a machine that provides new ways for people to communicate with other people . . . The biggest advances will come not from doing more and bigger and faster of what we are already doing, but from finding new metaphors, new starting points. —(T. Winograd, 1997) A New Yorker cartoon shows a person asking directions of a policeman. In the thought bubble of the policeman is a clearly drawn map of streets and intersections and a path to the destination. In the thought bubble of the questioner is a tangle of wormy lines mixed together like spaghetti. The policeman’s message didn’t arrive intact. The policeman disassembled an idea and sent out the pieces (sentences, perhaps), but when they arrived at the listener they didn’t get reassembled properly. What were the pieces? Something about them made a difference. This chapter deals with the instructional design issues of the message layer. A message is an abstract idea, not a surface representation. The message is a unit of meaning that the sender intends to send. It is independent of its surface representation, because a single message can be given many different surface representations that are faithful to its meaning and intent. It is important to make this separation clear, because the abstract message is so easily lost in the shadow of highly charismatic, colorful, moving, noisy, and flashy representations. The representation is just one part of what Etienne Wenger (1987) called the “interface language” of an instructional system. The message unit is another, less visible part of that language. Development tools make it easy to create representations. The harder task by far is designing a message structure capable of executing an instructional strategy adaptively and conversationally through a representation. In both technology-based instruction and live instruction intentions are formed about what needs to be communicated before deciding how to communicate it. There may be only a fraction of a second between deciding on a message and deciding its representation, but there is that fraction of a difference, and it is a valuable difference for a designer to know about. Where is the Message Layer Important Today? The message layer is the most critical to achieving the goal of adaptive, conversational instruction. Today the importance of the message layer can be seen in at least four different instructional design venues: 203

204 • Design in Layers

• Instructional simulations—Instructional simulations have to be adaptive because no one knows what the learner will do next. There is no constraint on the order of learner actions, so there must be a ready response to whatever the learner chooses to do, and it can’t consist of a long narrative. Simulations respond through the means of a learning companion that is capable of constructing messages appropriate to the most recent learner action. • Improved Web search engine technology—Web search engines accept and answer queries like “What does sodium chloride look like?” This ability to respond to inquiries from individual users with appropriate messages is due to better natural language recognition systems, the use of Web analytics, and smarter recommender systems. The ability to tailor responses to learners’ questions will continue to improve. • Instruction based on social interaction—Learners and instructors can use structured messaging patterns during problem solving as a tool to create productive, structured conversations. • Intelligent tutoring systems—Many varieties of intelligent tutoring systems are currently employed in schools for instruction in a variety of subject-matters. These tutors follow each learner action with a tailored response message based on the learner’s actions. What distinguishes these four threads of adaptive instructional research is not the instructional medium they use but rather the granularity of the message system from which a tailored response is created and the degree of fit that this affords between the learner’s need and the instruction’s response. In all four cases, the key to adaptation is the existence of a structured system of messaging in which the basic size of the message unit is deliberately chosen. The message as a basic structural unit is a powerful design concept, and it is the key to adaptive instruction. Deciding the basic units of communication—messages—requires its own set of strategic decisions. Deciding how to actually communicate them through representations that can be sensed requires a separate set of strategic decisions that call upon a different set of principles. This is the reason for separating the message layer from the representation layer. The focus of this chapter is on messaging systems. A later chapter (Chapter 10) describes the representation of messages. How Does the Message Layer Work Within a Design? The message layer carries out instructional strategies given to it by the strategy layer. Negotiating high-level strategy patterns with the learner is the province of the strategy layer. The strategy layer then uses the message layer to carry out the conversational details of strategy patterns. The implementation of a high-order strategy at lower levels of detail is described by Greg Rawlins (1997) in his book Slaves of the Machine: Napoleon could not have commanded an invasion of Russia and never did so. One day he ordered certain documents to be dispatched to Vienna, to Berlin, and to Petersburg; the following day saw such and such decrees and orders issued to the army, the fleet, the commissariat, and so on and so on—millions of separate commands making up a series of commands corresponding to a series of events which brought the French armies into Russia.  . . . A cook in Napoleon’s army . . . may have had no idea why he was ordered to prepare his cart and victuals. He could safely conclude that he was going to be asked to march somewhere in the next few days, but other than that he was too far down the chain of command to know what to do without being given precise instructions. —(pp. 41–42) A high-level strategic plan has to be broken down into lower-level plans and actions for execution. If a learner (or the instruction) decides that a demonstration is the next important strategic move, then

Design Within the Message Layer • 205

there exists the problem that a demonstration can last a few minutes and can require the exchange of multiple messages back and forth between the instruction and the learner. Together, the message layer and the control layer (Chapter 9) make the conversation with the learner possible, like the two opposite lanes of a freeway. Messages are represented to the learner, and the learner uses controls to speak back to the instruction. The kinds of messages necessary to carry out the demonstration are the concern of the message layer, and the designer (or the instructor) decides either in advance or at the moment of need what those messages will be. Though demonstrations can be made interactive, most often they are treated as a single monolithic instructional resource—a video segment or an extended visual and textual presentation. However, a designer must decide what is said and what is shown, and if it is non-interactive, the designer must decide the order of message presentation. The important point is that the monolithic demonstration is carried out through the mechanism of numerous smaller messages. These messages are designed using a set of principles particular to the message layer. These are the principles of structuring instructional conversations. The degree of conversationality is determined by the granularity of the messages used, the degree of interaction provided, and the presence or absence of smart algorithms. In the Napoleon example each level of command had to translate the order into the terms of the next level down. Napoleon gave out a general message, one to each of his subordinates, outlining the goal (“invade”) and the general approach (“walk east, you can’t miss it”). Each of the subordinates translated the order into messages the next level down could translate and pass along. For each subordinate, the command received was suited to the particular officer, the particular level, and the particular responsibility area. All of the messages were generated from the original, but the details of the means for carrying out the plan had to be determined at each successive level. Rawlins’ example illustrates how plans in the form of messages at one level generate more detailed plans at a subordinate level. This has an exact analog instruction: goals at different levels lead to messages, which lead to representations. A single strategy plan in this way passes through levels of translation down to a subordinate level that carries out the details of the originally high-level plan by generating specific representations. To the extent that intelligent decision-making becomes involved in this process, the instruction becomes increasingly adaptive. Application Exercise Try to imagine the missing levels of breakdown in Napoleon’s order. How many translations are there in an army before orders reach the level of the cook? What kinds of decisions are involved at each level? Are all of those levels needed? If so, what is the value-added at each level? A Simple Example of Strategy-driven Message Structure The planning of message structures during design can be illustrated using a common strategy for instructing procedures. For this example the performance goal is “Perform Procedure X”. It is not important to know what Procedure “X” is involved, since the architectural aspects of the design problem are the same for any procedure. Figure 8.1 illustrates the levels of translation that lead to specific instructional messages. Corresponding to the performance goal there is a design goal: something like “Instruct Procedure X”. In Figure 8.1 this design goal is decomposed into three standard strategic parts: • “Present Procedure X.” • “Demonstrate Procedure X.” • “Practice Procedure X.”

206 • Design in Layers

Design Goal

Instruconal Goal

Present Procedure

Demonstrate Procedure

Present Step 2

Present Step N

Present Step 1

Present Step 1 Name

Present Step 1 Acon

Present Step 1 Inial Cue

Present Acon Message 1

Present Acon Message 2

Present Acon Message 3

Present Step 1 Ending Cue

Pracce Procedure

Present Step 1 Cauons

Figure 8.1 Decomposition to messages for presentation of a procedure.

If we decompose the design goal “Present Procedure X” in terms of the content structure typical of a procedure, we can see this leads to smaller design goals: • • • •

“Present step 1 of Procedure X.” “Present step 2 of Procedure X.” . . . Etcetera . . . “Present step N of Procedure X.”

These design goals can be further decomposed, this time in terms of specific messages that can be used to instruct each of the steps: • “Present the name of step 1 of Procedure X.” (What will the step be called to identify it?) • “Present the action of step 1 of Procedure X.” (What action does the step consist of?) • “Present the initial cue of step 1 of Procedure X.” (How does one know when it is appropriate to perform the step?) • “Present the terminating cue of step 1 of Procedure X.” (When does one stop performing the action?) • “Present the cautions related to step 1 of Procedure X.” (What should not be done?)

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These few messaging goals will be sufficient for this example, although many more are possible. The first level of decomposition was based on a strategy pattern; the second was based on a content structure pattern; and the third was based on a messaging pattern. Where did these come from? They were all designer commitments based on an instructional theory or on the designer’s personal views of instruction. One common pattern prescribes presentation, demonstration, and practice. This is not the only way to characterize strategy for procedure instruction, but it is a common one. (Keep in mind that there is no single right strategy.) The content decomposition was likewise based on a designer commitment to a particular view of the nature of procedural content—that procedure content can be subdivided in terms of steps to be performed. The message decomposition was based on a designer commitment to the kinds of message that might be employed in procedural instruction. This could represent the designer’s view of the minimum set of message categories. There is no empirical basis for this choice other than the abstraction of message structures from previous, known-effective designs. A designer with different views might decompose the design goals differently, but eventually the decomposition would reach a messaging level. Figure 8.1 shows that at least one additional decomposition is required to map message elements to a set of representational elements. At the final level, the messages and representations obtained are small: they can be used as exchanges in a conversation that is driven by a conversation pattern determined by the designer. Figure 8.2 illustrates a similar decomposition for the demonstration element of the instructional strategy. It decomposes in the same way, first in content subdivisions and then into message

Design Goal

Instruconal Goal

Present Procedure

Demonstrate Procedure

Pracce Procedure

Demonstrate Step 1

Demonstrate Step 2

Demonstrate Step N

Demonstrate Step 1 Inial Cue

Demonstrate Step 1 Acon

Demonstrate Step 1 Ending Cue

Demonstrate Acon Message 1

Demonstrate Acon Message 2

Demonstrate Acon Message 3

Figure 8.2 Decomposition to messages for demonstration.

Demonstrate Step 1 Cauons

208 • Design in Layers

Instruconal Goal

Demonstrate Procedure

Pracce Procedure

Pracce Step 1

Pracce Step 2

Wait for Step 1 Acon

Present Step 1 Ending Cue

Give Step 2 Inial Cue

Accept Acon Response

Affirm OK Response

Present Procedure

Give Step 1 Inial Cue

Design Goal

Pracce Step 3

Enter Feedback Roune Figure 8.3 Decomposition to messages for practice.

subdivisions. Similarly, it can undergo a further subdivision that allows message elements to be mapped to representation elements. It should be kept in mind that this mapping can take place during development or that representations may be generated fresh at the moment of instruction. Figure 8.3 illustrates a different pattern of decomposition because the nature of practice requires interaction, and the message structure will now conform to the conversational logic typical of practice events. Messages must be created to elicit actions from the learner, provide action opportunities, display appropriate reactions to learner actions, and provide feedback. Feedback messages themselves may become quite involved and may themselves involve interaction as well, requiring additional conversational logic. The purpose of this example is not to promote a particular strategic viewpoint or to describe the “right” way to pursue procedure instruction. It is to show the role of message structures in a design and how they can be identified. Of particular interest is the way that, for practice and possibly for demonstration as well, the pattern of messaging corresponds with a pattern of conversational logic chosen by the designer. It is the logic and the messaging structure working together that define the level of conversationality of a design.

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A Second Example of Strategy-driven Message Structure What if instead of a procedure the learning goal pertained to understanding the forces at work during a process? A similar decomposition of design goals can be used, beginning with the same high-level strategy pattern (present–demonstrate–practice). At the second level of decomposition, however, steps are not an appropriate characterization of the content structure: a process proceeds in stages as causes (for example, learner actions) bring about observed effects. This structure of content has to be seen in terms of cause–effect relationships—an unfolding pattern of events that can follow more than one path to a conclusion. The performance during process instruction normally entails the description or prediction of the outcome resulting from the action of forces on the process system. In addition, it entails an explanation of why and how the outcome was produced. This explanation is couched in terms of invisible but real forces acting on each other in a dynamic way. Process instruction strategy often takes the form of a narrative. Figure 8.4 (from Gibbons and Fairweather, 1998) shows a high-level strategy of the form presentation–practice, with presentation including (1) the setting of a narrative stage, and (2) the description of the process paths resulting from different sets of causal conditions. Four types of practice performance are defined in this high-level strategy: stage-setting practice, explanation practice, prediction practice, and integration practice. Once again, these represent designer choices that may differ between designers, depending on instructional theory commitments. Many different event and activity arrangements could be used. What can be seen from Figure 8.4 is that each of the lowest-level elements in the figure will consist of: (1) a set of messages that can be related to a representation, and (2) a conversational logic. The conversational logics are not shown in Figure 8.4, and they vary depending on the designer’s personal theoretical views. The point of the example, once again, is that a message structure will be derived from the combination of strategic intent and content structure and will be matched with a conversational logic and representations. Compared to the first example, the message structure in this second example is more complex because the content structure is more complex. The conversational logic will be more complicated as well, as may be the rules relating representation elements and message elements. A Third Example of Strategy-driven Message Structure A third example can be used to show that the strategy plan and the structure of the content become the basis for determining message structure, conversation logic, and representation. Consider the design of a simulation that is based on interaction with a dynamic model, one that changes state as the learner acts upon it using controls. Simulation instruction usually requires the learner to perform procedures and operate on processes, but to do so with understanding and judgment (the hallmarks of skill). Between bits of procedural performance there are decision points where the learner must decide the next appropriate action. Experience with the model alone could convey some level of understanding, but the full value of model experience is obtained when the reactions of the model are augmented by commentary in some form that gives descriptions, explanations, directions, suggestions, and feedback. In addition, instruction might ask the learner to explain back to the simulation why a particular action was taken at a given moment, so both procedural and process knowledge would need to be applied. Descriptions, explanations, directions, and feedback are classes of message that may be required to augment the model interaction, depending on the strategy plan of the designer. Message structures from both procedure and process strategies are almost certain to be needed, in addition to message structures necessary to support the problem presentation, problem-solving support, and problem-level feedback (as well as process-level and procedure-level feedback).

210 • Design in Layers

Presentaon

Integraon Pracce

Present Event Paths

Compare Integrate

Model Analogize

Describe Explain

Present Event Field

Outcome States

Condions

Elements

Environment

Process Descripon

Explain Pracce

Stage-set Pracce

Stage-Seng

Predicon Pracce

Pracce

Figure 8.4 Decomposition to messages for process instruction (adapted from Gibbons and Fairweather, 1998).

The conclusion is once again that message structure depends on (1) the high-level strategy plan, and (2) the content structure. The conversational logic and the mapping of message elements to representations is in this case more involved, but the principle of relating them is the same. Application Exercise Find examples of procedural, process, and simulation instruction. Identify individual messages required to enact these examples of instruction: • • • •

Identify messages that become represented in verbal form. (Go behind the words.) Identify messages that become represented in visual form. (Go behind the visuals.) Identify messages that become represented in auditory form. (Go behind the sounds.) Identify messages that are conveyed without words, pictures, or sound (the unarticulated message).

Ontology The principle underlying the construction of message systems in the examples of the previous section is, in every case, ontology. An ontology in the modern, technical sense is a set of categories for managing information by assigning it to a class. Ontologies are invented for practical purposes:

Design Within the Message Layer • 211

especially to enable computations that involve the analysis, storage, retrieval, and presentation of information. The most familiar example of an ontology for instructional technologists is the system of tags incorporated into XML and HTML5. Using the tags, a designer indicates that a parcel of display information belongs a particular category. If the designer indicates that a parcel of text is assigned to the category, then a browser knows how to display that text because it has been categorized, and the browser software knows how to display it. On the other hand, if the designer assigns a tag to the text, it will be displayed as a title. An ontology always has a central theme or object for relating individual categories of information. Figure 8.5 shows a simple ontology with the theme of “car”. The categories related to car in this ontology are arrayed around the central theme: make, model, size of engine, color, year, etc. In Figure 8.1 there was a message-type ontology organized around “messages related to the presentation of a step”. This ontology is shown in Figure 8.6, and the message categories “step name”, “step action”, “step initial cue”, “step terminating cue”, and “caution” are arrayed around it. An ontology can be used to organize kinds of information. Almost anything can be used as a category tag in an ontology, because ontologies are invented to suit the purposes of the one who designs them. They do not represent truth, but rather utility. The message ontology in Figure 8.6 could be applied to any set of procedural steps. A more complete version with more message categories could be created. Table 8.1 contains a tabular representation of this more complete ontology. It shows the message elements that could be used for the presentation of any step in a simple procedure lesson. The unique content feature of a procedure is its steps. These have been placed as the row headings. Each column heading gives the name of a class of message elements that a designer has decided are needed for procedure instruction. Each message unit will require one or more media representations (which may include live instructor actions) to create the instruction. A similar table could be constructed for the demonstration messages of Figure 8.2 and the practice-related messages of Figure 8.3. What is the value of inventorying messages in this way, and why is an ontology a useful tool in making this inventory? The answer is that without a standard message structure for presentation, demonstration, and practice, the composition of the message may be haphazard, and the message designer may omit an essential item of information, which might cause

Make Convert?

Name

Car

Trans

Common errors

Model

Year

Engine

Body

Technique

Acon

Step

Tools

Color

Figure 8.5 A simple ontology based on car properties. Figure 8.6 An ontology based on the message properties associated with a procedural step.

Inial cue

Stop cue Cauons

212 • Design in Layers Table 8.1 Ontological Categories for the Messages of a Traditional Procedure Lesson Procedure Step

Step Name

Initiation Cue

Step Action

Termination Cue

Primary Indicators

Concurrent Indicators

Cautions, Warnings

Irrelevant Indicators

Proceed to Next

Step 1 Step 2 Step 3 Step N

confusion for a learner trying to perform the procedure. A similar principle holds for the process example in Figure 8.4 and the simulation example as well. The ontology table is only a tool for identifying a complete set of messages. Creating an ontology does not determine the order in which the messages are deployed to the learner through representations during instruction. In fact, a table makes it possible for the designer to see the full array of messages that pertain to procedure (or process or simulation) instruction and manipulate the order in which they are represented to the learner by creating a conversational logic. Rather than diminishing the designer’s degrees of freedom, ontologies of message structure create new possibilities by making message elements explicit and visible and by making the logic of using them manipulable. Another side effect of creating message ontologies is that an intelligent algorithm can be used with them, deploying them in a tailored order. A message ontology can be the basis for building any number of quite different message sequences. The designer’s opportunity is to imagine innovative ways to represent and orchestrate this kind of instruction. The Massing and Distribution of Message Elements During Instruction An ontology table not only clarifies discussions about messages among design team members, but it allows the designer to see alternative arrangements and sequences of message units. In particular, it allows the designer to provide message in a form that is not massed—given all in one big lump at one time. Messages can be distributed across instructional time, being used at one or more appropriate moments, just in time. Figure 8.7 shows a traditional pattern of lesson design for a three-step procedure which relies on massed presentations. Presentation of all steps (P1, P2, P3) is accomplished before demonstration (D1, D2, D3) and practice (Pr1, Pr2, Pr3). Many procedural lessons follow this pattern almost exactly. Figure 8.8 shows an alternative arrangement of messages in which procedure steps become the Pracce Pr1 Pr2 Pr3 … Prn Demonstraon D1

D2

D3 … Dn

Presentaon P1

P2

P3 … Pn

Figure 8.7 Message organization that relies on massed presentation of messages.

Design Within the Message Layer • 213 Step 3 P3

D3 Pr3

Step 2 P2

D2 Pr3

Step 1 P1

D1

Pr1

Figure 8.8 Message organization that is distributed to be closer to the performance of steps.

organizing principle, rather than the presentation of information, so that presentation, demonstration, and practice of a single step are accomplished before moving on to the next step. This illustrates a shift in the rhythm of message units that emphasizes the availability of information at the time it will be used. The organization in Figure 8.7 tends to produce instruction that masses presentation of information. A designer of this kind of instruction might be tempted to spend most of the effort on designing the presentation of the message and less on the practice. In the extreme version of this not-uncommon organization, practice is often omitted or shortened, and the learner receives mostly presentation and little opportunity to practice. The organization in Figure 8.8 exemplifies a shift to an organizing principle that places emphasis on practice. Instruction organized using this pattern can’t ignore practice because it occurs frequently and repeatedly. This shows that the designer is able to think in different terms about the inner architecture of the instruction. Once this barrier is crossed, a designer is able to think of a number of additional possible organizations of the lesson that elevate the creative organization of message units. Application Exercise Select a procedure and create an ontology table for it. Then use the table contents to create two non-traditional orderings of messages that you feel would be effective and interesting to a learner. Non-content-related Message Ontologies There are other kinds of message unit in instructional conversations as well. These are required for the operation and administration of the conversation itself. In conversational instruction there must be messages related to the negotiation and assessment processes. These can be placed within an ontological table like Table 8.1, though the column and row labels would be different. The important principle is that there is a systematic way of identifying and cataloguing messages within a conversational instructional system. The TREKKER system described in Chapter 13 provides a basis for systematic identification of non-content-related messages. Avoid Confusing the Message Layer with “Message Design” It is hard to talk about instructional designs without using the word “message”. In the traditions of instructional design, the term “message design” has been widely used to describe the principles for structuring and presenting messages. In the message design literature, the term “message” is

214 • Design in Layers

used in a different sense than it is used here. In that literature the focus is on the formatting, and presentation of represented media messages. In this book, the term “message” is more narrow and specific, referring to an abstract intention to communicate an idea, which has as yet no represented form. Why choose to narrow the use of the “message” term? Because the traditional use of the term “message” incorporates both “message” and “representation” and ties them together so closely that they cannot be considered, discussed, and designed separately. This requires the mapping of “message” to “representation” as was mentioned earlier. Separating message and representation designs gives the designer more options and is the very thing that facilitates the incorporation of intelligent adaptation into instructional designs. The Message Layer: In Theory and Practice Interest in the issues of the message layer is evident all around us in practical settings and in the literature. Several messaging systems are described below, with emphasis on providing a wide range of examples. Examples of Everyday Message Systems Revolving doors and elevators use message systems. When you step onto an elevator and press a button, you may hear a voice announcing that you are “going up” or “going down”. If you step into a high-security revolving door, you may hear one of ten or so messages that announce that you may not pass and that you are overweight for the door, or that someone is trying to “piggyback” out the door using your security clearance. These simple examples illustrate a basic conversational structure: the user acts, and the system responds with an appropriate message, however that message becomes represented (as a buzzer, a red light, or a voice speaking sentences). Most people have had an encounter with messaging systems when they call a bank or a customer support line. The message structure consists of the network of decision points that a key press can bring you to: you are given a message that asks a question, and you press a key to answer. The experience can be frustrating when there is no human and the system does not ask the right question. These examples are limited and highly constrained message systems. They do illustrate, however, the idea that the granularity and richness of the message system regulates the degree of responsiveness the user—or a learner—can expect. Advertisements provide a case study in everyday messaging systems. Ads are focused and carry multiple messages, often implicit. For example, the surface message of an automobile ad might say “Buy this car”, but the intimidating athlete frowning at you from the screen says “Buy this car; I dominate you.” Advertisers carefully target their ads at niche markets (such as 25-year-old college graduates in business) and carefully craft their messages, which are usually not stated in so many words. Instead, visual symbols like business figures closing deals and admiring friends are used to carry the messages “Buy this car and you have arrived” or “Buy this car and people will like you.” This example illustrates the point that representations often do not correspond directly with the wording of abstract message units. Every instructional experience conveys messages through subtext in this manner, often unintended ones that would surprise the designer, were he or she aware of how they were being conveyed. Examples from Telephone Technology One step up the ladder of message systems are systems in telephones that parse speech and determine the intent of the speaker within a limited semantic of telephone-related tasks (scheduling, taking memos, logging reminders, etc.). Sometimes the telephone may ask clarifying questions to be

Design Within the Message Layer • 215

sure of the speaker’s intent. Competition will enlarge the semantic boundaries over time, increasing the apparent insightfulness of these systems. It is important to recognize that the basis for the phone’s conversational capability is a context. The phones do not treat each new utterance from the user as if it were out of the blue. The phone-related tasks supply a context for interpreting the utterances. If a user asks questions that make no sense within the context, the phones are programmed to give seemingly intelligent, but irrelevant, replies. Systems for composing communications back to the user from the phones are based on prerecorded words and phrases that the phones can combine into meaningful representations (voice and text) to the user. The representations are preceded by the composition (invisibly within the phone) of a message—a kernel of meaning and intent—that is then given representation by both display and spoken phrase-building. These messages are based on the same semantic used to interpret the user’s input, and so the formation of messages has as its background the same context as message interpretation. The lesson from this example is the importance of context in message design. Individual messages are interpreted within a framework of meaning that the phone and the user share. Such a framework exists when a learner addresses instruction. The purpose of being at the instructional interface (live or technology-based) is in the minds (or programs) of both participants when the instructional conversation begins. That context can be used as a means of limiting the search for meaning when a learner makes a request, and the same context can be used as the basis for constructing messages and then representations back to the learner. Maintaining context is a balancing act. Conversational context exists when live instruction is conducted, and it becomes most evident when a learner asks a question that seems totally irrelevant to the instructor. Instructors and designers both should be mindful that the learner interprets instruction within a subject-matter context and that creating and maintaining that context of shared meaning is terribly important for the learner. Messages and New Information The function of context is to provide a setting for new information that is conveyed by a message. The momentary context is created by what has gone before. It is created by expectation, anticipation, and questions in the mind of the receiver. As mentioned above, context is something to be maintained, and it can change over the course of a conversation. A speaker can say the same words (a representation) at different times during a conversation, and because of a changing context the message conveyed can be different: • On seeing something for the first time, enthusiastic sincerity “Oh, I LIKE that!” • In a difference of opinion, personal preference expression “OH! I like that!” (Even if you don’t.) • In a fit of pique, sarcasm “Oh! I like THAT!” The message layer in this conversation uses the same representation (well, almost the same, except for the intonation) to convey much different messages. The context is what confirms the meaning of the message at a given moment, though. You know this because some of the intonations could be used in just about any part of the conversation and carry the meaning supported by that momentary context. What’s the point? It is that it is not the surface representation of a message that necessarily determines its meaning; it is the time at which the message is given representation, the shared context at

216 • Design in Layers

that moment, and the intention of the message sender that makes the difference. This is as true in math instruction as it is in casual talk. The reason a phone can understand what you are asking it to do is because of the context it assumes: you are asking it to schedule an appointment. The phone understands the context of making appointments, and so it knows how to handle appointment requests. It will look for key information in the message, ignore the emotional context expressed in your voice, and complete the transaction. Ask your phone what the square root of two is, and it will begin to throw out red herrings because it has no context for those kinds of question. Express an emotion to it, and it will fall back on a stock, flippant response, because emotions are not part of the context that it recognizes, and so it has no context that would allow it to abstract the new information from your communication. Examples from the Computer-based Instruction Message structure can be defined in advance of a conversation. This happens when, as in the example of the phone, only certain kinds of message are recognized. The phone does not tell the user in advance exactly what kinds of message are legitimate during a conversation. Some examples from computer-based instruction do provide such definitions for users. TICCIT: Component Display Theory In the early 1970s, before the computer was thought of as an instructional device, an ambitious experiment in computer-based instruction took place to demonstrate that it could be. One of two major systems in that experiment was the TICCIT system (Merrill et al., 1980). TICCIT was a timeshared, computer-controlled information television system. Terminals were connected by coaxial cable to a central minicomputer, which was at that time the cutting edge of computer technology. The goal of the TICCIT experiment was to demonstrate that learners could effectively be given a choice of how to conduct their own instruction. Learner control, which is today taken for granted, was at that time experimental. The educational literature at the time was full of the doctrine of individualized instruction, but few had workable plans for achieving it. The computer promised to make that possible. The solution TICCIT offered was a structured messaging system based on Merrill’s component display theory (CDT). CDT was a technological theory, not a scientific one. It specified a standard set of display types that lesson authors could create, regardless of the nature of the instructional goal, to provide a structured interaction with the learner. During instruction, learners ordered the display type they wanted in the sequence they desired. Ordering of displays was accomplished using a specialized keyboard (see Figure 2.2, Chapter 2) that named the display types and a small number of navigational commands. TICCIT’s instructional conversation was carried out using predefined message categories. In this case, the message and the representation of the message were conflated: text and visuals were packaged together and could not be accessed separately. The display types related to a specific learning goal constituted an ontological message structure. The complete family of displays was created for each objective. Information Mapping Robert Horn (1997) defined another ontological system for instructional purposes under the title of Information Mapping™, or Structured Writing. In a system that “deals only with that which can be written”, Horn defined a family of ontological structures called “blocks”, each having its own unique set of properties. Standard blocks included definition, description, example, non-example, classification tree, decision table, procedure table, and flow chart; but a designer could invent a block type at will. Blocks were arranged in the form of a table. A decision block might have columns that included

Design Within the Message Layer • 217

“If ”, “Then”, “And”, “Or”, and “Else”. A library’s decision rules that could be arrayed within this tabular structure might read: “IF the book is . . .THEN send the patron . . . AND send .” “IF the book is . . . THEN send the patron .” “If the book is . . . THEN send the patron . . . AND send .” Structured Writing provided a tool for technical writers and instructional designers that, at the time it was originated (in the late 1960s), was notable because it dealt with instructional messages in a structured way. The context of the messages provided by the tabular format and the consistency of the information categories used in blocks made information maps easy to read and information easy to locate. In Horn’s system, the message and its representation were inseparable, as with TICCIT. Knowledge Forum: Sophisticated Structured Conversations Marlene Scardamalia (2003) describes a much different structured messaging system, one in which learners create messages to each other in the context of pursuing a shared learning goal. Scardamalia and Bereiter (2006) describe learning environments called CSILE and Knowledge Forum™ in which learners jointly explore and experiment in the building of knowledge that is new to them. The computer provides a shared project space within which learners attack a knowledge problem, acting as members of a knowledge building team. The messaging tool that exists within the problem-solving space supports three main activities: theory building, experimentation, and opinion expression. Theory building takes place as learners exchange messages (called “scaffolds”) from a categorized set: • • • • • •

My theory I need to understand New information This theory cannot explain A better theory Putting our knowledge together.

Some messages express a theory about the phenomenon being studied; others propose informational goals; still others supply new information, contradict theories, or synthesize what has been learned. Experimentation stimulated by expressed theories and the need for additional information also proceeds through the use of structured message types: • • • • •

My goal Information My prediction is . . . My result is . . . I learned that . . .

Opinion expression likewise takes place through structured messages: • Opinion • Different opinion • Reason

218 • Design in Layers

• • • •

Elaboration Evidence Example Conclusion.

By using the categorized message system learners are supported in forming habits of mental exploration and knowledge building as they communicate with each other during their explorations. Message and representation are not entirely conflated since learners may submit their messages in a variety of media forms. Lessons from Intelligent Tutors Early designers of intelligent tutoring systems also found it possible to limit themselves to simple messaging systems because they began by building systems that dealt with verbal communications of factual content. An example of this is the SCHOLAR system designed by Carbonell, which read a semantic net and used slotted grammatical message forms to produce dialogues like: SCHOLAR: STUDENT: SCHOLAR: STUDENT: SCHOLAR: STUDENT: SCHOLAR: STUDENT: SCHOLAR:

Approx. what is the area of Brazil? 2,500,000 square miles. Wrong. Please indicate if the following statement is correct: the area of Paraguay is approx. 47,432 square miles. What is the area of Brazil? The area of Brazil is approx. 3,600,000 square miles. Now answer the question you didn’t answer before. False. Rephrase your statement. I can’t understand it. Incorrect. Very good. The area of Paraguay is approx. 157,000 square miles. The language in Brazil is French. Right or wrong? —(Quoted from Wenger, 1987, p. 34)

This dialogue illustrates the ability of the computer to construct natural-sounding sentences, but it also shows a lack of awareness of a conversational context (for example, with the unexpected question about Paraguay after an incorrect answer). Over time, tutoring systems broadened the conversational context. The goal of experiments became to create systems for question asking and answering related to the operations of a dynamic model. For example, message creation in the SOPHIE system took place through the concatenation of readings from a model, which gave the computer’s prose a strange robot-like quality. The generation of message directly from the model, however, made the difference between the message structure and the representation much more clear. The composition of messages directly from the model with little polishing by a representation function is what produced the stilted prose: Question: (What happens if the air temperature decreases to 20 degrees and there are no condensation nuclei) Answer: The (humidity of saturation) (decrease) from 24 to 17 mmHg because (air temperature) (decrease). The (relative humidity) (increase) from 100 to 180 percent because (humidity of saturation) (decrease) and (gradient of change) (sudden). The (water temperature)

Design Within the Message Layer • 219

(decrease) from 25 to 20 degrees because (air temperature) (decrease). The (rate of evaporation) (decrease) from 24 to 17 mmHg because (water temperature) (decrease). Then the (air condition) (supersaturated) because (rate of evaporation) (decrease) and (gradient of change) (sudden). —(Quoted from Wenger, 1987, p. 54) What would this answer look like if the specific model element names in this example were substituted with ontological variables? The from to because This pattern is repeated above multiple times. Like other examples that have been shown previously, this message system uses a slotted message structure, but in this case the assembly of a message is very apparent, and the lack of any additional processing to create a learner-friendly representation is notable by its absence. Intelligent tutoring systems have evolved a long way since the two examples shown here. What is important to take away from the examples is the idea that the message layer is an essential ingredient of adaptive instruction. Not every instructional design aims at adaptivity, but in the not-so-distant future instructional designers will have to address the issue, first with local capabilities for adaptation, but over time to an increasing extent this feature will become standard in certain types of instructional design. Message and Theory The message layer is the basis of conversational instruction, and it is related to theories of conversation building, of which a few of the most directly relevant and applicable are described below. Schank: Story Indexing Roger Schank (1995) has emphasized the role of stories in learning, remembering, and understanding. The tie between Schank’s theory and conversation is his concept of story indexing. Schank proposes that as a listener hears a story, it is understood in terms of indices—themes the listener finds embedded within the story that touch off a reminding. Schank devalues the keyword search for finding the right story at the right moment, noting that, no one asks a question during a conversation and is happy getting 1,000 search results back. The alternative to keyword search, according to Schank, is indexed search. Indexed search relies on underlying semantic dimensions of a story that the listener may or may not realize are being used to bring other stories to mind. Semantic themes can include goals of story characters, patterns, problems, situations, plans, dilemmas, consequences, and solutions. For example, a story about a wild pitch that got away from a pitcher might in the listener’s mind be an example of a pitcher’s strategic plan for striking out a batter, or it might be an example of a tired pitcher whose muscles can no longer control the ball precisely, or it may be an example of the wild path of a ball caused by aerodynamics and the way the pitcher held the ball before its release. Schank suggests trading keyword search for search by indices, a semantic search type based on meaning. He suggests that the semantic search is more appropriate for advancing instructional conversations for both computerized and live instruction. Schank’s indexing theory is pertinent to the design of instructional conversations in which shared meaning is paramount. Designers should understand that matching keywords is an insufficient basis for understanding the meaning of a

220 • Design in Layers

participant in a conversation and begin to develop a technology for determining the state of a participant’s understanding during an instructional conversation. This means driving the evaluation and assessment functions directly to the heart of instructional designs. Pask: Conversation Theory Gordon Pask created a conversational theory (Pask, 1976) to express the implications of cybernetic theory (Wiener, 1948) for human–machine learning systems and their interactions. Cybernetics is the study of controlled dynamic systems. Kopstein and Seidel (1973) assert that “systems and cybernetics are conceptually inextricably intertwined” (p. 24). Cybernetic systems influence their environments and then feel the effects as changes in the environment impinge on system controls, changing the state of the controlled system. This, in turn, again perturbs the environment, perpetuating a continuous cycle of change through the system’s controls. Cybernetic systems can be as simple as a cooling system connected to a thermostat. The cooling system cools the air around the thermostat, causing it to turn off the system, which begins a trend of air warming around the thermostat, which eventually turns the cooler back on, in a cycle that goes on until the whole system is switched off. Cybernetics is one of foundational sciences of the instructional design field (Branch and Gustafson, 2007; Pask, 1976; Richey, 1986, Chapter 2; Silvern, 1972), however, in general practice this is more often observed in the breach than in the practice. Ideally, conversation theories and the practice of conversational instruction are the means of implementing cybernetic theory. This point bears emphasizing: if cybernetics is ever to be implemented within instructional designs, it will be through conversational systems. Though Pask did not produce a practically implementable system to guide conversation designers, he led the way in introducing the principles of cybernetics and conversation into the discourse of instructional design and continued to do so longer than any other theorist. Luppicini (2008) has reemphasized Pask’s work after several years of relative neglect. Scott (2008), in the Luppicini volume, describes his personal experiences working with Pask in the creation of multiple research systems. Pask, says Scott, had “an unashamed commitment to cybernetics as a unifying discipline, regarding its conception as the greatest intellectual achievement of the 20th century” (p. 20). Indeed, cybernetics is deeply embedded in every aspect of our lives: we regulate our environment temperatures, our car speed, our shutter speed, and our heart rate using cybernetic devices. We reach the moon, our financial goals, and our maximum running performance thanks to cybernetic systems, which today mostly involve computers in some way. The exception to this predominance of cybernetic principles is education. Pask’s theory of instructional conversation accounts for: (1) conversations within one’s self, (2) conversations with other people, and (3) conversations with devices. The object of conversation is the reduction of dissonance between the conversants. If there is no dissonance—no difference in concepts—then there is no reason for conversation (Pask, 1980, p. 1006). Pask’s definition of conversation is “concept sharing” (ibid., 1980, p. 999). Pask contrasts communication and conversation: the goal of communication is “accuracy” and “veridicality”; the goal of conversation is “agreement”. According to Pask, “conversation is information transfer between organizationally closed . . . systems. It is a mechanism for conflict resolution” (p. 1006). The goal of conversation is the reduction of conceptual dissonance through changes in both conversational parties (which can include self, people, or devices). There is much more to Pask’s theory, much of it technical beyond the grasp of the average designer, because Pask’s aim was to be precise and complete. The everyday application of Pask’s principles is described more clearly in the work of Paul Pangaro.

Design Within the Message Layer • 221

Pangaro: Pragmatics of Conversation Theory Paul Pangaro became a follower of Pask’s ideas and developed them into practical, synthetic principles. According to his account: I became a student of Pask as no doubt countless others had begun: listening to his fascinating monologues, beguiling as much for what was not understood as for what was . . . I began to read the papers, put them down, pick them up again weeks later. My interest was raised with each encounter. I had previously been steeped in all the hardware and software and concepts that MIT could offer; and in a matter of months, nothing was more interesting than what Gordon had. And that was so much: a theatrical existence, an audacious theory, an artistic sensibility. And most useful of all, given where I was at that moment, I could read his papers and write code. —(Pangaro, 2001, p. 791) The most interesting applications of Pask’s principles by Pangaro for the practicing instructional designer are described not in the literature of instructional design but in that of cybernetics and business (Pangaro, 2009), and more specifically, in marketing. Pangaro’s premise is that: By focusing on the requirements for effective conversation—that is, by explicitly “designing for conversation”—designers of web services can create a high-value, efficient, and effective experience that improves the relationship of consumers to brands as well as between consumers and consumers. —(p. 4) The emphasis on consumer-to-consumer relationship is intentional and points to Pangaro’s unique perspective on the value of conversation to his particular audience—business people hoping to attract and retain customers. Pangaro’s plan is not just to have a conversation with the customer but to form and maintain a relationship. This point is important to instructional designers because it describes a use of conversations that is much closer to the use that an instructional designer would have for them. The instructional designer hopes, if possible, to negotiate a long-term relationship with the learner in order to gather enough data to anticipate the learner’s needs and adapt instruction to the learner’s characteristics and preferences. Pangaro’s model of conversation is shown in Figure 8.9. Several points are worth noting. First, as in Pask’s conversation theory, the goal is agreement—the reduction of dissonance—between the participants in the conversation. Second, if there is no shared language at the beginning of the conversation, then there must be one by the end, if the conversation is to end successfully. Third, Pangaro includes the context as the meeting ground for the conversation. This includes the place of contact, but also the time, the current activity of the participants that brings them together, their momentary interests and goals, and other factors. Context for Pangaro also includes the history of the contact, meaning prior exchanges, relationship, agreements, and shared history. In addition to the (1) context and (2) shared language dimensions that have already been pointed out, Pangaro’s conversation model includes: (3) the element of exchange, by which he means that messages from one conversant trigger reactions from the other conversant, (4) agreement, as already noted, and (5) action and transaction, meaning an exchange of actions in fulfillment of the agreement. For Pangaro’s audience, transaction may involve the exchange of cash and goods or the commitment to remain a customer. To an instructional designer, it may mean the willing performance of a task so that it can be assessed and so that feedback can be given that will result in performance improvement.

222 • Design in Layers shared

la nguage

goal

goal

agreem ent

e v a lu a t in g '

le a rn in g

In terface

participant A

participant B

action

(trans)action exchange

exchange context

Figure 8.9 Pangaro’s “skeleton of conversation”. (From Pangaro, 2009. Reproduced with permission of the author.)

The long-term view of Pangaro’s description of conversation differs significantly from other descriptions because it takes into account the continuation of engagement in conversation. The concern is not for the mechanics of a single exchange of information but for the growth of a commitment to remain engaged, even to deepen that engagement. This is pertinent to both marketers and educators and is exemplified by Pangaro with the example of the Web site of Nike+: A brilliant synthesis of relationship, community, and action/transaction is the web site of Nike+ (Nike-Plus). The site serves runners who have purchased a device that fits into their shoe and sends wireless data to an iPod carried by the runner. The iPod records speed and distance travelled, while offering the usual opportunity to play audio while on the run. On returning home and docking the iPod, the runner automatically transfers this data to the Nike+ web site, performance can be compared with the runner’s own prior runs as well as with a community of similar users, friends, and strangers alike. As a result, the runner is integrated into a community with common interests and uses those relationships to increase commitment to maintaining or improving performance while stimulating others to do the same. One result is the blurring of the community of peers and the brand itself. This leads to long-term trust, return of the consumer to use the products and services offered by the brand, and ultimately, an emotional affiliation and identification with the brand that can weather product defects and competition far better than one-way messages about benefits. —(p. 13) Dubberly and Pangaro (2009) describe the rhythm of the type of conversation illustrated in Figure 8.9 as a cycle of: • • • • • •

Open a channel. Commit to engage. Construct meaning. Evolve. Converge on agreement. Act or transact.

This cycle stresses commitment, engagement, the mutual construction of meaning (and language) and the evolution of the conversation toward a resolution of meaning. It provides a fresh perspective

Design Within the Message Layer • 223

on conversation that stands out in a literature full of communication-centered models that seem to take less of the human—the feelings, emotions, values, and prior knowledge, and the willing participation of the conversants—into account. Messages and Conversational Structure Winograd (1987–1988, 1997) also stresses the role of commitment, detailing in a model of human– computer interaction the stages of commitment making and fulfillment—what Pangaro would consider both the agreement and action/transaction elements of a conversation. Winograd’s description gives added definition to the negotiation of agreements and the negotiations that are part of agreement fulfillment in what is called “conversation for action”. Figure 8.10, from Winograd and Flores (1987), shows this detail. The nodes on this graph represent states that have been reached. At node (2) a request has been made. Action (A) leading to node (2) represents the action of making the request. The main path of commitment and fulfillment in this figure leads in a straight line from (1) to (5). At points (2) and (3) negotiations can branch off. The (2) to (6) path represents a counter-offer, which may be accepted or withdrawn. Likewise, once a state of agreement has been reached at (3), the agreement may be withdrawn, reneged, or an assertion can be made along path (3) to (4) that the agreement has been fulfilled. This, of course, would be accompanied by evidence of fulfillment. Path (4) to (3) represents the rejection of the fulfillment, and path (4) to (5) represents acceptance. Winograd’s representation of conversation in this detail provides a pattern usable by instructional designers, and it emphasizes that negotiation process that has been largely forgotten in not only technology-based instruction but instruction in general. This omission has led to the tacit acceptance of instruction as a process of force-feeding.

A Declares

Agreement 1

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s ter s oun A c unter o Bc

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Figure 8.10 The conversation for action pattern (adapted from Winograd and Flores, 1987).

4

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224 • Design in Layers

The Winograd and Pangaro representations of conversational issues coincide in that: • Both accept that agreement after negotiation is the goal of the process. • Both see their depictions of conversation processes as resulting in action and reaction. • Both demonstrate the role of thought before action on both parts. Pangaro’s representation stresses the importance of shared language that exists or comes into existence during conversation, and the importance of the context of the conversation in terms of the place, time, and history of prior conversations. Winograd’s representation of conversation stresses the details of the negotiations both after agreement and during acceptance of fulfillment. An important quality of both representations is their recursiveness: each conversational process can be seen to be a quality of itself. During a Winograd negotiation, for example, the reaching of state (3) at one level may be delayed while a recursed agreement and fulfillment at a lower level of detail is accomplished that opens the way to reaching state (3) at the original level. This is exemplified by the learner negotiating a final grade who must complete one detail of an assignment before the instructor is willing to negotiate the grade. Likewise, the architecture of the Pangaro representation is recursive in that the learning and exchange processes may require several iterations of the cycle in service to creating a few shared language terms. Application Exercise Select a transcription of an instructional session. Analyze it in terms of the message concepts that have been discussed in this chapter. • • • • • • • • • •

To what degree is it conversational? Can you detect patterns of messaging that are used? Can you detect an ontology that the instruction uses? Could you reorganize the order of the instructional messages and still come up with coherent instruction? Can you see messages that are only for the purpose of managing the instructional session, as opposed to messages that are about content? Can you detect new information contrasted with the background of discourse? What is the instruction emphasizing? Could you use a device like the TICCIT keyboard or an information map to parse the message? Can you decompose some of the sentences as content-free variable structures, as in the SOPHIE message structures? Can you index stories that are a part of the instruction? Can you detect stories (narratives) that most people would not normally consider stories? Can you imagine ways to make the instruction more conversational?

Conclusion This chapter has set out a definition of the message layer, shown how the message layer is related to the strategy and content layers, and shown how message structures are generated by interaction logic structures. It has also provided several examples of message systems in hopes of giving concreteness to a layer that is at the same time the most abstract of the layers but yet the most important to reaching the goal of adaptive instruction. With these concepts in mind, a designer is enabled to think more readily in terms of the “inner game” of instructional design. Many of the conversational

Design Within the Message Layer • 225

ideas offered here are ideals to be kept in mind for a not-too-future date. However, many parts of a design can today be made conversational by a designer who sees that conversation is not necessarily a monolithic property of a design but a property of certain modules of a design. Chapter 15 expands on the theme of modularity. The message layer is one of the most fruitful areas of research because it is the most undeveloped with respect to the goals of adaptivity, generativity, and scalability that designers must respond to in the future. Message definition and the construction of message-handling engines for use during instruction constitute one of the major technological challenges in the advancement of conversational instruction. In the long run, it is safe to predict that this area of design will flourish as designers recognize the leveraging power of messaging systems in improving the mass customization of instruction, while at the same time reducing the costs of design and development and improving its consistency. Time will tell.

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9

Design Within the Control Layer

There are three responses to a piece of design—yes, no, and WOW! Wow is the one to aim for. —(Milton Glaser) You’ve got to start with the customer experience and work backwards to the technology. —(Steve Jobs) Don’t make me think. —(Steve Krug, 2005) The control layer is the second lane of a two-way highway that connects the learner with the instructional system. This highway is the arterial over which the elements of an instructional conversation flow. The control layer and the message layer manage the substance of the instructional conversation, and the representation layer communicates this substance to all of the learner’s senses. Together, these three layers are responsible for what the literature commonly refers to as the “interface”. Only the representation layer is visible or audible to the learner. The message and control layers do their work behind the scenes. The term “interface design” has been criticized for painting the human–computer (or human– human) meeting place in sterile, technical terms. Buxton (2007) notes that more recently the idea of “interaction design” has become more prominent, and he suggests that a further migration to the idea of “experience design” is taking place. This is catching on in unexpected ways: a major fast food chain has experimented with a message on its food bags that proclaims, “Inside this bag is your food moment”. Who knows whether this is an experiment in irony, but it is clear that commodity providers of all kinds are exerting themselves to improve the quality of the experience of their products, not just their shape or technical properties. Sizzle has become a design issue, not just an advertisement ploy. The message, control, and representation layers of a design impact the learner experience most, and because they are highly integrated at the time of instruction, they could be treated in the terms of a monolithic hyper-layer interface design. This book deals with them separately, because from the designer’s point of view they have to be designed separately as well as together. In the past the practice of designing at only the representation layer has eclipsed attention to the design of the message and control layers. This chapter is about control systems and their part in interface/interaction/experience design. All three of these perspectives (technical, syntactic, and semantic) are considered here. Every 227

228 • Design in Layers

instructional system has a control system, whether the system consists of a live instructor or a blended technology-involved system. The goal of this chapter is to clarify the issues related to instructional control systems, their properties, and the principles for designing them and suggest new ways of seeing controls in terms of the languages of the learner. The role of the control layer with respect to the other layers will become apparent, and the issues of control design will receive muchneeded emphasis. We Could Start with a Bad Example Chances are you have a digital watch somewhere in a drawer that has lots of functions like multiple alarm settings, two time zone settings, a stopwatch, one or two lap counters, a countdown watch, and even a phone number database. You keep it in a drawer because you have lost the instructions for operating the controls. Worst case, you may have an alarm that goes off at 5:30 AM every day, and you don’t know how to un-set it. This is a control problem. Control systems today are more complicated in proportion with the complexity of the systems they operate. In a car there used to be a single turn signal on the steering column, all by itself. At some point it was joined by a windshield wiper control, then a windshield cleaner control, a cruise control, headlight brightness control, and most recently, stereo system controls. These controls have moved onto the steering wheel or the column for the convenience of the driver, but the performance tax this places on the driver is apparent when entering a new car or a rental. It takes several minutes and a few unexpected window washings before the new controls are familiar enough to become transparent. We should be able to operate systems without thinking about their controls. Control systems levy on the user what Hugh Dubberly calls “biocost”—the energy, attention, and stress people have to invest toward reaching a goal (Dubberly et al., 2010). The control layer is one of three layers—the message layer, and the representation layer being the other two—that constitute what is generally considered to be the “interface” of a system: its interactive surface. Within the last twenty-five years, interface and interaction design have become areas of research and publication in their own right. Numerous technical and popular books and journals are published on interface and interaction design. An interface can be considered a metaphorical place; your car’s interior can be considered an interface because that’s where you come into contact with the car while driving. As an interface, the car interior, which is carefully engineered, provides the interactive surface through which you express control of the car. Control Systems in Everyday Experience We are surrounded by control systems. Wherever you are at this moment—sitting, standing, walking, or driving—you are not many feet from a control system of some kind. Consider two places in your life where control systems abound: your car and your computer. In your car you probably have controls like these, and they offer the kinds of operations shown: • • • • • • •

Steering—Right and left, continuous adjustment Steering wheel tilt—Up and down, continuous adjustment Gears—Reverse and forward, multiple gears Overdrive control—On or off Four-wheel drive—On or off, high and low ranges Acceleration/braking—Continuous adjustment Cruise control—On, off, cancel, resume, accelerate, continuous adjustment

Design Within the Control Layer • 229

• • • • • • •

Seat adjustment—Up, down, tilt, continuous adjustment Entertainment media—Play, stop, FF, FR, eject, volume, track, station, etc. Air conditioning—On, off, temp, floor, passenger, defrost, continuous adjustment Air conditioning vents—On and off, side and center, continuous adjustment Exterior lighting—On and off, bright and dim, parking and headlight Interior lighting—On and off, brightness, automatic and on-demand (door open) GPS—You name it. It’s a small computer with its own operating system.

Through these controls you carry on a conversation with your car, tailoring its environment to your liking and needs, and moving it to destinations of your choice. Your computer has a much simpler set of controls. The problem with computer controls is that for instructional purposes they limit the “bandwidth” of the communication channel. All that is available on the average computer is a keyboard and a mouse. For portable devices the mouse is replaced by a touch screen. A revealing and fascinating history of computer interfaces and the interactions that take place through them is detailed in Designing Interactions (Moggridge, 2007). What becomes clear in Moggridge’s book is that styles, devices, and surface features for interfaces come and go  .  .  .  at the speed of light. It is as if there exists a giant ongoing research and development project being conducted across organizational boundaries by a handful of philosopher– technical-guru–designers for whom market success is only one of the criteria. These researchers are engaged in trying to humanize the human–computer interface: to make it a place for natural and rich expression. The ideas of these researchers multiply so rapidly that, often, new mechanisms for expressing control change before we become used to the old ones. Yet there seems to be an invisible barrier to expression past which we find it difficult to remove. It is a semantic barrier. According to Rawlins: Today’s computers are good because they do exactly as we say. And they’re bad because they do exactly as we say. To work at all, they need a language both they and we understand, even if neither of us understand each other’s native tongue. —(Rawlins, 1997, p. 42) Rawlins is talking about computer programming languages. There are literally hundreds of languages, but they essentially perform the same function: they provide a common space of commands that humans can use to direct the uncomprehending actions of a computer. They allow the communication of directions but not of meaning. The semantic barrier lies on the other side of these languages. No matter how sophisticated the languages are that we use to give it directions, the computer does not really understand (or care) what it is that it is being asked to do. The computer does not share meaning with us; it only shares languages and the limited understanding we can create with them. Application Exercise As mentioned above, control systems come and go. For example, the mouse and touch panel are late arrivals as computer controls. • Project yourself thirty years into the future. Describe how you will communicate control commands to your computer at that time. Remember that computers will be used everywhere: at home, at work, on the street, in the air, and at play.

230 • Design in Layers

The Barrier: Control System Semantics Returning to your car, consider one of its most useful conversational systems: the GPS (Global Positioning System) that helps you navigate unfamiliar roads. A good GPS system knows where you are and where it is possible to go. It knows about roads and intersections, it knows the difference between a freeway and a two-lane road, it knows destinations, and it knows what a route is. When you tell it your destination and your preferred route, it can work cooperatively with you to execute the route, even if you pass a turn-off and it has to recalculate your route. In addition, if you do not know a specific destination but you know you want to find a restaurant, you can ask it for suggestions, so it knows types of destination. It will give you choices within a radius you define. How would it be if our instructional designs had even this small amount of conversationality? The GPS can carry out a limited conversation with you because it shares a small, specialized semantic space with you: it “knows” the meaning of certain objects about which you want to converse—destinations, kinds of destination, routes, miles, directions, highways, human driving purposes, and most importantly where you are located at any given moment. Because you have a little shared semantic “knowledge” with the GPS, you can carry on a conversation with it as if you were communicating with a very limited person—only on a limited subject, and only using the stock of communication symbols the GPS is equipped with. Note that the conversation from the user’s point of view consists of control actuations, followed by actually driving the car. This semantic dimension is lacking in virtually all computer-based and Web-based instruction today. The computer has no idea what a “lesson” is, what “progress” means, what “goals” are, what your control actions mean, nor any of the other concepts related to instructional engagements. The Web is becoming more intelligent as analytics are being applied to large databases (mainly for marketing, as yet). To the extent that a semantic field was built into an instructional program, you could have the same kind of conversation with your instruction that you have with your car’s GPS, but that would mean that the instructional delivery system would have to know a lot about media resources, instructional purposes, methods, event structures, and you. In a live classroom, a shared semantic context (of meaning) exists because the learner and the instructor both possess a common experience of past instruction in a system where the rules are relatively uniform. When a learner raises a hand, it is a sign that the learner wants to say something. When the instructor gestures to a learner during a discussion, it is an invitation to speak. In those simple acts, the semantic of human symbolic language is invoked, and meanings can be exchanged. (Note: The basic gestural and postural languages of the live instructor are a much neglected area of instructional research.) This shared semantic is one of the strengths of the live classroom that allows it to be as adaptive as it is; but it is also the weakness of the live classroom, because the past experience that is shared includes a classroom culture which assumes that the instructor will act in a certain way and that learners will act in a certain way. The result is an instructional tradition so ingrained into both instructor and learner that its practices and expectations are very difficult to change. The challenge of control system design is designing a shared semantic field that allows learners to communicate with the instruction with a precision that allows the instruction to interpret increasingly complex and nuanced contexted meanings. Some degree of semantic can exist in even the most basic control systems for computer-based instruction. Figure 9.1 shows the different levels of meaning that can be involved when a learner presses a mouse button during a medical diagnosis exercise. The difference between the levels in the figure is a shared context of human-instruction meaning at each level. To the extent that both learner and system understand the context, the system can participate in the instructional experience intelligently. At the machine level (bottom of the figure), the computer only knows about the pressing of a

Design Within the Control Layer • 231

Semanc level

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“The learner realizes that a certain diagnosis might be suspected”

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Figure 9.1 Levels of semantic interpretations of controls for a hypothetical instructional application.

button, and the semantic field is very narrow—one that only the computer “understands”. At the top level of the figure, the computer and the learner both “understand” the elements in the context of the learning problem and have at least a minimally shared idea of what they mean. The chain of control interpretation begins as the computer becomes aware that a control has been actuated (box 1). The instructional application begins to make sense of the control’s meaning as it notices that a learner response has been entered (box 2), as opposed to an administrative command (such as “exit this event”). The application identifies the context that was being displayed to the learner by noting the time and location of the actuation (boxes 3, 4). The application then continues to give context to the control by identifying the instructional event that was underway (box 5)—a particular means goal being pursued. Next the application identifies the implication of the control for the currently active strategic goal (box 6) and performance goal (box 7). How well the computer “understands” the meaning of the control depends on how rich the sematic is that the computer is given that allows it to make interpretations at a given level. It depends on the shared human– machine language. The problem of the control layer is, therefore, a linguistic problem, even if a designer chooses to tell the computer nothing about what controls actually mean. Every design includes a language that gives the learner and the system at least some degree of shared meaning, even if that is a very minor degree. The designer’s job is to make sure that language allows the learner the fullest possible expression and a system reaction appropriate to the momentary context, within the limitations of technology and cost.

232 • Design in Layers

Application Exercise Consider what services can be supplied to your instruction if the control semantic is robust compared with a system with a relatively “dumb” control semantic. Approaches to Control System Design Learners learn by doing, and yet one of the most persistent problems during instruction is giving the learner chances to act, ask, and make choices during instruction. In a live classroom, the number of learners leaves each learner only a small slice of time for productive self-expression. In technology-based instruction, the problem is also limited opportunities for action and responding because of development costs, the limited expressiveness of computer controls, and the limited imaginations of many designers. Control layer design is a subset of interface, interaction, and experience design and needs to be seen by designers in that light. The considerations of interface, interaction, and experience represent the technical, syntactic and semantic dimensions of a design: technical because there is a technical aspect to any structural design; syntactic because there has to be a certain arrangement and ordering of events during control use; and semantic because ultimately the transparency of a control system to the user relies on the shared meanings behind controls that allow the learner to form goals and express action plans for reaching them. This chapter will treat control system design in a way that corresponds roughly with these three views of what is being designed: • A navigation approach that deals with the technicalities of surface concerns of a control design and how controls allow a learner to express movement through choice places, instructional spaces, display landscapes, and modes, maintaining an orientation. • A linguistic approach that considers controls in terms of an object–action language commonly employed in control systems to provide interaction. • An experiential/aesthetic approach that deals with how meanings are exchanged using controls and how increased conversationality can be achieved through attention to the semantics of learner–instruction conversation. Navigational Approach Controls facilitate navigation, and instructional experiences consist of spaces, places, and landscapes to be navigated. At one level, the control layer is preoccupied with navigation, orientation, and wayfinding. A design defines spaces, places, and pathways. Spaces are, metaphorically, open areas; areas of transition; room for moving between; room for maneuvering, deciding, and changing. Places are arrival points; destinations; events; locations within a field; places a learner wants to go; places a designer hopes a learner will choose to go; engagements; things learners can stumble across; areas of enticement and curiosity; interesting questions, and problems. Pathways are the bridges that carry learners across and through spaces, allowing them to view the landscape, scan the options, establish bearings, and maintain orientation. Every design has all three: spaces, places, and pathways. Sometimes destinations are fixed and non-negotiable, so spaces are traversed quickly, and there is no opportunity to look around, to consider options, to participate in choice-making, or even in some cases gain an orientation to the whole. At the opposite end of the spectrum are designs in which pathways are vague and hard to detect and places are few and incompletely or nebulously defined.

Design Within the Control Layer • 233

INTRO

TEST 1 3

2 4

5 6

4.1 Pronoun – Referent Agreement 4.2 Agreement with Compound Referent – OR/NOR 4.3 Agreement with Compound 4.4 Agreement with Collecve Referent

7 8

Press ENTER to see Addional topics

PREREQUISITES UNIT 5 LESSON 4 MAP Figure 9.2 A map of places within a TICCIT space, showing the lesson segments within a unit and the permissible pathways between them. Lower segments have to be completed before upper segments. (Adapted from Merrill et al., 1980, p. 53. Copyright by Educational Technology Publications. Reproduced by permission of the publisher.)

There are reasons for both of these patterns of design. Some subjects, learning goals, and learners require a great deal of structure; others require less. Some instructional goals, such as learning how to learn, require that the learner confront a space in which there are few, if any, places, and therefore few, if any, pathways. In some cases it is important that the learner be the one to fill the space with places and pathways. In some cases a designer may simply arrange places within a space without pathways to encourage serendipitous browsing. Add a physical setting and this turns into a museum display area; add a search engine, and this turns into the Web. Navigation, orientation, and wayfinding are the concerns of control systems, regardless of the configuration of spaces, places, and pathways in a design. A learner either is given or figures out a model of the learning space and its places and pathways. Later in this chapter this will become an important insight. Then the learner has to learn how to use the controls provided to navigate the space. A visual diagram or textual list of the space contents (places and pathways) is normally, but not always, provided. Figure 9.2 shows a unit map from the TICCIT system described briefly in Chapter 2. Its space is completely defined by places (boxes representing lessons) and pathways (lines between boxes). In this case, places are arranged hierarchically, and completion of a higher lesson requires completion of the lower lessons connected to it. Within a lesson, the TICCIT space contains information and action points. Pathways between them are shown in Figure 9.2. Navigation across these pathways is accomplished using a special set of learner control keys depicted in Figure 2.2 in Chapter 2. At the unit map level, the TICCIT example represents fully defined pathways between segments that must be followed in a prescribed order to reach specific place destinations. Within a TICCIT lesson, pathways are also defined, but the order of following them is under the learner’s control. Figure 9.3 shows the set of pathways that exists within an instructional segment. The places in this space consist of fixed instructional displays that the learner can access in any order, just by pressing a key. There is no prescribed order for going from one to another.

234 • Design in Layers 6 RULE HELP

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Figure 9.3 Pathways between information and action places within a TICCIT segment, created by control actions using the specialized TICCIT keyboard. (From Merrill et al., 1980, p. 23. Copyright by Educational Technology Publications. Reproduced by permission of the publisher.)

The important point here is that there had to be controls like the special keys on the TICCIT keyboard to allow selections to be made. The mouse had not matured as a commercialized control device at the time of TICCIT: TICCIT was implemented on a minicomputer, and mice only matured later, for use on microcomputers. Moggridge (2007) describes the fascinating history of the mouse. The development of the mouse as a control device took a long time—in computer years. It went through many versions and trials with users before today’s familiar styles and conventions emerged. Figures 9.4, 9.5, and 9.6 show a visual space representation of a control system of a different kind: one used in a product called “Habitat Hike” (Gibbons and Sommer, 2007). These figures provide views into a more complex learning space defined in terms of both information and action points, with emphasis being on decision-making and action.

Design Within the Control Layer • 235

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Figure 9.4 The main Habitat Hike control environment, showing an orientation area and multiple responding areas. (From Gibbons and Sommer, 2007. Copyright by Sense Publishers. Reproduced by permission of the publisher.)

In Figure 9.4, for example, the major orienting view is supplied by a red line that snakes up the mountainside, in the upper left corner of the display. This is Borah Peak, the tallest peak in Idaho, and the red line defines a path to the top of the peak. The task goal of the Habitat Hike simulation is to “climb” Borah Peak, stopping at seven separate habitats along the way, solving a visual riddle with respect to the ecological web of life represented by each habitat. As a learner passes through each habitat and solves the riddle at each one, the red line advances until the learner is rewarded with a 360-degree view from the top of Borah. The riddle posed at each habitat is to discover one thread of the energy web within the habitat— one set of organisms that fit into four key habitat roles: primary producer, consumer, predator, and decomposer. Two kinds of control are provided for indicating choices for filling the empty slots at the bottom of the display: (1) the titles on the crumpled topographical map on the left, and (2) the empty slots themselves, which are clickable with a mouse. (Yes, the mouse had finally been brought into the mainstream by then.) Both the topographical map titles and the empty slots are controls, because they represent points where an action can be expressed. Another set of controls is located inside the habitat viewing area, which consists of a 360-degree panorama of the habitat area, superimposed with organisms representing each of the four habitat roles. The learner’s job is to scan the habitat (using view navigation controls actuated with the mouse), select a role to be filled (by clicking a map or slot control) and then indicate the organism to fill the slot by clicking on it. Note that clickable organisms are highlighted. Some are very small, and some are very large, so some exaggeration of size was necessary.

236 • Design in Layers

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□ Energy I I Decompose M aterial & Recycle Nutrients Figure 9.5 Organisms in the habitat energy web, showing the multiple threads that could be used to solve the habitat riddle. (From Gibbons and Sommer, 2007. Copyright by Sense Publishers. Reproduced by permission of the publisher.)

When a slot is filled, instantaneous feedback is provided that contains either a congratulatory message or a correctional message. In both cases, the learner is given not only right/wrong feedback, but an explanatory message as to why the choice was right or wrong as well. It should be noted that the structure of the slot-filling control system is intimately related to the instructional goal, which is to recognize the key roles in the energy web in each of the seven habitats. The abstract relationship between roles in the seven different habitats is the essential content, not the names of specific organisms. This energy narrative is hinted by the captions “Flow of Energy and Nutrients” and “Energy” and “Nutrients” close to the role slots. These slots, then, represent the “story” or the narrative that Habitat Hike is hoping will be learned. This display contains still more controls. The Red-Headed Sapsucker character in the upper right corner is a source of hints. Occasionally this figure nods its head and chirps in a way that attracts attention to itself, increasing the likelihood that the learner will click on it and discover that it provides clues to assist solution. A “clear all” control resets the slots back to empty, and a “food web” control leads to the display in Figure 9.5, which is a display of all of the organisms in the habitat and their relationships in terms of capturing energy, passing energy up the energy web, predation, and decomposition. This is an informational display, but it is also a large control panel, since clicking on any of the organisms in the web causes each organism to move dynamically to the configuration shown in Figure 9.6, which shows for any given organism what it eats, what eats it, and what decomposes it. In this way, clues are provided that can lead to future role-filling choices.

Design Within the Control Layer • 237

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M

I’m the submarine of this habitat! I can swim up to 180 feet underwater without emerging, and stay subm erged up to 20 minutes. This helps me m ake a living underwater where I eat the leaves, stems, roots, and bulbs of many aquatic plants. Som etim es I also eat clam s, snails, crayfish, frogs, and even fish. My lips com e together behind my incisor teeth so I can eat underwater without drowning. I build a little cone-shaped house out of cattail reeds. I also dig tunnels in the banks as a place to find shelter from predators. I can outswim coyotes, foxes, and eag les when I’m in the water. Unfortunately for me, otters and mink can catch me in the water or on the shore.

Figure 9.6 Rearrangement of the energy web information in Figure 9.5 obtained by clicking any of the organisms. (From Gibbons and Sommer, 2007. Copyright by Sense Publishers. Reproduced by permission of the publisher.)

Control System Dimensions Habitat Hike exemplifies the close integration of the message, control, and representation layers to create an interface, interactions, and an experience. The navigation approach deals with how controls allow learners to express movement through spaces to places over pathways, maintaining orientation during the journey. The control layer helps to define navigation, orientation, and wayfinding. The role of the control layer is to promote expression of learner choices through action. In the case of Habitat Hike, which was designed for use in a public kiosk setting, success was measured in numbers of minutes that the average user remained engaged. Despite the complexity of the controls, minutes of use were double the average for a museum display. This leads to the conclusion that control complexity does not necessarily dampen engagement when an interesting narrative is unfolding and a mystery is being explored. Application Exercise Examine the controls of a system that allows you to navigate a space of some kind (e.g., Google Earth, Star Walk, a university card catalogue system, etc.). • What are the destinations in this navigation system? • How well does the control system express your desires to move?

238 • Design in Layers

• How is the space itself represented? • How would you simplify this system? • What would you add? The TREKKER Metaphor A metaphor that can be used by designers to integrate control, message, and representation designs can be supplied by what will be called here the TREKKER concept. A trek is a journey across unfamiliar territory. A trekker may spend a good deal of time lost in trees and bushes, but occasionally the wanderer comes to a prominence in the clear that can be used to note major features, establish orientation, read maps, consider options, select pathways, make plans, select waypoints, and set out again along the charted course. Styles of trek differ: a novice may follow a recommended pathway, but an experienced trekker may devise a customized course that promises more adventure and more satisfaction. This image of the trekker applies to both novice and expert learners. Trekking, or self-directed learning, is a major goal of instruction, a process described by Butler and Winne (1995): Self-regulation is a style of engaging with tasks in which students exercise a suite of powerful skills: setting goals for upgrading knowledge; deliberating about strategies to select those that balance progress toward goals against unwanted costs; and, as steps are taken and the task evolves, monitoring the accumulating effects of their engagement. As these events unfold seriatim, obstacles may be encountered. It may become necessary for self-regulating learners to adjust or even abandon initial goals, to manage motivation, and to adapt and occasionally invent tactics for making progress. Self-regulated students are thus aware of qualities of their own knowledge, beliefs, motivation, and cognitive processing—elements that jointly create situated updates of the tasks on which the students work. This awareness provides grounds on which the students judge how well unfolding cognitive engagement matches the standards they set for successful learning. —(p. 245) Self-direction is learned over time and through experience, in the same way that walking is learned: with early intensive support that eventually tapers off. The length of the taper and the pattern of removing scaffolds varies, according to the learner. What a designer can do is supply learners with the information and controls they need to make the decisions that are theirs at any given moment. As control is relaxed, self-direction becomes possible. The TREKKER framework is useful to the designer whether the learner is experienced in selfdirection or not. The metaphor suggests questions that the learner needs answers for at each decision point during instruction. At each point on a learning trek the designer must provide: (1) information the learner can use to support decision-making, and (2) controls the learner can use to express choices. Figure 9.7 shows that this creates a number of points, at which the questions in the learner’s mind are different, requiring different kinds of information and control support. It recognizes differences between choices being made in the space between instructional events (“spaces between places”) and choices being made within instructional events (“within a place”). There is an important reason for this distinction. Handling transitions between instructional events—for example from lesson to lesson—is normally considered the province of the kinds of learning management systems (LMSs) currently in wide use. Such systems treat what goes on within instruction as a black box. They assume that the lesson or event is self-contained and that a score will be passed back to the LMS following its completion, to be registered in a database. Th is is the

Design Within the Control Layer • 239 Transitioning Between Instructional Events Seek Orientation

Evaluate Status

Form Goals

Plan Actions

Provide Info

Seek Orientation

Evaluate Status

Form Goals

Plan Actions

Give Control

Transitioning Within Instructional Events

Provide Info

Seek Orientation

Evaluate Status

Form Goals

Plan Actions

Give Control

Seek Orientation

Evaluate Status

Form Goals

Plan Actions

Figure 9.7 The TREKKER framework that can be used to supply information to learners for self-direction, regardless of the level of the learner’s self-direction ability.

“grade book” metaphor of the current LMS. Conversational instruction cannot tolerate such a black box approach; it must be able to support learner choices within instructional events. The questions in the learner’s mind during the transition between events may differ from those in the learner’s mind at places within an instructional event. (Note: A later discussion in Chapter 12 on the strategy layer will seem to conflict with this distinction between decisions outside of an event and decisions within an event. The real conviction of the author is that instruction consists of decomposable events and that all events are simply combinations of smaller events. The distinction here between between-event and within-event decisions is an appeal to the traditional understanding, which for most designers at the present time tends to draw clear boundaries between events. In a truly conversational instructional system the emphasis on media “events”—such as “lessons”—will probably give way to a different kind of progress-marking points—something more like “goal attainment” or “competency achievement”. That is, the emphasis on divisions of instruction based on physical objects—media events, or “lessons”—will be replaced by something that measures progress for the learner, rather than the designer.) Figure 9.7 shows that whether the learner is between or within instructional events—instructional places—a similar process is taking place in the learner’s thinking. The trekker’s process involves: (1) seeking orientation, (2) evaluating status (progress against an existing plan), (3) forming goals, and (4) planning actions that will move the learner toward attainment of the goals. Table 9.1 samples the questions of the learner at each of the “between instructional event” points in Figure 9.7. The lists of questions in this table are not exhaustive, but they do suggest the kinds of questions a learner may have. Table 9.2 samples the questions of the learner at each of the “between instructional event” points in Figure 9.7. Once again, the questions are suggestive, not exhaustive. One of the major blind spots in learning management (which legitimately should be called “learning support”) systems today is that even if significant strategic choices were provided to learners by the designer, one of the keys to supporting intelligent learner choices would still be an information-and-control system that nurtured the learner’s decision-making process at a detailed level—within instructional events. As the adaptivity and conversationality of instructional systems improves, this supporting function will become a more important design consideration.

240 • Design in Layers Table 9.1

Questions the Designer Should Anticipate during Decision-making as the Learner Moves between Instructional Events. Provide Information

Give Control

Seek Orientation

1. What did I just finish? 2. What is next?/What can be next? 3. What is expected of me at this point? What do I expect of myself? 4. How does what I just learned relate to my options?

1. What options/controls do I have at this point? 2. Where are the control actuators?

Evaluate Status

1. What status information can I obtain? 2. How well have I done on previous events? 3. How does my performance compare? (With peers, etc.) 4. How am I doing compared to time constraints? 5. How am I doing compared to my own prior plans? 6. How am I doing compared to my own goals?

1. How do I obtain status information? 2. Where are the control actuators?

Form Goals

1. What goals are already set that I must achieve? 2. What goals can/could I form for myself? 3. Does the system have any recommendations for me?

1. Where are the control actuators for surveying goals? 2. How may I express new current goals and activate them? 3. How can I access my current and past goals? 4. How do I specify the balance of initiative at this point? 5. How can I prioritize or order my goals?

Plan Actions

1. What actions are possible at this point? 2. Which actions are under my control? 3. What action plans do I have in effect at present? 4. What are the implications of different action plans?

1. How do I effectuate actions that are possible at this point? 2. How do I obtain system recommendations? 3. How do I express my action plan at this point? 4. How do I modify an existing action plan?

Table 9.2

Questions the Designer Should Anticipate during Decision-making as the Learner Moves Within Instructional Events. Provide Information

Give Control

Seek Orientation

1. Where am I within this event (time, location, goals, percent completed)? 2. What else is there in this event to see/do?

1. How do I obtain orienting information? 2. Where are the actuating controls?

Evaluate Status

1. What are my currently active goals? 2. How far do I have to go to reach completion? 3. How far do I have to go to reach criterion? 4. How does my performance compare so far? 5. Am I proceeding according to plan? 6. How close am I to reaching my goals?

1. How do I obtain status information? 2. What status information can I obtain? 3. Where are the control actuators?

Form Goals

1. What goals may I form at this point? Types? Scope? 2. Which goals may I control and which not? 3. Can I prioritize and order goals?

1. How may I express and activate new goals? 2. How can I access current and past goals? 3. How do I express the balance of initiative at this point? 4. How can I prioritize or order my goals?

Plan Actions

1. What actions are possible at this point? 2. Which actions are under my control? 3. What action plans do I have in effect at present? 4. Does the system have any recommendations? 5. What are the implications of different action plans?

1. How do I effectuate actions that are possible at this point? 2. How do I obtain system recommendations? 3. How do I express my action plan at this point? 4. How do I modify an existing action plan?

Design Within the Control Layer • 241

Design Questions for Navigational Controls The examples in the previous section demonstrate that there are multiple levels of control and multiple types of control. This leads to many, many design decisions that are not readily apparent to the novice designer. The following sections deal with some of those detailed considerations to give an example of the kinds of issues a designer may have to deal with. Types of Control and Control Operation Decisions about “types” of control involve everything from mechanical device choices, control operation, control realism and fidelity, the synchronization of control actions, and arrangements that make controls more accessible to disabled users. Devices A device consists of any means that a learner can use to express a control action or a response. A hand raised during class is a device, as is a mouse. In some cases (for example, games, some simulations, and some problem-solving environments) control devices become highly specialized. The concern of the designer in device design is to identify the range of devices through which the learner can express control actions. This can raise questions about: • • • • • • • • • •

What devices will be used as controls? Will multiple devices be used at one time? What kinds of device actuation will have meaning? How will the system know when a device action is complete? What feedback/tracking representations will tell the learner that device operations have been received and are being processed? What factors of ease of device operation will be critical? What will devices look like? Where will devices be positioned relative to the learner’s space? Will training in device use be necessary? Will warm-up exercises in device use be needed?

Control Operation Control operation may consist of more than a single key press or mouse click. Sequences of control operation are often required to express the full “sentence” of nouns and verbs that contains the learner’s intentions. Sometimes the nouns and verbs will literally be nouns and verbs in a text response. Control operation may require multiple actions in a sequence. Control operation design describes how controls are operated, and the control modes and contexts for each control. Design issues include modes, control action, feedback, and tracking during control operation. • • • • •

Will device operations be discrete or continuous? How important is the ability of a device to respond to fluid action? Within what contexts will a specific control be expressible? What sequences of action constitute a control operation? Will a control be active only within certain contexts (modes)?

Realism/Fidelity For controls used to simulate real actions or metaphorical actions (e.g., Google Earth controls and the “flying” or “navigating” metaphor), realism and fidelity become a factor. The realism/fidelity

242 • Design in Layers

sub-layer is an example of a sub-layer that is not always required but that can become critical in certain kinds of application. Timing and responsiveness can also be very important. • • • • • • • • •

How critical will the “feel” of actuation be to learner performance? How sensitive must controls be? How realistic must devices be? What level of fidelity to a real-world device is required? What dimensions of realism and fidelity will be of importance? How will realism and fidelity be measured? Will realism and fidelity be more important for some control operations than others? Will the realism and fidelity of control operation need to escalate over the course of instruction? How critical will exact timing and responsiveness be for each control? How much time can a device action take?

Control Synchrony If it is necessary for two controls to operate at the same time, or if the operation of one control requires a second control to be operated in some sequence, then control synchrony becomes an important consideration. • What hierarchy of importance will exist for resolving conflicts of device actuations? • What control actuations will be permitted to act in synchrony, and which will be locked after one actuation? • What timed or synchronous sequences of control operation will have meaning? • Are some simultaneous control actions interpretable as a single control action? • Will length of control actuation have meaning? • What is the priority order (of interrupts) for the processing of control inputs? • How will simultaneous inputs from multiple users be judged? Layer-related Controls Each layer provides some functionality to the integrated instructional system. There are often controls that relate to specific layer functions. For example, controls that terminate a browser session are part of the representation at the same time as controls that provide navigation within the browser and controls that provide choices within a single displayed document. Determining the potential contribution of each design layer to the control set is the concern of the layer-related controls decisions. • • • • • • • •

What controls, if any, will be given to the learner over content? What controls, if any, will be given to the learner over strategy? What controls, if any, will be given to the learner over the control system itself? What controls, if any, will be given to the learner over messaging? What controls, if any, will be given to the learner over representation? What controls, if any, will be given to the learner over data management? What controls, if any, will be given to the learner over media-logic? Will controls related to different layers be combined into single controls?

Levels of Control “Levels” of control refers to the perspective point from which a learner actuates a control. • Administrative controls—Controls that start, terminate, or pause an event. Controls that request help from the system. Controls that reach outside of an event in some way to perform a function.

Design Within the Control Layer • 243

• Navigational controls—Controls that allow a learner to move through spaces, among places, and along pathways—both literal and figurative. • Action controls—Control actions that resemble some real-world action or an action that is part of a skill or use of a body of knowledge being learned. • Statement controls—Controls that allow learners to express themselves to the instruction without being first asked. • Query controls—Controls that allow a learner to ask a question of the instruction. • Negotiation controls—Controls that allow learners to negotiate goals, roles, means, or any other decision that will influence the future course of an instructional plan. Application Exercise If you were to design a “dashboard” for learners that gave them access to information to help them self-manage their own learning, what information would you supply on the dashboard? How would it be conveyed to the learner through the senses? Linguistic Approach At one level, the control layer is concerned with the creation of languages. This involves recognizing space, places, and pathways and acting within an environment, taking initiative, and demonstrating competence. Control Languages The underlying mechanism of the control layer is language (Crawford, 2002). The acts of a learner are expressed in terms of objects (nouns), actions (verbs), and modifiers (adjectives and adverbs)— not literal words, but the symbolic words of a control language that the designer creates. Consider the examples below Control operation usually creates a message to the instruction system/instructor of the form: • • • • • • • •

By With respect to the On Convey the Of To carry out Of Within the present .

To see how this confusing statement applies, let’s revisit the automobile control example given earlier. Setting the cruise control would be expressed this way: • • • • • • • •

By With respect to the On Convey the Of To carry out Of Within the present .

244 • Design in Layers

This sounds a little stilted, but it is a language statement that could be programmed into the cruise control unit (with a little translation into programming language) and still understood by me, the driver. This sets up a language for communicating with a device about the operations of the device. The language allows me to send control commands to the device. We (the cruise control unit and I) would have a common language of action. When we open a file on the desktop of a computer, the control language expression might be like this: • • • • • • • •

By With respect to the On Convey the Of To carry out Of Within the present .

What might an instructional control operation mean? We could use the example in Figure 9.1 to fill in the blanks in two different ways, one of them for non-intelligent software, and one of them for intelligent software or a live instructor. The non-intelligent software first: • • • • • • • •

By With respect to the On Convey the Of To carry out Of Within the present .

Then the intelligent software: • • • • • • • •

By With respect to the On Convey the Of To carry out Of Within the present .

The difference in these examples is that in the non-intelligent one the computer has no ability to make any decisions except those it always makes in order to execute program commands. In the intelligent example, the computer makes those decisions, but it is enabled to participate in additional decisions, should it be called upon to do so. It “knows” something about what is happening and has additional programs that allow it to carry out additional activities in support of instruction.

Design Within the Control Layer • 245

• • • • • • • •

By With respect to the On Convey the Of To carry out Of Within the present .

Instructional Control Language In simplest terms, the operation of a control produces a the message. A designer specifies the nouns and verbs of the control language during design in terms of: (a) elements that can be acted upon, (b) the actions possible on the element, and (c) the contexts in which the action can occur. This only sounds complicated. You use control languages every day that are exactly according to this principle. When you click on an object on a computer display you are in effect saying to the computer “. . . on this object . . .” Then when you select a command from a pull-down menu, you are saying “. . . perform this action . . .” That is a command in the form of “ the : “”. The key questions for the control language designer are, then: • What things can be acted upon? • What actions can be performed on each object? • Under what conditions? In what conditions? Or when? This third question brings up the concept of context-sensitivity. You may have noticed while you were word-processing that if no text is selected, certain pull-down menu choices are greyed-out— not available. But if you have selected a word, several menu commands then become active. This is because when you select a word in a word processor, you enter a new mode—sometimes called a context or a state—even if it doesn’t seem like it. When something is selected, you have said to the computer “”—meaning that you have selected a “noun” in the control language. Then the computer waits in that mode for the rest of the command, the verb. If you click anything that is not a verb, the computer assumes that you don’t want to complete the command and reverts back to the original mode (aka context or state). If you select a verb—by clicking an icon or an item on a pull-down menu—then the computer executes the command and the selection is bolded, italicized, or deleted—depending on the command you selected. The principle of context-sensitivity is a general principle of control systems. Controls in a word processing environment are one thing, but how do the principles of the language and context-sensitivity apply in other kinds of interaction? Consider a few examples. • Example #1: A computerized two-dimensional simulation. You are using a computer display to control the temperature and pressure in the boiler of a power plant. Before you is a display of the boiler’s control panel, showing both controls and temperature and pressure indicators. You decide the temperature is reaching a critical value. You click on (or touch, or move your eyes to) the temperature control. You drag and drop (or drag your finger, or blink twice) and the control value changes. You have the .

246 • Design in Layers

• Example #2: A simulation using real equipment. You are facing the temperature and pressure controls. They are real knobs. You move your hand to the temperature knob. You turn it to the right value. You have the . • Example #3: A non-computerized simulation using assistive technology (a communication board). You are unable to speak your commands, and you are impaired in your ability to move your hands in more than simple movements, but your mind is bright. You have on your lap a command board with a drawing of the control panel, and you are told that the temperature is rising. You move your finger to the temperature control and tap it once. Then you move your finger to the icon for “increase value” and tap it the correct number of times. These example show that the the form of communication through controls is a generalizable principle. The value of the principle for an instructional designer is that it supplies a basic design pattern for designing interactions. The designer becomes interested in asking the following, which lead to the formation of a control language: • • • • • •

What symbols will be used to represent “things”? What physical actions will be used to represent actual actions upon “things”? What is the syntax (order of actions) of the control language? What is the meaning of each the unit? How complex will language expressions be allowed to be? Will speed/action quality be a factor in the form of the language?

Control language systems can be used for navigation, interaction, simulation, performance assessment, and even rudimentary natural language communication. For example, a set of pull-down menus like those in Figure 9.8 could be used to give commands during a math interaction in which the learner was practicing solving simultaneous linear equations. Figure 9.9 shows how this same pattern could be adapted for management training.

Add

Equaon 1

To

5

Subtract

Equaon 2

From

-5

Mulply

Equaon 3

By

Divide

10 -10

Figure 9.8 Pull-down menu used to construct a limited control language interaction.

Send

Memo1

To

Johnson

Request

Offer

From

Wilson

Share

Conversaon

About

The Board

With

Shareholders

Refuse Figure 9.9 Pull-down menu tailored to a management application.

Design Within the Control Layer • 247

Robotic Languages Robotic languages translate action intentions into servo-mechanistic movements and convert readings from sensors into digital signals that can result in further robotic actions. Robotic languages are useful in instruction when learning is accomplished through learner design. An early robotic language called LOGO did not involve mechanical actuation, but current robotic construction kits allow learners to construct computer–mechanism interfaces and then program them with interesting actions. Gestural Controls A whole new world of control options has been opened by gestural controls of the kind used in game systems. These include a variety of controls operated by swinging, pushing, hitting, or snapping, as well as controls embedded in floor or wall pads. Game control systems are easily adaptable for use as navigation controls through virtual spaces, traversing space, and zooming in and out. Luckily, designers don’t have to program their own software to connect with this type of control system, because system architecture and routines for the game systems are usually open through software developer kits. Control Issues As the number of control options increases for designers, new challenges appear, including maintaining simplicity, keeping controls transparent, determining the priority and interactions among controls, keeping straight the interdependence of control settings, and carefully planning modal partitions in which some controls become temporarily unavailable (such as a menu item which disappears). All of these issues are familiar to simulation designers. The control layer prompts the designer to specify patterns of alignment among learner intention states, control language primitives, control language expressions, conversational patterns, and mechanical control device operations. This alignment creates a chain of interpretation from a mechanical act on a device to its intended meaning. Several quality concerns should become goals for the designer: • Affordance—Does the control look like a control and invite an action? (See Norman, 2002, 2004.) • Proportion—Are controls represented in a salient way and in proportion to their importance in a particular context? • Responsiveness—Are controls timely in their response? Are delays minimal? • Feedback—When a control is actuated, is there some signal to the user that shows what is happening or what has happened? These questions create the demand for a coherent, standard plan for control system design and control conventions. Not only should the control system be coherent within one instructional product but within product families as well. The designer should specify quality standards for the controls and their operation. Application Exercise Describe the language you use to operate your telephone in terms of the the statements. The Experiential/Aesthetic/Semantic Approach Instructional experiences consist of spaces, places, and landscapes to be navigated, but in the case of instruction, the whole experience is greater than the sum of the parts that make the experience

248 • Design in Layers

possible. At one level, the control layer is preoccupied with the generation of shared meaning that makes efficient and moving experiences possible. Buxton (2007) proposes that designers should “put experience front and center in a design” (p. 9). Permitting user experience to take center-stage means making control systems that are semantically rich but as transparent as possible to the user. “What we are creating”, says Buxton, “is less a product than a context for experience” (p. 10): It is not the physical entity or what is in the box . . . that is the true outcome of the design. Rather, it is the behavioral, experiential, and emotional responses that come about as a result of its existence and its use in the real world. —(p. 10) Rheinfrank and Evenson (1996) describe the difference between a black box design and a transparent box design: One thing that designers can do to help people learn [what they can do at an interface] is to reveal functionality through transparent-box design, rather than to conceal it through black-box design . . . In a black box, all functionality [and therefore meaning] is opaque, or hidden from view, and people accomplish their goals by pushing buttons that signify nothing whatsoever about the inner workings of the object . . . In a transparent box, functionality is revealed, and people are provided with the opportunity to comprehend the inner working of the artifact they are using. —(p. 71, emphasis in the original) One would say, also, that a shared meaning is created between the user and the artifact. The user understands the artifact and can begin to use it as a tool. The user can adapt the artifact for personal, inventive, purposes. Interface and Affect What summarizes the experiential/aesthetic approach? There are several windows into this issue in a volume edited by Winograd (1996) titled, Bringing Design to Software. Liddle (1996) describes designing the first interface through which the user communicated using “spatial, gestural, and nonverbal interaction techniques” (p. 19). He identifies three design issues that were the key to this design: (1) the display of information, (2) the command or control mechanisms, and (3) the user’s conceptual model. According to Liddle: “The most important component to design properly is the third, the user’s conceptual model. Everything else should be subordinated to making that model clear, obvious, and substantial. This is almost exactly opposite of how most software is designed” (p. 21). Liddle describes this model as “what the user is likely to think, and how the user is likely to respond” (p. 21). Winograd describes it this way: “Every object appears in a context of expectation that is generated by the history of previous objects and experiences, and by the surroundings in the periphery—the physical, social, and historical context in which the object is encountered. —(p. xxiii, emphasis added) The context of use, the user’s intentions at the time of use, the history of the interaction and all previous interactions, the affective state of the user, and the user’s purposes are the factors in the design of an interface. It is this context that allows communication to take place through controls with a minimum of effort and direct attention required to make the expressions themselves. It becomes, like excellent music, defined as much by what is not played as by what is.

Design Within the Control Layer • 249

Crampton-Smith and Tabor (1996) note the necessary qualities of an interface: “clarity, legibility, predictability, and economy of means are essential” (p. 43): These characteristics, however, are not only useful, but also affective—the timetable shown [in an example they are presenting] evokes an emotive response—a congenial feeling of clarity, predictability, and so on. The graphic design implies, whether or not intentionally and truthfully, that the rail company is dependable, efficient, and trustworthy. —(p. 44, emphasis in the original) Interface and Product Architecture An interface does more than report information and accept control commands. “An important part of the product . . . is a coherent model that tells users what the product is and how they operate it—its central organizing model” (Crampton-Smith and Tabor, 1996). “An interaction designer who thinks about information in terms of what users do with it, and the context in which they use it, may imagine approaches different from those suggested by the way that the system is engineered” (p. 49). The experiential/aesthetic approach to interface (and therefore control) design requires a separation of the internal structure of the software from the external face that it presents to the world. Buxton (2007) explains how the external face must be interpreted in terms of other interfaces the user has to deal with: Increasingly the technologies that we design are not islands—that is, they are not free-standing or complete in their own right . . . Rather, they are social entities . . . They have different properties and capacities within a social, and physical, context than they have when viewed in isolation, independent of location or context. —(p. 11) Kapor (1996) notes that one of the barriers to the building of more user-friendly interfaces is the focus of interface designers, who often are also the designers of internal program mechanisms. He makes a comparison between the architect and the engineer: Architects, not construction engineers, are the professionals who have overall responsibility for creating buildings. Architecture and engineering are, as disciplines, peers to each other, but in the actual process of designing and implementing the building, the engineers take direction from the architects. The engineers play a vital and crucial role in the process, but they take their essential direction from the design of the building as established by the architect. When you design a house, you talk to an architect first, not an engineer. Why is this? Because the criteria for what makes a good building fall substantially outside the domain of what engineering deals with. You want bedrooms where it will be quiet so people can sleep, and you want the dining room to be near the kitchen. The fact that the kitchen and dining room should be proximate to each other emerges from knowing first, that the purpose of the kitchen is to prepare food and the dining room to consume it, and second, that rooms with related purposes should be closely related in space. This is not a fact, or a technical item of knowledge, but a piece of design wisdom. —(p. 4) The placement of the kitchen close to the dining room is an artifact of architectural thinking, not engineering thinking. It is not that the engineer wouldn’t know from owning a home about the convenience of the arrangement; it is that the engineer’s attention is focused on answering different questions. For example, the ease of getting pipes connected might overshadow concerns about the

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user’s use of the interior space. Winograd and Tabor (1996) describe it in this way: “The architect starts from the look-and-feel of the problem—Vitruvius’ commodity and delight. The engineers and builders are more concerned with the firmness of the construction: with issues of economy, safety, and constructability” (p. 10, emphasis in the original). More evidence of the difference in focus between the architect and the engineer is found in the supposed long-range concern of the architect for the life cycle of the product. “What properties of our software”, ask Winograd and Tabor, “will make it remodeler friendly, while preserving its firmness?” (p. 12, emphasis in the original). The issue of remodeler friendliness echoes Brand’s (1994) concern for the layering of building designs so that different layers can age and be replaced gracefully rather than destructively—the major theme of this book. Architectural thinking is what can relate an interface to the user’s context and the context of other products. An architect’s work makes a statement. A building can be designed to fit in with its surroundings, or it may be designed deliberately to call attention to itself, sending the message, “notice how different I am”. In either case, the product proclaims what it is and must also declare to the user how to use it. This is more the job of an architect than an engineer. As technologies mature, they take on a life of their own, and their life begins to influence our lives. Experiential interface design is changing our culture in a way described by Liddle (1996): When the Wright brothers first flew an airplane, or when Benz drove the first automobile, the wonder was not that people could drive or fly easily, but that they could do so at all. These machines eventually left the enthusiast realm and became forces of change in society, because the principles of their use became more important than the technology of their construction. —(p. 30) Interface and Commitment One of the most important meanings shared through an interface using controls is the intention to be bound by a commitment—an agreement of some kind between the instruction and the learner. Commitment to a mutual goal is necessary for an instructional system to support a learner in a dynamic, adaptive way. This implies not only a moment of commitment, but a renewal of the commitment moment by moment and adjustment of the support plan dynamically as needs, goals, and levels of commitment change. This is not only a concern for adaptive, conversational instruction but for human–computer interface design in general. Winograd and Flores (1987) realized this and documented a basic pattern for achieving and maintaining communication and commitment across the interface through controls—what they term conversations for action. Figure 9.10 illustrates this pattern. The “conversation” for action is a recursive pattern in two parts: one part in which an agreement is reached (states 1 to 3), and another in which the agreement is fulfilled (states 3 to 5). The pattern is recursive because it occurs within itself, over and over. That is, in the process of reaching an agreement, there may be negotiations that require intermediate agreements to be reached and fulfilled. The recursive pattern is beneficial for two reasons: (1) it makes the conversation generative and definable in terms of multiple goal formations and fulfillments, and (2) it implies that a single mechanism can be fashioned that attends the formation and fulfillment of agreements at different levels of granularity. The same process that tends the formation and fulfillment of goals at a high level can also be used to tend the formation and fulfillment of very low-level goals as well. There are many models of conversation in the literature that highlight information transmission, processing, and responding after thoughtful consideration, but the heart of conversation is even more: the mutual commitment between its parties that keeps the conversation going. That

Design Within the Control Layer • 251

Agreement 1

A requests

B Promises

2

B Asserts

4

A Declares

5

s ter s oun A c unter o Bc

Aa cce pts

3

s

Br eje cts

ithd raw

B rejects

Aw

A withdraws

raw s

8

Aw ith d

A withdraws

Refusal or Renege

6

B reneges

Negoaon

7/9

Figure 9.10 The conversation for action pattern. (Adapted from Winograd and Flores, 1987.)

commitment is signaled between participants through verbal, gestural, postural, and expressional messages that are exchanged that have the effect of imposing control on the conversation—not controls of compulsion, but controls that communicate mutual intent that can be reversed at any moment. Seen through this lens, instruction is a process of the formation of agreements, and the basic metric of the instructional conversation is the compact to continue conversing, of which controls are an important element. The compact in this view becomes a basic unit of instructional design that typifies at the same time the gross structures of the learning management system and the minutest exchange during an explanation, a demonstration, or a performance. The compact, or at least the willingness to form and fulfill compacts, is, then, the gravity that attracts the learner and the instructional system, even if only for an instant. This realization accentuates the importance of controls, acting on both sides of the conversation, for instructional design. Application Exercise How well does the Winograd and Flores pattern of conversation fit your concept of instruction? Consider the possibility that the act of reaching and fulfilling a single commitment can require the making and fulfilling of other, more minor commitments, each sharing the same Winograd and Flores pattern. What are the implications of this? Control Theories The theory that pertains to the control layer is cybernetics. Cybernetics is the study of systems and how they are regulated, moderated, and guided through feedback. This is exemplified by temperature regulating the opening of a flower bud, a quarterback adjusting arm movements to intersect

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the path of a moving pass target, and a live instructor adjusting an answer based on a learner’s question but also on the learner’s mood indications. Cybernetics is an essential property of systems; it cannot be divorced from systems; it is the means by which systems self-adjust, change, adapt, carry out their functions, and survive. It is an indispensable part of the study of general systems theory. The central principle of cybernetics is that as a system carries out its functions it influences the environment in which it operates. In turn, changes in the environment brought about by this influence then return influence back to the system. The implications of this simple principle are staggering because systems are made up of systems, which are themselves made up of systems. Moreover, systems co-exist side by side and intermingle intricately, even when they are not parts of each other. Therefore, the influence of a very small and relatively minor system can, through cascading influence on other systems, become a very large net influence on many neighboring and component systems. This cascading effect and feedback within systems and between systems can amplify or dampen subsequent effects, sometimes with enormous consequences. Though its origins lie much earlier, cybernetics supplied the subject of a series of multidisciplinary conferences from the mid-1940s to the mid-1950s that solidified it as a research subject. Original and leading thinkers from scientific, design, and social science disciplines gathered to discuss the possible convergence of their fields and the unification of the sciences (Heims, 1991). Following the conferences, the influence of cybernetics spread rapidly, and today the topic pervades virtually every field of design and science, even if the terminology of cybernetics is not apparent in its literature. The original conception of cybernetics was technical and mechanistic because the concept of cybernetics was then connected with computers, robots, automatons, weapon systems, and systems that needed little or no human. The concept of control was also prominent, as in the control of something’s or someone’s behavior. These associations may be among the reasons some people find the concept of cybernetics less than “friendly”. The term “cyborg” brings to mind the evil, controlling “Borg” of Picard’s Star Trek. The extent of the influence of the original cybernetics is described concisely by Heylighen and Joslyn (2001): Cybernetics had a crucial influence on the birth of various modern sciences: control theory, computer science, information theory, automata theory, artificial intelligence and artificial neural networks, cognitive science, computer modeling and simulation science, dynamical systems, and artificial life. Many concepts central to these fields, such as complexity, selforganization, self-reproduction, autonomy, networks, connectionism, and adaptation, were first explored by cyberneticians during the 1940s and 1950s. Examples include von Neumann’s computer architectures, game theory, and cellular automata; Ashby’s and von Foerster’s analysis of self-organization; Braitenberg’s autonomous robots; and McCullough’s artificial neural nets, perceptrons, and classifiers. —(p. 3) Concern with the mechanicalness and deterministic nature of cybernetics led one of its original founders, Heinz von Foerster, to lead a break-away movement in the mid-1970s that asserted itself as “second-order” cybernetics. Second-order cybernetics begins with the realization that the goals and structure of the systems the first-order cybernetician tries to control are set by the cybernetician. The system is a thing apart from the controller of the system. However, not all systems are mechanistic: there are systems of living things, and there are human and animal social systems. These are also amenable to description, study, and influence by cyberneticians.

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According to Glanville (2001): The relation of first order Cybernetics to second order Cybernetics is like the relationship between the Newtonian view of the universe, and the Einsteinian. Just as Newton’s description remains totally appropriate and usable in many instances (including flights to the moon), so first order Cybernetics also retains its value and frequently provides us with all we need (for instance, in many control arrangements). And just as the Newtonian view is understood to be a special, simplified, restricted (and slow) version of the Einsteinian view, so first order Cybernetics is a special, simplified, restricted (and linear) version of second order Cybernetics. —(p. 1) Cybernetics as the basis for control layer decisions in instructional design is closer to the secondorder sense of cybernetics. Control during conversation in an instructional conversation is constantly modifying the course of the conversation, at whatever level of granularity it is applied. The designer acts as a co-designer of instructional conversations. Early instructional design literature represented cybernetics as a key theoretical idea for the instructional design field (Lewis and Pask, 1965; Silvern, 1972), especially for intelligent instructional systems. For instructional designers cybernetics is the principle most central to designs that can adapt to changing circumstances. Today, in practice, the principle of cybernetics exists mainly in the evaluation function that is a prominent part of the ADDIE process. The cybernetic principle is readily detectable in the familiar feedback loops built into most ID models. Moreover, designers are taught that instruction should adapt to the user, but the tools and concepts for doing this are just maturing. Cybernetics as a topic study is not the typical fare today for instructional design students, even at the graduate level. The complexity of instructional products is sure to escalate due to competitive pressures and technical and conceptual breakthroughs. Then the principle of cybernetics will become quite relevant. Conclusion This chapter has viewed design at the control layer from three perspectives: (1) in terms of navigating among spaces and places across pathways, (2) in terms of control languages the designer invents, and (3) in terms of shared meanings between the learner and the instructional system that allow them to adapt to one another during an instructional conversation. Winograd and Tabor (1996) convey the sense of how control systems can be seen from multiple views: A work of architecture can be seen in terms of three interlocking domains: material components, spaces, and experiences. So an architect might conceive of a building primarily as (1) an assembly of walls, floorplates, and columns; (2) a cluster of spatial volumes, some squat, some lofty; or (3) a sequence of feelings induced in the user—of welcome, awe, constriction, and release . . . Obvious parallels can be drawn to software, which traditionally has been designed with a focus on the computing itself: algorithmic form, function, and implementation. The software-design field is now turning to understand the nature of the human–computer interactions—the metaphorical spaces that people inhabit—and to the experience that software offers the user. —(p. 15) This can (and should) be the attitude of the instructional designer as well.

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10

Design Within the Representation Layer

All our knowledge begins with the senses, proceeds then to the understanding, and ends with reason. —(Immanuel Kant) He is the true enchanter, whose spell operates, not upon the senses, but upon the imagination and the heart. —(Washington Irving) Dialogue should simply be a sound among other sounds, just something that comes out of the mouths of people whose eyes tell the story in visual terms. —(Alfred Hitchcock) The representation layer produces something the learner can experience through the senses; it is the only layer that does this. The concerns of the representation layer, therefore, include orchestrating all of the signals sent to the learner’s senses, their staging, and their choreography. Publications related to the representation layer, with only a few exceptions, deal with the principles for producing individual types of media. There is the equivalent of a large library of books and online resources of this kind, and rapid changes in media technologies add to this library literally every day. The representation layer is the visible layer, therefore it is the charismatic layer, and it can (and frequently does) become the designer’s main preoccupation during design conception. It is not surprising that this layer has historically assimilated and obscured the importance of the other layers. The focus of this chapter will be the issues of designing representations that incorporate multiple media forms working together. The important questions of representation have to do with complete sensory experiences, not individual media forms. Designers need to be oriented toward concerns of synchrony, timing, and the complementarity of media channels in a world where dynamic representational forms are rapidly becoming the standard.

Explanation is the Main Problem of the Representation Layer The critical design problems of the representation layer are not media production problems; they are problems related to making explanations. Representations supply raw materials out of which learners build understandings. During representation, design principles are applied that harmonize 255

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and synchronize multiple media forms for clear, efficient, and focused communication—not of information, but of ideas (Tufte, 1997). The communication must lay bare the essence of an idea and give proportion to its relationships with other ideas. Saul Wurman, author of Information Architects (1997), describes how this task is complicated by the “tsunami of data that is crashing onto the beaches of the civilized world” (p. 15). He notes the confusion between simply illustrating data and supporting understanding. Because of their access to computers, like everybody else, designers do make prettier piecharts, now in 256 or in millions of colors, now in three dimensions, now exploding apart in wedges, floating in space, with shadows on some ethereal background. But apparently they are applauded by other graphic designers and by clients who don’t seem to care about understanding, or are convinced that jazz and beauty and design as they know it—making things prettier—is the wave of the future. —(p. 18) “Prettier” can complicate rather than simplify the learner’s task of deciphering a representation. But, according to Tufte, “when principles of design replicate principles of thought, the act of arranging information becomes an act of insight” (p. 9). Tufte proposes that visuals (and, by extension, other media expressions) should have an inner structure, coherence, and meaningful unity in addition to an attractive exterior. Tufte’s book, Visual Explanations, names “strategies . . . for presenting information about motion, process, mechanism, cause and effect” (p. 9). He speaks in terms of “strategies . . . found again and again in portrayals of explanations, quite independent of the particular substantive content or technology of display” (p. 9). These strategies are of particular interest to an instructional designer. Multiple media forms used in instruction should lead to understanding by speaking with a common, clear, coordinated voice, and the principles for the design of representation should reach across and harmonize their influence. Application Exercise Have you considered representations for their abilities to explain? • Collect several examples of representations that explain something well. • Identify how the explanation is accomplished. What are the key properties of the representations that make it work? Maturity of the Representation Layer In terms of technological maturity, the representation layer is the most advanced of all the layers. Creating representations has become the basis for multiple profitable industries whose techniques of message communication have become quite sophisticated: movie-making, advertisement, publication (in all media forms), music, news, and all of the performing arts. Because representation is a mature layer, there are some things that can be said about it: • • • •

The number and complexity of design decisions has increased. Specialization has advanced significantly. A high level of specialization has created a number of design sub-layers. Specialization within sub-layers has proliferated in many directions.

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Specialization in skills Specialization in tools Specialization in processes, procedures, techniques Specialization in professional structures Specialization in theories, philosophies, “schools of thought”. A sophisticated consumer audience holds heightened expectations of media. Design and production have become cost centers. High-quality design and production require a greater investment. More design planning is required in advance by thoughtful practitioners.     

• • • •

Generative Representation The culture of high-level media production is growing. Thanks to more powerful computers, equipment, and sophisticated software with easy-to-use interfaces, what used to be impractical production designs are falling more and more within the abilities of the average design team. This trend toward increasingly powerful, dynamic, and efficient representations will intensify in the future as high-end software tools continue to make more powerful representations available to a larger producer base. For instructional designers to take advantage of this bonus, they should begin to think of more powerful ways of conceiving representation designs. Dynamic capabilities require dynamic thinking. In the past, tools for creating static representations have tended to guide representation designs, and designer thinking has tended to conform to what tools could do. This has tended to limit designer imagination. As a result, for many years representations were single-use artifacts and hard to revise. Often changes led to discarding the old version and starting fresh. However, the increasing use of the computer in all forms of media production has lowered production effort and increased the reusability of elements from earlier versions. Moreover, improved tools for visual modeling have made it possible to create graphical models of objects and then capture still and motion sequences from the model. This gives the ability to produce a number of useful representations from a single visual model. At the time of instruction, representations are created either by: (1) grabbing pre-constructed representations from a library, or (2) by generating a representation at the time of instruction. This defines a continuum of possibilities: representations may be pre-composed or constructed from elements on demand. The usual practice falls between the extremes. Past practice has tended to favor pre-construction and storage of representations because that was what development tools made most accessible to the designer. However, advances in software now make it much easier for the designer to move further toward the real-time generation end of the spectrum. Representations can be constructed at the time of presentation based on a decision made at the moment of need. Application Exercise Have you experienced representations that were customized to you, in real time, as you used them? • • • •

Describe some examples. Were they customized because you had operated a control? What features of the representation were customized? What features of the representation were the same for all users?

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Who Designs Representations? Should instructional designers select better artists for their staffs, or should they themselves become better at conceiving representations? The best answer is probably “both”. Representation has become specialized to the extent that a dangerous gap has opened between the designer and the production artist. Designers sometimes abdicate the design of representations to production artists, and production artists often claim that their judgment in the design of representations is superior to that of the designer. What is needed is a middle ground in which the production artist and the instructional designer focus on the common task of principles for representation design aligned with the demands of instructional purposes. This middle ground is currently largely vacant; there are few bodies of advice that describe at a principled level how to take advantage of new media production capabilities to serve instructional purposes. Even more important is the lack of a coherent body of guiding principles and theory for the design of instructional representations that have a force and influence similar to representations produced in other industries such as filmmaking and advertising. The concepts of instructional representation need to catch up with the tools and technologies available for producing representations and the design concepts of other fields. Principles for Representation Design How can we unify the fragmented media conceptions we currently use? One approach is to bring representation designers onto projects earlier in the project life cycle. Closer and earlier collaboration between instructional design and representation design places the focus on representations that: • • • • • • •

are explanation-centered rather than decorative; support “doing” rather than “telling”; change dynamically in response to “doing”; rely on readily interpreted graphical forms; balance visual, textual, and auditory forms in a non-conflicting way; unify multiple media forms to present a single expressive voice; involve deeper, more abstract principles of communication.

Representation design principles are classified in the sections that follow under nine headings. These principles are not new, but the grouping of principles and the order of their presentation provides a cumulative description of representation design principles for supporting instructional purposes— purposes that involve cognitive and emotional impact. This list is cumulative in that it begins with the lowest semantic levels—the most basic levels of meaning-making—and builds to higher levels. Later sections will make some very rough but illustrative comparisons with levels of processing of representations within the mind. Many excellent works describe these principles in detail, including Visual Language for Designers (Malamed, 2009), Visual Explanations (Tufte, 1997), Information Architects (Wurman, 1997), Multimedia Learning (Mayer, 2009), The Cambridge Handbook of Multimedia Learning (Mayer, 2005a), Information Graphics (Harris, 1999), Understanding Comics (McCloud, 1993), Making Comics (McCloud, 2006), Illustration (Male, 2007), Communicating Ideas with Film, Video, and Multimedia (Shelton, 2004), Stopping Time (Jussim and Kayafas, 1987), and Visual Language (Horn, 1998). Universal Principles of Design (Lidwell et al., 2003) presents general design principles, most of which apply to the creation of representations also.

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Principle #1: Contrast Contrast is the beginning of representation. Without contrast there is no representation. A mark on a blank page represents something only if it contrasts with the rest of the page. Likewise, if there is no variation in sound waves from moment to moment, there is no representation. In a dark, silent, still place there is no representation and therefore no information. Contrast, therefore, is not a technique: it is the basis of representation. A designer creates meaningful contrasts: what Gregory Bateson would call “differences that make a difference” (Bateson, 1979, p. 99). Contrast is essential to representation, and it is active at every level of human perception, processing, and learning. Designers create contrast through change: change in shape, change in color, change in audible sound, change in position, change in movement. Change attracts our senses and puts them on alert; it causes the element that has changed to move into the foreground; it gives existence to something in our attention and in our thinking; and it impinges on both our cognitive and emotional processes. The principle of contrast is what allows us to perceive. At one level, contrast created through change is what allows us to notice the marks on a page and the tones of a sound. At other levels, contrast created through differences allows us to sense structure, proportion, similarity, difference, trace, symbol, and story. These allow us to create information useful for thinking and adapting our action to our environment. Design considerations related to creating contrast include the following: Salience—Contrast creates salience, which can be described in terms of degrees of salience. How much does a particular element stand out from its background? Proportion—Contrasts between multiple salient elements in a representation create proportion among them. The most salient become the proportionally most noticeable and important. Comparability—Proportion and differences in salience automatically invite comparison. In comparison there is much new information. New information—Most of the substance in an instructional communication is background created solely for the purpose of foregrounding some portion of the substance in a way that can be noticed. Clarity—A designer may choose to blur or smudge certain items of information, metaphorically speaking, in order to create questions. New information may be presented directly or by implication. Aesthetic—Every contrast has an aesthetic: some emotional value, either positive or negative. Lines drawn on a page are assessed in aesthetic as well as cognitive terms. Principle #2: Framing An important part of interpreting a representation is gaining a sense of where the receiver stands with respect to the scene. In everyday experience we take it for granted that we are experiencing things through the perspective of our own eyes and ears. This tells us where we are with respect to what we are sensing, and it tells us what kinds of things we should expect to see. When representational experiences are composed for us by others we have to orient to the landscape the designer has selected and determine where we stand in relation to it. We must determine the type of representation we are experiencing. Is it real? Metaphorical? Schematic? Abstract? What are its most salient elements? What can we detect of the designer’s purpose? We scan and rescan— play and replay—the experience in search of these answers. Framing issues include: Window—In a representation there is a “window” of some size created through which we view what the designer has prepared for us to experience.

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Point of view—We stand at some viewing point as we look through the window. Are we above a map, looking down? Or have we been transported to the countryside depicted by the map? Or have we zoomed from one to the other? Orientation—Within this framing of things, what are the major orientation points by which we can determine whether we have moved—or the frame? What are the points of reference that we can use to measure movement? Scan—Within the frame, how has the designer arranged for the scene to be scanned? Is there a pattern? Or is the learner expected to find a pattern? How large is the landscape? Metaphorical stance—Is the framed scene real or does it contain some degree of abstraction from the real? If abstraction, what kind? Principle #3: Structure The mind looks for structure in representations. Structure is what allows us to orient within the world the designer has created. When change creates contrast, we orient to what has changed and attempt to understand it: “What is this? What am I experiencing? What are its parts? How are they related? What is the whole? What is the purpose?” The mind looks for familiar structural patterns. Only when it finds structure can it deal with the purpose and meaning of the structures. Issues of structure in representations include: Layout—What things are there to sense? Where are they? What are they? How are they arranged? Proximity/grouping—Things placed close together usually indicate that they can be perceived as being related. Relation—Proximity and direction relationships lead to questions about meanings that are intended by the designer. Functional differentiation—Different parts of a representation provide different kinds of information. Maps and diagrams often have legends that aid in their interpretation. There may also be links to additional resources and reference information. Symmetry—Symmetries also provide interpretive clues. These can include physical symmetries and ideational symmetries. Simplification/distortion—A learner may try to detect the degree of simplification that has been applied to a representation or the degree to which it has been distorted to give some parts more weight. The learner interprets the degree of literalness of the representation. Is the audio a historical person’s voice, or is a narrator simply reading the famous figure’s words? Sequence—A representation is sensed in sequence because change implies the passage of time. Auditory experience is sequenced; visuals use scan patterns and gradations of salience to draw the eye in a sequence of moves. Several clues built into a visual or audio representation reveal its organization of ideas: Direction—Relationship and interpretation sequence can also be controlled by attention-directing devices that serve as road signs that direct the scanning. Hierarchy/network—Digging deeper into the meaning of a relation, one detects either a hierarchical or a network (non-hierarchical) relationship between salient elements. These provide interpretation clues. Discoverable purpose—A learner interprets all of the structural clues to determine the designer’s purposes for the representation. Principle #4: Trace We notice patterns—not just static, two-dimensional patterns, but dynamic, multi-dimensional patterns of change over time. Most often these patterns do not persist in our senses, but they can be

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renewed each time we have an experience of the same kind. A designer can capture patterns that make similar time-change structures salient. These captured patterns can be called traces—visible or audible contrails of captured experience created by unfolding representational events. A designer can use traces to reveal to a learner otherwise difficult-to-notice patterns. When we become aware of traces, we can use them to compute trajectories of future events (“Will that ball hit me in the head?”): even ones that take years to unfold (“Will my retirement account have anything left in it?”). Audible traces are important in language learning. Processes can be either sped up or slowed down and captured in media form so that we can see similarities with other processes that take place in a normal time frame. Charts and graphs of all kinds are a form of trace. The opportunity that instructional designers have is in conveying more of the message that is normally carried by text into more complex and interpretable trace diagrams. Newspapers like USA Today and magazines like National Geographic have become masters at providing information in a graphical form that shows multiple influences on trends and allows multiple comparisons. Rosling (2006) adds dynamic motion to depict change over time. Through the manipulation of time and space with traces, we make it possible for the learner to see new contrasts and new structures. The issues related to creating traces include: Dimensionality—Traces can chart many dimensions at once and include many variables in a single trace. The challenge is to make a compact representation of all of the data without creating something too complex for the audience. Layering—Layering in this case refers to layering of the kind employed in a visual development program like Photoshop. Multiple layers of a visual can represent different variables for comparison purposes. Visual and audio represented together constitute layering as well. Transparency/hiding—Transparency and hiding refer to the degree to which multiple layers can be made visible at the same time by creating layers that are semi-transparent. This results in see-through traces superimposed on a base which permit the learner to explore individual layers. Dynamism—A trace is not necessarily a static thing, nor does it of necessity describe something that has actually happened. Learners experimenting with different variable values create data for traces that are subject to all of the variations that have been described here. As variables change, a new trace is superimposed over the old ones, providing a family of traces that can be studied. Timing—A trace can show processes which are speeded up or slowed down so that they can be studied. The innovative work of Harold Edgerton (Jussim and Kayafas, 1987) with stop-action and time-lapse photography played with time and captured time–space traces artistically. Navigability—Unlike many simple representations, traces are usually so detailed and contain so much information that they cannot be comprehended in a single scanning. This implies that the ability to navigate the dimensions of a trace representation and to study local details is an important aspect of trace design. For audio traces, this includes play/replay capability, and for visual traces, it means that controls for zooming and panning may be needed. Principle #5: Symbol Once we are able to identify what it is that we are seeing and hearing, we begin to interpret the meaning of the experience. For this we rely once again on our store of past experiences and the information and emotion they evoke. A thing (sight, sound, etc.) becomes a symbol mainly because it is tied to the emotional experiences as well as information. Symbols become culturally connected. The

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representation layer is therefore closely related to cultural preferences and appeal. The issues related to the creation and uses of symbolic representations include: Universality—Some symbols already exist and can be used, but the populations in which they have meaning will vary. The same symbol can mean different things in different cultures. Symbols can be created. The purpose of much of our instruction is to create new symbols and meanings for learners. Mathematics, music, the sciences, the arts, linguistics, and the humanities are all heavily involved in the creation of new symbolic meanings. Interpretability—Symbol meaning should be unambiguous; references to and uses of symbols should be consistent. This makes them interpretable and allows them to be used in self-guided learning. Emotional value—Symbols, new or existing, come to have emotional association because learning itself is tied into the emotions. Designers should include the establishment of these emotional values among their goals. Principle #6: Story Stories are created as symbols interact. Understanding the story of Goldilocks depends on owning symbols for “bear”, “little girl”, and “cottage in the woods”. Without these symbols and their meanings and their associated emotional values, the bears represent no menace, and the cottage in the woods seems like any other house, rather than like a mystery to explore. The days when we can count on there being a shared symbol for “porridge” are fast slipping away. Luckily, the contents of the bowls are not as important to the meaning of the story enacted in the interaction between the bears and the girl. Sights and sounds used to create instructional experience draw upon existing symbol systems and arrange them in new ways to represent new meanings through stories. This takes the concept of story far beyond the fairy tale definition of the term into the sense of story proposed by Roger Schank in the book, Tell Me a Story (1995). For Schank a story is a structured account of events that we use for communication purposes: “Communication consists of selecting the stories that we know and telling them at the right time. Learning from one’s own experiences depends on being able to communicate our experiences as stories to each other” (p. 12). Knowledge, then, is experience and stories, and intelligence is the apt use of experience and the creation and telling of stories. Memory is memory for stories, and the major processes of memory are the creation, storage and retrieval of stories. —(p. 16) This is a strong position on the role of story in learning, and not all psychologists agree, but Schank’s research in the 1980s demonstrated that stories can be used to simulate portions of the understanding process. Stories are clearly part of the learning process. If so, then they must be considered part of the instruction process as well. Our conception of story needs to change so that we can see that when the numbers 5 and 2 and a “+” are arranged in a particular way, they can come to have meaning as a story about addition. A mental schema is formed through exposure and practice that allows a learner to “read” the story of any equation that uses familiar symbols and standard meanings. Arranged in another configuration, the symbols lose their story value. It is possible to see most of what we do in instruction as the telling of stories using symbolic representations. Some of the issues surrounding the use of story in representation designs include: Narrative arc—Stories are dramatic structures. One of their appeals is emotional: we become engrossed in a story if it touches our interest and reminds us of prior feelings in some way. Classical story structure has a beginning, a middle, and an end. A story depends on an arc of

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rising emotion that begins with the introduction of a goal or a mystery and hits its peak at the climax of the story with the resolution of the mystery. Development—The progression of the story through its stages constitutes the development of the story. A story unfolds. As with a joke, there is an art to telling a story. Scene—A story consists of a series of scenes. A scene begins with a goal and follows the interaction of story characters (which may consist of molecules or economic resource flows just as easily as people) through their paths toward a goal, which often is blocked, resulting in the need for another scene. Structure—Scenes are arranged in a sequence most calculated to build suspense, the emotional value, of the story. Each scene sets a new goal—a new anticipation—based on the previous scene and advances the action of the story toward the main goal of the story. In an instructional story this may consist of the delivery of nutrients through a cell wall or the crowning of a new British king after much war and intrigue. Principle #7: Question Stories start with a question (a mystery) and (usually) end with an answer. However, in instruction the best stories end with more questions than answers. For example, a graphical poster may depict the process involved in waste disposal and at the same time open a number of questions about the amount of waste that accumulates every day and whether it could be recycled. An audio segment used in a history course might give the learner a speaker’s words but also reveal the underlying uncertainty in the speaker’s tone and hesitations. These things tell a story but leave questions hanging in the air. Questions lead to learning because they supply the need for more stories to be told which lead to answers—and further questions. A glossy and colorful representation surface may attract attention, but that level of interest is short-lived. Representations should be designed in a way that engages the learner in question asking and engagement in stories. Issues of using questions include: Anomaly—A question is stirred by an imbalance, an asymmetry, the unexpected, the unexplainable. A representation can present an unexplained scene or an unexplained sound that begs for a question to be answered. All learning begins with a question. Too much of our instruction answers questions the learner is not asking. Invitation—A representation should accompany an anomaly with an invitation or a challenge to answer. This can be accomplished by the representation itself or through a hint or suggestion. A single sound such as the creaking of a hinge poses both an anomaly and a host of questions: Where is the hinge? What door? Why does it creak? Where does the door lead? What is behind it? A famous radio show many years ago used the creaking hinge effectively to invite listeners into an Inner Sanctum. Principle #8: Access Having questions that you can’t answer is frustrating, especially when the answer lies in a part of a representation that is inaccessible. If you do not have the ability to access the content of representations that have gone past, you are frustrated. Learners should be able to access parts of a representation. This implies having controls that allow access to the parts. This defines an interface between the control layer and the representation layer. Representation planning is, therefore, intimately integrated with the control layer. Access issues include: Pace control—For sound, this is the ability to speed or slow the pace of the information being delivered. It may involve something as simple as a pause control that allows the learner to ponder for a moment before proceeding.

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Speed control—A control for speed hastens or slows the presentation of the visual or audible representation itself—not the pausing of the presentation but the elongation of the experience. Slow motion playing of a representation of a foreign language utterance might allow a learner to capture the key elements of a correct pronunciation. Focus control—This kind of control would allow the learner to focus on some part of the representation for closer inspection. Zoom—We have the habit of thinking that a visual representation has only one dimension— the flat one—and no depth. Not only might a learner want to zoom in to see details more closely, but a computer representation with a new kind of zoom might take a viewer into a second or third layer of detail not visible to the naked eye; thus the viewer would gain a sense of relation of the parts to the whole. Pan—Similarly, a learner might find value in a representation so large that it could not be fit into a single display, which would necessitate a panning control to view it. Repeat control—In language instruction, in auditory directions in a lab exercise, in listening to an historic speech, and in many other cases, a repeat control could be most useful for individually directed study. Principle # 9: Inter-media Coordination Have you experienced the situation where the text in a book and the text elements of a diagram don’t match? Where the sound arrives three seconds after it’s supposed to and the interviewee is mouthing words that haven’t been heard yet? Or have you seen text on the screen that did not match what the narrator was saying? Or large amounts of text on a screen being read to you word for word? Inter-media coordination requires orchestrating timing and content across media channels. The combination of media channels should vanish from the learner’s awareness, making the representation itself transparent to the user. We are so used to this quality in most media that we do not think of it until something goes wrong. Media events using different channels should use those channels to strengthen each other’s message, acting in concert to amplify and create saliencies. This challenge is quite important, especially in instruction that blends the actions of a live instructor with the resources and interactions supplied by a technology device. Blending should be accomplished in a manner that does not call disproportionate attention to the media being used or to the live instructor. The coordination of media functions needs to be seamless, so that the learner can stay focused on the story being told jointly by the instructor, the media, and the learner. Coordination issues include: Synchrony—Different media channels must be designed to remain in sync. Movie makers recognize that this means that some multi-channel information must arrive instantaneously—a shot and the smoking of the gun—and that some information must arrive slightly out of sync to give the receiver time to process one input and then the next. Interference/complementarity/echo—The representations being made over one channel should not mask those of another channel. Channels should amplify, explain, echo, complement, and strengthen each other. Style—Representation is the surface at which a style is manifested to the learner. A style includes everything about the media design. It includes the messages between the lines that tell learners how the designer regards them. In the back of a learner’s mind in live instruction as well as media-delivered instruction is the implicit question, “Who is talking?” and “Who do they think they are talking to?” Style answers that question.

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Application Exercise Find representations that exemplify each of the principles listed above. Collect notes on examples that seem to you to be particularly effective.

Understanding Representation at a Different Level Much of the literature on designing representations is written from the point of view of the designer, with the designer in the privileged position of knowing and deciding what is good for the learner. In contrast, at the beginning of this chapter, Edward Tufte was quoted as saying that, “when principles of design replicate principles of thought, the act of arranging information becomes an act of insight” (Tufte, 1997, p. 9). This is taken to mean that designers in the act of creating a representation of an idea that they think they understand find the essence of the message becoming more clear and refined, and that they reach new insights, through the act of representing, about what they thought they knew. This places the designer in the role of a learner as well, during the act of designing. For this reason, the testing of representations for their effectiveness and economy of communication is simply common sense. Not only does the designer learn more of the subject-matter in this way, but he or she learns more about designing representations. Tufte explains: “Those who discover an explanation are often those who construct its representation.” Among such people, Tufte cites: “John Snow in 1854 finding evidence needed to end an epidemic and skillfully presenting the evidence; [and] Richard Feynman developing space-time diagrams for quantum electrodynamics” (p. 9). He concludes: “all these quick-witted creators and discoverers demonstrate methods by which to represent, describe, illustrate, and, indeed, construct knowledge” (p. 9). Representation is more than creating media surfaces. It also involves finding an economy of expression that lays deep structures of knowledge open to inspection. From inspection, learners take away elements they use to construct understanding and competence. Communicating More Than Information Wenger (1987) describes instruction as a process of “knowledge communication”, expressing reluctance to define either “knowledge” or “communication”, for fear of converting what are fragile and uncertain concepts into formulas. At the time of his writing, there was a clear movement in that direction within the community of computer-assisted instruction designers. As an alternative, Wenger offers this vision: Now imagine active books that can interact with the reader to communicate knowledge at an appropriate level, selectively highlighting the interconnectedness and ramifications of items, recalling relevant information, probing understanding, explaining difficult areas in more depth, skipping over seemingly known material  .  .  .  intelligent knowledge communication systems are indeed an attractive dream. —(p. 6, punctuation in the original) Wenger claims to be conducting an “intertwined investigation of communication processes and the nature of knowledge” (p. 6). The dream of an adaptive instructional system, conceived over fifty years ago has persisted, but has yet to be realized. However, progress in design and development tools, increased understanding about how learning takes place, and powerful delivery systems hold out much more promise now than ever before. The sticking point now is the training of everyday designers and their ability to convert new concepts into practical applications. Everyday design thinking has remained for the most part at the formulaic level because designers have been taught

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to approach it at that level. They are not taught, for example, to approach design in terms of models of knowledge and their communication. They are taught about mental models and the creation of simulation models, but they are not for the most part taught how to incorporate their knowledge about models into instructional designs. Figure 10.1 is Wenger’s illustration of different approaches to the architecture of technology-based instruction. It represents three points along a continuum, from completely packaged instructional designs on the left to instructional designs that are completely generative on the right. The ovals represent a knowledge source, and the rectangles represent representations of knowledge. The difference between the extremes of the continuum is that at one end the representation of knowledge is conflated with the external representations. That is, the instruction is pre-composed, and the instructional system does not have any internal representation of the knowledge in computable form. At the other end of the continuum, the instructional system has an internal, computable representation of the knowledge, and this representation is capable of influencing the external representations made available to the learner. The systems to the right adapt the external representation at the moment of instruction. At the center of the figure are systems in which part of the “knowledge” is hard-wired into fixed messages and their representations and part of the “knowledge” is generated (computed) and given expression at the time of instruction (for example, in a simulation). Such systems are typified by having just those parts of the system dynamic that can profit the most from the ability to adapt. Likewise, systems in this zone have some of their knowledge of how to instruct hard-wired into set messages and representations, while some of their instructional strategies, messages, conversational interactions, and representations are generated from a set of rules or general patterns at instruction time. It should be emphasized that what is illustrated in Figure 10.1 is a continuum. The left side of the figure identifies totally hard-wired, pre-composed instructional systems with fixed instructional strategies and representations. The great majority of instructional designs produced today are of this type due to several factors, including cost, time constraints, and team skills. However, a major factor in the persistence of this design architecture is the patterns in designers’ thinking: designers are taught or shown by example how to design products of this type, and it is convenient, safe, cheap,

Epistemic source

External Representaon

Internal Representaon

External Representaon

Internal Representaon

External Representaon

Figure 10.1 An illustration after Wenger (1987) of different approaches to the architecture of technology-based instruction. The left side of the diagram represents systems in which “knowledge” (as captured in content, strategy, message, and representation) is tightly bound to its external representation. The center represents systems in which some portion of “knowledge” is tightly bound, while some other portion is computable, fluid, changeable, and therefore dynamic. The right side of the diagram represents system in which everything (content, strategy, message, and representation) is computable and therefore dynamically determined at the time of use. (After Wenger, 1987, p. 314.)

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and less risky to do so. The right side of Figure 10.1 identifies systems that generate instruction entirely from internal knowledge representations and instructional process rules. Wenger terms these “ideal systems”, as at the time of his writing they represented the ultimate goal of intelligent tutoring researchers. In fact, the feasibility of such systems has been demonstrated both in the laboratory and in general use. Johnson, Rickel and their research team designed and developed an intelligent instructional system called STEVE (Johnson et al., 2000; Rickel and Johnson, 1998) whose architecture is fully generative (the right side of Figure 10.1). STEVE’s virtual-reality representations—of a ship’s boiler room—are fully computer-generated from three-dimensional graphical models that provide for a movable point of view: speech in STEVE is also computer-generated and therefore sounds somewhat robotic. STEVE is capable of carrying out instructional procedures in both instructor-led and learner-led modes. In instructor-led mode, a tutor (learning companion) named Steve, represented by an avatar, demonstrates procedures for boiler operation, moving from location to location during the process. In this mode, the learner, also represented by an avatar, can ask questions, such as “Why?”, and perform actions, even disruptive ones, which STEVE is capable of stopping and correcting. In learner-led mode (that is, the learner can say “Let me do it”) the learner can perform procedural steps, being corrected by STEVE when errors occur. STEVE is an example of a system that is generative in all aspects. Its representations are generated moment by moment. This kind of system, not uncommon in simulations, requires a different approach to the design of representations than one in which the representations are fixed. Indeed, this is the kind of design where the designer must construct a model of an environment, and of learning companion actions. The point of this extended discussion of system architectures in a chapter on the representation layer is to show that though traditional approaches to representation have made it easy for designers to concentrate on the surface features of representations, alternative architectural views make it easier for the designer to consider using dynamic models in representations. This is more attractive because of the increasing availability of visual modeling tools, thanks to early experimentation with sophisticated simulations and now the boom in gaming software development tools. Now more than ever before, a designer can more easily think of representations in dynamic terms, and can think of instruction in conversational terms rather than fixed strategic terms. In this view, the representation layer is more fully integrated with other layers, and yet it has its own architecture, expressed in terms of both static and dynamic representation functions that can be fed and controlled by other layers, including the content layer. What is communicated during instruction is more than information. Instruction as a conversation can be seen as a “tuning” process during which: 1. A learning companion converts its internal knowledge model into an external representation for consumption by the learner. 2. The learner interfaces the external representation and abstracts from the elements that can be used to construct an evolving understanding (in the form of a mental model or schema). 3. The learner acts on the basis of the evolving knowledge model. 4. The learning companion interprets the learner’s acts, comparing the diagnostic information they yield with its own idealized knowledge model, and then selects its own next actions and crafts or fetches its own representation. As the learning companion and the learner interact, each is tuning or refining a model of the other’s knowledge. The learning companion tries to diagnose the state of the learner’s model, and the learner is trying to explore the companion’s knowledge model. This is a very simplistic description of

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a very complex instructional interaction, but it defines in general an approach to design architecture that demands a different kind of designer thinking, and a different level of awareness of the architecture of the design. This requires a deeper insight into the processes of communicating understandings and competence through external representations and control actuations. Even if the designer is presently limited by cost, time, or skill, this view defines a path of personal study, research, and experimentation for a designer that can lead to career-long improvement in a working world that is rapidly changing and becoming more challenging. Application Exercise This section described a different view of instruction. • How does it differ from your way of thinking about instruction? • What do you see as its major benefits and challenges? • How might this view of instruction change your way of thinking about designing? The Processing of Representations The complexity of processing of representations by the mind staggers the imagination. The following observations from an article in Science (Van Essen et al., 1992) reveal a processing system characterized by “modular design, hierarchical organization, and the presence of distinct but intertwined processing streams” (p. 419). The description explains that: Thirty-two distinct cortical areas associated with visual processing have been described on the basis of anatomical, physiological, and behavioral information. Twenty-five areas are primarily visual in function; the remaining seven are also implicated in other functions such as polysensory integration or visually guided motor control . . .  To date, 305 pathways interconnecting the 32 cortical visual areas have been identified with modern pathway tracing techniques. This constitutes nearly one-third of the number there would be if the network were fully interconnected . . .  For some pathways the laminar pattern suggests ascending (forward) information flow from a lower to a higher area. These are generally paired with reciprocal pathways that have patterns suggesting feedback from a higher to a lower area. Other pathways have patterns suggesting lateral connections between areas at the same level. Systematic application of these criteria leads to a hierarchy containing ten levels of cortical visual processing plus several additional stages of subcortical processing. The visual hierarchy is extensively linked to centers associated with motor control, other sensory modalities, and cognitive processing. —(p. 419) The concept of brain modularity and levels of processing for the visual faculty alone makes it clear that a designer is not addressing a single “decoding” capability. Jackendoff (1993) surveys the “languages” of the mind. For speech alone, he identifies sound structure (intonation, stress contour, prosodic structure, rhythm), phrase structure (lexical categories, phrasal categories), and conceptual structure (concepts: objects, events, places, actions, properties, amounts). On this view, then, in order to understand a spoken sentence, the brain must translate information from the form in which it was detected—auditory input—through phonological form

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and syntactic form to conceptual structure . . . This does not mean that these translations must be accomplished in a strictly serial fashion. —(p. 9) For vision, Jackendoff identifies three levels of (processing) representation: primal sketch, two-anda-half-D sketch, and 3D model. Clearly, given the above account of the processing-center description visual system, there is not a correspondence between Jackendoff ’s levels and Van Essen’s except in the broadest sense, especially since Van Essen describes the feedback loops in the processing system that tend to define processing modules. Jackendoff describes a “language” of music. He identifies musical surface (sequences of tones, pitches, durations, amplitudes, and timbres), grouping structure, metrical structure, time-span reduction, and prolongation reduction. Jackendoff describes Schenker’s concept of a structural skeleton: “which of the events in the piece—which of the notes and chords—are structural anchors on which patterns of elaboration and ornamentation are hung” (p. 13). “Understanding a piece of music involves, at the very least, constructing all these musical structures in one’s mind (unconsciously of course), just as one constructs phonological, syntactic, and conceptual structures in the course of understanding a sentence” (pp. 13–14). Jackendoff also describes “body representation that encodes internal states of muscles and joints, as well as the locus and character of body sensations, such as pain, tension, heat, and so forth” (p. 15). This includes haptic perception (perception of shape by touch) and body position sense. All of the complex functions associated with processing representations are the concern of the instructional designer. It is not possible to associate a particular module identified by Van Essen or a “language” named by Jackendoff with specific principles of representation design, but it is important for instructional designers to realize that the processes they are dealing with are non-trivial and non-unitary. If vision is processed in thirty-two cortical areas, over 305 pathways, and at ten levels of processing (which numbers are almost sure to have changed through intervening years of research), then the concept of a unitary visual sense is untenable, and if there are multiple “languages” of the mind, each related to a sensory system, then the concept of a unified processing of sensory inputs doesn’t hold together. Jackendoff makes the claim that: The various specialized computational capacities of the mind are carried out, not by a single type of general-purpose device, but by a variety of computational devices, each specialized to deal with a particular form of information or to translate information from one particular form to another. —(1993, p. 17) If this is a reasonable explanation, then the designer must understand how to speak to each sense and, more importantly, how to speak to multiple senses at once, in their native languages, and how to synchronize and orchestrate them to maximize communication between the models within the instruction and the models ultimately comprehended by the learner.

Imagination and the Representation of Complex Knowledge A colleague of the author grew up in Mainland China. During the 1970s computers were scarce, but computing had a strong hold on this friend, and printed resources describing programming languages and basic computer architecture fell into his hands. The drive to know how to program a

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computer was so strong that he began to imagine writing programs and then running them on an imaginary computer. By doing this, he trained himself to be a highly competent computer programmer and software system designer. He now owns his own software company in America, a businessman creating innovative products. Because paper was also scarce, this colleague had to hold the programs that he designed in mind rather than writing them down. The model of the computer running the programs was also in his head. Years later, when a software problem was posed to him, he could still design complex programs without writing them down, and when a design had been thought out, he could sit down and rapidly input the program code, which would almost always run correctly. With sufficient practice, complex structures like computer programs can be created in the memory using imagination without what we normally think of as the essentials: paper, pencil, and computer terminals for running and testing the programs. It is easy for instructional designers to think that their representations are essential to the learner—representations that include text, audio, music, visuals, haptics, and kinesthetics. At the same time, experienced instructional designers know how difficult it is to represent ideas that go beyond a certain level of complexity. Abstract concepts, complex highly interrelated models, and dynamic systems can be easier to picture in the mind than they are to represent using media. Diagrams become so crowded with lines that they become impressionistic in nature rather than exact. The more complex and abstract the subject-matter, the more complex the representation becomes, until at some point the designer must rely on the power of the imagination to carry part or all of the representation load. Most often the way to the imagination is a verbal description or a narrative. The Star Trek episode “Darmok” provides an excellent example of this. At the beginning of the episode, Captain Picard and the Enterprise crew receive communications from an alien vessel that seem friendly and are expressed in familiar words but in expressions that consist only of noun phrases: “Lowani under two moons”, or “Rai and Jiri at Lungha”. After repeated attempts at communication, frustrating on both sides, the alien leader forces Picard to the surface of a planet that is deserted except for a menacing monster. Dathon, the alien leader, tosses Picard a knife for self-defense and speaks the phrase “ Darmok and Jalad at Tanagra”. Picard is now not only mystified, but he is anxious about his safety as well. Will he have to fight Dathon? Not so, for the monster emerges and attacks and wounds the alien captain, who eventually dies. In the process, Captain Picard begins to understand that the alien language describes—only in phrases—the great heroic events of the alien culture—ones that form bonds of brotherhood through shared hardship or battle. At the end of the episode, the aliens record a new story: “Picard and Dathon at El-Adrel”. This episode is difficult to understand until halfway through, when the utterances and gestures of the alien captain start to unravel the language and the viewer begins to hypothesize about meanings in the alien language. A learner-watcher has to wait a long time for the payoff of understanding. When it comes, however, there is a strong emotional response, the alien captain has arranged the experience on the planet surface, knowing that he would probably die, so that he and Picard could understand each other and a bridge between his people and the Federation could be built, despite the language barrier. The viewer of this episode learns how a complex communication concept—a concept about the structure and semantics of an alien language—can be conveyed (to the learner) through narrative means. Certainly, the scriptwriters could have summarized this point in a few sentences—a few verbal symbols—as has been done here. However, the summary could not possibly have the emotional impact conveyed by the actual episode. Through dramatic means, the script designer enlists the emotions and the representational faculties of the imagination to create a much more memorable experience. As the viewer watches, the imagination is constructing internal representations of how meanings are conveyed by the alien language. In the same way, the imagination of the Chinese

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colleague was able to create memorable representations of program structures in the imagination, which, it seems, is also a representation system that the instructional designer can call upon and should perhaps attempt to strengthen. Application Exercise It is easy to represent simple ideas. Complex ideas present more of a challenge. • Find examples of representations that deal with complex and subtle ideas. • Analyze the examples to try to understand what principles were used to achieve the communication of the complexity and subtlety. Learner Participation in Representation Learner participation in the creation of representation during learning holds implications for design architecture, and it requires some new thinking by the designer. Learner-contributed representation does, however, have a long history. Marginalia Learners participate in design even when the designer does not intend it. How often have you opened a library book and found someone else’s markings on the pages? The practice of scribbling on the page is not modern. From earliest times, readers of hand-written texts (which were in short supply) would add marginalia—translations of words, editorial statements, notes on a new idea—in the margins. Some books became so filled with comments from readers that one could barely read the original text. This necessitated recopying the book. Often during this process the marginal notes and commentaries were copied into a book of their own. Certain kinds of marginal or inter-textual notes that gave meanings of words or translations of foreign words were called glosses, and these were often gathered into a glossary when the recopying took place. Today the common equivalent of the marginal note is the comment or reply added to a Web article or blog. The largest collection of marginal notes ever collected is called Wikipedia, a growing, learner-created encyclopedia of four million-plus articles, for which the average number of accreted comments and changes approaches twenty. Product Form and Learner Participation In a period of intensive social media use, the participation of the learner in the co-creation of representation is almost a given. That is to say, users today expect to contribute as well as consume. This gives relevance to Krippendorff ’s trajectory of artificiality (Krippendorff, 2000), which defines a continuum of designed product types with increasing levels of social participation, with products that the user simply consumes at one end, to products that cannot exist without user participation at the other. The product types define six different architectural types along the continuum that require a different level of user commitment—and a different type of architectural organization: • Product—The user consumes the product, using it just as designed by the designer, for an intended purpose (example: packaged instruction). • Logo, Brand—The user chooses from a family of products which one to use (example: Khan videos). • Interface—The user is given access to services and functions that permit personal expression (example: desktop interface to computer programs).

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• Network—The user is put into contact with other users and permitted to communicate with them (example: e-mail). • Project—The user is expected to contribute to a common task within a space created by the designer (example: Wikipedia). • Discourse—The user adopts and helps to further develop a language and paradigm of discourse for use by a community of like-minded users (example: professional community). Products, Brands, and Interfaces do not require and do not invite creation of representations by the user/learner. In contrast, Networks, Projects, and Discourses do not function without the user’s contribution, which is in some form of representation. Collaboration Spaces Krippendorff ’s Project and Discourse product types describe collaborative spaces created by a designer. Spaces of this type may be for the purpose of carrying out design projects, for collaborative problem solving, resource pooling, or the creation of community. One of the designer’s main concerns in the creation of collaborative spaces is the matter of representation: What kinds of representation does each require? What kinds of things do participants need to be able to contribute? In what form? What types of representation tools does this require? The creation of collaborative spaces is a major sub-field of information technology. There is a whole new category of designed collaboration spaces that contain the phrase “massively online” in their title, from online gaming to online instruction. New kinds of business and service enterprise are being experimented with, including open courses and open-course university offerings. These can allow the learner to create and share documents through free online services. Document linking is possible using the Web. This in effect creates mega-documents shared by special interest groups formed for, among other things, learning. Application Exercise Several brand-name software services are offered today that represent the top three categories described by Krippendorff (Network, Project, Discourse). • Make a list of brand-name services that fit into each of these categories. (Warning: The hardest to find will be Discourses. But keep searching, because they are there: whole new ways of thinking.) Gesture and Meaning Live instructors use their hands, posture, gaze, movement, and their whole body to punctuate representations: • A speaker raises the pitch of delivery, pauses, folds hands, looks significantly in a sideways direction, and then, with a quick forward turn of the head, delivers a significant phrase. • A teacher drums the podium with a finger, speaking slowly, the finger moving like a small jackhammer for each new spoken phrase. • An instructor stands at the whiteboard, first pointing and looking at the whiteboard and the drawing on it, now turning toward the class, holding out an arm toward them, now turning back to the whiteboard and with a marker circling several times one part of the drawing that has clearly been circled several times already. The instructor writes a word by the circles and turns back toward the class, still pointing to the circles.

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What is going on here? These may be very effective speakers, teachers, and presenters and instructors using their whole person as a part of a representation. The gestures, the pauses, the pounding of the podium, the posturing—all are typical parts of a live representation. Gesturing is part of the representation, but at the same time a modifier of the representation. It is an overlay that emphasizes, exaggerates, points to, repeats, makes more salient, or otherwise highlights some other part of the representation. Gestures can be physical or mediated: they can be a sweeping arm, or a flashing red arrow on the display. They can be a beeping sound or a rising voice level. In some classrooms gesture can take the form of positioning: when a formal doctrine is being given, an instructor stands behind the podium; when practical commentary or personal opinion is being offered, the instructor steps aside from the podium and then returns back to the podium when it’s time to resume the doctrinal representation. Gesture is unavoidable. In a live instructor, it is almost impossible to stand stiff and motionless during the delivery of representation. With media representations it can be different: a set of slides can look and feel stiff and motionless, with every slide appearing identical to the last—slide after slide of bullet points arranged on the same irrelevant but colorful background. What is the function of gesture? It is to give texture and proportion to representation. It creates saliences that indicate parts of a representation to be foregrounded and noticed—more important parts, pivotal parts, memorable parts, parts more central to the structure of a message. Gestures, whether live or mediated, are rhetorical devices. They act as a pointing finger, directing our attention, helping us to notice that some parts of a representation are more critical to the message than others Gestures are closely related to the concept of new information (Prince, 1981). Just as a stage provides a background for the movements and speeches of an actor in a drama, most of a representation (visual, auditory, haptic, or kinesthetic) merely provides a background for some item of “new information” that represents its main message. A famous example by Prince shows how intonation, a form of gesture, adds meaning to representation: A. John called Mary a Republican, and then SHE insulted HIM. B. John called Mary a Republican, and then she insulted him. The emphasis of the words in the first sentence changes the interpretation significantly. In the first sentence, Mary is insulted by being called a Republican; in the second, Mary’s insult to John merely follows John’s action. The emphasis in the first sentence is a gesture that changes sentence meaning significantly. Gesture in Live Instruction Gestures in live instruction range from the raising of an eyebrow to wild arm-flailing, elevated voice pitch, and elevated volume. Gestures can signal acceptance, controversy, anxiety, comfort, happiness, and anger. An actor’s skill consists in part of control over gestures. Some actors are known for a particular style of gesture. Don Knotts, for example, had mastered an agitated shaking that signaled anxiety or fear. In one recent animated film, a character touted the impact of his “smolder” expression. An instructor, as both an instructional designer and an instructional delivery system, has to decide how to use gesture for maximum effect. This is a personal and stylistic choice, but even if a deliberate choice is not made, gesture still occurs, because it is part of the act of interpreting to anticipate gesture. Gesture in Technology-based Instruction Gesture in technology-based instruction takes many forms. It is accomplished through augmentation or distortion that draws the attention of the learner: distortion of time, space, or feature,

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accomplished through the manipulation of salience, proportion, arrangement, point of view, speed of change, and other means. The challenge of creating gesture for technology-based instruction is determining the kinds of gesture to be used and the rules governing their use. Gesture can be pie-inthe-face direct, as in adding flashing arrows or colorful text, or as subtle as the turning of STEVE’s gaze to look toward the learner’s avatar while delivering a verbal message and then turning the gaze to the object to be acted upon before performing a procedural step. In technology-based instruction, there is a considerable gulf separating fixed representations and dynamically composed representations. In the former, the designer builds gestures directly into pre-composed representation resources—visual highlighting of visual media, auditory highlighting of audio. In the dynamic gesturing, since the exact order of representations cannot be determined in advance, the designer must choose rules that add gesture dynamically to representations. This is one of the challenges of turning a dynamic visual model into a model that can teach. Application Exercise Gesture is a part of your own live instructional style. • Try delivering a three-minute presentation without making any gesture, including facial expressions. • Try delivering a three-minute presentation on a complicated concept that uses only physical gestures and whiteboard drawings (no text, please). • What reflections do these exercises lead you to about the extent of gesturing during instruction and its addition to the instructional representation? Representation Mapping and Layering Representations have many sources. They involve the integration of many messages (from the message layer). These become mapped together onto a representation space (visual, auditory, haptic, and/or kinesthetic) either before or at the time of instruction. Different layers contribute different parts of a representation: • The content layer may contribute to representations in many forms: from fixed verbal content to computed variable values from a simulation model. • The message layer, acting out goals and plans from the strategy layer, may contribute conversational elements that lead to representation in a number of possible forms. • The data management layer may contribute orientation and current status information that the learner needs for self-direction of learning. • The control layer may contribute controls to be represented in their current state (available/ hidden). • The media-logic layer may contribute session/event controls. • The strategy layer contributes the messages and actions of the learning companion. How do these different sources come together into a coherent representation that includes visuals, audibles, haptics, and kinesthetics? The answer can be the layering of the representation itself. Layering is familiar to users of operating systems with windows. When windows overlap, one must appear in the foreground and others are either completely or partially obscured. Layering is familiar to users of graphical and photographic or content development tools. Different layers not only contain content but have layer properties as well—for example, transparency or opacity. In audio production the

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use of layers is common. In the production of sound effects for animated movies, as many as twenty or thirty layers of sound may be overlaid on each other to create a single sound effect. Animated feature movies provide excellent examples of this that are sometimes explained in their “making-of ” documentaries. In all of these types of media production software the layers of a creation are “flattened” to produce fixed (static) products, but in a dynamic representation system, the layers of a display can be dynamically active. Clicking on the proper control can move a layer to the foreground, make it semi-transparent, and in many other ways make the multiple layers of the entire representation (of all media forms, visual and audible) learner controllable. Simplifying Representation Through Integration So many messages need to come together that the representation surface can become over-complex, and the cognitive load of using it can break up the “transparency” of the interface, foregrounding it in the processing of the learner, where it does not belong. This can be avoided by simplifying the representation through the integration of individual elements into a coherent whole, producing a higher level of representation organization. Over time, integration happens naturally as interface concepts of an entire design field mature. For example, Moggridge (2007) documents how the operating system controls that are represented to a user have had to become clustered and integrated over time as additional functions became controllable. For example, the original concept for the desktop metaphor was sketched on a napkin by Tim Mott (ibid., p. 53). It afforded only controls directly related to the metaphor: a desk, in/out trays, a file cabinet, a printer, and a wastebasket. Compare that with today’s desktop, which must have not only content files and folders (no longer any file cabinet, nor printer), but areas for launching applications, areas for controlling launched applications, areas for finding files and folders that are hidden from view, areas for monitoring and OS-level functions, areas for performing OS-level functions, and areas for managing the computer and its configuration. This is a far more complex working context than the original sketch of the desktop took into account. The browser interface has undergone a similar evolution. It must display controls for managing window size, browser settings, locating “favorite” links, finding new links, moving back and forth between prior links, displaying multiple open links, and minimizing or terminating the browser session . . . then there’s the page content itself. On-page controls provide even more representation clutter. How can these things be simplified? Or, speaking in terms of instructional designer interests, how can instructional representations be simplified? Representations of instructional controls and information areas share a structure similar to those of the OS and browser: learning events must be launched and terminated; settings must be accessible and changeable; the display surface must be adjustable; external resources must be accessible and playable; multiple open sessions must be possible; and within an event there must be the possibility of obtaining event status and orientation information so that the learner can self-monitor progress. Unlike the OS and the browser, however, instructional representations must additionally afford control within events and all that that entails. Designs should not present the learner with a unique palette of control choices, so solutions will undoubtedly involve the evolution of a learning representation standard that anticipates the broad range of representation and control possibilities. This will reduce the need for learners to reorient every time they cross vendor boundaries, which will clearly be a phenomenon of the future. Simplification may not be just deciding on a better way to allocate screen areas to general functions. It may entail using the semantic of what is being represented to combine elements of meaning in creative ways. For example, the problem of crowded, over-complex displays impacted the aviation

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industry years ago, as the number of individual systems providing flight progress data to the pilot multiplied. Fragmentation of pilot attention became a major cause of critical errors, and now pilots are specially trained in cockpit management techniques. This helped the attention problem, but it did not address the problem of representation semantics for flight itself. Individual indicators in a cockpit provide readings for flight variables such as position, orientation, speed, relation to the ground and other aircraft. David Still has described a system for the compression of multiple indications into a single representation that relates all of these together meaningfully in terms of the total situation of the flight. In a system named OZ Still and his research team (Still and Temme., 2003) combined multiple indicator variables into a single display. Figure 10.2 shows an example of an OZ display. In this static graphic, the display looks crowded and confusing, but when it is in motion during flight, it becomes easy to read because the parts of the display change in synchrony with the movement of the aircraft in relation to the ground. Flying is a dynamic process in which the moment-to-moment energy levels of the aircraft are what the pilot is really managing, and the OZ representation combines several discrete indications into a representation that gives individual indications meaning in terms of the aircraft’s momentary state. In addition, since the display is dynamic, the trace of change in all of the indications over time, in one integrated display, creates a context for interpreting the progress of the flight in terms of flight semantics—the meaning of the action or the event itself. The purpose of this example is not to promote an aircraft display system but to illustrate a concept about the simplification of representations through the integration of discrete parts at a higher, semantic, level that provides a new kind of context for the ready interpretation of the state of a dynamic process. Instruction is one such dynamic process, with multiple factors that change moment by moment. However, during the average instructional event, the learner is normally not exposed to information on the changing factors that might aid self-management—in a way that treats learning as a dynamic process. One could ask, “Should the traditional learning management system become just such an integrated part of the representation, giving updated information moment by moment?”

Figure 10.2 The OZ aircraft pilot display that combines key flight factors. (From Still and Temme, 2003. Reproduced with permission from the author.)

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This is a distinct possibility, and one that should be explored. Doing so would certainly add new meaning to the term “learning management”. Representation and Other Layers The representation function is responsible for giving visibility to the functions and operations of all of the other layers. Controls available at a given moment need to be displayed; options for negotiation of strategy, content, goals, roles, and initiative need to be provided to the learner; session management choices that end, begin, and pause instruction have to be made visible; progress reports, performance data, and recommendations from the learning companion must find an outlet. These are accomplished through messages sent to the message layer function and on to the representation function. Application Exercise The computer display has become very cluttered with controls and indicators. • Design an instructional dashboard that gathers controls and indicators together in an OZ-like display. • How does it simplify the operation of controls? • How does it unify the information a learner needs to make informed choices about what to do next? Message-to-Representation Mapping Media resources—stored or generated—need to be aligned with messages, because messages in service to a strategic plan are what provide one side (the instruction’s side) of the instructional conversation. Two options confront the designer. Either: (1) make all representations uniquely defined and artistically designed, or (2) identify some regularity within the message system structure that permits rules to drive the channeling of the message to media representations. This second option assumes that the designer wants to follow a consistent pattern of message-to-representation matching based on message characteristics. This entails the assignment of certain categories of message to certain media channels. Message categorization generally follows one of two schemes: (1) categorization by message type, or (2) categorization by message content affinity. Categorization by message content affinity would be chosen if there was a body of messaging that for some reason needed to be placed in the same media channel. Perhaps this means that a document exists that can be reused that already clusters a number of desired messages together, or perhaps a video resource exists or is planned that clusters messages in the same way. Categorization by message type has already been described in Chapter 8 (on the message layer) in terms of ontology building for planned instructional message elements. Elements like those described in Chapter 8 can be matched not only with a media channel, but also with representational features such as placement within the representation, visual or auditory qualities, and gestural dynamics. Media Selection In an earlier day, the topic of media selection would be prominent and close to the front of a chapter on representation design because representations are provided through different media forms, and the media selected for a project either constrain or empower a design. Media selection in the early

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days used to be a chicken-and-egg question: if particular media were chosen before the design was created, then the designer had to deal with the constraints this imposed. If on the other hand the design could be made before the delivery media were chosen, then the designer could select media to support the unconstrained design, so long as costs were adequately controlled. Which one should come first, designers asked. Media selection was an important issue in the 1950s and 1960s, when many new media forms were emerging and government funds were readily available. Designers found themselves asking which media were the most effective for instruction, so research trended heavily toward media comparison studies. Another relationship studied was cost–benefit trade-offs: the study of what mix of media supplied the best economic and instructional balance. This type of research was prominent in the 1970s. Government and military organizations were especially interested in questions of this type because of the volume of training they conducted. Large corporations also found these studies of interest. Today interest in media selection as a major design process has waned, largely because of the computer and the maturing of multi-media technologies. The computer has absorbed the functions of most other media forms into a highly portable and ubiquitous device. Separate media forms tend to be used only in specialized situations and locations. The unsung hero of this media revolution has been the video projector. It has made the computer more usable in more settings than any other development, with the exception of the Internet and Wi-Fi. With the increasing portability of projectors, instruction can take place anywhere there are students. Today the practice of media selection, at other than the institutional level, can best be summed up in terms of the media-to-representation mappings just described. This type of media selection might be called micro-selection, because it deals with a very fine-grained level of decision-making for media application. This should be considered a good development, because it opens the door in principle to adaptive forms of instruction that can make very low-level kinds of individual decisions when that is desired. Conclusion Though representation is one of the most mature of the layers, it is also the one that holds the most interesting design challenges and possible futures. The representation layer is one that appears across many design fields. What lies ahead for instructional designers is to discover the principles that are unique to instructional representations: representations that must tell a story at both detailed and big-picture levels; representation that must portray dynamism across time; representations that must display multi-dimensional causes and effects; and representations that must capture attention and both answer and pose questions at the same time. Despite it’s being a mature layer, the surface has only been scratched.

11

Design Within the Content Layer

Teachers need a clearer idea of what they are about. It needs to start with a clear idea of what it is that the student is supposed to understand. —(Carl Bereiter, 2002, p. 126) The value of considering the content layer apart from other layers is readily apparent to a simulation designer. With that type of design problem the designer loses a familiar and comfortable design backbone that most designers depend on without even realizing it: the narrative verbalization—the instructional monologue—which so often is used as the central structuring thread of an instructional design. With simulations you simply cannot “tell” the content with an explanatory presentation, because the very nature of a simulation is to create constant, unpredictable interaction between the learner and a dynamic, changing model. An extended narrative is out of place: there is nowhere to put one. In a simulation the content is in the form of the model and its behavior, and the learner learns by interacting with the model: the pilot flies a plane that never leaves the ground, a surgeon repairs an eye that only exists in a computer, a golfer launches a 175-yard 3-iron drive inside of a garage. Each of them receives feedback and learning support, from either a live or computer-based learning companion. This kind of instruction parallels how we learn in everyday settings. We interact with the world’s systems and learn how to use them from their response to the interaction. During this process we do not expect or want lectures and lots of telling. But instructional designers find that narrative verbalizations are less demanding and less expensive to design, so we have a lot of them, except in high-payoff, high-risk, and high-danger instruction. There we learn from simulations. The issues of how to recognize differences in content structures, select specific structures to be captured, and then capture them are concerns of the content layer. The content may take many forms. The focus may be on skills, tasks, knowledge of cause–effect systems, facts, rules, metacognitive ability, attitudes, dispositions, values, or some combination of these. The concern of the designer is to identify the subject-matter clearly, accurately, completely, in measurable terms, and in a framework that can be related to the existing knowledge structures and performance capabilities of the targeted learner. This chapter describes the range of design questions relevant to the content layer.

Dealing with Content: Recent History and Traditions In order to put the issue of content in perspective, it is important to understand some instructional design history that leads to how content tends to be seen by designers in current practice and why. 279

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The most common practice in dealing with content has been to combine it with the concerns of the strategy layer—the layer that deals with instructional goals and goal structures—so that each one was structured in terms of the other. For example, Ralph Tyler, in his masterful 1949 work Basic Principles of Curriculum and Instruction, described the centrality of instructional goals to instructional designs and speculated that there were different types of goal. He suggested that different goal types might be more appropriately addressed instructionally using different strategic approaches. His student, Benjamin Bloom, who was concerned with the measurement of performance, followed Tyler’s lead and proposed the existence of three main divisions, or domains, of performance: cognitive, affective, and psychomotor. Within these domains he proposed a number of sub-classes of performance arranged in an order of increasing complexity, so that in order to do the higher-level performances it was necessary to have mastered the relevant lower-level performances. At first, Bloom’s categories—arranged within a “taxonomy” or system of categories—were used mainly by test item writers, but before long simple reasoning led instructional designers to the speculation that for each class of performance objectives there might be an appropriate instructional strategy for learning the performance. This was the beginning of the conflation of the content and strategy layers. This concept of categorizing instructional objectives became very popular, and in 1965 (1965a) Robert M. Gagné published a very different category system for learning objectives that subscribed to the same principle of matching objective categories with strategy patterns. Both Bloom’s and Gagné’s taxonomies flourished, and both taxonomic systems evolved over the years and underwent multiple revisions. Both systems are still in use today by novice designers. There were important differences between the two systems. Bloom’s objectives classes were expressed in terms of mental operations that could be applied to any subject-matter. On the other hand, Gagné’s classes were expressed in terms of content structures: concepts rules, and so forth. Gagné appropriated knowledge structures proposed by current psychological research. The earliest of Gagné’s taxonomies was strongly influenced by operant (behaviorist) theory (Gagné, 1965a). This led to learning goal types based on the operant, an idea from B. F. Skinner, defined in terms of stimulus–response units. All of Gagné’s classes in the earliest version of his taxonomy involved some variation on the operant theme. In later versions of Gagné’s taxonomy, behaviorist structures were joined by structures borrowed from information processing psychology (Gagné, 1970, 1977, 1985). Regardless of the new categories added with each successive version of the taxonomy, some influence of behaviorism was always retained in at least one of Gagné’s objectives classes. This seems to indicate that Gagné was trying to be faithful not just to one psychological theory but to the research findings of a variety of learning psychologists. Gagné himself affirmed this: “The aim has been to reflect recent and current research on human learning, and the implications these advances in knowledge have for the formation of what has come to be known as instructional theory” (Gagné, 1985, p. xi). He also described his taxonomies and work as more of an exploration than a statement of universal truth: Eight different classes of situation in which human beings learn have been distinguished— eight sets of conditions under which changes in capabilities of the human learner are brought about . . . From the standpoint of the outside of the human organism, they seem to be clearly distinguishable one from another in terms of the conditions that must prevail for each to occur. Might there actually be seven, or nine, or ten, rather than eight? Of course. —(Gagné, 1965a, p. 57)

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In effect, Gagné was trying to use the best results of the scientific enterprise to create temporary scaffolding so that the work of design could proceed on the best footing currently available. Gagné’s later work on objectives taxonomy not only increased the number of individual categories, but Gagné’s description became multi-level in nature, describing categories of categories of objectives. Were Gagné writing today, his current taxonomy might be much different from where he left off. Mixing Content and Strategy Unlike Bloom, Gagné had combined content structures with strategic structures, implying that if there was a kind of knowledge structure called “concept”, then there was an instructional strategy appropriate for teaching a concept. According to Gagné, the strategy would set up conditions under which concept learning would be most likely, hence, Gagné’s most famous book was titled The Conditions of Learning. The principle of objective-related strategy was widely promoted and exists in many designers’ minds today as a kind of gold standard for selecting instructional strategy. Gagné’s book was a best-seller and went through four editions. Likewise, Bloom’s work was highly influential. It has been picked up by his former colleagues and continues to grow and mature (Anderson et al., 2001). Regardless of the brand loyalties of designers to either of these two taxonomic systems, the standard reasoning for instructional strategy selection has become: (1) determine the content or performance structures that exist in the subject-matter, and (2) construct an instructional strategy that follows the generally accepted pattern for objectives of that type. Over the years, research themes have formed around different types of behavior and content structure. Content Becomes Independent Gagné’s objectives classes were considered to be hierarchical in nature, just like Bloom’s. That is, in order to attain mastery of a higher-order objective it was considered necessary to have mastered a certain number of prerequisite objectives of a lower hierarchical order. The difficulties in applying Gagné’s system of objective types became apparent as it was noted by designers that more sophisticated forms of performance existed than were accommodated by even the highest forms of objective in his taxonomies. In effect, it became apparent that the research-basing of Gagné’s objectives classes needed an update, not because of any fault of Gagné’s, but because researchers and theorists had concentrated their research on isolated laboratory tasks rather than the kinds of fluid and adaptive performance required in everyday settings. In response, Gagné continued to evolve his system of categories through several versions, trying to keep up with changes. The new kinds of instructional goal that became relevant to instructional designers over the years included complex problem solving, design, skilled performance in unpredictable environments, the ability to monitor and guide one’s own performance, and the ability to manage one’s own learning activities. It was in these areas that Gagné found that his system was lacking, and he tried to include them. Many designers trained in the 1970s and 1980s were taught strong allegiance to objectives taxonomies. Many found it hard to communicate to clients why the basic categories were important and to make the argument that they were comprehensive (now, of course, considered highly questionable). Over time, most designers discovered for themselves higher-order categories of performance that were missing from taxonomic systems because they were asked to design instruction for the more complex forms of behavior missing from the taxonomies. This chapter is about the content layer. During design a designer decides what there is to be learned and how it will be partitioned, captured, and inventoried. This requires that the designer have on hand a store of design language terms that describe the nature of learnable content—the elements of content and their internal patterns of relationship. Using these terms, a design team can build into a design the implications of content different structures and textures. These terms do not represent static entities;

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they represent the substance of performance capability. From this point on in this chapter, emphasis will be placed on describing content in terms that are related to complexes of performance capability. The views expressed will be guided by the philosophy that descriptions of knowledge should be made in terms of the manner in which the knowledge will be put to use once it is absorbed. Application Exercise There are several alternative taxonomic systems for classifying instructional objectives. Look in the literature of the time and find some of these systems. (Fink’s, Yager’s, Harrow’s, Simpson’s, Thomas’, the SOLO taxonomy, Marzano’s taxonomy, Krathwohl’s, and Shulman’s. The list could go on.) • What underlying principles differentiated these systems? The Content Layer How does an instructional designer deal with the question of what there is to be learned? It is an important question, because learning has to be assessed, and the designer has to be able to describe the performance to be measured. If there are different kinds of performance to be measured, then the designer needs to know how to select among techniques for eliciting and evaluating different kinds of performance, to determine when they reach a targeted level. If we speak of knowledge in relation to performance ability, then we are implying an ability to use content structures that evolve over time through practice. This treats “knowledge” as a dynamic quality and an ability to do something. Outside the mainstream of instructional design literature, artificial intelligence (AI) researchers for a long time investigated the separation of the content layer from the strategy layer (Wenger, 1987). This began at about the time that Gagné was formulating his first taxonomy. An important feature of the AI research was that although it identified knowledge-related structural elements, it did so in terms of how they were used in some type of performance. From this research there is an extensive literature containing proposals for the structuring of knowables, and by implication, how a designer can deal with them. This chapter will review several different ways of looking at knowables. Then it will apply those ideas to what instructional designers do. No one knows what form knowledge and performance ability take in the mind, and it is conceivable that a thorough description would involve multiple levels of detail (e.g., cognitive process, neural pattern, neuron, synapse, etc.). In the absence of the thorough description, designers still have to capture content and devise content-sensitive instructional strategies. The best solution at present is to propose a variety of useful approximations. In the descriptions that follow, keep in mind that in most cases the designer was not trying to describe the comprehensive reality of knowledge, but rather a useful approximation to some of the structures involved in some aspect of human performance. Given that proviso, what constructs can a designer use to capture and deal with the structural essence of knowables in a way that leads to design and learner performance advantages? Computability Because many of the ideas that follow arose from research on learning and artificial intelligence, the concept of content computability is important to keep in mind. For advanced designers, the question of content has often hinged on the question of computability. The computability of “knowledge” or “performance” is an issue because of the dream of achieving instruction, some portion of which can be generated at the time of need rather than creating instruction that is pre-composed, pre-assembled, and stored in an archival memory. This dream has never been fully realized for the everyday designer, but in some important ways it has been realized in the laboratory and in the high-end training world. For example, when a pilot steps into the cockpit of an aircraft simulator, the path of

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the experience is not pre-stored in a computer. As a pilot acts on a simulation in unpredictable ways, it computes the impact of each action using a computed model. As the model produces new values, they are displayed to the pilot, who decides what to do next. The simulation engine in this case uses a computable model of the aircraft’s behavior, stored in equations and rules and executed when needed. How to do this involves software technologies that had to evolve through experimentation. An interesting example of this evolution is provided by Bass et al.(2003, see Chapters 3 and 8). At the beginning of every simulation exercise, in effect a new aircraft is created inside the computer’s memory, and a pilot interacts with it. In order to capture and implement this kind of model, the designer has to be aware that content can be represented dynamically. As later sections of this chapter will show, there are many ways that content can be captured that align content structures with their strategic application. The choices of the designer in this respect are, in fact, strategic and often condition the types of design that can be created. Early experimenters in artificial intelligence wanted to know just how far the computability of “knowledge” and the computability of “teaching skill” could be computerized. Notice that it is not only subject-matter content they tried to capture, but also the content or rules of the instructor in relation to the skill of teaching (Buchanan and Shortliffe, 1984, see Chapters 25–29). Obviously these researchers foresaw productivity gains from being able to do this. Instead of having large development teams working to create unique hand-made bodies of instruction that anticipated every eventuality, they saw that it was possible to provide the computer with content formulas and data in a computable form and then cause a fresh experience to be computed for each new learner. Theories related to the content layer are theories of content structuring and computation (when that is possible). Intelligent tutoring and adaptive instruction depend on a consciousness of content structures. Even if computable content is not practically attainable, most designers will benefit from the deeper understanding of the structures that describe and approximate human knowledge and performance, because it will point them toward what to expect to find as they conduct content analyses. A Survey of Content Structures What is the range of content that needs to be described and captured? A better way to put this might be: What is the range of content that it is useful and productive to describe and capture? There are several approaches to answering this question in the literature. For example, it would appear that there are major “levels” of performance knowledge: • There is subject-matter (domain) content. • There is content that governs how to apply subject-matter content. • There is content about how one learns and solves new problems in the subject-matter area. An instructional theory called Cognitive Apprenticeship (Collins et al., 1989) exemplifies this. Cognitive Apprenticeship defines four types of learnable content—types of content that support particular types of performance. • Domain knowledge—“The conceptual and factual knowledge and procedures explicitly identified with a particular subject matter” (p. 477). • Heuristic strategies—“Generally effective techniques and approaches for accomplishing tasks that might be regarded as ‘tricks of the trade’ ” (p. 478). • Control strategies—“control the process of carrying out a task . . . Operate at many different levels. Some are aimed at managing problem solving at a global level and are probably useful across domains” (p. 478).

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• Learning strategies—“Strategies for learning any of the other kinds of content just described” (p. 479). All of these aspects of learning can be influenced either directly or indirectly through instruction, some more than others. Therefore, an instructional designer should be able to identify them through analysis. The sections that follow describe different ways “learnables” have been described in the literature. In each case, the goal is to point out a practical application related to the knowledge structures that are described. For additional discussion of subject-matter structures, see Chapter 5. Semantic Networks The first attempts at explaining knowables to a computer were very literal and very basic. Jaime Carbonell coded information into a network of associations—a semantic net—which could be “read” by a computer and then combined with strategy to create interactions between the learner and the networked information. Carbonell’s system was named SCHOLAR. SCHOLAR knew how to read the information from net and form it into factual sentences for display to the learner. From a semantic network SCHOLAR could extract relationships and use a standard sentence-structuring template to construct the statement “The area of Brazil is approximately nnn square miles.” It could form a similar statement about the location (latitude, longitude), bordering countries, capital, rainfall, and any other properties associated with Brazil or any other country included within the net. Through the same type of mechanism it could form questions for the learner to answer: “What is the approximate area of Brazil?”. It could check answers using keyword recognition, and that means it could become confused, in which case it would ask for another response from the user. The important idea in SCHOLAR for instructional designers is that sometimes it is useful to think of content in terms of interconnected networks of terms, concepts, and entities like this. When all possible links in a network of this kind are drawn, you encounter incredible complexity. However, a simplified version of a network that includes only priority relationships of selected kinds can be useful as a way to capture and represent the most important semantics within a body of information. The familiar process of concept mapping (Novak and Cañas, 2006) is sometimes used by learners to capture semantic information. Designers can find it useful for a similar purpose in subject-matter expert interviews. It can be a useful tool for team communications about content as well. A semantic net contains specific values, but it has an underlying structure. That structure is described in Chapter 8 as an ontology. The concept of ontology is becoming increasingly important in data mining. Ontologies are used for conducting searches in data-rich venues like the Internet and also for the kind of reasoning used by recommender systems such as your favorite online book or music store. A practical ontology can also be seen at work when you go to a regular bookstore and ask for “the of an for a ”. The clerk fills in the missing elements of the ontology and gives you some title suggestions. A consistently organized semantic network can contain different kinds of structure. These can include or be used to create different kinds of lists, such as: • Category groupings (e.g., Brazil, Argentina, Chile, etc.) • Ordered sequences of elements (e.g., step one, step two, step three, etc.) • Superordinate–subordinate inclusion arrangements (e.g., South America, Brazil, Brasilia). These internal structure options create specific ontological types: the concept class, procedures and processes, and the hierarchy respectively. All of these are useful to instructional designers as list structures for capturing subject-matter, which normally possesses all three types. One special form

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of expression called “propositional knowledge” consists of readings of portions of a semantic net (e.g., “The annual rainfall in Brasilia is nnn inches”). Another type of content representation, created as small chunks in semantic nets, is termed by AI researcher John R. Anderson the “working memory element” (e.g., Capitals–Brasilia–Buenos Aires–Santiago). In Anderson’s theory of knowledge these small chunks extracted from semantic nets, work in conjunction with “ . . . if . . . then” rules called “productions” as a fundamental unit of human performance. Anderson’s is one example of where a means of knowledge representation and capture does coincide with a theory of learning and performance, and this manner of structuring content has been used in a successful commercial application of intelligent tutoring in highly structured subject-matters in public schools (Ritter et al., 2007). True semantic networks are used to capture subject-matter structures. The lines representing connections between nodes of the net have a definite meaning to the creator of the net. However, in everyday use, when concept maps are created, connections can be of different kinds and are often labeled to give them specific meaning. Carbonell’s SCHOLAR system was a laboratory experiment. Carbonell’s use of semantic nets is an example of how instructional content can be separated from instructional strategy. A single semantic net like SCHOLAR’s could be interpreted by multiple strategic engines employing different strategies. Each one would produce a different kind of instructional experience. The preceding discussion focused on the nets and only briefly mentioned the strategic engine of SCHOLAR, which was capable of reading the nets and forming didactic statements and questions. In addition to this capability, Carbonell’s strategic engine had to be able to make decisions that included: • • • • • •

Which topic to take up next Which topic was unnecessary because it was already known Which answer could be considered correct When to give feedback What kind of feedback to give When to declare that SCHOLAR did not understand an input.

The difference between the networks used in SCHOLAR and the less formal concept maps that have been described in this section is computability. Carbonell’s semantic nets had to include markers that could in principle be used by the computer to tell it what kind of element it was dealing with and how each was related to other elements computationally. This brings to mind the recent innovation of XML-tagged content and the ability it provides for both display and databasing engines to interpret how to deal with it. Application Exercise Perhaps without realizing it, you possess numerous semantic networks. • Have a friend pick a word. In ten seconds, write down all of the words you can that relate in your mind to that word. • Analyze the relationships between the words you wrote. Are there different categories of relationship? • Now pick another word and in ten seconds write down all of the words you can that are related to the original word only along a single dimension (e.g., they are physically alike, or they are all synonyms, or they all begin with the same letter).

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• How hard was it to stay in the single dimension? • How many words came to mind that you could not use because they were from another dimension? Cause–Effect Models An interesting thing happens when the relationships between elements in a semantic net indicate some form of influence being exerted between them. For example, if you were to include into a net the relationship that the number of inches of rainfall in a particular city raised or lowered the level of a nearby river by a certain amount, you have given the rainfall amount a computable relationship with the level of the river. This relationship can be expressed in different ways. For example, it can be expressed as an equation dealing in quantities (rainfallininches = riverlevel x coefficient), as a qualitative and non-numerical relationship (greater rainfall = > higher river level; less rainfall = > lower river level), as a production rule (IF rainfall is heavy, THEN river rise will be larger), or in other ways. Directed relationships among the elements of a network turn the network into a model, and relationships may take a number of forms. The most common examples given of dynamic models are digital, all-or-none, on–off examples: “turn on the light switch, the light goes on; turn off the light switch, the light goes off ”. However, the kinds of models that are most useful to an instructional designer are more complex and may include: • • • • • •

Multiple elements which influence a single element A mixture of quantitative and qualitative relationships Complex rules of influence which involve multiple AND/OR/NOT relationships Sequential or timed relationships Relationships which have greater importance or priority over others Conditional relationships which rely on the co-occurrence of other relationships.

A set of relationships of this kind might look like this: IF the air temperature is above 32°F (melting point of water ice) ANDIF the level of particulate matter is high ANDIF the air is saturated with water vapor (nearing 100 percent) ANDIF the air cools relative to its original temperature ANDIF water droplets form ANDIF turbulence is sufficient to cause water droplets to collide ANDIF larger droplets form from collision ANDIF droplet mass becomes greater than any upward force exerted THEN droplets will fall, creating a form of rain. A professional meteorologist would use a much more complex, numerical model than this one. Models have inner dynamics that can be complex to understand, especially when time is a factor. Hunter (2009) describes the complex tidal system of what later came to be known as the New York Harbor, which was faced unknowingly by seventeenth-century explorer Henry Hudson in 1609: The Hudson River estuary features convoluted tidal dynamics, as seawater floods and ebbs along its many miles and among its bays, straits and feeder rivers, while opposing the outflow of freshwater, which itself follows a seasonal cycle in volume.

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Tides in all river estuaries move as an enormous wave, changing speed and range according to the local geometry of tidal course. (These waves are normally imperceptible but can build into a visible standing wave, or bore, in the higher reaches of an estuary.) Waters normally are “slack”, neither flooding nor ebbing, around the times of high and low tides. But in an estuary like the Hudson, the tide typically continues to rise after the point of high tide, continuing for two hours into the ebb period. Even in the general area of Upper New York Bay, the tide is slightly idiosyncratic, complicated by the fact that tidewaters reach it from more than one direction. The flow in the East River (actually a tidal strait) is answerable to the tide on Long Island Sound, which is about 70 percent greater than that of the Hudson estuary and has a cycle that falls more than three hours later. While the mean tidal range at the Brooklyn Bridge is about four and a half feet, only eleven nautical miles away at Whitestone, near the entrance to Long Island Sound, it is more than seven feet. Two hours after high water is marked at the battery, the wavelike character of the tide means the ocean is still heading up the Hudson River at about one knot. But Upper New York Bay has already begun draining on the ebb through the narrows at less than a knot—and moving through the Ambrose Channel south of Coney Island at more than a knot and a half— while Long Island Sound is being replenished via Hell Gate at almost two knots. Three full hours after the high water has been marked at the Battery, the nearby Hudson is finally slack, while the East River is ebbing at Hell Gate at four knots and the bay is exiting the narrows at about a knot and a half and pushing through Ambrose at two and a half. And while all of this is happening, the high water of the wavelike flood tide continues to ascend the Hudson River. —(pp. 146–147) In this complex model description, locations (the narrows, the Hudson), substances (seawater, river water), and forces (incoming tides, outflowing tides, and the invisible waves they create) interact over time, producing a rich, dynamic pattern capable of supporting inferential thought. This model can be viewed in either simplified terms or in much more detailed terms. The detail and complexity of a model of interest to an instructional designer is related to the existing knowledge of the target population and the instructional goal. It would be possible to create a model of rain conditions that consisted solely of complex equations, charts, and graphs. So the designer must ask: What level of detail would be appropriate for an advanced meteorology student? What level for a beginning meteorology student? What level for a high school student? What level for an elementary student? Since it is possible that they would be studying the same model, the answer to these questions is: It depends. Here you can see the need for a designer to negotiate the level of complexity in the expressions used to generate the model with the subject-matter expert. Here also it becomes evident that models are always approximations to reality. Therefore, there is always some difference between them and reality: they do not represent “knowledge”. Object Models One form of model of special interest to instructional designers is the object model (Resnick, 1994). In an object model, entities are identified and given existence as things that live in a community of other objects, sending and receiving messages among themselves and acting out the implications of the messages. The computer has been intentionally left out of this definition because object models can be enacted by learners as a group as well as computers. For example, in a role-play situation, if each role is described as a profile, then the role player is acting as an object in an unpredictable scenario that will unfold in terms of the interaction among the objects (role players). Change the definitions of the different objects and you have a new unfolding of the model.

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Object models are also of interest because of their properties when enacted by a computer. A model can be made to be self-explaining to some degree, meaning that as a learner interacts with a model, the model supported by strategic augmentation can tell the learner what is happening inside of itself. This can include links to the visual representation of the model, which would change dynamically and in an unplanned sequence as the interaction proceeds. Not all models can be captured and enacted in this form, but many can. Bayesian Models A second type of model that is gaining in relevance to instructional designers consists of a network that is structured on the basis of probabilities. These are called Bayesian networks. Bayesian networks can be used to reason about the probability of future events, given the history of prior events. The name “Bayesian” indicates the fact that Bayesian networks are produced using the techniques of Bayesian statistics, techniques described in a book with an intriguing title: The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines and Emerged Triumphant from Two Centuries of Controversy (McGrayne, 2011). Bayesian statistics supports reasoning, based on patterns: the probability that a submarine will be in a particular place, based on where it has been in the past, and the probability that the next letter will be an “r”, given that the preceding letters were “l-e-a-r-n-e”. Of course, the word could also be “learned”, and it would take more pattern data to improve the prediction of that network, including the probability that the word normally chosen was “learner” as opposed to “learned”. Bayesian-based reasoning is common sense, and we apply it every day without necessarily making computations. If Wally was late to the previous three meetings, what are the chances he will be late to today’s meeting as well? What if he was late to the previous ten meetings? Does that increase the probability he will be late today? The use of Bayesian nets in an instructional design is bound to increase because the actions and decisions of a learning companion—live or automated—are made within the context of an ongoing learning conversation within which repetitive patterns can be detected and used to make choices and recommendations. Formal Bayesian modeling techniques are currently out of reach for the average instructional designer, but not long ago (less than a career span), it was not within the reach of the average designer to create professional-looking graphics, to edit video, to edit audio, or to modify photographs. Once the underlying principles of those kinds of systems were worked out in research and development laboratories, the movement to the everyday workplace was swift, because there was an existing demand. Today, when you take a computerized adaptive testing (CAT), chances are that Bayesian statistical techniques are being employed to choose the next most informative item. Moreover, once the test is over and you turn to your Internet radio station, when it recommends a song, chances are high that Bayesian statistical techniques had a hand in that also. One interesting aspect of Bayesian techniques is their ability to use patterns to make predictions across almost any set of variables. One useful and perhaps unexpected example is the possibility of using Bayesian techniques to match the emotional and personality variables of a learning companion to those of the learner. Ball and Breese (2000) describe the use of Bayesian methods in the design of intelligent conversational agents that relate qualities like “friendliness” to variables such as “gesture”, “face expression”, and “positive words”. Rickel and Johnson (2000) describe how the layered architecture of an intelligent agent called STEVE allows it to generate actions such as the shifting of its gaze in the direction of a learner while speaking. Some areas of STEVE’s pedagogical knowledge are encoded in Bayesian networks.

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Application Exercise Think back to your high school biology (or chemistry, or physics) course. How many systems can you name that you learned about? (Biological systems, chemical systems, physical systems.) • How did your biology (or chemistry or physics) teacher describe the operation of these systems to you? • How useful do you think it would have been to have an interactive system that you could manipulate and experiment with? Task Structures For many years a staple of analysis methods has been task analysis. Semantic networks capture relationships; cause–effect models capture relationships of mutual influence, and task analyses attempt to capture expert performance models. These performance models describe how humans interact with the cause–effect models in the world around them to produce desired outcomes. It is in performance models that we see the different interesting levels of: (1) subject-matter performance, (2) problem-solving performance, and (3) learning performance. It is also in the area of performance models that reflective actions such as thinking about thinking (metacognition) and self-evaluation show up. Analyzing Subject-matter Performance Every subject-matter is associated with the performance of tasks. Reading involves decoding and comprehension. History involves processes of research and source evaluation. Economics involves the application of statistical methods and interpretation. Choosing examples from three domains that do not call for physical acts emphasizes that a task is often not a physical act. Even tasks drawn from the domain of sports that we might consider to be physical have a very large cognitive component. We might, for instance, compare the statistical predictions of the economist with the trajectory plotting of a wide receiver about to catch a long pass. Task analysis in general is a method for decomposing a human performance for the purpose of inventorying its component parts (Jonassen et al., 1999; Gibbons et al., 1999a). A wide variety of task analysis methods have been used in instructional design since the 1940s. These methods differ in the amount of detail they capture about the tasks themselves and their relationships to one another. Analysis methods differ in capturing: • • • • • •

Details of the steps in a performance The sequences involved in performing steps Decision-making carried out during performance Hierarchical relationships among tasks Conditions under which component tasks are performed Standards to which tasks are performed.

Task analysis is rooted in efficiency studies and job definition questions raised by the second industrial revolution that occurred around the beginning of the twentieth century. Task analysis can be used to describe functions of all kinds, including highly cognitive functions. Analyzing Problem-solving Performance In addition to analyzing performance which involves the manipulation of subject-matter content, task analysis is useful in identifying two kinds of problem-solving knowledge possessed by virtually

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every subject-matter area: (1) “rules of thumb” or heuristic strategies used in solving problems, and (2) rules for selecting a heuristic to be used in a particular problem-solving situation. Rules of thumb become apparent when a complete novice experiences a car that won’t start in the morning. In attempts to get the car started the rules of thumb applied might include checking the transmission lever (5 percent chance of succeeding), turning the key on and off several times (2 percent chance of succeeding), lifting the hood to look at the motor (no help at all), jiggling the battery wires (1 percent chance of succeeding), and calling a friend (50 percent chance of succeeding). These may or may not be applied in any particular order as the level of frustration rises. This is where the rules for selecting a heuristic (the second type of problem-solving performance) come into play. Someone who has watched a more expert problem solver or who has obtained a better mental model of the electrical system or of troubleshooting might apply certain steps in a certain order determined by the information provided by each step. The first steps might include taking care of checks that can be made without exiting the driver’s seat: transmission lever, battery indicator lights, re-try cranking. Then there might be a moment of reflection while a list of possible causes is generated. Then the list of causes might be pursued systematically to eliminate possibilities in a rational order, finally zeroing in (or not) on the cause. This more systematic choosing of steps to execute and the choice of a next step based on what the previous step revealed constitutes heuristic selection performance. Both the application of rules of thumb (which are mini-performances) and the selection of rules of thumb (a meta-performance) can be analyzed with respect to a given subject-matter area. In analyzing library search techniques a perceptive analyst will find a multitude of tasks that are often neglected (and improperly instructed) in the training of novice library users. These two kinds of task are a gold mine of value-added for a perceptive instructional designer. In comparison with the easy-to-identify mechanical tasks that are easy to spot, these tasks have high leverage value for the learner. Analyzing Learning Performance The goal of instruction should not simply be to instruct within a narrow range of factual and conceptual subject-matter but to impart capability. A capability with great value-added for an organization is the capability for building new knowledge through reasoning, inference, and personal experimentation. It is possible to instruct a learner in how to learn within a particular subject-matter domain, and this is amenable to capture by task analysis. There are many approaches to analyzing the kinds of performance that are most useful in this respect. In some cases there are universal principles that can be taught that will increase the ability to conduct self-guided learning within any domain. Mainly these consist of what can be considered “meta”-cognitive skills: reflection, close observation, pondering, reasoning, experimentation, articulation of new learnings, critical thinking, looking for epitome ideas, and summarizing. Critical thinking can be included in the list of universal learning performances. Most often the best approach to instructing these things is through patterning of instruction to emphasize these partial performances repetitively rather than teaching the skills directly (Resnick, 1987). In addition to universal skills, there are domain-specific skills that promote continued learning within a subject-matter area. We might describe these things as “thinking like a  .  .  .  ”(fill in the blank). Those who continue to improve their expertise in any domain of performance do so not just through interest or desire but by knowing how their domain is organized and how new knowledge is produced within it. Developing the skills of “thinking like a . . . ” can depend on whether the designer can come to understand “what it is to be a . . . ”

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Application Exercise Task structures are everywhere in daily performance. We commonly break large tasks into smaller tasks to simplify their planning and execution without being aware of it. Consider how you decompose such tasks. Take one of the tasks listed below and list the sub-tasks that it entails. Identify: (1) performance steps, (2) problem-solving tricks of the trade, and (3) selection rules for deciding what to do next during problem solving. • Task: Complete a trip expense report. • Task: Paint a room. Attitudes and Values Attitudes and values exhibited during performance are in most cases an integral part of the performance. Attitudes and values must be described as if they were part of the content of a performance. A key distinction exists between attitudes and conative states (Snow, 1989). An attitude is a favorable or unfavorable impression or feeling about someone or something. Rather than being absolute, attitudes are considered to be relative: “I like this more than that”. Consequently, the measurement of attitudes is usually carried out using a kind of scale we are all familiar with where we have to express a degree of liking for something. (“Do I agree, agree somewhat, not disagree . . . ?”) In contrast, conative states—or conation—is considered to be a quality of wanting to act or a state of feeling desire to act (or not act). Conation implies the gravity toward or away from acting as well as an impression or valuing. What difference does this difference make to an instructional designer? Whereas attitude measurement records preference independent of performance, the measure of conation has to be taken in the act of doing something. An instructional designer who creates performance environments creates at the same time a stage for measuring, or at least approximating, conative state. Application Exercise • Make a list of things that you perform in which your attitude plays an important part in performing acceptably. • Now make a list of things you perform in which conation, the intention or desire to act, plays an important role. • Compare the lists. What are the differences in the performances? Skill The goal of most instruction is not simply the transference of information or even the ability to think in terms of subject-matter concepts: it is the ability to competently perform a sequence of actions, within a demanding environment, and to a high criterion. This is the skilled performance. Skill is not a kind of knowledge: it is a learned capability and a form of adaptive behavior. Skill is the combination of well-learned individual capabilities into a coherent flow of performance. During skill execution, decision-making constantly adjusts the course when necessary to achieve a desired result. The execution of skill demonstrates human cybernetic processes at work. As the execution of the skill progresses, self-observation of the performance and decisions about how to make adjustments are instantaneous and constant. Think in terms of a parachutist watching the ground approach and using the harness lines to make adjustments that will lead to a better landing spot. Skill involves all of the kinds of “knowledge” described in previous sections acted out in combination. The tendency of instructional designers to want to categorize kinds of behavior has made it

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harder to envision the trainable dimensions of skilled performance. Geoff Colvin, in his book Talent is Overrated (2010), observes that in organizations the scarce resource today is “human ability”. Colvin contrasts ability in this case with raw talent, indicating his belief that skill, not inborn capability, is more important. Instructional designers are sometimes trained to concentrate on the fragmented performances and conceptual elements. Experienced designers tend to have a more holistic view of what is to be learned, and they see performance from a wider viewpoint as well as in its details. In particular, the designer understands how to bring small performances into whole, competent, fluid performances by following principles like those described by van Merriënboer and Kirschner (2012) for training skill while also training individual tasks. The analysis of skill requires the description of extended performance sequences under a variety of performance conditions. The description of multiple scenarios that a performer must respond to under a variety of conditions is useful in this respect. Application Exercise How many skills do you possess? Everything from walking and talking to breaking marathon records is a skill. Driving is a skill. So is eating with a spoon. Can you remember when you didn’t have those skills? If not, observe an infant who is learning these things. • Consider a skill as something that improves over time as you practice and receive feedback. How many more things can you add to the list of skills that you use every day? • How many of your skills do you deliberately and consciously work on to make improvements? • How do you improve a skill that you are already quite good at? Erroneous Knowledge Woolf (2010) identifies multiple types of content that the designer will often need to capture that represent: “misconceptions  .  .  .  well-understood errors, or incorrect or inconsistent facts, procedures, concepts, principles, schemata, or strategies that result in behavioral errors” (p. 269). The value of collecting these kinds of mis-knowledge is that they allow the designer to anticipate classes of error and specific errors to provide for their detection and remediation during instruction. This kind of knowledge is something referred to as “buggy” knowledge (Wenger, 1987). Application Exercise Recall some of your own teaching and learning experiences. • • • • •

Consider the number of errors you made while you were learning. How important were the errors and failures in your learning? Is it possible to contemplate instruction that kept a person from making errors? Would that kind of instruction be a good thing? Why or why not?

Abstract Content The types of content described to this point imply that there is some way to represent the content in a computable way. Content in this sense is usually thought of as consisting of atoms or elements that can be enumerated and manipulated with rules of logic (digital or otherwise). For many design

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problems this level of thinking about content is sufficient. However, there are cases where content resists being captured and inventoried in such a directly useful way. Bereiter and Scardamalia (1989) describe a form of learnable content related to “learning to learn”, which is the fourth of the major content types named by Collins et al. (1989) in their description of Cognitive Apprenticeship. Though it is possible to list principles related to learning to learn, there is no body of content that can capture its essence computably. For this reason, Bereiter and Scardamalia designed a type of learning space called an Intentional Learning Environment (ILE) in which multiple learners can jointly solve problems that lead to the creation of new (for them) knowledge. Bereiter (2002) explains that learning how to create new knowledge through problem solving is a capability that in the future will be more and more critical for youth and adult workers trying to adapt to life in a world where creativity, innovation, and the ability to learn both independently and collaboratively will be at a premium. Bereiter asks the questions: How might a nation or an organization double its rate of knowledge production? How do we educate the populace to be knowledge workers? How does an organization become a learning organization? These questions pose novel challenges, which our ancient theory of mind has never had to wrestle with. Also, they involve queer juxtapositions of terms—knowledge production, knowledge work, learning organization—resulting in expressions whose meaning is unclear. These expressions don’t, in fact, make much sense under a theory that has knowledge consisting of objects in people’s minds. Yet there is a widespread conviction that they refer to very important things. This is not a happy state of affairs. To correct it, I believe, we need a new theory of mind. —(p. x) It is the inability to describe certain kinds of knowledge in atomistic, capturable, and computable form that led Bereiter and Scardamalia to devise the concept of an intentional learning environment. Within an ILE, learners are guided by “scaffolds” while working collaboratively within the common space to create new knowledge: • Learners contribute their part to the problem solution by creating resources within a space using the scaffolds. • Resources become linked into argumentational clusters through the use of additional scaffolds. • Peer critiques of theories and evidence-based arguments use still other scaffolds to refine what the group “knows”. As the solving process moves forward supported by the family of scaffolds, conclusions emerge, and—over a period of days or weeks—new knowledge is created. This knowledge is not Nobel Prize quality, except to those who have jointly created it. Solving activity within the ILE takes the form of highly structured conversation. The scaffolds suggest categories of contribution and so discipline the process of working jointly, while at the same time providing a living model of learning discourse. The subject of the problem and the actual “knowledge” that the solving team creates are not as important as the process they experience (and observe) as a group. Hewitt (2004) shows that this knowledge creation process, though originally implemented on a computer (Scardamalia and Bereiter, 1994), is also applicable within a classroom setting. The relation of this discussion of ILEs to content analysis is that there exists a kind of knowledge that does not yield to traditional notions of atomistic knowledge capture. Yet these can be modeled

294 • Design in Layers

through group processes in a way that allows the learner to observe and, through a process of assimilation, master them. (For another example of this type of learning environment, see Brown and Palincsar, 1989). Application Exercise Have you ever had an experience working with a group of people where the excitement of solving a problem made you a learning group of the kind that Bereiter describes? • • • • •

What is it that creates the excitement in such a group? What was it that you learned? How would you describe it? Would you like to have that kind of experience again? Would you like to design that kind of experience for others? How would you do that?

The Recursive Nature of Content Structures Previous sections have described how several different kinds of content can be identified and captured. This is based on a practical technical need of designers and does not constitute a scientific statement about the nature of knowledge. The ability to talk of types of content does not constitute a position about the true nature of knowledge, because how we see “knowledge”—individually and as a professional group—changes. When behaviorism was the theory du jour, behavior was characterized in terms of operants and operant chains (perhaps another content structure to add to the list developed in this chapter). Information-processing psychology and cognitivism added their views on the nature of knowables. Bereiter, representing a constructivist perspective, describes knowledge in terms of the discourse that creates it and refuses to give it any specific form. The idea of content analysis is for the use of the designer, and the designer must work with whatever terms the theories of the times provide. Given that content structures are inventions based on what we can currently discern within human behavior, one thing we can say is that whatever types we can see, they seem to be made of themselves. That is, operants are chains composed of smaller operants. Procedures consist of component procedures. Processes, analyzed, turn out to consist of sub-processes that are in turn composed of smaller processes that can be decomposed recursively down to the degree of absurdity. This idea may seem trivial, but it is important for the designer to acknowledge. A designer chooses the span of levels to be captured during analysis. As tasks are broken into sub-tasks and concepts are broken into sub-concepts, how far does the analysis proceed? Surprisingly, that decision is relative and is determined by what the learner already knows, as revealed by the target population analysis. You stop analysis of content when it reaches the point that the learner already possesses. The Non-exclusivity of Content Analyses The concept of content and performance analysis has become over-simplified within the profession of instructional designers. The trend has been toward simplification—some would say over-simplification. There was a time when task analysis was performed to the exclusion of other forms of analysis. Then, as now, there was no standard narrative on how to integrate analyses of different kinds. As a consequence, some designers found themselves performing task analyses on content that did not yield useful results, because the type of content and the desired performance were not task-oriented. This led many to feel their time had been wasted and that analysis in general was unproductive. For

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a period of time, the public conversation on the topic of analysis lost interest, and there was a loss of perspective about what the designer was trying to accomplish by performing analysis. A few authors have attempted to describe how to combine multiple forms of analysis usefully. Examples include the ETAP (Extended Task Analysis Procedure) described by Reigeluth and Merrill (1984) and the MCAP (Model-Centered Analysis Process) described by Gibbons et al. (1999b). Reigeluth’s Elaboration Theory (Reigeluth, 1999b) likewise combines multiple facets of analysis in a coherent way. Jonassen et al. (1999) describe multiple analysis approaches. Gibbons et al. (1999b) define the input–process–output pattern as a unifying theme of analysis and compare several quite different analysis approaches using the pattern. In this view, each variety of analysis is considered as if it were reductive: subject-matter structure is shown to be decomposable into sub-units of its own type, as described in the previous section. Though this is far from unifying disparate analysis methods, it does illustrate one underlying pattern of virtually all analyses: the decomposition of larger content units into smaller units of the same type. What remains to be described is how the outputs of different types can be related together in a way that can produce insights for the designer and a deeper understanding of interacting content structures, possibly leading to a new explanation of how deeper understandings can be communicated to the learner. These few examples have not had sufficient influence to promote the idea generally that multiple analyses and their integration might be preferable to single analyses. Many of the most popular instructional design models still convey the sense that analysis is mostly monotonic. A grand theory of pre-design analysis does not exist. However, no subject-matter and no performance is as trivial as our current design models and practices make them appear. Relating the Content and Strategy Layers The content and strategy layers together supply the central structures of a design: without them, there is no design. During design, layers mutually influence each other, but during instruction the job of all layers is to support the functions of the content and strategy layers. Most designers have been trained to think that a single chunk of content (say, “C = πD”) becomes matched with a performance (say, “solve for D”) to create an instructional goal. This relationship—

Performance objecves

Instruconal objecves (typed)

Content

Instruconal strategies (typed)

Figure 11.1 The mixing of content and strategy into fixed instructional designs.

Self-contained, fixed-content, fixed-strategy instrucon

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the welding of instructional goal type to instructional strategy type—has become one of the fundamental patterns in modern instructional design practice. It is embodied both in Bloom’s taxonomy and in Gagné’s taxonomy of learning objective types. This mindset places the content and strategy layers into a relationship depicted in Figure 11.1, where content and strategy are intermingled within packaged instruction: content and strategy become treated in fixed, static terms. While the idea of crossing content element with strategy pattern was once revolutionary, experience has shown that it has an upper limit of applicability. That system works at the level of isolated performances executed toward relatively small and fragmented bodies of content, but the system becomes difficult to apply when extended performance and complex subject-matter are involved. In such cases, a structured approach like van Merriënboer’s 4C/ID model (van Merriënboer and Kirschner, 2012) must be used to phase instruction on fragmented goals, through stages, into integrated sequences of skilled performance. The more complex job of instructing skills at higher levels of integration as suggested by van Merriënboer often leads to performance environments that are simulated. In such environments, neither content nor strategy can be considered static. Simulations provide dynamic models of content with which learners interact and dynamic learning companions that support momentary needs, based on the constantly changing instructional goals. Figure 11.2 shows that the models themselves become the source of content. The intent of these examples is to illustrate how a designer might think strategically about bridging the gap between content, goals, and strategy in a way that moves beyond the traditional pattern. Content Sub-layers Over time several factors will force designers to give more consideration to the issues of the content layer. Among them will be the increased use of simulation and accelerating emphasis on high-performance training. This kind of training requires a finer-grained analysis of performance. Most of

Performance objecves

Content

Content modeling engine

Strategy modeling engine

Instruconal objecves

Strategy Rules

Figure 11.2 Content supplied by a dynamic model.

Design Within the Content Layer • 297

the sub-layers listed below are speculations concerning the content analysis skills that will grow more prominent in the future, as content analysis becomes more specialized. Three main sub-layers of the content layer in this estimate include: • Content methodology—This sub-layer will deal with questions of matching analysis methodologies with subject-matter characteristics and the kinds of performance that are desired outcomes of instruction. It will deal with questions like: How many varieties of content will be analyzed and recorded? What will the varieties be? (e.g., domain knowledge, metacognitive knowledge, attitudes, values, etc.) Will a particular theoretical system influence the partitioning of units of content? How many kinds of analysis method will be used? At what point in the design or development will each content element be fleshed out in specific detail? • Content capture and inventory—This sub-layer will specialize in conducting analyses. It will deal with questions about how best to approach the analysis process, given the resources, personnel levels, time, and expertise of subject-matter experts. Its questions will include: How will epitome knowledge be identified (e.g., as per Reigeluth’s elaboration theory)? How fine-grained will analysis be, in terms of detail? How will the dimensions of the structural element be matched with the needs and abilities of the target population? How will completeness and accuracy of the content be judged? • Content operation during instruction—This sub-layer will be concerned with computable content structures. If a designer is creating a simulation, the content will probably be expressed during instruction as a state model—a set of variable values produced by a program running inside a computer or enacted by learners. These values supply “live” data to the other layers (functions) of the design describing the momentary state of the simulation model. A verbal expression of content is not sufficient in this case. How will the content be represented in a dynamic (computable) model? How will content data be passed to other design layer functions?  

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Conclusion Many approaches to analyzing content have been described. Will it ever be possible to create completely generative instruction—instruction that is generated computationally from kernels of knowledge and strategic rules? That vision that was created many years ago with the advent of instructional computers. But whether or not a computer is involved in a design, a designer makes decisions about content analysis and partitioning. History shows that what is in the laboratory will be in our workshops tomorrow, so new designers should be looking forward to the expanded role that will be played by content analysis in the future.

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12

Design Within the Strategy Layer

I never teach my pupils; I only attempt to provide the conditions in which they can learn. —(Albert Einstein) Everything an instructional designer does is strategic. It is strategic in the same way Napoleon’s order to invade Russia was strategic. That order was a sweeping concept for a military operation that had to be broken down into its low-level operational implications. Invade. What does that mean for the artillery soldiers? What does that mean for the logistical planners? What does that mean for the movement of troops? Most of our plans for daily living are formed by breaking larger goals into smaller ones. We’re going on a vacation. What does that mean for transportation? What does that mean for finances? What does that mean for packing? We take big plans, big intentions, and break them down into more detailed plans until they represent individual things we have to do to prepare for whatever we are planning. Once the plan is made, it can be executed, though some details are always left fluid until much later. There is an interesting pattern in this for instructional designers. We get a grand idea about how instruction can be carried out, but then we immediately begin to break that down to its implications. What does that mean for facilities and infrastructure? What does that mean for finances? What does that mean for media creation? What differs from designer to designer is how the breakdown occurs. Everything depends on what a designer chooses to “see”. Some designers are able to see the gross features of a strategy. Some designers recognize structures of finer granularity. The main idea of this work is that sometimes a better set of structures can lead to “cheaper, better, or faster” designs. Most instructional designers feel comfortable talking about instructional strategy at the level of the monolithic plan. But the problem facing the profession of instructional designers at present is to give a coherent account of the different sub-layers of instructional strategy in as much detail as possible. This will reveal areas of strategy that require more attention through research and documented practice. This chapter is a speculation about those levels. A complete treatment of strategy design would require many books. This chapter will define strategy design questions in terms of several sub-layers, concentrating on areas that may have the greatest impact for the learner. The list presented here is by no means complete. The complete list that will evolve over time depends on what designers and theorists are able to “see” in the future.

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The Relation of the Strategy Layer to Other Layers If everything an instructional designer does is strategic, then every design decision should be considered an addition to the strategy. This includes decisions about: • • • • • •

The partitioning of knowable content The formation of instructional messages and communication intentions The representation of instructional messages in sensory form The provision of controls for learner use The arrangement of execution means and logic The gathering of data and its use in tailoring instruction to the individual.

These are all concerns of other layers, and yet they are strategic decisions. The seven layers described in this book are treated as if their concerns can be separated, but in reality they are all part of the designer’s strategic plan. Separating them permits design decision-making and design specialties to be described and examined in more detail and correlated with research and theory. The progress of technology always leads to subdivision and specialty in this way, and so the single comprehensive statement of instructional strategy principles and theory that may have seemed possible in the past is no longer tenable. We must consider separately the many facets of strategy that come together into a design and the guiding principles from research and theory pertaining to each area so that we can see the richness of the choices and possibilities that face the designer. In the future there will undoubtedly be additional subdivisions in our understanding of strategy, and so there will be more strategic layers and sub-layers, and the number of options open to the designer will be the more numerous. This pattern of growing detail is typical in the growth of any technology as quality expectations rise.

The Learning Companion and the Strategic Function Figure 12.1 was used in Chapter 2 to show how the structure of real-world performance venue matches that of an instructional environment. Within the bounds of both kinds of environment there exists a performer and usually one or more cause–effect systems acted upon; the performers in both contexts are in the process of using and improving their expert performance models. Perhaps the most significant difference between the real world and the artificial instructional world in Figure 12.1 is the presence of a learning companion function in the instructional case. Such a function may exist in the real world in the form of a tutor or a mentor, and thus the real world and the instructional world can blend with each other. However, the need for a learning companion closer at hand typifies the instructional world, and, in many cases, it adds value if we create a distinct, artificial world where the learner can experience the content combined with augmentation from an active companion. There are some important nuances to this view of instruction: • Instruction is characterized as augmented performance that nurtures performance in stages from its rudimentary state to some desired level of expertise. This stands in contrast to the idea that instruction consists of delivering information. • Instruction begins by establishing a performance environment and becomes didactic only to the extent necessary to support an attempt at performance, never taking the responsibility for learning away from the learner when the learner is capable of self-direction.

Design Within the Strategy Layer • 301

ENVIRONMENT

EXPERT PERFORMANCE MODEL

LEARNING COMPANION LEARNER

CAUSE–EFFECT SYSTEMS

Figure 12.1 The scenario that relates the artificial world of instruction to the real world of everyday performance.

• The instructional environment may only be an approximation to real environments, so the job of the designer is to preserve those qualities of the environment that have the highest payoff in performance improvement. Providing performance opportunities at the appropriate level of challenge is one of the learning companion’s main jobs. Application Exercise Have you experienced instruction of the type described above? Many people haven’t. • Examples of this type of instruction can be found. Identify examples that are accessible to you and describe them. • Identify examples of this style of instruction in the literature of the instructional design field. Look at the work of Howard Barrows, Ann Brown, John Bransford, and Roger Schank. Strategy, Goals, and the Message Layer Chapter 2 also described the different kinds of goals held by learners and designers during the instructional conversation. Figure 12.2 reintroduces this goal structure to set the stage for clarifying the relationship between the strategy and message layers. The columns of the figure represent the learner’s perspective (right column) and the designer’s (left column). According to this figure, instructional goals form in both the learner and the designer (or instructor). Whether or not they match depends on negotiations that should be carried out before instruction begins. As the term is used here, an instructional goal (also called instructional objective) describes a performance target for instruction, and it may differ from the real-world performance that it resembles. The extent to which instructional goals lead to real-world performance levels is a designer decision determined by project goals, criteria, resources, and constraints.

302 • Design in Layers Describes the instruconal goal the designer hopes the learner will adopt.

Describes what the learner wants the instruconal goal to be and what he/she will be sasfied with.

Strategic Goal

Describes what things the designer might do strategically to help the learner reach the current performance goal.

Describes strategically how the learner intends to go about reaching the current instruconal goal and the level of effort that will entail.

Means Goal

Describes what specific acons the designer intends to undertake to help the learner reach the current strategic goal.

Describes what specific acons the learner intends to undertake to reach the current strategic goal.

Instruconal Goal

Figure 12.2 The relationship between learner and designer goals at different levels of strategy.

Both the learner and the designer form strategic goals that define the broad outlines of how each of the participants would like to approach the instructional goal. Strategic goals preferred by the designer and the learner might initially differ, so once again a negotiation (carried out with the learning companion) brings them into alignment. This results in a plan shared by the learner and the learning companion that consists of relatively large blocks of intended activity: “demonstrate a good example of the performance”, or “test performance ability”. Means goals describe how the learner and designer each want to proceed to achieve the strategic goals (one at a time). Means goals describe one or more plans for carrying out the conversational exchanges that will accomplish the instructional goal. The designer designs a set of messages that can be used, as needed, to organize the learning companion’s side of the conversation. The message layer rules guide the conversation, which can require decision-making on the fly at finer levels of detail. The bird’s-eye view of this process sees that higher-level goals are in a constant cycle of generating and then fulfilling new lower-level goals in the same way that Napoleon’s generals were creating and adjusting their goals and means in order to meet changing circumstances. When a lower-level goal is fulfilled, control passes upward to the next higher-level goal, and the cycle continues. This is exactly the pattern followed in an everyday conversation as circumstances and goals change from moment to moment. For example, upon initiating a conversation with a friend, there may exist three higher-level goals: “find out how her mother is doing”, “negotiate the return of my wheelbarrow”, and “tell her the time of the meeting”. During the conversation each of these conversational goals will require one or more conversational exchanges (exchanges of message), and some of them may require skilled wording and sequencing of ideas. (After all, she has had that wheelbarrow for a long time now.) The lower-level goals lead to the selection of messages as appropriate. This equates to the constant selection and execution of means goals in service to strategic goals. Application Exercise Instruction involves the goals of two or more persons (or instructional systems) who are engaged in a conversation. Consider the last conversation you had before reading this. Replay the conversation in your mind. How many goal creations and fulfillments can you identify? If you find this hard to do in retrospect, then go and have a conversation (put down the book). Notice the goals that shape the conversation.

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What is an Instructional Strategy? Though the term “strategy” has been used freely in the discourse of instructional design, there has been little conversation about the conceptual framework of instructional strategy. This leaves designers without a way to compare and evaluate strategy theories. Theorists who have strategy concepts to promote often use the term opportunistically to refer to their particular interests without cross-referencing their ideas with those of others. Reigeluth’s 1999 volume of Instructional-Design Theories and Models is very helpful in this respect. In this chapter a set of key topics for instructional strategy is outlined, representing an evolution from the set that might have been defined fifty years ago, and the set fifty years from now will be equally different, because the conversation among theorists will have progressed just that much more. For now, however, we need a set of topics that can organize our discussion about instructional strategy in today’s terms. This chapter (and the other layer chapters) are an attempt to provide a framework for discussion drawn from current topics in the literature of instructional design, and topics as well that may pertain to instructional design that are suggested by literature from other fields. Progress from this point will alter the set of topics in the same way that Gagné’s initial set of learning objectives altered over time (Gagné, 1965a, 1985), as has Bloom’s (Anderson et al., 2001). In this chapter the term “strategy” will refer to planned or generated sequences of events that are determined by the participants in an instructional conversation, using a basic set of events that the designer anticipates and prepares for in advance. The designer’s principles and advance planning constrain what kinds of conversations can take place. Therefore, strategy plans fall somewhere along a continuum that includes at one extreme fully determined and fixed strategies and at the other end strategies that are completely adaptive at the time of instruction. These opposite extremes have very different implications for what the designer designs. During strategy design, a designer specifies a variety of structures that are the constituents of “events”, which are intersections of goal, time, space, place, artifacts, activities, and social structure. During the design of strategic plans the main concern is ensuring that ideals (theories, principles) are translated into event plans and artifacts that faithfully implement the desired values and maintain the balance of intellectual and emotional forces the designer intends. This calls for a mode of thinking closely related to the validity checking of traditional research, where the fit between a set of theoretical ideas and an embodiment is judged. This principle is described clearly in Mislevy’s (Mislevy and Risconcenti, 2005) evidence-centered assessment design. A strategy plan must align with the plans of other layers, achieving a coherent integration of the overall design. During design, strategy plans influence other layers and are in turn influenced by them. (Note: Strategy plans include planning for initiative sharing.) Then, during instruction, the functions designed for the strategy layer “take charge” of the other layers and orchestrate their functions to carry out the kind of instructional conversation the designer has decided to offer. A major strategy-layer activity during instruction is the coordination of constantly shifting goals and means at whatever levels of detail the designer has incorporated, within a conversational exchange that includes mainly actions, but also verbal expressions. Deciding just how to decompose the strategy layer’s design problems raises important questions about what kinds of structures are being aligned. Design within the strategy layer may involve any of (but not necessarily all of) the following kinds of structural unit: • Setting unit—The learner will be located at a physical place (or places) during instructional encounters. • Social unit—The learner may have social interaction with other persons at different times and in different ways during instruction.

304 • Design in Layers

• Role unit—The learner will be expected to carry out actions that comprise one or more anticipated roles. • Initiative unit—The learner will be expected to interact within a given set of initiative-taking and initiative-yielding patterns. • Communication infrastructure unit—The learner will carry out social interactions using some communication medium. • Time unit—The learning encounter will take place at one or more moments in time. • Goal unit—Learning related to the subject-matter unit will be focused in terms of one or more instructional goals. • Subject-matter (content) unit—The learning will be focused on one or more elements of subject matter defined within the content layer. • Strategic activity unit—Learning will be pursued with the aid of a set of augmenting strategic events selected by or supplied to the learner. This type of unit describes strategic augmenting events. • Interaction units—Learning will take place as a conversation between the learner and the instructional source. This type of unit describes conversational elements and patterns. • Data recording units—Learning interactions will produce large quantities of data. This type of unit describes data recording events. When these kinds of units are given definition and dimension by a designer, they can be aligned— integrated with—each other. The result is a set of descriptions for the intersection of: • • • • • • • • • • • •

a defined physical setting a defined social setting, including defined role expectations and defined initiative expectations for actions carried out using a defined communications/delivery infrastructure during a defined time block or moment allocated to a defined subject-matter element that is lensed through a defined instructional goal that is pursued according to a defined strategic event plan consisting of a described set of conversational interactions monitored by a described set of data recordings followed by publication through a described set of progress or change indications.

A design consists of a set of designed event, content, and goal structures within which multiple agents can make expressions that carry out a dynamic instructional conversation.

Application Exercise You may be enrolled in a class of some kind, or you may be able to recall a class you have attended recently. (It does not need to be a class on instructional design.) Examine the event structure of the class. What constituted an “event” in the mind of the instructor? A class period? Something within a class period? How granular was the instructor’s concept of “event”? Compare multiple classes to see if you can detect instructors with a more or less well-formed concept of “event” in their course designs.

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The Questions Answered in Strategy Designs Strategic decisions create conceptual structures and relate them to physical artifactual structures. At this layer conceptual and physical artifactual structures are mapped together in such a way that the physical structures can carry out the ideals of the conceptual structures. The design problem within the strategy layer consists of several related sub-problems. Strategy brings the learner into contact with elements of content as the learner attempts a performance; it augments this experience in ways that support the performance efforts of the learner. Strategy layer design decisions supply values to the variables suggested by Figure 12.1: • • • • •

The organization of a learning environment Provisions for interaction with cause–effect systems The ability to observe models of expert performance (provided by the learning companion) The opportunity for the learner to attempt performance related to instructional goals Augmented or scaffolded by different kinds of support from a learning companion.

Strategy Sub-layers The strategy layer is comprised of several sub-layers, each representing a different set of design problems—questions to be answered. The sub-layers each deal with a major architectural aspect of strategy: goals, assessment points, places of instruction, instructional events, and so forth. The sub-layers that are most generic include the following. Instructional Goals Instructional goals provide the conceptual focus for practice and assessments in an instructional design. They are strategic because they represent a degree of approximation to a real-world performance goal. They can also define intermediate levels of competence—performance plateaus—for practice and assessment on the way to higher levels of competence or expertise. The process for converting performance goals into instructional goals is described in detail in a later section of this chapter. Briefly stated, it consists of a logical process of determining which performance tasks, conditions and standards of performance will be manifest in instructional assessments and practice settings. A sampling of design questions for this sub-layer include: • What kinds of instructional goals will be created? (E.g., domain knowledge, metacognition, etc.?) • Will goals be derived according to a particular theoretic system? • How will instructional goals be related to each other? (Hierarchically? Topically? Temporally?) • Will instructional strategy choices be linked to goal categories? (That is, will existing taxonomic systems for goals be used?) • How close to real-world performance will the approximation of instructional goals be? • How granular (low-level) will learning goals be? • How specific or precise will instructional goal statements be? Will instructional goals be stated formally? • Will some goals be considered more important or critical than others? • Will clustering of instructional goals be used to create learning progressions? • What will be the quality standard for the expression of goals? • How will goals be generated?

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• • • •

When will goals be generated? (e.g., before design, during design, during instruction?) Who will participate in goal generation? (Designer? Instructor? Learner?) How will subject-matter expertise be supplied during instructional goal creation? How will the accuracy and currency of subject-matter expertise, as reflected in the goals, be assured? • How will the completeness or sufficiency of the instructional goals be determined (validated)? • Will instructional goals be supplied to the learner? If so, in what form? • Who will be responsible for selecting/forming instructional goals during instruction, and how will that be accomplished? A designer will answer the questions on this list that have the highest relevance to the project. The priority of these questions will differ from project to project. Assessments/Performance The assessments/performance sub-layer is where the designer decides how to use assessments and interaction events strategically. Numbers, types, and placement of assessment events are all determined. Merging assessment and performance into instruction seamlessly is a necessary means of achieving adaptivity. The granularity of assessments must match the granularity of learning goals. The assessment/performance sub-layer is the interface between the strategy layer and the control layer. • • • • • • • • • • • • • • • • • •

How will assessments be used strategically? How will each instructional goal be measured? How will assessment points be chosen? Will intermediate assessment points be used to monitor progress toward terminal assessment points? What kinds of assessment items will be used? (Objective? Performance?) Will diagnostic assessments be used to reveal learner needs and readiness? How frequently will progress toward goals be assessed? Will each assessment be high-stakes or formative? Which assessment events will be formal and informal? How will assessments be blended with other strategic functions? Will the difficulty of tests be modified dynamically over the course of instruction? Will assessments be visible to the learner or unobtrusive? Will both individual and group assessments be employed? How will self-assessment be used? Who will be responsible for assessing the attainment of instructional goals? (Computer? Instructor?) What will be the procedures for conducting assessments? How will assessment data be passed to the data management functions? How much processing of assessment scores will be necessary before data can be sent to the data management function?

Data Recording Data recording, which is a major necessary step in achieving adaptivity, is a strategic choice. This factor, along with the granularity of instructional goals and assessments, regulates the ability to track progress, decide when remedial interventions are recommended, determine the pace of forward movement, and assess the impact and fitness of the instructional system itself. The data recording

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sub-layer is the interface between the strategy layer and the data management layer. It is also one of the interface points between the strategy and control layers. Questions at the data recording sub-layer include: • How often will data be captured? At what points? • What kinds of data will be captured at each point? • When and how will data be reported for strategic purposes to the learner? Setting and Siting Deciding the setting of instruction has become an important factor in instructional strategy now that mobile media have made the classroom and training room no longer the default choice of instructional venue. The means of instructional delivery—a teacher, a computer, a Web site, a conference, or a conference call—is no longer assumed to be tied to a specific location. The setting sub-layer is concerned with answering the question: “Where will learning take place?” Questions of siting deal with the provisioning of the learning environment (with furniture, electrical power, etc.) when a facility of some kind is designed or an existing facility is refitted for use. • • • • • • • • • • • • • • • • •

Is instruction strategically linked with one or more specific sites? Are the characteristics of the individual setting an important strategic element? Will instruction be fixed to a particular setting or group of settings? Will instruction take place at multiple sites? Will instruction be independent of specific sites? Will instruction involve indoor/outdoor sites? Will multiple sites be linked? What communication requirements will exist among sites? What will be the standard physical configuration for an instructional siting? What is the minimum configuration of equipment, software, and networking per site? Will some instructional settings/sites be more important than others? Will one or more settings/sites be used for centralized control? Will the configuration of communications media be equivalent for all settings/sites? Do settings/sites need to be reconfigurable? What qualities of atmosphere will be necessary/preferred at the settings/sites? Will mobile equipment be necessary to carry out the strategy? What procedures will be carried out at each setting/site? What personnel will be required to operate each setting/site?

Social Context Social context—the patterns of interaction between learners and instructors—has always been a part of instructional designs, but old assumptions no longer hold concerning the roles of the teacher and learner, relationships and interactions between learners, and expectations about how learning will proceed within a social milieu. The social sub-layer is concerned with answering the question: “What roles are defined for participants in instructional conversations, and what responsibilities are attached to each role?” • • • •

What roles are assigned within the learning group? Are group roles and responsibilities formal, public, and permanent (or otherwise)? What interpersonal relationships are anticipated/essential among learners and instructors? What learner–learner social relationships are expected or hoped for independent of or outside of formal learning events?

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• • • • • • • • • • • • • • • • •

What communication patterns are most important, most desired? What are the responsibilities of each role? Is the size of the learning group specified, and if so, what are the sizes? Which roles are considered most important? What is the balance of initiative between roles? What initiatives are allocated to each role? How is initiative exercised? What is the balance of expected contribution by different participants? Does instruction involve a fixed or a changing learner/instructor group? (That is, group coherence.) What is the desired pattern of change over time in learner–learner and learner–instructor social relationships and communication patterns? What is the desired quality of the learner–learner relationship? What is the desired quality of the learner–instructor relationship? What is the desired social tone of communications? What is done to stimulate the formation of appropriate learner–learner and learner–instructor relationships? Are there special provisions for supporting learner–learner and learner–instructor communications? Are learners or instructors trained in correct communications procedures, protocols, standards and expectations? Is the execution of roles and responsibilities by group members monitored, evaluated, and fed back?

Initiative Sharing Learner control and learner participation became a more strategic choice and topic of study with the advent of the instructional computer. Interest in adaptive instruction has increased as learning research has pointed to the value of learner decision-making during instruction in support of the learning of problem solving and metacognition. The umbrella questions of the initiative sharing sublayer are “Whose decisions will direct the path of instructional activity?” and “How will initiative patterns change as learning progresses?” • Will there be points during the instruction at which initiative between learner and instructor can be negotiated? What are they? • What are the aspects/elements of strategy that are subject to choice at choice points? • What support is available to the learner for decision-making at choice points? • Are choice points formal or informal? • Are strategic choice points fixed regular or negotiable? • Are choice points the only time at which the direction of instruction can be influenced? • Will a formal procedure be followed at choice points? What is the procedure? • Are certain choice points more important than others during instruction? • Are certain choice points more binding than others? • Who initiates choices? How does a learner become aware of choices? • Is there a shift anticipated in the locus of initiative over time? • Is there a fading of decision-making support over the course of instruction? • What is the expected tone of interaction at choice points? • What are the expected protocols for respect? • What are the ethical expectations of learners and instructors?

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• How are instructor guidelines for handling choice points and decision support communicated? • Will instructors be trained how to make decisions at choice points? • What data items will be supplied to learners and instructors for decision-making at choice points? Scope Dynamics Scope dynamics refers to the relative size or scope of the performance a learner is attempting at any given moment during instruction. Scope dynamics determines the size of step learners are asked to take between instructional events. Each event has a scope, which consists of a performance task, a set of conditions, and a set of performance standards. The scope dynamics sub-layer is concerned with answering, “How can structures regulating the scope of performance be used to adjust the level of challenge to the learner in a strategically beneficial way?” Scoping structures are described in more detail under the heading of “Creating Instructional Events: Work Models” later in this chapter. • • • •

Will scope dynamics be used as a strategic variable? Who will determine the size of step (level of increased challenge) at each choice point? What data will be available for making that decision? How many options (in terms of increasing the challenge level) will be available at each choice point?

Scope Trajectory/Sequencing Research supports the value of dynamic trajectories that can be created using deliberately chosen progressions of scope. The scope trajectories sub-layer is concerned with answering the question: “How will the designer use patterns and variations of dynamic task scope and difficulty over time to strategic advantage?” • Will trajectories of content scope be planned to structure instructional sequences, either under learner or system control? • What principles of scope escalation will be used? • How will the size of progressive steps in the trajectory be determined? • What rules will be used to adjust scope escalations either to a group’s pace or an individual’s pace? • Will progressions be linear, or will they include an acceleration factor? • Will learners be coached either in the selection of increasingly complex events or in the construction of their own? • Will learner initiative be incorporated in the escalation plan, and if so, how? Task/Activity Activity alone is not a sufficient condition for improving learning. It must be activity that is valid with respect to the knowledge and behavior patterns the designer and learner have agreed will be learned. It is counterproductive to emphasize an instructional strategy that simply arranges surface activities without paying attention to underlying mental task structures. The task/activity sub-layer of strategy deals with the alignment of surface instructional activity with the mental task structure. It answers the question: “What surface activity structures best elicit the desired mental operations that are the learning target?” This question is asked for each instructional event. Questions at this layer include: • What is the essential core of the behavior the designer hopes the learner will engage in? • What variety of surface activities will lead to this kind of engagement at a deeper level?

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• What specific surface activities will the learner be asked to perform during each instructional event? • What patterns of relationship will exist among activity types? • What is the activity plan for each event type? Timing/Synchrony What was once a clear divide between synchronous (classroom) and asynchronous (time-and place-independent) instructional patterns has now narrowed to where a learner may engage in both synchronous and asynchronous (online) activities within the time frame of a single instructional event, in pursuit of the same instructional goal. The importance of synchronizing the requirements of instructional activities is the focus of the timing/synchrony sub-layer. The timing/synchrony sublayer is concerned with the question: “What blend of synchronous and asynchronous activities will be used during instructional events?” It is concerned with the coordination of technology and instructor functions (blending) and with coordination of learning group events. • What sequence of synchronous and asynchronous activities (both individual and group) is required for each event? • Will there be a principled shift in the mixture of synchronous and asynchronous activities over the course of instruction? • What role–role and activity combinations require synchronous participation? • What role–role and activity combinations require asynchronous participation? • What role–role interactions are time-sensitive? • How (in what patterns) will synchronous and asynchronous activities be blended? • Which instructional events must be carried out in an ordered sequence? • Which instructional events must be carried out by a fixed time? • Which instructional events must be carried out at a fixed time? • Which instructional events must be carried out within a specified period of time? • Which instructional events may be underway simultaneously? Augmentation As a learner engages with interactive content models, a greater burden is placed on learning directly from the model interactions, and strategy can be thought of as an augmentation to the model experience by an intelligent companion (living or automated). The augmentation sub-layer deals with questions about the general patterns of augmentation (conversation) the designer will employ in support of the learner’s performance experience. The augmentation sub-layer is the interface between the strategy layer and the message layer. • What forms of augmentation to the learner’s experience with the content will be provided? • Will specific augmentation forms target specific content, domain knowledge, and metacognitive learning? • How will different initiative-sharing modes be implemented with different forms of augmentation? • How are learner/instructor roles affected by different augmentations? • What messaging plan will be used for each type of augmentation plan, including types of messages employed and conversational rules for administering them? • Under what initiative conditions will each type of augmentation be offered? • What fading levels of support will be offered for each augmentation?

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Artifacts and Environments A shift in design practice from a “product” orientation to a focus on the use of learning environments, artificial activity worlds, and experiential learning artifacts has broadened the perspective of the designer about the nature of the “thing” being designed. The artifacts and environments sub-layer deals with the nature of the designed artifact. It is concerned with the question: “What designed, constructed, or natural artifacts, physical or virtual, including environments, modeled simulations, physical objects, and software will be embodied in media elements to be produced?” • • • • • • • • • •

What best describes the nature of the artifact being designed? Will natural (non-designed) resources be used in instructional events? What artifacts will be designed and constructed for the present project? What standard media elements will be designed/constructed? What physical devices or physical models will be designed/constructed? What physical environments will be designed/constructed? What virtual environments will be designed/constructed? What computerized dynamic models will be designed/constructed? What existing media resources will be repurposed or reused? What characteristics (physical, mechanical, content, operation, etc.) will designed artifacts possess?

Cultural Design Instructional design has become a worldwide enterprise, and so designers have to create products that are capable of reaching the widest possible audience. The cultural design sub-layer raises questions concerning how instruction can be configured for cross-cultural appeal and effectiveness. It deals with the question: “What provisions will be made to accommodate the instructional experience to the needs of learners from multiple cultures and special (e.g., disability) needs?” • What properties and features of artifacts, events, and strategy will be used to promote multicultural communication? • What patterns of language will be used? • What strategic patterns will be given emphasis? What options will be available? • What assignments of roles and relationships will be given emphasis or made reconfigurable? • What guidelines for symbolic/pictorial element usage will be followed? • What value/belief system congruencies will be emphasized? • What specific disabilities will be accommodated? Engagement When instruction is treated as a form of conversation, either party to the conversation may choose to engage or disengage. Principles for achieving and maintaining engagement should be treated as a major design factor and may be qualitative and stylistic as much as structural. The engagement sublayer deals with the question: “What dimensions of the design will be used to promote a desire for prolonged engagement within the user?” • What properties/qualities of instructional events, sequences, or designed/constructed artifacts will be designed to stimulate, maintain, or increase learner engagement? • What event properties/qualities/types/styles will be used to promote engagement? • What social/personality/grouping/relationship factors will be used to promote engagement?

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• • • • • •

How will manipulation of challenge level be used to promote engagement? What narrative features will be used to promote engagement? What interaction features will be used to promote engagement? How will dramatic factors such as narrative be used to promote engagement? What practices related to rewards/certifications/recognitions will be used to promote engagement? What use will be made of interpersonal expectations to promote engagement?

Summary Other strategy sub-layers and design questions may exist in the mind of the designer. The ones above have been presented to stimulate thinking about instructional strategy at a more detailed level. Defining sub-layers is a way to improve both the depth and breadth of designer thinking about the use of strategy during instruction. Application Exercise This list of sub-layers of the strategy layer is by no means complete. If you were to suggest a sub-layer or two that were of value to a designer, what would they be? Remember that a layer is defined in functional terms, specialty terms, or theoretical terms. • Perhaps you have been reading in the literature about a new strategic development. Does it suggest a sub-layer to you? • Perhaps you can think of a theory that offers a designer distinct advantage. Does it suggest a sub-layer? • How about a function of instruction that you are familiar with that is not covered under one of the sub-layers in the list above. Does that suggest a sub-layer? If you think you have identified an advantageous sub-layer (maybe a specialty of a design team member), then what reasoning would you use to defend its value to another person? How would you respond to their objection that it was not “scientific”? Documenting Strategies and Complete Designs An instructional strategy is multi-sub-layered. It is not reasonable, therefore, to expect that a simple diagram can summarize all of a design’s aspects. How do you represent a design in a sharable form, and what are the issues related to design documentation? In this respect, a lesson can be taken from software design. The Universal Modeling Language (UML) (www.uml.org/) was originally invented by a trio of software tool designers whose competing systems for object-oriented program development could not describe program designs adequately. They jointly produced a merged specification representing all three of their notation systems, providing a family of diagrams capable of describing the inner complexities of an object-oriented program. Each diagram looks into the finished program as if through a filter that hides certain elements and relationships and exposes others. Elements recur across multiple diagrams so that in one diagram an object can be seen in its formal relationship to other objects, while through another view the same object can be seen in relation to its functions within the program. One is reminded of looking through colored lenses that hide or reveal secret coded messages. There is no single diagram that reveals the entire program design, but the effect of all of the diagrams together is to enable a design team to relate every object and every function within a

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framework in a way that can be communicated among team members, critiqued, and validated. Moreover, different specialists find certain diagrams more useful than others. This allows specialist team members to participate in the design (and documentation) of different parts of the program. UML diagrams are divided between two categories: (a) structural diagrams, and (b) behavioral diagrams. Structural diagrams, of which there are seven official types, identify the elements that are part of the program system: everything from individual objects to the physical hardware elements of the system on which the program will run. One diagram, the package diagram, describes how the program is divided into functional groupings. This should be noted in relation to the discussion of modularity in Chapter 15. Behavioral diagrams are used in UML to describe what happens as the program is run. Seven types of activity diagram capture the flow of events, use cases for different kinds of user, communications among program elements, and show the timing of events. UML is extensible, in that a designer who finds a type of relationship is needed that is not covered in the standard diagrams can invent a diagram that captures that set of elements, relationships, or behavior. This means that a designer is not limited by the design notation system. The important lesson of UML for instructional designers is that a design—any design—is comprised of multiple elements and multiple layers and that complex and dynamic interrelationships among these can be represented if only a system of representation guidelines can be established that can be understood in common by professionals. It is easy to draw parallels between the diagram families provided by UML and the concept of layers proposed in this work. Architects also have evolved a system of diagrams and specification formats that over time have come to provide a standard means of capturing and sharing designs. Any architect can read another’s design. Moreover, engineers and contractors can contribute to the design because they too can read the diagrams. Yet other design communities have likewise created public design notation systems that allow teams to work on design problems together, each specialist contributing his or her part to a larger whole. Think, for instance, of musical notation and the tool that provides for composers and musicians. What kind of a system could be evolved for the expression of instructional designs that was capable of capturing this kind of design information? Documentation technology has advanced one additional step beyond the familiar systems described above to the point where the documentation of a design becomes also part of the mechanism for building the design. This has been achieved in many fields, but most notably in aviation design. Sabbagh (1996) describes the design of a modern jet aircraft in Twenty-First Century Jet: The Making and Marketing of the Boeing 777. The problem facing an aircraft design team is complexity and the multiple layers of functionality that are necessary parts of an integral design. Sabbagh describes the traditional approach to aircraft design: Traditionally, new planes had been designed in two dimensions. Drawings on paper had been used as the basis of the manufacturing process. But to design a plane entirely in this way, with over 100,000 different three-dimensional parts, and then to trust that the two-dimensional drawings had accounted for all of the complexities of the three-dimensional airplane would have led to endless unpleasant discoveries at the assembly stage, as a piece designed by one designer arrived at the factory and turned out to be impossible to install because another designer had failed to leave the right amount of space. —(p. 58) Sabbagh describes how the design process was made three-dimensional using computer models that could detect space violations in which a pipe tried to occupy the space already used for a spar. Not only could the software help create the design and detect conflicts, but then, when the plane was

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ready to be assembled, it could take a major role in that as well. This is because the tools used to cut, mill, condition, and assemble parts—for example of the wing—had also been designed by computer and could assemble wings more rapidly and more accurately. The applicability of this example to instructional design will become clearer in Chapter 15, which describes modularity and mass customization. The design of instruction is conceptually more complex than the design of an aircraft because instruction has few physical features and has the additional dimension of time. Envisioning how elements of instructional media and logic can be brought together to create experiences that have both intellectual and emotional components is probably the greatest challenge that humans and computers face. Design documentation systems that can not only support the recording of a design but participate in its testing and assembly will become competitive advantages in an educational marketplace that is becoming increasingly active. Application Exercise The idea of design documentation became a problem when the computer was introduced as an instructional tool. • Do some research to find out how designs were documented in the period just after computers arrived on the scene. • How do designers document Web designs today? Look at some of the how-to books on Web site design (there are many of them). How much documentation of the design do they recommend? What form do they suggest documentation should take? • If you were inventing a documentation system to communicate designs among a design team of five people (you, an artist, a programmer, a writer, and a database specialist), what documentation system would you use? Creating Instructional Goals from Performance Goals The process of turning performance goals obtained from the early stages of content analysis into instructional goals that represent to-be-measured instructional performance targets can take place through a series of logical transformations. Since the analysis may contain more content than can be practically dealt with, given the constraints on instructional time and resources, these transformations begin with a selection process that identifies those content elements of highest priority. (Recall that the content analysis is a large net that pulls in tasks, semantic networks, and associated conative and affective states.) After the selection, content is still expressed in real-world performance terms, so an approximation transformation can be applied. This transformation changes performance statements that are impractical to measure in the instructional settings into statements of performance that are attainable and measurable during instruction. For example, if the performance is to steer a boat through a narrow passage and the training resources provide for neither boats nor narrow passages, then the next best approximation to the full performance—one that can and will be measured during instruction—can be substituted. This may result in a statement of performance measurable in a simulation. If this transformation is applied, it must be done with the expressed caveat that the instruction in that case has not certified that full performance ability has been attained. The approximation must take into account not only task performances, but knowledge of conceptual, cause–effect relationships and conative and affective factors as well. If learners are not subjected to stressful conditions of performance, for example, the training cannot claim to have prepared them (and tested them) for performance under stress.

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This transformation removes one of the problems that has disconnected real-world expectations from instructional goals for some time. There has existed a gap between what is instructed and measured and claims of impact on workplace performance. This transformation removes that gap and makes claims for instructional effectiveness more trustworthy. This gap has become so pervasive that it has come to permeate the reasoning of the public in general about expectations of instruction and education. Instructional outcome measures that do not match real-world expectations have made possible the perpetuation of an educational system whose output is measured statistically rather than exactly. This may suffice for the purposes of policy-makers and administrators, but its application to the management of learning for individuals has created a system largely incapable of accounting for individual progress and unable to administer needed remediation for slower students and extracurricular learning opportunities for faster ones. When a designer is satisfied that the approximations represent the real and attainable instructional goals, another process of breaking apart each goal can be applied for the purpose of identifying within them hidden implications for instruction that may lead to the creation of additional sub-goals. Several factors can lead to the specification of these sub-goals: • • • • •

Objectives that impose large new memory loads on the learner. Objectives that assume that a body of cause–effect knowledge already exists in the learner. Objectives that require complex decision abilities the learner may not already possess. Objectives related to complex discriminations or concept categorizations. Objectives that require learners to carry out complex or coordinated motor actions with which they are not already familiar. • Objectives that require the learner to deal with unfamiliar emotional or conative aspects of a performance. These sub-goals represent learning prerequisites for the higher-level goals from which they originated. For each goal and sub-goal the designer should specify the range of conditions under which assessment of performance must take place. Also, if there is a requisite criterion for performance, that should be expressed. Though the specification of instructional goals can take place through a series of logical transformations, there is an intentional looseness to the process as it is described here. In the past, particularly in the period of history when instructional design models were rapidly proliferating, the issue of instructional objectives was treated in a mechanistic fashion. Design modelers tried to bring the complex decision-making processes of the designer under the control of well-defined procedures. This progressed to the point where designers were experiencing “paralysis by analysis”—the condition where detailed, sometimes bureaucratic process specifications were inhibiting forward movement of design projects. Design models themselves grew in some cases to outlandish proportions. This did not produce a proportional increase in instructional impact. In fact, an argument could be made for the opposite. In the long term, this led to a movement away from formalized design processes, and, especially, suspicion of detailed processes for deriving instructional objectives from real-world performance requirements. Where there was once an active academic discussion on the nature and creation of objectives there is now a general silence—and a lack of guidance on deriving objectives. The discussion in this section has attempted to describe a possible path from real-world performance expectations to practical objectives that does not repeat the mistakes of the past that tried to turn design into a set of steps. (I must confess that I know this problem from the inside, having been at one time a proponent of highly specified design procedures, but that fever has passed.) However, just as it is possible to go overboard with process specifications, it is possible to neglect an essential design activity. It is important for designers to define instructional objectives as targets

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because without them there is no standard for measuring whether the time and effort spent in instruction has produced a result. Objectives are part of the measurement of value-added. The process described above tries to walk the narrow line between too much and too little guidance on this important, though troublesome, aspect of instructional design. Application Exercise The issue of instructional objectives is a real puzzler. On the one hand, designers don’t want to be bothered too much with them, and on the other hand, the quality of the objectives goes a long way to determining the focus, effectiveness, measurability, and adaptive quality of the instruction. • Prepare to teach another person the key principles of instructional objectives, as if they were a novice instructional designer and you were their trainer. Identify the key principles. Determine how you will involve the learner in some form of practice. Express the criterion you will use to judge the quality of their work.   

Creating Instructional Events: Work Models Raw instructional objectives are not a sufficient basis for defining instructional events—the points in time when instruction will take place. An objective states a goal; an event identifies a moment in time and an activity. The process of creating instructional events requires the designer to match one or more objectives with one or more moments in time and one or more activities. It is a mapping process that always occurs but of which many designers are not consciously aware. This sometimes causes confusion. The need for mapping objectives and activities to instructional events comes from the realization that any of the following situations may occur: • One instructional objective may require more than one occasion for instruction and practice: one-to-many mapping. • Multiple objectives may be grouped for some reason by the designer into a single instructional event: many-to-one mapping. • Multiple objectives grouped for instructional purposes may require many occasions for instruction and practice: many-to-many mapping. • One instructional objective may only require one instructional occasion: one-to-one mapping. The bookkeeping situation for designers trying to account for objectives and their practice requirements is complicated by the idea that each time an objective or group of objectives is re-encountered a higher expectation may be held for the performance (a higher standard), or the learner may be ready to experience the performance under more challenging conditions. One of the principles of instructional sequence design is expressed by Burton et al. (1984) in terms of “increasingly complex microworlds”—the idea that the level of challenge should be continuously adjusted to the learner’s capability. This idea is also expressed in Vygotsky’s concept of the zone of proximal development, a zone in which a learner can be pressed to perform at the edge of capability, under conditions of support normally referred to as scaffolding, in order to increase capability in steps that are sized to the learner’s momentary ability.

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This book assumes that these principles are valid and that the designer should have conceptual tools for defining rising sequences of this type. The tool described here is the work model—an abstract structure that maps instructional objectives onto repeatable event structures (Bunderson et al., 1981; Gibbons et al., 1995). Work model definition is based on the assumption that: (1) a designer will be able to make a rough estimate of the amount of practice a learner will require to reach targeted performance objectives (at desired condition and standard levels), or that (2) the designer will build adequate negotiation space into curriculum plans for adjustment of difficulty on the basis of the actual performance progress of the individual, allowing the number and challenge level of instruction and practice occasions to be adjusted to individual needs. The work model construct provides a tool that can be used for both situations. It has the added benefit of producing the raw materials out of which syllabus sequences can be easily built. Constructing work models is a relatively simple mapping process that involves estimating: (1) practice volume requirements (based on the capabilities of the target population), and (2) escalation patterns for conditions and standards (based on the spread of target population abilities). These estimates lead to grouping patterns and repetition (multiple event occurrence) patterns. One of the benefits of using work models as raw material for syllabus sequence-building (what has been referred to as “macro-sequencing” in Van Patten et al., 1986) is that it avoids the built-in assumption of static syllabus designs that every learner must follow exactly the same path of intermediate goals to reach competence. Instead, it offers a tool for adjusting the path and the rate of progression to the individual. This can happen if the designer designs to the lower end of the learner continuum and provides larger steps for the more capable learner within that framework of work models. Application Exercise The concept of work models is not prominent in the literature of instructional design, but the practice of using progressively challenging performances can be found in many places. Work models are a formalism that makes the definition of progressive performance plateaus easier to understand and work with. • Identify a learning experience from your past where you can tell that progressions of performance (work model-like event structures) were implemented. • Consider how you would form a progression of work models that could help a child learn to walk. If you have already experienced helping a child walk, describe the succession of performance plateaus that you used. Strategy Design and Theory Instructional strategies emerge from theory—privately held or public. Therefore, some form of theory guides design decisions (see Chapter 6). Edelson (2006) describes three categories of design decisions common to instructional designing: • Decisions about the design process: These are decisions about what steps to follow in constructing a design, who to involve, and what roles they should play. • Assessments of the design context: These are decisions about the goals, needs, and opportunities that should be addressed by a design. This category also includes decisions about the

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design context that must be addressed, such as challenges, constraints, and opportunities in the context. • Decisions about the design itself: These are decisions about the design itself. This category includes decisions about how to combine design elements and balance trade-offs in order to meet goals, needs, and opportunities. —(p. 101) Edelson links these categories of decisions with distinct types of theory: domain theory, design frameworks, and design methodology. One type of domain theory Edelson identifies is outcomes theory, which: “Describes the effects of interactions among elements of a design setting and possible design elements. An outcomes theory explains why a designer might choose certain elements for a design in one context and other elements in another” (pp. 101–102). A designer who claimed not to be using theory would create random, meaningless designs, not based on any consistent ordering principle. Theories used to generate instructional plans are synthetic theories—theories about how things can be arranged for effect. The formal theories used by instructional designers are usually termed instructional theories. Instructional research traditionally narrows the number of variables studied to isolate effects and connect them as much as possible to a particular cause, usually in support of a particular theory or a variant of a theory. During design, the goal is not to “prove” a theory but to integrate multiple theories in order to achieve a desired effect. The argument of this book is that every layer and sub-layer constitutes an entry point at which theory can influence a design. In fact, that reasoning is reversible: one characteristic that might convince a designer to “see” a particular layer is an existing, coherent body of research and theory literature. Each layer chapter in this work suggests what kinds of theory relate to designs within that layer, making as little reference as possible to specific, opposing theories. Instructional designers who consciously try to apply theory in their designs sometimes labor under the misconception that a single theory is adequate to guide a design. On the contrary, every design is informed by literally hundreds of theories: content theories, theories of representation, theories of strategy of many kinds, and others. All that is in question is the validity and research credentials of the theory that is used. Does it have empirical support? A designer should learn how to evaluate theories carefully, asking critical questions instead of accepting them at face value. Prototyping and testing is a way of validating theories on a small scale. Design-based research is a method for discovering and validating them on a larger scale. Edelson (2006, pp. 103–104) defines four stages in the process of theory evolution through design-based research: 1. Creating designs that to begin with are “research-driven”. 2. Creating designs that are “thoroughly and systematically documented”. 3. Conducting formative evaluation in “iterative cycles”. 4. Reasoning by retrospectively seeking design-specific lessons “to identify appropriate generalizations in the form of domain theories, design frameworks, and design methodologies”. Combining Theories in Strategy Designs If multiple theories inform a strategy design (and designs at other layers as well), as suggested by the list of sub-layers described earlier, then there arises a question of how to select theories and how to avoid a clash of theories. This is a designer skill that is not governed by strict principle, but rather by judgment. Several suggestions for selecting and combining theories and learning the skill are given below.

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Select for the Target Population There exist many narrowly defined instructional theories that inform designs for niche populations: children, ADHD children, children with reading disabilities, and so forth. There are theories that give recommendations for adult instruction, instruction of the handicapped, and instruction of teenage learners. A thoughtful target population study (see Appendix A) and familiarity with the theories that pertain to specific populations can strengthen a design. Such familiarity is only gained by long-term study and cannot be converted into a formula or summarized in a list. A designer might consider keeping a scrapbook of theories: readings accumulated over time that become useful for a particular project. Select Theories that Maximize Within Constraints A careful current training and resources analysis (see Appendix B) will identify constraints of time, funds, or other resources placed on a project. The very purpose for conducting the analysis is to inform the design. Theories for rapid design processes can remove development time constraints; theories for effective use of media can loosen or remove media constraints; theories that support efficient learning strategies can reduce instructional time constraints. It should be apparent that many other kinds of theory related to logistics, organizational behavior, and product economics are applicable as well. This suggests that the instructional designer’s toolbox of theory is not confined to psychological and structural theories and that part of a designer’s value-added is the breadth of the theoretical base he or she is able to comprehend. Prioritize Theories Some theories are more powerful than others. A theory called the power law of practice (Newell and Rosenblom, 1981) appears to have wide applicability and can have considerable effect. The power law would guide a designer to increase emphasis on parts of the instructional experience related to practice with feedback. In comparison, theories about font size or background color, though they may be important for some parts of the design, may be less important for defining major design structures. Seek for Economies Financial economies are important to designers, but the economies of the learner can take higher priority. Learning economies deal with the amount of effort the learner must expend to achieve a goal and how long the effort must be sustained (see Kahneman, 2011). Different designers have different homely theories about stress, motivation, and satisfaction during learning. Regulating these factors in the light of theory may create higher financial costs but lower the cost to the learner. In the future educational market of increased competition, this may be the highest-priority economy. Read the Label on the Theory Many designers think of theories in broad terms and may fail to recognize that synthetic theories have limits—boundaries—to their effectiveness. Theories for memory instruction are not effective for teaching skill. Likewise, skill instructional strategies are less effective for promoting memorization. The literature of the instructional design field is not accustomed to considering theory from this perspective. Designers should apply theories within the boundaries that define their claims, but they should not dismiss a theory when it is used beyond the instructions on the label and the results are disappointing. Analytic assessments of theories presented side by side, like that in Reigeluth’s Instructional-Design Theories and Models, Volume II (1999), should be studied carefully, and if theorists do not identify theory limits, then designers should proceed with caution, experimentally.

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Ensure the Validity of Theory Application McDonald and Gibbons (2009) describe how theoretical ideas sometimes lose their edge as they are incorporated into designs: The writings of instructional researchers and theorists contain many innovative ideas, which have given practicing instructional technologists very specific and detailed approaches for creating high-quality instruction . . . Yet when practitioners adopt one of these instructional approaches, they sometimes are not able to maintain the level of quality the innovating theorist originally envisioned. Essential principles of an instructional approach sometimes seem to be lost as it is translated from the original theory into practice . . . As a result, many designers feel trapped—always aiming for powerful outcomes, yet often only achieving limited results. —(pp. 377–378) Designers should feel the same concern for establishing the validity of their research materials and measures as researchers. Do designs and materials faithfully embody the core principles of the theory the designer intends to use? Trace the Operational Principle of the Design The familiar practice of copying the surface of working designs should be extended to copying the parts of the design that actually account for the design’s effect. The concept of an operational principle (see Chapter 7) is a key to tracing the source of a model design’s effectiveness. Part of gaining expertise as a designer is learning to “see” into a design in order to discern the essential from the merely decorative. Make Sure Theory Drives the Design, not Tools Novice designers tend to follow the lead of tools and design structures that development tools afford. In live instructors this takes the form of copying the elements of a lesson for which media can be easily prepared. More experienced designers realize that tools (and media production systems) are created for a commercial market by designers who are not instructional designers. One way to maintain some degree of independence from tools is to imagine designs as their chosen theory and strong principles would dictate and then look for the tool that can execute the design. In organizational settings, of course, this is sometimes not possible because tools are chosen by committees who are not instructional designers. However, that should not lessen the designer’s reliance on imagination first and tool second. Be Willing to Experiment Edelson’s guidelines for learning from design-based research (research-basing, documentation, formative analysis, and retrospection) are within the reach of every designer. Every project, even the most mundane assembly-line operation, can be an occasion for learning something new from having designed, whether it be something new about instructional theory or design theory. It is important not to discount, however, the value of carefully documenting designs and constructing formative evaluation data systems that provide useful information to supply retrospection. It is also important to create a personal theory base by constant professional reading in sources that stretch the mind just beyond the familiar. Over the course of time, these practices pay off in both career achievement and personal satisfaction.

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Attend Try-outs in Person When Possible Nothing spoils a well-imagined design based on good theory as much as watching a learner suffer through the experience of using it. Many designers are isolated for one reason or another from try-outs, and yet it is from witnessing try-outs that the most authentic and detailed data can be obtained. Application Exercise Theory is an intimidating concept to many instructional designers because it seems so far removed from everyday practice. Most designers are experienced in designing without giving a lot of thought to what theory they are using. However, without realizing it they are applying personal theories about how things work and how they can be made to work. It is important for designers to recognize their own theories. • • • •

Recall the last time you designed instruction. Recall the main structures you included in your design. Why did you choose those structures for your design? If someone asked you to change the structures you had built into your design, which structures would you be agreeable to change, and which would you defend and resist changing?

If you will reason about the ones you would defend, it will lead you to recognize some of your own personal theoretical positions. How many of them are there? Conclusion Most texts on instructional design stand for a position and promote a particular methodology, theory, or process. You may have begun this chapter expecting to be given the “right“ answer for the design of instructional strategy. It is not the mission of this work to supply that, because no book could possibly do it. There is no right answer, just good ones and better ones, and the outcome is judged on the basis of many variables, not just one. The instructional design literature has been mainly a literature of position advocacy: voices that claim to have the silver bullet for instructional strategy, the best technique for creating representations, or the best method of analysis. There has been so little discussion of the scope of design theories that most designers are not aware of the wide range of decisions they face. In the absence of this broad spectrum view, knowledge about designing becomes fragmented by the claims of those who have different views. “Which one is right?” we begin to ask, and then we become captured by today’s fad. The intent of this work is to identify design questions rather than to offer short answers for them. For any of the questions listed in the preceding sections, numerous theoretical or methodological options exist. The task of the designer is not to find the “right” answer from among the many options, but to learn to use all of the options, each in the right context, at the right time, for the right purpose. This is not achieved through process following but through the designer’s skill and knowledge, which is developed over time and through study. The organizational principles of strategy introduced in this chapter will hopefully accelerate the individual designer’s development, through self-directed and peer-supported study, of expertise in strategy design and help the designer to see just how many choices there are and how much there is to learn about designing.

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13

Design Within the Data Management Layer

The fundamental educational task is to design settings for education that are flexible and adaptive enough to handle these differences which derive from an individual’s cultural milieu and his or her own uniqueness among other human beings. —(Robert Glaser, 1977, p. 1) In order to provide the kind of information required for making instructional decisions, tests and assessment procedures need to have certain general characteristics. These measurements need to provide information on how well a student is performing in relation to certain desired goals or objectives. Through this information, both the teacher and the student become aware of the ways in which performance meets, exceeds, or falls short of certain criteria of accomplishment. —(Robert Glaser, 1977, p. 78) A great deal of data is generated during instruction. The design for data management defines the “memory” for this data and how it will be used beneficially, for adaptive purposes. Data from instruction has many uses, but traditionally the amount and kinds of data gathered and analyzed have been relatively trivial. The trend today, however, is toward the gathering and analysis of much larger amounts of data, especially on the Web, where analytic techniques are ascendant. More data capture, as well as during- and after-instruction analysis, will over time become the norm. Instructional designers should therefore begin to think more in terms of how data management can be applied to improving the instructional experience and its fit to the individual. Data management includes the collection, warehousing (storage and archiving), analysis, and interpreting of data from instructional events. It includes also the distribution of data compilations to the strategy layer, to the learner, to the sponsor or parent, and to the designer. The data management function also collects and analyzes evaluation data on the performance of individual instructional events and of the instructional system as a whole, which can be used for improvement. Designing within the data management layer produces plans for data collection and analysis, some of which may be automated by computer software, and others that are carried out by live data gatherers. These plans specify the data to be gathered, the instruments used to gather it, and the procedure for gathering. They also specify the rubrics for analysis, interpretation, documentation, and distribution of reports that may be provided to the learner, the system, and multiple administrator, sponsor, and designer stakeholders. 323

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Data Management Provides Support for Adaptive Instruction During instruction, data on learner responses accumulates at an enormous rate and can include any set of variables the designer or instructor has decided in advance to collect. One of the key decisions a designer makes is a trade-off between the amount of data collected and the amount of data that can be processed within time and resource limitations. This is a question of how granular the data can be and how detailed are the decisions that can be influenced by the data. Shute (Shute and ZapataRivera, 2012) proposes that two of the key questions of adaptivity are: (1) what to adapt, and (2) how to adapt. How a designer answers these questions determines the granularity of a system. Some data may be used at the time of instruction, and a history of the learner’s choices can be kept for later analysis. Data can also be used at the time of instruction to determine options for negotiation of the level of learner control. Four Areas of Data Management Planning Four areas of data management planning can be defined in terms of the purposes and audiences for which the data is gathered. The time frame for the use of the information also plays a role in defining these areas: • Data for the learning companion and the learner—Data for real-time support of instruction, including adaptation of the instructional experience to the learner. Portions of this data are also provided to the learner to support choice-making. • Data for the evaluator and the stakeholder—Data for the improvement of instructional designs and plans. • Data for learner analysis—Data for the purpose of obtaining a better profile of an individual learner or group of learners. This data is not used at the time of instruction but is digested after instruction to learn more about the learner’s characteristics, preferences, patterns, etc. • Data for the researcher—Data collected on an experimental basis to meet the needs of individual research studies or research programs. Data management is treated as a separate layer because the concerns for data collection, analysis, and reporting come from all of the other layers in the form of questions about the effectiveness and impact of designs within an individual layer, questions about how layers operate together to carry out instructional plans, or questions about learner reaction to particular layer designs. The design of a data management system involves every aspect of the design and how it works for the learner, the instructor, the automated elements of the system, and all of the stakeholders of the system whether or not they are directly involved in instruction. Questions Questions are the driving force behind data management designs. What do the designer, the instructor, the learner, and the other stakeholders want to know? Need to know? What are the questions whose answers will improve instruction, improve the learner experience, improve our ability to understand and meet the learner’s needs adaptively and to learn how to design by improving designs themselves? We can explore the answers to these questions by considering the four areas of data management planning just named. Data for the Learning Companion and the Learner During instruction, the control of events is negotiated between the learner and a learning companion. In some cases, the learning companion takes primary control of instruction and offers choices

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to the learner. In others, the learner is encouraged and even incentivized to participate in instructional decisions and goal setting through negotiation. In either case, the learning companion plays a part—either a major controlling part or a part on the side. The companion that needs the data in real time may be a human or a computer routine. For example, classroom responder systems, quizzes, discussion, and similar sources can make real-time data available to a human learning companion. The learning companion is a mentor that functions either in the foreground or in the background, and can be either human or artificial. As Rickel (2001) shows, an automated learning companion’s features can be implemented in the form of a highly nuanced agent, acting in roles that have emotional as well as cognitive impact. The concept of negotiation with a learning companion does not mean that every decision has to be negotiated but rather that the designer must deliberately choose which decisions should involve the learner, consistent with a plan for cultivating self-direction of learning. The number of potential choices the designer could offer is staggering. Therefore, the choices offered the learner must depend on a judgment by the designer of how much control can be given, what choices the target population is ready for, and which choices the learner can benefit from the most. The sections below describe data management design issues from the perspective of constructing learning companions that increase the learner’s capacity for exercising choice. Data Management Supports the Learning Companion The learning companion augments experience by adding commentary, feedback, direction, suggestion, or other artificial support for a learner’s direct experience with a task, a situation, a project, a dynamic model, an object, or a problem. The data requirements of the learning companion are derived from the list of decisions the learning companion is empowered to make by the designer. A designer who pre-makes all instructional decisions, providing only one path through instruction for every learner alike, leaves no decisions up to the learner or the learning companion. In that case, the learning companion is essentially dormant as a functional module. More exactly, the learning companion is hard-wired into the instructional experience and acts the same for all learners. However, when the designer offers choices to the learner, those choices define the scope of activity for the learning companion. The designer may choose to offer to the learner choices from any or all of the other design layers: content focus, content order, instructional goal, instructional mode, instructional location, level of conversationality, types of controls, types of representation, and learning platform. The possible dimensions of companion function are limited only by the designer’s imagination. The designer chooses one or more dimensions to be either mandatory or negotiable for the learner. What the designer chooses becomes the basis for the plan for data use by the learning companion. If the learner decides not to make the choice in a proffered area, then the learning companion must be able to make the choices for the learner based on data. If the learner accepts a proffered choice, the learning companion must be able to monitor the learner’s outcomes and offer recommendations or corrections as needed. Each area of the design made negotiable adds to the scope of activity of the learning companion and therefore defines the kinds of data that the companion needs. Data Management Supports Self-directed Learning Choices The learning companion monitors strategic plans in which some choices are made on the learner’s behalf, while others are allocated to the learner through negotiation. The main concern of the learning companion—live or automated—is to promote the judicious use of choice on the learner’s part, so that the learner will need the learning companion less and less over time. One of the goals

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of target population analysis might be to determine which level of choice the learner can be given beneficially. As was described in an earlier chapter, many designers feel that instructional goals on “learning to learn” can be as important as goals for the learning of domain knowledge. For example, the design movement called “constructionism” (Papert, 1980; Harel and Papert, 1991) addresses goals of learning to learn independent of subject-matter concerns. Constructionism simply uses the designer’s domain learning goals, whatever they are, as a vehicle for learning to learn goals. What instructional decisions can be allocated to the learner? In theory, every decision that the designer makes while constructing an artifact can be given to the learner. If a learner is making every instructional choice, then the instruction becomes a radical form of self-instruction. The first person that should have a right to data resulting from instruction is the learner. This can be provided through a reporting system that supplies data on a more or less constant basis throughout instruction. Such a system was described in Chapter 9 as the TREKKER system, because a learner is in constant need of information, like a trekker arriving at a clear prominence after hiking through the woods. A TREKKER system provides the kinds and amounts of information to the learner for decision-making at the point where the information is needed so that strategic learning choices can be made by the learner. This defines the need to report status and progress to the learner. It may be useful to review the questions defined by the TREKKER framework in Chapter 9 in the light of data management requirements they could produce. Data Management Supports the Evaluator Instructional designs consist of many elements. A designer would like to know how well each one served its purpose—activities, problems, sequences, physical and social arrangements, artifacts, resources, and the activities of the learning companion. This data can be used in an evaluation process to maintain and improve the quality of the instructional experience. Appendix C, titled “Evaluation Planning”, describes the mechanics of making an evaluation plan. There it can be seen that defining data needs for evaluation uses questions as the beginning point, just as in the case of the TREKKER. Planning an evaluation is not difficult, but there are always more questions to ask than there is time to gather the necessary data for answers. This makes the evaluation planning process a strategic process in which the challenge is to choose the high-payoff questions whose answers can lead to the most improvement on the dimensions of most importance to the designer and the design stakeholders. In some cases the goal will be to improve instructional quality; in others the goal will be to reduce instruction time; in yet others the goal will be a combination of making the instructional experience more motivating and reducing the costs of development and maintenance. The circumstances of the evaluation determine its priorities. Data Management Supports Learner Analysis Current instructional design practice includes the gathering of data on the target population of learners for the purpose of influencing the design toward their needs, preferences, and abilities. The process guide in Appendix A, titled “Target Population Analysis”, describes how this process is normally carried out. The process is usually performed manually, with the results of the analysis being captured in a word processor file. In some cases, project resources of time and money do not allow a formal target population analysis; in such cases there may not even be a formal document. Shute (Shute and Zapata-Rivera, 2012) discusses in detail the possible range of data that may be possible to characterize learners for meaningful adaptivity. She also describes alternatives for data gathering only after instruction has begun in order to avoid being frozen in old data patterns.

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In some projects, however, the fitting of student characteristics, existing knowledge, and circumstances not only favors but demands that a more thorough target population analysis be performed. Designing instruction for students with learning disabilities makes it mandatory to be more particular in adjusting the design to the learner’s skills and abilities. For this reason, designers of special needs instruction often are not from the general designer population but must be specialists in the target population. One example of this is designers who craft instruction in basic communication skills for special education students. In the early years of instructional design, it was considered sufficient to conduct a target population analysis once for all purposes. However, as the precision of instructional techniques matures, it will be increasingly important to conduct more thorough analyses and update them frequently, moving ever closer to the ability to adjust the instructional experience to the specialized needs and characteristics of the learners of a niche population. This will become the basis for a growing subfield of data management within instructional design. Some commercial organizations have already begun to amass large amounts of data on individual users. Your favorite book store or online music station are already improving the art of recommending to you what they think you will enjoy or want the most. On the one hand, this simplifies things for you, the consumer, but it also raises the issue of narrowing the user by trapping him or her in old data patterns. This borders on being an ethical question. In any case, the practice of gathering of analytics data will grow, and in order to stay competitive many organizations will expand their commitments by finding better data collection plans and means. Instructional designers in the future will find it imperative to use data analytics. Analytics and Cost Trade-offs The data management layer has always been important because it has always been necessary to determine learner progress and accomplishment. However, in the future the data management layer and data analytics will become increasingly separated from other layers, and the area of study which is now called “learning management systems” will increasingly merge with it. As this happens, one of the important trends will be the increasing granularity of the data gathered and analyzed. Where at present it may be considered sufficient to record that a lesson or exercise has been completed along with a score, in the future more data will be required. How well did the learner do on the performance test? What areas of the performance were weak and might require more exercise and experience? How long did it take the learner to master the performance? What kinds of errors tended to be made during the performance? How consistent is the learner’s performance once mastery has been accomplished? How quickly does performance ability decay? How often does learning need to be reviewed and renewed? With collected data, designers can “tune” instruction, trimming it where extra and unnecessary practice is being provided and reallocating training assets in areas that are more problematic. These are questions that in some areas of training are vitally important. For example in the training of high-payoff, high-risk, complex skills such as pilot training these questions are already of such importance that specialized training environments have been designed to give this data not only to the trainer but to the learner as well. This data recorded in such systems permits the ability to “replay” the experience, to which a learning companion can add commentary. The simulation industry for pilot training has adopted this method extensively. Most aviation simulators today are equipped with playback systems that allow the details of a training exercise to be replayed to spot areas of both proficiency and need. Lesgold (2012) points out, however, that capturing data has both economic and instructional impacts. Instructionally, the replay of problem-solving sequences is beneficial during a reflection period following practice, but the details of such a system have cost implications. Interestingly, Lesgold concludes that the more costly system is not always the most beneficial.

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The growth of data analytics and data management is a reality. Over the next few decades, this is a topic which designers will be expected to understand. Adaptive instructional designs will become increasingly common, and powerful tools will become available which place adaptive routines within the hands of the average designer. Just as things that seemed technically difficult but exciting thirty years ago have become commonplace and boring today, these advances in tools and techniques will become the norm, and instructional designers will be expected to deal with these cost–benefit trade-offs. Data Management Supports the Researcher Data capturing and analysis functions are a means of improving the theoretical principles upon which designs are based. This depends on a carefully executed and documented design approach called design-based research (van den Akker et al., 2006; Kelly et al., 2008). First, a designer determines the theoretical influences that will be applied in a design. Specific theory may influence only one layer at a time, or it may influence the entire design. One of the implications of layers is that individual layer theories can be implemented independently, freeing the designer from the fallacy of single-theory product designs, and producing information on multiple theories at once. For example, a designer may test a specific theory of conversational interaction or a representational theory. Next, a product is designed that implements the theoretical base(s). Care is taken in this process to ensure a valid application of the theory. Careful records are kept of the correspondence between design features and theoretical principles, as the designer interprets them. In this way, other design researchers are able to examine the reasoning behind theory-feature relationships. The product of design is then tested. A designer seeking to test a brand new application of theory in controlled (near-laboratory) circumstances will try to control every possible confounding variable and may resort to techniques closer to traditional educational research. On the other hand, a designer seeking to test the robustness of an application that has been tested in the laboratory and found workable may release the product into general use in the hands of typical users in typical settings (Brown, 1992). During the design of data management functions, if there are research questions, the data management plan will include provisions for collecting the necessary data. Instructional designers should become familiar with the design-based research process and include it into their toolbox of research skills. Application Exercise Instructional designers are taught to consider the needs for assessment and the needs for gathering evaluation data, but they are not often taught that these are both elements of a larger data management system. What do you see as the implications of the need to gather data for all of the areas of need described in the preceding sections? Sub-layers of the Data Management Layer Data management design includes making plans for: • • • • •

What data to capture How to capture it How to interpret it/analyze it How to report it How the learning companion will use it.

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These sub-layer headings provide a framework for discussing below the issues and questions related to data management design. Each of the sub-layers represents a functional area of the cybernetic process for guiding and adapting the path of instruction, evaluating instruction, maintaining a model of the learner’s current knowledge, and supplying research data. Deciding What Data to Capture The data captured in a data management system depends on the questions the designer hopes to answer. Data for Learning Companion Functions A learning companion requires data to the extent the designer intends it to influence strategic choices. If instructional choices are in the hands of the learner, the learning companion acts as a monitor and advisor and needs enough data to notice patterns and make recommendations to the learner. The scope of these actions is determined by the designer. If the learning companion is making instructional choices, then it needs data to determine when to advance its pre-planned sequence. Of course, there are numerous possible configurations of the learning companion between these opposite poles. In either case, the data required by the learning companion is learning-goal-oriented. Either the learner or the learning companion (which may be a live instructor) selects the learning goal. Data of interest to the learning companion includes anything that facilitates reaching the goal. Duval (2011) differentiates between easy-to-get and significantly useful data: Of course, one of the big problems around learning analytics is the lack of clarity about exactly what should be measured to get a deeper understanding of how learning is taking place: typical measurements include time spent, number of logins, number of mouse clicks, number of accessed resources, number of artifacts produced, number of finished assignments, etc. But is this really getting to the heart of the matter? —(p. 14) Data collection depends upon measures. It is measures of real progress toward the learning goal that constitute useful data. Data collected must be actionable, and it must be worth collecting, because there is a cost associated with every step of the data collection, analysis, storage, and reporting process. The part of the design that provides the best guide for selecting performance data for capture is the assessment plan, since it defines points at which progress toward learning goals is measured. The first class of necessary data shows whether performance goals—whether set by the learner or the learning companion—are being reached. When data shows that progress is not being made, then a second class of data is needed (if the learning companion is to have a diagnostic function), along with routines for detecting faulty performance patterns. This is similar to the use of data by any professional. For example, your doctor checks certain key indicators when you go in for a regular checkup. If you have no specific complaints, and if the key indicators are within range, no more data is required. However, if you have a specific complaint, or if one of the key indicators (say, blood pressure) is out of safe range, additional data will probably be gathered through additional tests. Live instructors follow this two-tiered pattern of data collection in almost exactly the same way as a medical doctor. Learning performance problems can lead to diagnostics: diagnostic performance tests, conversations with the learner, and so forth. However, the instructional designer has to anticipate all of the performance data needs—the first as well as the second class of data—from the beginning and incorporate all of the necessary data collection and diagnostic analyses into the design.

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Ensuring the Validity of the Data One problem that threatens this ideal is the invalidity of most instructional measures. Though instructional designers are schooled in design processes for instruction, the emphasis on designing valid assessments is much less. Consequently, the problem arises akin to the doctor using a broken blood pressure cuff. Moreover, the question of adding diagnostics is often moot, since most instruction does not offer them. Frankly, they are not required in every case, but in many cases—especially when knowledge is new or what can be termed “troublesome” knowledge (Meyer and Land, 2006)— they are very important. Well-prepared instructional designers should understand the means of securing valid measurement data. Mislevy and his research associates (Almond et al., 2003; Mislevy and Risconscenti, 2005) describe a layered approach to educational assessment called evidence-centered design (ECD). According to Mislevy and Risconscenti (2005): Assessment design is often identified with the nuts and bolts of authoring tasks. However, it is more fruitful to view the process as first crafting an assessment argument, then embodying it in the machinery of tasks, rubrics, scores, and the like. This approach highlights an important distinction between testing and assessment  .  .  .  While specific tasks and collections of task constitute one way of going about gathering information relevant to an assessment, assessment is a broader term and refers to processes by which we arrive at inferences or judgments about proficiency based on a set of observations. —(p. 1, emphasis added) Mislevy’s ECD approach ties the design of assessments closely to the design of instruction. It names five assessment design layers that are different in name but not in principle from the seven instructional design layers described in this book. It will be useful to describe Mislevy’s five layers before proceeding with the issue of data selection: Domain Analysis Layer Mislevy’s domain analysis layer corresponds with the content layer described in this book: “The domain analysis layer is concerned with gathering substantive information about the [knowledge and performance] domain of interest . . . This includes the content, concepts, terminology, tools, and representational forms that people working in the domain use” (Mislevy and Risconscenti, 2005, p. 7). The ECD analysis of the subject-matter domain is thoroughgoing, in the same way that analysis in the content layer is described in Chapter 11: It may include the situations in which people use declarative, procedural, strategic, and social knowledge, as they interact with the environment and other people. It may include task surveys of how often people encounter various situations and what kinds of knowledge demands are important or frequent. It may include cognitive analyses of how people use their knowledge. —(p. 7) Note that the descriptions of knowledge and performance captured are not purported to lead to assessments directly. Instead, a logical process unfolds through the design of the other layers that allows the designer to separate single task statements from assessments. This is especially important in complex subject-matters and in the testing of metacognitive and problem-solving performances such as one might encounter in science, math, engineering, law, medicine, and design—any

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subject-matter that involves complex system diagnosis and prescription. Note also that, just as with the design layers that this book describes, layer decisions are not necessarily made in a specific order: decisions within one layer influence decisions within the others. Domain Modeling Layer The ECD domain modeling layer is an intermediate layer that allows the designer to decouple domain knowledge from specific assessment acts. Instead of moving directly to the creation of assessment instruments, the designer creates an “argument” that will tie evidence gained through assessments to competence claims. Reconsider the medical analogy: if your trip to the doctor was about a heart complaint, the doctor might have to perform a number of tests and amass considerable data in order to make a more firm diagnosis. Evidence might have to be compiled from lab tests, treadmill tests, and early medical records. From these the doctor could make an argument for what might be wrong. But the logic of argument follows a general pattern, regardless of the ailment. In the same way, in making diagnoses of knowledge and performance ability, especially in complex or high-stakes cases, multiple sources of data and certain patterns of reasoning might be required to make a more certain judgment: testing the performance in different ways, under different circumstances, and at different times. According to Mislevy: Technical details—the nuts and bolts of particular statistical models, rubrics, or task materials—are not the concern yet in this layer. Rather this layer articulates the argument that connects observations of the student’s actions in various situations to inferences about what they know or can do. —(Mislevy and Risconscenti, 2005, p. 10) This layer of ECD is what separates and yet links the abstractions from the domain analysis with assessment patterns. This layer is one of the (previously) missing links that allows a logical correlation between the inference being made and the evidence to be gathered. It is a mapping layer, mapping out the logic that will be used in making inferences about performance from different kinds of data sources. Conceptual Assessment Framework (CAF) Layer This layer of ECD is an additional linking layer. It links the patterns of argument to kinds of data: In the conceptual assessment framework (CAF) we begin to articulate the assessment argument . . . in terms of the kinds of elements and processes we would need to implement an assessment that embodies that argument. The structures in the CAF are expressed as objects such as variables, task schemas, and scoring mechanisms. —(Mislevy and Risconscenti, 2005, p. 16) Note that this is just one step away from specific test items. What is determined here is the link between the argument and kinds of test situation, classes of data collected, and mechanisms for turning raw data into interpretations. In the CAF, many design decisions will be put into place to give concrete shape to the assessments we generate. These decisions include the kinds of statistical models which will be used, the materials that will characterize the student work environment, and the procedures that will be used to score students’ work. —(p. 16)

332 • Design in Layers

Mislevy explains: When we have done the work in the CAF layer we will have enhanced the assessment argument expressed in operational terms, primed to generate a family of tasks and attendant processes that inform the target inference about student proficiency. —(pp. 16–17) Assessment Implementation Layer “Implementation” as it is used in the name of this layer does not refer to using tests, it refers to creating them, just as a computer programmer would refer to implementing a program in code: that is, writing the code: “Implementation encompasses creating the assessment pieces that the CAF structures depict: authoring [writing] tasks, [statistically] fitting measurement models, programming [computer] simulations and [writing] automated scoring algorithms, and the like” (Mislevy and Risconscenti, 2005, p. 24). In this layer the assessments are finally created. These can be anything from items on an objective test to simulated problem solving. The assessments actually created can include anything from a quiz for a lesson test to high-stakes standardized test. The implication is that when ECD is employed, it is not necessarily the form of the test item that is affected, but the logical links between the test item and the inference about knowledge and performance the test is designed to allow. Assessment Delivery Layer This layer involves the testing process, the gathering of data, its interpretation, and the reporting of the results: Even the most enviable library of assessment tasks can say nothing about the students in and of themselves. These libraries provide only potential for learning about what students know and can do, unrealized until students begin to interact with tasks, saying and doing things that are then captured, evaluated, and synthesized into evidence about the claims at issue. Any assessment requires some processes by which items are actually selected and administered, scores are reported, and feedback is communicated to the appropriate parties. —(Mislevy and Risconscenti, 2005, p. 24) Note that this process of testing is not simply a matter of placing a sheet of paper in front of a learner. It involves making the specific test that the learner will take, administering it, scoring it, recording the outcome, interpreting the outcome, and reporting results. This perhaps unexpected list of steps makes evident one of the important points about ECD: all of the work was not spent in constructing one test, it was spent to construct a testing system. This realization is important for several reasons: • Even though we have become accustomed to testing once and not returning, the more complex performances we train today are not so easily assessed. • Repeat assessments of performances under varying conditions are often required to assure valid measuring of competence in real-world settings. • In a time of increased accountability, designers need a more robust understanding of how to improve the validity of their assessments. • Today the emphasis on computerized testing is leading to the proliferation of adaptive tests. Designers need an understanding of the logical frameworks used to produce multiple, equivalent, and valid versions of tests that purport to test the same knowledge.

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• Testing systems in many cases need to be cumulative in their measurements, assessing not only the latest knowledge accumulated but earlier, perhaps prerequisite, knowledge as well. Summarizing Why this extended description of the ECD layers in a section on selecting data to collect? Because: • Assessment data is the right kind of data (among other types) to feed a learning companion, living or computerized. • The discussion lays bare some of the unspoken assumptions about assessment that have allowed us to become lax in the design of tests. It makes us think twice about how we have been designing assessments. • The discussion makes it plain that instruction and tests originate in an analysis of content and performance. • The discussion clarifies the logical and interpretive machinery needed to make an assessment valid when you need a valid test: something we have neglected to teach many of our designers. • The discussion shows how a system of tests can be created, rather than a finite number of specific tests, whose keys can be compromised. • The discussion shows how tests can be made current and yet retrospective, increasing the confidence that what was learned before has not been forgotten. • This approach to assessment design works at many levels, including large standardized test development, district testing projects, professional test development, and course test, lesson test, and even quiz development. If there is a favorite reason from the author, however, it is that this approach closely links the processes of instructional design and assessment design in terms of the layered structure of both parts of design. These are too often separated in the minds of designers, as if instruction and assessment could be treated separately. Design and assessment have floated off into their own specialist worlds that need to be reunited, because in an adaptive system the assessment process lies right at the heart of the instructional system, providing moment-by-moment data on progress. Learning companions must make finer-grained decisions about how to support learners, but that depends on performance data. How to Capture Data Capturing data means obtaining it from an assessment or an evaluation (directly or by manual entry), transcribing it into a storable format, and placing it into a data repository. Since data arrives from multiple sources and in many native forms, the designer must choose how it will be captured and stored in a form ready to analyze. Performance Data: Learning Management Systems (LMS) Learning management systems (LMS) were originally developed for centralized, one-stop data amassing and storage related to K-12 computer-based instruction (Watson and Watson, 2007). The function of the LMS was that of a grade book. An LMS was able to launch students into learning experiences from a central place, retrieve control back after lessons, and automatically record scores. As LMSs matured, they separated from specific lesson suites and became a product in their own right. Eventually, off-line experience data could be entered manually into the grade book through an instructor interface. Since then, both the role and the architecture of the LMS have evolved at an accelerating rate. Early on, home-made LMSs were insular in their architecture, and they could collect data only from a limited number of sources, such as completed lessons or tests. Eventually, the LMS growth strategy

334 • Design in Layers

morphed into obtaining learner performer and profile data directly from a larger number of sources, including an organization’s own information infrastructure containing student registration and schedule information. In this way, data could be integrated across course and even degree program boundaries. Career and talent management became possible; this moved the LMS closer to the heart of an institution’s business concerns—educational or corporate. It also made LMSs larger and more expensive. As the software became more central to the interests and business processes of the institution, breakdowns in the software turned into costly disasters rather than inconveniences, and the closed proprietary architecture of some LMSs made them hard to tailor to local circumstances. Open-source LMSs relieved some of this problem, and several commercial LMS products have grown in popularity as institution-level tools, while the closed systems have experienced a shrinking market. Mott (2010) describes the rapid aging of the grade book LMS metaphor, observing that, though LMSs are still a relatively new type of product, they have already become a “symbol of the status quo that supports administrative functions more effectively than teaching and learning activities” (n.p.). He cites that 90 percent of colleges and universities have standardized, institution-level LMS implementations. Also, the growth in the number of corporate and for-profit universities since the early 1980s implies increased reliance recording training results and using them for tracking the development of skill and talent pools, which requires some form of LMS. Mott notes that a shift is needed toward LMSs as instructional tools (from the learner point of view) rather than management or administrative tools (from the organizational point of view). That view is compatible with the conversational metaphor of instruction. Rather than viewing the data management process as a function apart from instruction, separated by a software barrier, the conversational pattern sees the collection and capture of data as an integral instructional function. But this means that the basic structure of the data that is collected by an LMS will have to change. A later section discusses an alternative approach to structuring. Mott points out several structural properties of LMSs, especially those used by academic institutions, that influence not only the means of data collection but the symbolic or metaphoric nature of the LMS as seen by the learner. The structural property is the semester-length or courselength time division assumed by most LMSs. The symbolic effect of this is that the LMS interface represents to the learner a grade book kept by a (virtual) instructor. As Mott indicates, “LMSs are teacher-centric. Teachers create courses, upload content, initiate threaded discussions, and form groups. Opportunities for student-initiated learning activities in the traditional LMS are severely limited” (n.p.). Consequently, Mott notes: Courses developed and delivered via the LMS are walled gardens, limited to those officially enrolled in them. This limitation compares content sharing across courses, conversation between students within and across degree programs, and all of the dynamic learning affords us of the read-write Web. —(n.p.) Though it is easy to collect progress data within a walled garden, we should consider that the structures of the past may be hindering progress and that the grade book metaphor for collecting data may be favoring convenience over a more principled but powerful solution. Consider the possibility that we are collecting data on the wrong structural units. Structures for Data Collection Leavy (2011) suggests that data structures for the future LMS might be aligned with assessment structures: “The future of the LMS and electronic learning content are entirely intertwined with a

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model that looks more like the exploding market for assessment-driven learning applications rather than the PowerPoint posted to a website/LMS” (p. 4). One might argue that lesson scores represent assessment structures, but the lesson construct is defined so differently by different designers that it loses its meaning. Instructional objectives as a structuring entity are a move in the right direction, as they represent competency plateaus. However, these still represent a gross level of structure incapable of feeding moment-by-moment performance information to the learning companion for influencing conversational exchanges when that is desired. For example, as a learner progresses through a practice exercise, there is likely to be a staged increase in the complexity of practice items along some dimension (whether they are standard test item types or problems for solution). If a learner succeeds in answering the easier items acceptably but runs into problems with more difficult problems, there must be some way of characterizing the level at which performance runs aground, and in a responsive instructional system, there must be a way to provide feedback and remediation that can put the learner back on track. This concept of diagnosis of performance during practice and prescription for remediation purposes is almost as old as the concept of formalized instructional technology. A review of the history of instructional design models and the structures they incorporate by Gibbons et al. (2013) describes successful efforts to incorporate this cycle into instruction in the first decade of the 1900s and at many points thereafter. In fact, the ideal of diagnosis-and-prescription is perhaps the single most persistent theme in all of instructional technology. Figure 13.1 shows that data may be collected at not just one but several levels of granularity in order to support conversationality. This figure suggests that the assessment choices the designer makes are a major factor in the level of conversationality that can be achieved in a particular instructional system. As a previous section has suggested, data availability depends on the structure Instuonal level Program level Course level Unit/Competency area level Topic/Objecve/Event level Sub-topic/Sub-event level Interacon sequence/Strategy level Conversaonal exchange level Single interacon level

Figure 13.1 An illustration of several levels of granularity at which data collection may support the conversationality of instruction.

336 • Design in Layers

of assessment goals the designer has chosen. Once again evidence is presented for the centrality of assessments to conversational instructional design. This, in turn, is directly related to the granularity of the content and performance analyses performed within the content and strategy layers. The Compact as a Data Structure Whatever the level(s) of granularity data collected, it is collected in service to an existing instructional goal in order to answer a question about the learner’s progress toward that goal. Figure 13.1 shows that the goal may exist at the institutional level—perhaps a goal to graduate with a diploma. Though we have come to see this as strictly a commitment of the learner to achieving a high aspiration, it is actually an agreement that is made: the learner enters into an agreement—a compact, as it will be referred to here—to meet certain minimal requirements, in which case, the institution agrees to provide the learner with a certificate stating that the requirements for a degree have been met. If either party to this compact fails in their obligation, then the other can hold them accountable. The notion of compacts exists at every other level of the instructional conversation. On signing up for a course, the learner agrees to meet certain obligations to the instructor, and the instructor agrees to use a certain minimum standard for certifying successful completion. (Many students would say this standard can be somewhat arbitrary, and in light of the foregoing discussion on assessment preparation, it is hard to disagree. It’s tantamount to signing a contract containing blanks.) The compact idea is applicable at any level of the instructional conversation to which the designer decides to apply it. Whenever a learner enters into an agreement with the instructional system, then recording of data against the goal formed by the agreement is possible and desirable. The data can be used to track progress toward the goal and certify when it has been reached. There is an unfortunate tendency for the data to be collected being specified first, without thought about the goal structure it serves and the questions about performance that are being answered. The compact should be an important construct in the thinking of the instructional designer. In conversational instruction, a compact is an agreement to converse—to enter into an instructional relationship. Just as in an everyday conversation in the hall, when one party decides to break off conversation, the conversation effectively ends, even if the other party keeps talking. The compact represents the intention of the learner and the instructional system (live, technologybased, or blended) to continue to be intentionally engaged in a conversation toward some form of goal. According to Baron (2011): The LMS must evolve from systems that simply automate teaching, learning, and research collaboration to technologies that also facilitate, and even drive, true innovation—innovation that fundamentally changes how academia works. The ability to post a syllabus (a staple automation function of any enterprise LMS) needs to be complemented with capabilities that embrace the participatory culture of our students and faculty. Ultimately, if the LMS cannot be evolved beyond a tool to automate education, it will likely become extinct. —(p. 2) Seeing instruction as the fulfillment of a compact between the learner and the instructional system brings Baron’s “participatory culture” to the center of instructional concerns. It also brings assessment of progress toward compacted goals to the forefront, and assessment becomes an essential part of the instructional mechanism at whatever level the designer has decided to pursue assessment. Likewise, data pertaining to the evaluation of the instructional experience relies on goals the designer has decided to set within the evaluation plan. That data also is collected through the LMS, but by this point the identity of the LMS begins to morph into something new—something more

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than the event-launching and results-recording tools we have become used to in the past. The LMS merges with the instructional process itself as the cybernetic principle that allows the instructional process to self-correct under the influences of the learner and the designed strategies for instruction. Application Exercise A compact is essentially an agreement or a contract (formal or informal) between a learner and an instructional system. • • • •

Is the instructional system you are currently engaged with a compacting system? Do you think that signing up for a course is a species of compact formation? If so, are there compacts within courses? What are the implications of a compacting perspective of education for our current educational delivery traditions? • What might change is we were to agree to see education as a process of compact formation and fulfillment?

How to Interpret Data Figure 13.1 suggests that data on performance is highly contextualized. Data from a single conversational interaction does not yield to interpretation, independent of knowing the performance goals operative at the moment at every level. The levels of granularity shown in Figure 13.1 and the existence of goals within each of the levels at any moment during instruction provides a context for interpretation of performance data and patterns of conversation and interaction. According to Pellegrino et al. (2001): An assessment is a tool designed to observe students’ behavior and produce data that can be used to draw reasonable inferences about what students know . . . The process of collecting evidence to support the types of inference one wants to draw is referred to as reasoning from evidence . . . This chain of reasoning about student learning characterizes all assessments, from classroom quizzes and standard achievement tests, to computerized tutoring programs, to the conversation a student has with her teacher as they work through an experiment. —(pp. 42–43, emphasis in the original) Interpretation of data captured during instruction, which is used for progress tracking, diagnosis, prescription, and evaluation of the instruction itself, is a process of pattern-spotting. During instruction that consists of fixed sequences, interpretation is built into the instructional product, and the decision contingencies are fixed. However, when learners are involved in any way in choice-making during instruction, interpretation is possible—from the simplest issues of knowledge detection to the most complex issues of motive and conation. Pellegrino et al. (2001) describe an “assessment triangle” that summarizes the factors involved in making interpretations of what learners know. Figure 13.2 illustrates this triangle. The three corners of the triangle represent: • Cognition—The theory or set of beliefs about how learners represent knowledge. A theory about how the knowledge in a domain is structured, or how higher-order performance within the domain is structured. These become “targets of inference” and are closely related to the analyses of the content layer.

338 • Design in Layers Observaon

Interpretaon

Cognion Figure 13.2 The assessment triangle. (Adapted from Pellegrino et al., 2001, p. 44.)

• Observation—The choice of tasks learners will be asked to respond to and how the learner is expected to respond in a way that can yield inferences. • Interpretation—The “methods and tools” that can be used to reason about outcomes from the data observed. These include chains of reasoning. The concepts of evidence-centered design of assessment described above provide a context for relating the three corners of this triangle to reach conclusions about learner knowledge states. It is worth noting that this basic structure of reaching conclusions is recursive: it holds for observations at the individual conversational level as noted in the quotation above, and it also holds at the level of mass administered standardized tests. The triangle represents a mode of thinking where assessment and interpretation of data are involved. Kay and Kummerfeld (2012) show how the horizon of interpretation for accumulated data can stretch across a lifetime. How to Report Data The TREKKER system described in Chapter 9 provides the framework for reporting data to the learner. Other stakeholders also receive reports that answer their specific questions concerning the progress of the learner and the operation of the instructional system itself: • Responsible adult—Non-adult learners normally have interested parents or guardians who are anxious to participate in the educational process. • Instructional administrator—The administrator of the instructional system takes an interest in evaluation reports: to know whether the instruction is being carried out according to standards of the system, to know whether the instruction is being effective in reaching learning goals, to know whether the instruction is increasing the desire to learn among students and the patterns of self-directed learning that will help students become independent, life-long learners, to know whether instruction is being accomplished within financial and organizational constraints. 

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A designer must create periodic and on-demand reports that supply appropriate aggregations of data to these stakeholders to help answer questions pertaining to their unique perspective and set of responsibilities. How to Use Data Through the Learning Companion The learning companion is the pivot point of adaptive instruction, and the data management layer supplies information necessary to maintain a balance at that point, supporting trade-offs between learner choice and direction, decisions about when to tarry and when to move ahead, and judgments about when to speak and when to remain silent. The data produced by data management functions does not make these choices, but it feeds the decision processes of the learning companion that does. The theory that links the functions of the learning companion—which is centered in the strategy layer—and the functions of the data management layer, is cybernetic theory (Wiener, 1954, 1965; Glanville, 2008). Cybernetics is the operational principle of all self-correcting, adaptive systems. The cybernetic principle allows systems to remain alive and responsive within their environment. The processes of cybernetic systems are sensation, perception, processing, decision-making, and response. The first three of these can be considered data management functions; the remainder may be considered learning companion functions, depending on the modularity chosen by the designer. The choices of the learning companion are based on instructional and strategic goals negotiated jointly by the learner and the learning companion. Adaptation is not an absolute concept. Each adaptation is made with reference to: (1) the most immediate goal, at whatever level of granularity that goal represents; (2) the level of resource granularity the designer has provided; (3) the adaptation variables of interest to the designer; and (4) the granularity of the data capture, analysis, and interpretation the designer has provided for. The key to designing practical adaptive systems is carefully defining in advance the scope of adaptive action within the design. Adaptations can be made with respect to virtually any variable or combination of variables the designer chooses. Moreover, once target variables have been chosen, the number of ways adaptation can be accomplished approaches infinity. Adaptations can be made with respect to variables of content, control, messaging, representation, and all of the multiple sub-layers of strategy (scope, sequence, social, etc.). Whatever choices the designer makes, it will be the coordination of the data management layer design and the learning companion design that makes it feasible and workable. The sensitivity of adaptation to scope, and the close relationship between scope and interpretation is illustrated in an example quoted by Glaser and Silver (1994). In the process of assessment construction on a mathematics project, the following item was created: Yvonne is trying to decide whether she should buy a weekly bus pass. On Monday, Wednesday, and Friday she rides the bus to and from work. On Tuesday and Thursday she rides the bus to work but gets a ride home with her friends. Should Yvonne buy a weekly bus pass? (Bus company fares: One day—$1.00; Weekly pass—$9.00). The test designers decided that the best, most reasonable answer would be to buy five one-day passes rather than a weekly pass. According to Glaser and Silver: At a subsequent meeting, the teachers met to discuss their students’ performance, which had some surprising aspects. In particular, many students indicated that Yvonne should purchase the weekly pass rather than paying the daily fare, which the teachers believed to be the more economical choice. Curious about this unexpected answer to what the teachers believed to be a rather straightforward question—a multi-step arithmetic story problem involving multiplication of whole numbers—they decided to discuss the problem in class and ask students

340 • Design in Layers

to explain their thinking. The ensuing discussion with students provided an interesting illustration of their application of out-of-school knowledge and problem-solving strategies to a mathematics problem. Many students argued that purchasing the weekly pass was a much better decision because the pass would allow many members of the family to use it (e.g., after work and in the evenings), and it could also used by a family member on weekends. Students’ reasoning about this problem—situated in the context of urban living and cost-effective use of public transportation—demonstrated to the teachers that there was more than one “correct” answer. This experience made it clear to the teachers that if their goal was assessing what students know and are able to do, then it was essential that students not only provide answers but also explain their thinking and reasoning. —(p. 409, emphasis added) If there is a moral to this story that applies to assessment construction, then there is one also for the design of adaptive instruction, which depends heavily on assessment and on the interpretation of assessments. It is that more data is required for interpretation than simple final scores. Assessments for adaptive purposes must be, of necessity, diagnostic and formative as well as summative. This provides more argument for driving assessment more deeply into the heart of design and the instructional process.

14

Design Within the Media-Logic Layer

Computers themselves, and software yet to be developed, will revolutionize the way we learn. —(Steve Jobs) The media-logic plan describes the complete logistical and operational system for instructional delivery and locates it within the working environment of the organization (business, military unit, government department, school, or university). The media-logic plan provides for the execution for all other layer functions. The forms of media-logic most familiar to designers today include mainly software: learning management systems, navigation interfaces, and record-keeping systems. These, however, are only the most visible concerns of media-logic design. The wider spectrum of media-logic concerns includes: • • • • • • • • •

Defining learning spaces and places. Specifying technology devices and software. Specifying guidelines for blending instructor-led and technology-based instruction. Defining the learning management system operations. Designing the architecture and specifications of software. Designing the “packaging” of the system into conceptual and physical modules. Planning Internet, Intranet, and communications connectivity. Providing for learner privacy and system security. Controlling and maintaining versions of software and applications that are produced.

Media-logic is the engine that makes everything happen; its concern is operations—those of the technology, and those of the live instructor. The name of the layer—“media-logic”—should be clarified. It is based on the insight that the media-logic layer is the convergence point of: (1) media devices (including instructors) and their logic with (2) the logic of the instructional conversation. Figure 14.1 shows that conducting the instructional conversation is the first media-logic priority. The conversation is executed either by a live instructor or by technological means. A key issue of media-logic layer design is how to carry out the conversational logic without letting the execution logic of the medium overshadow and constrain it. The designed conversation logic should determine the execution logic, whether the medium is technological or human in nature.

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6

The organizaonal working environment

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The organizaonal training environment

Spaces, places, devices, connecons

4 3

Media modules 2

Execuon logic 1

Conversaon logic

Figure 14.1 The priority of media-logic concerns.

Execution logic, in turn, influences the formation of media modules. Modularity is described in detail in Chapter 15. Briefly stated, the principle of modularity is that the design of a functional system is most efficient and maintainable when its parts are created in the form of modules that are independent but capable of operating together. Most major consumer items today are designed with modularity, which is why you can move a hard drive from one computer easily to another or replace one video adapter with another and expect the computer to work properly. Media modules may consist of resources or execution logic that drives how they are used. The nature of the media modules is that they require spaces in which they can be used, places where they can be used, devices that can use them, and connections that give access to them. This is the next level of media-logic planning. Spaces and places refer to the setting and siting of instruction. The “setting” of instruction specifies the kinds of surroundings or spaces needed for media module administration—a large space? a private space? a dark or light space? an inspiring space? “Siting” of instruction specifies the range of locations and times in which modules can possibly be administered—out of doors? on the go? 24/7? or only at scheduled times? “Devices” refers to the kinds of hardware and software that are used to administer media modules. It includes models and manipulatives used during instruction as well as one unexpected “device”—the live instructor. The inclusion of humans in the device category is explained at length later in this chapter, but here it should be said that the instructor is part of a system in which the effective function of one part depends on the predictable functioning of the other parts. “Connections” refers to the kinds and capacities of network access required to administer media modules. This can include anything from no access to high-speed broadband access to the Internet or to the organization’s Intranet. Spaces, places, devices, and connections reside within the organization’s training environment. This in turn resides within—and in many cases is integrated with—the organization’s working environment, meaning that instruction becomes integrated with work tasks in some way.

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Application Exercise Examine the training operations of your organization. • Where do you see evidence of conversational logic used in instruction (either in live or technology-based instruction)? • Where do you see evidence of computer logic being employed? Where do you see instructors following a prescribed instructional logic? • What does your organization treat as the smallest unit of instructional modularization? A course? A lesson or a session? An exercise? A media resource? • What are the spaces, places, and devices your organization uses for instruction? Where is connectivity to instructional resources available? In buildings? On the road? • How well are the training functions of your organization integrated within the daily workplace and work functions? The remainder of the chapter considers each of the levels of media-logic planning in Figure 14.1, starting with execution logic and working outward. Each of these levels can be considered sub-layers of the media-logic layer: each one is an area of specialized decisions. Each has its own design language, design factors, professional literature, and theoretical principles. Though the exposition of these sub-layers takes a certain order, and though there is a logical dependency of each sub-layer on the previous one, it is important to remember that the many decisions represented within each level of Figure 14.1 are made in no particular order. Decisions within each sub-layer influence decisions in all of the others. Decisions in one sub-layer should not be finalized without making sure the choice fits well with decisions within the others. Cross-checking of decisions in this layer, as with all other layers, avoids embarrassing situations such as a new media center that is not sufficiently flexible to accommodate growth in new instructional technologies or learner volume, or an instructional product that has serious conflicts with the organization’s IT system. Sub-layer: Execution Logic Execution logic (see Figure 14.1) consists of the directions followed in the administration of instructional conversations. These directions pertain to the activities of a software product, a human instructor, or a combination of both. A look at the history of human–technology involvements in the past will give perspective to the issue of execution logic and the role of the human instructor. In the early days of computer-based instruction, many media forms were used side by side with the computer: slides, audio, videotape, videodisc, and others. In many cases the instructor’s role in these systems was minimized (for examples, see Atkinson and Wilson, 1969b). Media-logic in those days consisted mainly of computer code, recorded pulses on audiotape, or some other kind of device control mechanism. Eventually the computer assimilated most media forms. Today, the computer can execute many forms of media, but the trend in the use of the instructional computer is away from the self-contained product concepts of the past and toward the blending of the media role with the instructor role (Bonk and Graham, 2006). The question is no longer whether the instructor or the computer (and other media forms) will be the main means of delivering instruction, but how the two will blend their efforts harmoniously and effectively. The best way to characterize this change is to describe recent trends in the direction of: (1) precision instruction, and (2) blended instruction.

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Precision Instruction Research in instructional method shows that more precise instructional techniques can improve effectiveness, by giving guidelines for the role of the instructor. Two examples are given below to demonstrate this concept. The examples show how different instructional theories lead to disciplined activities of an instructor that increase instructional effectiveness. Problem-based Learning Howard Barrows is the best authority on problem-based learning, as its popularizer and having a long history of design research studies aimed at improving the method (Barrows and Tamblyn, 1980; Barrows, 1992; Hmelo-Silver and Barrows, 2006). Problem-based learning (PBL) achieves its effect through the precision application of principles that guide instructor choices moment by moment without directing specific actions. PBL leads instructors to avoid many of the kinds of actions that constitute the bulk of traditional instructional technique. There are multiple forms of instruction that term themselves PBL, but the example and guidelines used here will be those proposed by Barrows. The point is to show how instructor actions constitute an element of the media-logic plan. PBL, according to Barrows, poses ill-structured problems to groups of learners. The groups solve the problems in the presence of a facilitator. The facilitator’s role is clearly defined in terms of appropriate activities. These activities constitute the heart of the PBL technique and differentiate the Barrows version of PBL from others. Hmelo-Silver and Barrows (2006) explain that, “the facilitator guides students in the learning process, pushing them to think deeply, and models the kinds of questions that students need to be asking themselves” (n.p.). In PBL the facilitator is an expert learner, able to model good strategies for learning and thinking, rather than providing expertise in specific content. This role is critical, as the facilitator must constantly monitor the discussion, selecting and implementing appropriate strategies as needed. —(n.p.) Hmelo-Silver and Barrows identify the following as examples of legitimate instructor strategies: • • • • • • • • • •

Using open-ended and metacognitive questioning. Pushing for explanation. Revoicing. Summarizing. Generating/evaluating hypotheses. Mapping between symptoms and hypotheses. Checking consensus that the whiteboard reflects the discussion. Cleaning up the board. Creating learning issues. Encouraging construction of visual representations.

The details of these strategies must be left to the reader to explore in the literature. Unfamiliar terms like “revoice” and the reference to the “whiteboard” should be interpreted within the context of the Barrows PBL method. The key observation at this point is that certain kinds of instructor activity are encouraged, while others are enjoined, most of them being the kinds of activity most instructors are inclined to use when they action out their part in traditional instructional culture. These include activities like lecturing, assigning homework, and giving tests.

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To use PBL as it is described here, an instructor must assent to using a new, unfamiliar, and non-traditional instructional method. More than one technique, it is a suite of techniques used together in a sequence that adapts to the moment. Therefore, it is necessary with PBL to train and continually monitor instructors to ensure that they are the “expert learners” and “models” that the method requires, because it is the model they create for the learner’s inspection that becomes adopted as part of the learner’s own repertoire of learning strategies. The facilitator in this case becomes an extension of a more comprehensive plan of instruction that exists within a specialized environment—one in which a family of problems is employed, in which a broader instructional strategy has been imposed, and a different set of instructional goals is operating. The instructor’s discipline is necessary: it becomes part of the media-logic plan because the instructor, rather than being at center-stage, acts in cooperation with a larger strategic plan to allow learners to take a greater degree of responsibility for their own learning, and that new role must be modeled for them by the facilitator. Reciprocal Teaching Reciprocal teaching (Brown and Palincsar, 1989) likewise asks the instructor to abandon some traditional methods in order to become part of a larger instructional plan. This does not mean that the instructor submits to control but that he or she assents to the validity and effectiveness of the larger plan and decides to increase the effectiveness of time spent by accepting the discipline of the method and employing it to achieve a greater degree of precision. Similar to PBL, the activities of the instructor are not rigid directions to be followed but guidelines and principles. To a casual observer, a reciprocal teaching session for reading instruction looks like a traditional reading circle. To an informed observer, however, there are noticeable differences. In reciprocal teaching, the instructional strategy is in leading the learners to create a visible model of an invisible mental process of reading comprehension through a joint effort, with learners acting together in pre-assigned roles. The roles include each learner taking responsibility for asking questions that represent just one function of the comprehension process that normally takes place invisibly within the mind of a competent reading comprehender. According to Brown and Palincsar, the comprehension “roles of executive, skeptic, bookkeeper, educator, and so forth are executed overtly” (p. 401). As in the example of PBL, the teacher’s role is non-standard and more disciplined. Research has shown the reciprocal teaching method to be effective if each participant accepts and carries out their role, including the instructor. The model of comprehension—a thing normally invisible—becomes visible to members of the group experientially time and time again, until it becomes familiar to them and can be assimilated into their personal approach to analyzing and comprehending a reading by the individual learner: “The reciprocal teaching procedure renders . . . internal attempts at understanding external . . . In the course of repeated practice such meaning-extending activities, first practiced socially, are gradually adopted as part of the learner’s personal repertoire of learning strategies” (p. 415, emphasis in the original). The point is that learners, by acting together in their roles, create the model for observation and internalization. The instructor’s role is not to deliver information but to prepare each learner beforehand to fill a particular role and then, during instruction, to monitor each learner, noting how well the role is being carried out, nudging things back onto track when necessary. The instructor never dominates the group’s thinking and questioning process, and never lectures. The implication of this type of instructional role for the instructor is that the instructor becomes a part of a larger phenomenon created jointly by an entire group of learners, but this is only possible if the instructor acts in harmony with a disciplined media-logic plan in which the instructor becomes the intelligent media factor.

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Blended Instruction The changing role of the instructor illustrated in the foregoing examples highlights an important trend toward blending the role of the instructor as part of a delivery system. This becomes an important topic in a media-logic plan. How will the activities of the instructor be integrated with the use of technological devices in a seamless blending of delivery mechanisms and pedagogical techniques? Blending does not always ask the instructor to modify favored instructional methods. In most descriptions of blending the instructor directs, and the technological means are seen as tools in the instructor’s hand. According to Graham (2006), blended learning can consist of: (1) combining instructional modalities or delivery media (mechanism), (2) combining instructional methods (technique), or (3) combining online and face-to-face instruction (proximity). One could argue that the moment an instructor steps to the whiteboard, blending begins with the instructor retaining a central position. At the other end of the spectrum, blending becomes almost totally media-centered as an aviation instructor and student step into an aircraft simulator together. In using the whiteboard, the instructor creates representations that words and gestures alone can’t create. At this near end of a continuum the instructor’s logic controls the medium of instruction. At the far end of that continuum the instructor becomes just one element within a physical and operational environment, using the environment to supply experience that words and drawings can’t. This continuum and the logic of the instructor’s actions is addressed in the media-logic plan. It does this by defining in a principled way the instructional functions allocated to the instructor and those allocated to media and by giving guidelines for carrying out the functions. Computer Logic Media-logic up to this point has been described in terms of how human logical forms blend with technological means. Computer software logic is a critical part of this blending. What are the architectural elements of computer logic from the point of view of the instructional designer? Instructional designers create strategic event structures. A designer must map the abstract event (strategy) structures onto the logic structures of the medium in a convergence zone (see Figure 14.2). Computerized instruction requires detailed logical structures. The computer is capable of calculating instructional sequences for individual learners, but it can only do this if it is given detailed instructions. Computer logic can be fixed and rigidly sequential, or it can involve computed—even

Instruconal strategy plan

Development tool

Instruconal messaging constructs

Tool logic constructs

Convergence zone Figure 14.2 The convergence zone where abstract strategic constructs and logic tool constructs come together.

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Events Operator

Crane

Learner

Figure 14.3 The crane metaphor of instruction.

intelligent—sequences, but either way the directions to the computer must be exact, because the computer really doesn’t know what is going on. The work of the computer during instruction can be illustrated metaphorically using a crane to represent the computer and a crane operator to represent the computer logic (see Figure 14.3). The crane in this illustration is moving crates (representing instructional media resources) from a storage area and placing them in front of a learner. Each crate contains an instructional “event” of some size. It can be very large—an entire lesson—or very small—a single graphic, audio, or text resource, or a bit of execution (decision-making or modeling) logic. This metaphor describes the function of a computer during instruction. The computer (the crane itself) doesn’t know what it is doing; it is just carrying out instructions from the operator, moving crates. What makes all the difference in computerized instruction is: (1) the size or scope of the event inside the crate, and (2) the kinds of decision-making intelligence given to the crane operator. The crane operator determines what happens next, which may be decided entirely by the learner, through negotiation with the learner, or entirely by the crane operator. If crates are small, the scope of each event is small, and a great variety of textures and instructional experiences can be achieved. Likewise, if the crane operator is given an intelligent set of decision-making rules, the instruction can be highly adaptive. Everything depends on the contents of the crates and what the operator knows. These are designer choices. Keep in mind a few points: • The crates can contain programs as well as media resources. • The size of the crates and the rules of the crane operator exist along a continuum. Things do not have to be all one way or another. Different areas of the instructional design can have different crate sizes and different operator rules. • More than one crate can be open at one time. This means that one crate can contain a dynamic content model (e.g., a chemical reaction in process, or an airplane in flight), and another crate can contain a learning companion function that guides the learner’s experience with the model. These points lead to the issue of software and product modularization, which is treated in a later section of this chapter and in Chapter 15. Development Tool Logic Different development tools and tool families supply different kinds of computer logic structures. This equates to different sizes of crates and different kinds of rules for the crane operator. Designers

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need some familiarity with the differences because development tools are all about crate sizes and operator rules. Development tools determine how hard it will be to build crates and fill them and how hard it will be to specify the rules for the crane operator. These factors are major determinants of the cost in time and effort of developing the instruction. The best way to discuss different types of tool constructs is to consider the history and evolution of software tools, because it is a story about the balance between reducing cost and effort, while at the same time keeping pace with rapidly escalating expectations of product quality. The problem addressed by computerized development tools is one of finding a common language with which to speak to the computer. Rawlins (1997) describes the problem: Today’s computers are good because they do exactly as we say. And they are bad because they do exactly as we say. To work at all, they need a language both they and we understand, either if neither of us understand each other’s native tongue. Fortunately, we’ve had the problem many times before. To solve it we invented pidgins. —(p. 42) Then Rawlins defines “pidgin”: When speakers of two different language groups meet with no interpreters around, they rapidly make up a paralanguage—a pidgin. Initially, the pidgin is very clumsy because it has no native speakers, but children who grow up hearing only the pidgin generalize it, and it quickly slips into a true language. —(pp. 42–43) Development tools represent pidgins that allow humans to communicate intention to computers. Initially designers had to communicate with computers in their own language, but as Rawlins explains: “Over the centuries and across the globe, successive waves of invasion or trade forced colliding languages to pidginize. Today the same thing is happening between us and computers” (p. 44). What have been the major milestones in the development of human–computer pidgins? • Assembly language. Originally assembly language was the primary means of communicating instructional designs to computers. This resulted in long programs, primitive capabilities, and inscrutable code like: ADD 12E3 B429 JUMP 2A15 • Programming languages. Programming languages were developed that replaced tens of assembly commands with a single command. These languages included FORTRAN, COBOL, BASIC, Pascal, and C. Each of these provided its own set of imperative commands and its own way of structuring data in the computer memory. Still, programming with these was complicated. Even a simple instructional routine could require hundreds of lines of code. Sethi (1996) and Cezzar (1995) give overviews of the many new programming languages that emerged and the relationships between them. New categories of language aimed at particular kinds of programming problem were also invented. Languages were invented to create “thinking” computer programs that could accomplish some degree of problem solving. During this period, artificial intelligence became an important topic. Examples of these languages include LISP and Prolog. 

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Languages like SMALLTALK were invented for model-building. They were based on a new metaphor of programming called “object-orientation”. Today object-oriented languages like C++ and JAVA have become very important. • Visual development interfaces. Some standard programming languages evolved to combine object-oriented features with visual programming interfaces. The goal was to reduce the time and level of expertise involved in programming. A spate of languages like Visual Basic and Visual C++ appeared. The problem of writing code still existed when a designer wanted to do something sophisticated or out of the ordinary. Unfortunately, instructional computing often asks the computer to do things that are out of the ordinary, compared with everyday uses of computing. 

Instructional designers do not need to know how to program in programming languages, but they need to know how to talk with those who do. Design team members are more likely to know how to use a multi-media development tool. These have specialized logical structures. • Early authoring “languages”. When programming tools specifically for instructional purposes began to appear, they had program commands geared to instructional actions, such as accepting and judging answers to multiple-choice questions, creating graphic displays, and keeping scores on quizzes. These features appeared slowly at first, but as the time and effort payoff from using them became apparent, the number of languages multiplied. They do not persist in use today, however, because there were further labor saving and quality-adding developments. • Authoring systems. The goal of authoring systems was to make creating computerized instruction even easier. Keep in mind that the product expectation in those days was different. People were just beginning to be dazzled by computer graphics and the excitement of branching through non-linear sequences. Several authoring systems used the structural concept of the “frame”—a display component coupled with a logic (branching judging, interaction) component. Framebased systems proliferated, in all but a few cases replacing interest in language-based systems. However, authoring systems were hard-pressed to keep up with steadily rising expectations and numbers of new users, who, it turned out, expected training, users’ manuals, and support when they ran into difficulties. User groups formed to exchange ideas, products, and reusable product parts, sometimes called “widgets”. • Multi-media development systems. Authoring systems were born, lived a wonderful life and have mostly died out. They were overtaken by: (1) the rapid advance of multi-media technologies, (2) the rise of the Internet, (3) the growing size of unwieldy programs, and (4) the explosive growth in expectations of product performance. An idea that did not entirely die with authoring systems was the frame construct. Over time, while authoring systems were still current, software developers created “buffet” frames: frames that could be populated with both display and logic components selected by the designer from a menu. This concept lived on as development tools continued to proliferate and became increasingly specialized. Software developers stopped creating omni-capable, one-tool-does-it-all programs into highly sophisticated tools for the production of display content (photos, video, animations, graphics, audio), and logic. Logic tools tended to specialize according to the delivery channel (Web site, online book, classroom, mobile). In the normal course of things, tools became more object-oriented to provide greater modularity and the accompanying ability to lash-up and remix both displayable content and logic modules. Logic tools became the central point for the assembly of display content into publishable instructional event modules. • Indexing, recommender systems, and repositories. As designers produce modular products, many of them are reusable. Major projects to collect these resources and make them available

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have amassed large repositories that create the problem of searching through for just the right one. Indexing systems that attach “tags” to each item make the task of finding a resource easier for the user with the right set of search terms. Recommender systems are capable of using the tags to suggest resources of all kinds for remixing, and the sharing of resources under specialized copyright agreements makes reuse easier. • Tools for creating diverse “product” types. The front of progress has expanded like the front of a brush fire: what used to be a compact field of educational computing based on the “lesson” structure has become more and more diverse. The instructional “product” concept itself has lost its clear definition. Blogs, wikis, podcasts, and online collections have increased in use. The design of interfaces, networks, and user-contributed “projects” (e.g., Wikipedia) has increased (Krippendorff, 2000). Online forums for mutual problem solving and sharing of common interests are more numerous. These “products” which were once technical challenges to build are becoming more approachable due to specialized tools that converge toward minimum levels of programming expertise. • Intelligent tutoring tools. As research continues to improve the quality and approachability of intelligent tutoring systems, tools for building them are beginning to emerge. Commercial successes of intelligent tutoring will bring what is now on the edge of most designers’ capabilities into the mainstream as tools become more user-friendly. This pattern recapitulates the pattern of early authoring languages and their evolution into common development tools. Instructional designers should anticipate this development by becoming familiar with the principles of intelligent tutoring. The growth of computer applications in education and training and the accompanying tidal wave of multi-media development tools and options is so steady and relentless that summary books and articles that cover the whole subject—design to production to implementation—are impossible to find. Specialization has set in to a degree that a single designer finds it impossible to singlehandedly create products in the same way that designers could just twenty years ago. This brief summary has had to omit some specialized tools that are in use—for the creation of simulations, for authoring mechanical interfaces and robotic systems, and for interfacing with adaptive appliances and software for special needs learners. A designer cannot possibly know all that there is to know about computer development tools, but it is important to be familiar with the basics of software logic tools and recognize how software development affects the design process. Logic Patterns Development tools supply the building blocks for larger patterns of computer logic. It is at the level of these patterns that strategic constructs converge with computer logic constructs. From the number of development tools that are available and the number of types of product that can be designed, you might suppose that there would be a multitude of logic patterns, but there are surprisingly few. They are named below in terms of events. Remember that an event can be of any size: a single graphic, or an extended interaction: • Linear sequence of events. One event is followed directly by another event, the same event every time. • Conditional branch between events. As control is passed from one event to another, a variable value is consulted to determine which will be the next event. • Cycle of events. Conditional events follow each other in a cyclic pattern. Since the branch after each event is conditional, the cycle can be broken whenever a certain variable value is reached.

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These pattern types seem too few in number to account for all of the variations in computer behavior during instruction, but that impression vanishes when the fact is recalled that event structures are made up of event structures in a recursive fashion (see Chapter 12 on the strategy layer): events are made of events, which are made of other events. Applying these three basic patterns at many different levels of event size creates an almost infinite number of possibilities. One of the most interesting and useful event patterns is the cycle. It is the basis for most simulations. In a simulation a learner interacts with a dynamic model. Virtually all dynamic models are cyclic in nature. The value of interacting with a model is that realistic performances and realistic system responses can be simulated. Application Exercise Two basic classes of logic have been described: the logic that the instructor follows in choosing what to do next and how to do it, and the logic programmed into a computer, telling it what to do next. Examine two or three classes conducted by a live instructor. • Look for signs that the instructor was deciding what strategic direction to follow next. Notice revisions in strategic logic. • To what extent does the instructor follow a set pattern for instruction, and to what extent is the class conducted in a way that adapts to the circumstances of the moment? • How well does the instructor seem to be aware of an unfolding plan? How disciplined and principled are the instructional plans of the instructor. Find two or three examples of computer-based instruction. Compare the examples in terms of the kinds of logic they employ. • • • •

Do displays come up as a single unit, or do parts of the display change separately? How many different basic patterns of computer event do you detect? How different are the examples in terms of the variety of interactions they support? How adaptive is the instruction to the individual?

Sub-layer: Media Modules The media modules sub-layer deals with how the instructional designer’s products fit within the computing environment of the organization. Within the media modules sub-layer (see Figure 14.1) the designer has to show awareness of basic concepts of software architecture. Computer support of instruction is being absorbed into the information technology (IT) architecture of organizations— what is called the enterprise architecture—and media modules have to be delivered from within this enterprise architecture. Twenty years ago, this would not have been an instructional design book topic, but today designers need to be aware of how they add value to an organization. Since organizations are increasingly centered on a computing infrastructure, and since computing is becoming a more central feature of instructional systems, the designer’s contribution to (or tax on) the organizational IT economy relies on knowledge of how instructional computing and record-keeping create value. This begins with the designer’s definition of media modules. What is a media module? It is a self-contained unit of resources and/or logic that the instructional designer wants to treat as an independent, executable, pluggable, reusable, remixable element to be combined as part of an instructional product. When instructional logic and media-logic come

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together, they create media modules. These modules represent an end product to the designer, but they are just the beginning point for integrating software and hardware structures of the design within an organization’s enterprise software architecture. “Module” is a much-used term, especially in the computing world. Chapter 15 deals with the implications of modularity in more detail. For present purposes, it is useful to treat the term as if it referred to an independent, ready-to-use element of instruction, of some size and complexity. Modules can be large or small. Their size is a strategic decision that has both instructional and economic implications. The members of the IT department within an organization (and virtually every organization has one these days) will be familiar with the term “module”, but the IT definition will differ somewhat from the instructional designer’s definition. Theirs will be centered on software modules. The instructional designer will be thinking in terms of independent media modules—modules that use both instructors and computers to carry out instructional functions. These instructional modules must be integrated with the organization’s enterprise software system. This is accomplished using a learning management system (LMS) of some kind. Learning Management Systems A learning management system was at one time almost an afterthought with computer-based instruction designers. An LMS then was necessary for launching lessons and retrieving and recording lesson test scores, but it was not considered as interesting a topic as the instructional product itself. LMSs were not the first concerns of early designers. They did not set out deliberately to build LMSs. They were necessities created by individual projects. A designer would create a handful of lessons for a client and realize near the end of the project that the user would need an interface to select lessons and mark completion. Customers found this interesting but, ironically, once they obtained the data there was no place to send it, and they had no idea how to use it. All that changed, and LMSs began to take on more features, such as reporting score summaries to learners, and printing out progress summaries for the organization. LMSs became full-featured products in their own right. Large and small organizations have learned how to use LMS data. The LMS has become a tool for managing learning services for entire organizations. This is true for corporations, schools, universities, government, and the military. As LMSs have become more sophisticated, they have acquired a number of useful student and instructor functions, which may include: • • • • • • • • • • • •

Scheduling instructional resources, events, personnel, devices and spaces cost-effectively. Organizing course content and syllabuses. Delivering computer-based parts of the instruction. Integrating instructional services and resources obtained from outside sources. Supporting learner–learner and learner–instructor communication and collaboration. Centralizing recording and reporting results of instructional experiences. Communicating training results and records to within-organization clients, learners, instructors, and designers. Tracking completion records and learning progress for individuals across multiple training experiences. Registering certification and qualification levels for individuals. Gathering and interpreting evaluation data on instructor-led and computer-based experiences. Gathering data for quality control and maintenance of the instructional product. Managing extra-organizational learners, including suppliers, partners, and customers (Hall, 2002).

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• Supporting the tailoring of instruction to individual learner needs. • Administering assessments where appropriate; recording assessment results. • Tracking training costs in support of overall training systems management. Interfacing Media Modules What does all of this have to do with the designer’s choices regarding media modules? The designer today works within a framework of organizational goals and larger organizational policies, training styles, and software tools, including the LMS, which becomes the focal point for planning, executing, and evaluating training activities. A fortunate designer will be included in the planning and evolution of this framework. At a minimum, however, the designer will have to work within the existing framework, producing media modules that fit within the context of organizational policies and style, and that interface with the LMS software. This entails having a coherent and consistent plan for what constitutes a media module capable of hooking into the organizational system. Designers can help to define a corporate modularization strategy if there is not one, and they can work to improve an existing one by experimenting with designs. Most importantly, modules must be designed in such a way that they convey the needed data to the LMS across the software interface that links the media module with the LMS. Application Exercise Observe the LMS that your organization provides. If possible find additional examples. • What kinds of basic unit or instructional events can the system launch (run)? • What kinds of data are transferred back to the LMS once an event is completed? • Is there data kept by the system that is not reported to the learner? Sub-layer: Spaces, Places, Devices, Connections Just as designers work within an organizational framework of policies, styles, and software, they also work within the context of the organization’s information technology hardware, network, and computer standards. This brings the designer into contact with the world of another high-level administrator, the Chief Information Officer (CIO), who is the leader of the information technology services for an organization. More will be said about this person later, since the CIO and the Chief Learning Officer (CLO) have a close relationship. The policies of the IT department deal with: • • • •

The use of computer devices What kinds of devices can be connected into the organization’s computing networks Which physical spaces offer network connections Where mobile connections can be used.

All of these issues are important to an instructional designer because they represent the hardware, software, and physical environment for instructional delivery. As is the case in software planning, a fortunate designer will be included in the ongoing planning of software services, but at a minimum the designer will live and operate within the world defined by IT policies. Media-logic plans specify the use of devices and connections, meaning connections to the organization’s Internet and Intranet services. Most organizations have learned from (sometimes hard) experience that hardware and connectivity standards are essential to control of the computing

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environment. The important issues become capacity, supportability, security, scalability, usability, availability, accessibility, stability, interoperability, and affordability. Hall (2002) refers to these as the “-ities” of IT support. The implication for the designer is that designs have to live comfortably within the IT framework. This may include using IT-approved development hardware and software, even when the designer would have chosen differently. Application Exercise Interview a member of your organization’s IT department. • Ask if there is an organizational standard that takes into account the “-ities” that Hall mentions. • Which of these planning factors has the highest priority in your organization? Ask the IT interviewee to rank-order them. • What are the implications of placing one of them in a position of more importance than the others? Sub-layer: The Organizational Training Environment The LMS is a media-logic control tool that is fast becoming the administrative tool of choice in the hands of new top-level administrators—Chief Learning Officers (CLOs), and Chief Information Officers (CIOs). Together, these leaders shape the training and educational environment of the organization. There is increased organizational support for instruction and training due to a growing need to know the state of organizational competence. Credentialing and currency of workers has become an important competitive issue in technical areas, and advanced training in leadership marketing, sales, management, and administration have become similarly important in executive training. For this reason there has been an explosion in the number of corporate universities, institutes, and academies, which usually fall under the responsibility of an organization’s CLO (Allen, 2007; Gerbman, 2000; Meister, 1998; Prince and Stewart, 2002). According to Hearn (2001): “In 1993, corporate universities existed in only 400 companies. In 2001, this number jumped to 2,000” (n.p.), with projected estimates of 3,500 by 2010. A corporate university, institute, or academy serves several synergistic purposes that include strategic training, quality and cost control, culture building, and the promotion of creative and innovative problem solving. Corporate universities (including university organizations in the military and government) contribute as much to the social and cultural aspects of an organization as they do technical competence. The General Electric campus at Crotonville mixes recreational activities with seminars by world-class speakers in order to project a balanced image of its high-level leadership to what it expects will become its future leadership cadre. GE was the first to create a corporate university. The Defense Language Institute at Monterrey concentrates on training of foreign languages. It is just one of many examples of a university-type organization sponsored by the military. The corporate “university” organizations, some of which are at a central location, but many of which are virtual, often serve larger volumes of students than traditional universities. Many do so at a distance. They depend heavily on LMSs because of the volume of students and the number of courses and certifications they must track. Corporate universities bring the worlds of the CLO and the CIO much closer together. Media-logic decisions have an important economic significance for both the CLO and the CIO. Consider, for example, the general rule of thumb that only half of the lifetime cost of a software application (such as computerized instructional event) is spent on its original development. The

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other half is spent over the lifetime of the product maintaining, revising, updating, and upgrading (versioning) it. The software development and maintenance costs of minor design decisions can be amplified across the entire body of an organization’s computerized training. Likewise, the directions given to live instructors, also described in this chapter as media-logic decisions, can impact the efficiency of the learning experiences, as well as their effect. Organizational Standards for Media-Logic Media-logic decisions at the level of the individual instructional event have been shown in previous sections to have impact on several other sub-layers of media-logic. In the organizational standards sub-layer the administrations of the CLO and the CIO are influenced by media-logic decisions. A well-run CLO organization will create a general media-logic plan and media-logic standards in cooperation with the CIO. Instructional designers should ideally have important influence in the shaping of this plan. The plan will define the problem-solving space for the designer with respect to the usage of computer services. It has a major impact on the manner in which blending of instructional modes can occur. Importantly, it defines how home-made (within the organization) and other-sourced instructional products can be used together in a professional manner. At this level of concern, standards are constantly evolving that influence the work of the instructional designer, down to the level of the media-logic of the individual event. The designer should be aware of current standards for technology-based instruction. The main purpose for such standards comes under the heading of “interoperability”: that is, the philosophy and economic proposition that products developed by one source should be usable by others. The implications of the standards movements are that minimum hardware configurations must be met to “play” software products, that software must conform to specific “packaging” guidelines that allow it to “play” on the hardware, and that the structuring of the instruction must meet certain minimum standards to qualify for sharing. The standards are in a constant state of change. As this writing takes place, a change is being considered by two of the major standards bodies that would merge their interests. By the time this is published, it is certain that further major changes will have taken place, so rather than providing specific citations for the standards as they now exist, a better way of describing them will be to mention the standards by name and try to describe them in a brief context of how they emerged. They can be researched by the reader by name, either as current projects, or (if sufficient changes take place in the near future) as historic topics. (Note: As readers look at the documents for these standards, it is important to keep in mind that the term “content” as it is used in the standards carries a much different meaning from how the term is used in this book. Here content refers to an abstract representation of what is/can be known and no more. In the standards, content refers to bundled combinations of subject-matter, strategy, representations, and media-logic packaged together in a form that is capable of passing data—for management purposes—across an interface to an LMS.) IMS The IMS project, incorporated in 1999 as IMS Global Learning Consortium, originated as an attempt to standardize instructional resources so that they could be brokered across organizational boundaries. The origins of the project were in the early 1990s, and discussions among the participants in the project went on for a number of years before incorporation. The participants in the project have changed considerably over time, but the major stable members of the project have been an international group of hardware, software, and publishing organizations with a considerable financial stake in the future of standards for online instruction. More recently universities and government organizations have joined the project, perhaps due to the increased interest in online

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instruction. LMS vendors also participate, because the heart of the IMS Global effort is a standard that allows a common LMS architecture to launch activities and manage data for any uniformly structured instructional product, regardless of its originator. AICC The Aviation Industry CBT Committee (AICC) was formed in 1988 as a standard-setting organization for the aviation industry. Because the aviation industry was an early adopter of computer-based training (the “CBT” in the committee’s name), it encountered the problems caused by non-standardization of hardware, software, and what then was referred to as courseware much earlier than other industries. As in the case of the IMS project, the AICC committee began as a consortium of parties with common economic interests: sellers of CBT, computing hardware, LMS software, authoring systems, and government and commercial aviation training consumers. Adoption of the standard by aviation training designers and eventually others greatly encouraged the sharing of educational software among airlines flying the same planes. SCORM SCORM stands for Sharable Content Object Reference Model. Like IMS and AICC it is a standard-setting activity with origins in the government and military, another early adopter of computer-based training. The concept of SCORM originated in the mid-1990s. In 1997 the Advanced Distributed Learning (ADL) Initiative was established to provide a laboratory base for development and testing of evolving SCORM standards. In 1999 SCORM was formalized as a standard by an executive order by then-President Clinton. In 2006 the Department of Defense promulgated an instruction requiring the use of SCORM. Of all of the standards efforts, SCORM has placed the most emphasis on instructional strategies. Whereas the other standards are most interested in packaging products for sharing, SCORM has consistently shown the tendency to look inside the package in hopes of teasing apart what is contained there, which could eventually lead to more adaptive designs. The SCORM literature contains more descriptions of potential applications, many of which represent formidable design programming challenges where standard-setting is concerned. Of all of the standards initiatives, SCORM has the best prospect of resulting in adaptive forms of instruction. For example, it is the standard that has made the most explicit statements on the separation of subject-matter from strategic logic. It is the standard most closely aligned with the theory of design layers. However, there is still a great deal of theoretical and practical work to be done. The Value of Standards The momentum behind the standards projects is strong enough that eventually organizations that want to use brokered or shared instructional products will have to be properly equipped with standard-compliant hardware, software, and connectivity to do so. Likewise, any organization that wants to sell or widely share its products will have to be in conformance with a standard. The formation of standards has been an important trend in every stage of the computer world’s growth. A few people can still recall the time when every new computer design was a complete package of hardware and software that came “bundled”, meaning that the purchaser was required to buy the whole package. The revolution in computer designs precipitated by Fred Brooks and the IBM 360 computer family design (Baldwin and Clark, 2000) ended product bundling, and it opened the door to modularization, which means that the purchaser might buy the central processor from IBM, but could buy the memory unit and printers from another vendor. The principle of modularization is what made the microcomputer you used to have on your desk possible in practical terms, and in economic ones as well. It is what made it so that you could replace a hard drive from one manufacturer

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with another from another manufacturer. The drives may have been different on the inside, but they behaved identically at the interface where they plugged into the computer. For different manufacturers to build plug-compatible and replaceable units, computer standards had to be worked out by committees just like the IMS, AICC, and SCORM standards committees. Similar standards had to be worked out for operating systems, programming languages, application software. You can use the Internet today because basic standards were worked out at great effort that programmers follow with respect to the kinds of protocols that constitute the Internet’s software. There are browser standards, keyboard code standards, and electrical plug standards, all of which had to be worked out by industry committees, along with standards for every modular part of your computer: the hard drive, the memory chips, and the mouse. The standards for these things make them interoperable. The same standards phenomenon is well underway within the educational computing community, with three main standards evolving in parallel. Who knows what the future will hold? Standards processes require visionary leadership to bring them to a successful end. In the case of instructional computing, there is a great deal at stake, and the people making the decisions must approach the instructional architecture issue with a vision in mind. The instructional design world is holding its breath. The whole media-logic question is at stake. Application Exercise The scope of the training environment used to be the organizational training department. Today that scope extends even beyond organizational boundaries into the realm of universal standards. Consider the impact of this on your practice as an instructional designer. • What benefits might you obtain from this wider agreement on how to design instruction? • What downside might there be for you if you have to design within a standard that you do not fully agree with? • Consider how the balance between standardization and innovation will affect you. Sub-layer: The Organizational Working Environment The final sub-layer of media-logic design involves the instructional designer in the context where learning really occurs—in use. The media-logic plan describes how instruction will be executed in places that it can be most effective. In earlier days, that used to be the training classroom, equipped with whiteboards, overhead projectors, and an instructor. In many cases this is still a very effective place for some of the instruction. However, before that training happened at the workplace. The wheel has turned full cycle, and today new approaches to instruction are bringing instruction back into the daily working environment, where knowledge is applied. This means that the hard line that at one time was drawn between the training place and the workplace is blurring, and in some places disappearing. By implication, it also means that designers must plan for execution of the instruction in everyday performance settings. This includes instruction and assessment (which may be mixed together in a way where it is not apparent that assessment is occurring). What are the additional media-logic plans the designer must make for workplace instruction? Clearly this challenge brings the designer into collaboration with managers, because the workplace is their territory, and interruptions to the workflow and productivity are out of the question. However, there are many ways instruction can be executed by integrating it with ongoing work in a nondisruptive or minimally disruptive way: techniques that accomplish this include Web-based desktop

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instruction, expert task mentoring, modeling, peer mentoring, workplace study groups and learning communities, learn-then-teach methods, coaching, apprenticing, and job aiding. The media-logic plan for these methods includes supplying directions to those in the workplace who will carry it out. This includes workers and their supervisors. The problem posed by the need to direct workplace instructional activities, however, is that it is more than a one-time thing. It is a matter of helping it to happen all of the time, automatically. It has to do with the expectations, attitudes, and habits of the people in the organization: it has to do with the corporate training culture and whether the organization is a learning organization. The Learning Organization With the publication of The Fifth Discipline: The Art and Practice of the Learning Organization (Senge, 1990) organizations began to change the way they looked at themselves. The view of businesses, the military, government organizations, schools, and universities as systems of learning as well as performance is seen now as a source of competitive advantage. Senge promotes the view that organizations are dynamic systems, or learning organizations: “Organizations where people continually expand their capacity to create results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning how to learn together” (p. 3, emphasis added). Such an environment changes the place and value of the instructional designer within the organization, moving the designer decidedly more toward its center. It is important to see this shift as part of a long-term trend rather than as a fad. The concept of a learning organization can be seen from two perspectives: (1) an organization that learns by accumulating the knowledge possessed by its members, and (2) an organization that has a culture of learning and teaching. The perspective of interest here is the second one, because it precedes the first. One way to look at it is that if the members of an organization are not oriented toward learning individually, and being aware that this is the expectation, then it is unlikely that the organization as a whole will be able to enlist its members to harvest its collective learning, because learning and sharing will not be a part of the organization’s culture (Kim, 1993). An instructional designer is one of the major but unacknowledged influencers of organizational learning culture. Designed instructional experiences communicate much more than subject-matter and information. As well, they convey messages about what the organization thinks of them and what the organization expects of them. This includes messages from the types of learning activities chosen. Activities that involve learners in cooperative and collaborative learning with each other are more likely to send a message that sharing learning and seeking learning is part of how the organization works. Administrators may see the attitude toward learning as something that can be imposed from the top down, and to some extent that is probably correct, but the willingness to take part in knowledge creation and sharing is an individual decision, one that becomes more likely if everyone is doing it and if the habit of doing it is a part of how learning happens in the first place. Application Exercise The workplace is becoming the place where learning is taking place. • How does this benefit the organization and its competitive position? • How does this benefit the learner? • If you were consulting with an organization about how much training to bring into the workplace, how would you advise them?

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• What kinds of activities would you suggest they bring into the workplace? • What kinds of activities would you recommend they keep separate? Conclusion The media-logic layer is significant in several ways: • Its decisions have a major impact on the cost of creating and delivering instruction. • Its decisions are related to one of the most active and changeable areas of instructional design: media systems—devices, software, and connectivity. • It is the place where conceptual designs for instruction become physical designs and conceptual modularity becomes physical modularity. • It is concerned with the export of instruction across a range of delivery media. • It is where imagination in design meets feasibility. • It is the point where concepts of instructor-led instruction blend with concepts of technology-based instruction. • It is the point where the full surface of the learner’s experience—including individual instructional events and what happens between events (management functions)—becomes a single integrated plan. • It is the point where the range of skills required to produce the instruction becomes accountable. • It is the place where the tools required to create the instruction are decided. • It is the place where the role of the instructional designer in the context of the larger organization becomes clear. • It is the point where the impact of the designer on organizational culture becomes most apparent. • It is the point where learning enters the workplace and the false boundaries of demarcating training and performing are most likely to become blurred. The media-logic layer has long been underrated in the literature of instructional design. The underlying coherence of its many separate topics is seldom described in the literature. Yet being aware of its breadth of influence helps the designer bring design into the center of the organization and helps the prepared designer to make a clear expression of value-added.

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III

The Designer’s Value-Added

The chapters in this section outline possible future uses of layers and their application in the formation of brokered instructional products. Chapter 15: Layers and Modularity This chapter describes the concept of modularity: one implication of the concept of layers. Modularity is a general design theory that has revolutionized the design of commercial products. Mass customization is, in turn, implied by modularity. It provides a plan for organizing the engines of instruction as well as a concept of how educational industries can be made more responsive to the individual user. Modularity raises the prospect of reaching the goals of adaptivity, generativity, and scalability of instructional experiences. Chapter 16: Adding Value to the Organization This chapter addresses how a designer participates in the decision-making fabric of the organization, attempting to solve organizational problems and demonstrating the value-added of instructional design. Challenges to designers posed by a rapidly changing design landscape are identified. Designers are advised that staying relevant in this rapidly changing environment will require constant study of innovations in technology, instruction, design, and learning.

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15

Layers and Modularity

Man is, after all, a very finite being in capabilities and powers of doing actual work, but when it comes to planning, one mind can in a few hours think out enough work to keep a thousand men employed for years. —(Washington Roebling, designer of the Brooklyn Bridge) In the early 1960s a revolution in computer design took place that changed the basic design principles and economics of the computer industry. That revolution has made possible the affordable, configurable personal computers that we use today. Baldwin and Clark (2000) describe this revolution in a book titled Design Rules: The Power of Modularity. They describe how the design modularity of the 360 family of computers sent a shock wave through the business plan of the digital giant IBM and eventually led to the establishment of an entirely new economic structure in the computer industry. Companies with new names appeared overnight, and even greater revenue from the sale of computers and computer components began to flow through new channels into new hands. The cause of this revolution was an idea about how computer systems could be designed modularly—made from “black box” components which interacted with each other but which were replaceable, changeable, upgradable, and mostly independent of the larger computer system within which they functioned. Today the modules of a desktop computer represent black boxes that can be swapped for other more powerful modules with only minor setting changes to an interface between the module and the computer. If a hard drive is too small, the old one can be popped out and swapped for a larger one. Want to extend your computer’s memory? Slip in an additional memory module, so long as the interface is configured properly. The message of this economic wake-up call was that the different components of a computer no longer had to be designed or manufactured by the same company. To join the new components market all that a company had to do was make black boxes (boards, drives, printers, etc.) that acted in a standard way at the interface to the rest of the system. The designer could make a black box that did different and better things, just so long as it communicated properly at the interface. Black box components could be moved among different brands of computer. This meant that IBM, which had previously held a firm grasp on the market for all of its computer components, lost control of that market, and other manufacturers began to make and market IBM-compatible parts. In your daily activities you experience the effects of modularity in many places: for example, every time you plug in an electrical appliance. You know that the electrical power that will flow 363

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through the standard plug and the standard wires to your standard appliance will be a standard voltage, regardless of who made the electricity, the plug, the wire, or the appliance. The only time the modularity of your modern technological world becomes apparent is when it breaks down. My daughter Rebecca plugged her American-standard curling iron into a European-standard outlet by mistake and didn’t realize the error until a super-heated appliance singed off her bangs—a teenage trauma caused by different modularization standards that for some reason used the same kind of interface (the plug). The modular design of computers was an enormously important change for IBM and other computer companies, who were forced to revise some of their business plans. In this chapter the abstract ideas of modularity that fuelled the computer design revolution are applied to the practices of the instructional designer. Modularity is of interest to instructional designers because it illustrates a kind of change that has already overtaken other industries and produced new value. It defines a trend that is certain to engulf the instructional design industry as well, changing how it creates instructional products, services, and experiences in the future. The first signs of this change are already appearing, and your challenge as a designer is to understand its importance, because Baldwin and Clark describe the results as being value-added by the designer and to the designer’s organization: When implemented faithfully, modularity greatly reduces the costs of experimenting with new designs. With modularity enforced, it is possible to change pieces of a system without redoing the whole. Designs become flexible and capable of evolving at the module level. This in turn creates new options for designers, and corresponding opportunities for innovation and competition in the realm of module designs. —(p. 6) Today, the concept of modular design is being applied in many areas: industrial manufacturing, clothing, building design, office space, and fast foods. When you order a customized computer or custom-fit clothing or a pair of custom-designed running shoes online, you are dealing with a company that has adopted the modular design principle into its product and is using it to achieve mass customization, the delivery of customized products and services instead of mass produced ones. When you tune in to an Internet radio service that allows you to create your own radio station and pick which music you want to hear, or when you ask your online movie service to recommend a movie you might like based on your past likes and dislikes, you are taking part in mass customization, and the company that you are dealing with has formed itself around a modular product concept and mass customization. Baldwin and Clark say of these firms: Think of development in the past few years in financial services, energy, autos, communications, food, retailing, computers, and entertainment. In these and many other industries, changes in products and technologies have brought with them new kinds of firms, new forms of organization, new ways of structuring work, new contracts and relationships, new ways of bringing buyers and sellers together, and new ways of creating and using market information. These changes in products, technologies, firms, and markets are not a passing phenomenon, like froth on the waves, caused by shifting economic winds. These are fundamental changes driven by powerful forces deep in the economic system, forces which moreover have been at work for many years. —(p. 1)

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Where does the principle driving this change reside? At the level of engineering design, computers proved amenable to an approach we call “modularity in design”. Under this approach, different parts of the computer could be designed by separate, specialized groups working independently of one another. The “modules” could then be connected and (in theory at least) would function seamlessly as long as they conformed to a predetermined set of design rules. —(p. 6, emphasis in the original) What Baldwin and Clark describe is the impact of design architecture on the economics of the designer’s industry. The application of modularity within instructional design holds the same revolutionary promise that it held for the computer industry: if instruction is conversation, then that implies customization to the individual user, which requires a modular view of the product or service, which means that the product or service has to be viewed in terms of layers. Application Exercise Consider the modularity used to support rapid customization of the product in fast-food restaurants. • How many basic ingredients are combined at the time of placing your order to produce your meal? • How many ingredients get used and reused in different combinations? Consider now your favorite sit-down restaurant. • How many basic ingredients do they have to keep on hand to produce your order? • Does this differ between types of restaurant? What about the steak house? What about the Chinese restaurant?  

How can Modularity be Applied to Instruction? The term “module” has a long history in the instructional design field. However, the term has been used in so many different ways in the past that it has no definite meaning. The term “module” is most often used to refer to some amount of instruction administered using some media form. It can refer to a single short instructional session, a series of sessions, or an extended workshop. It is a catch-all term. This common non-definition does not seem to refer to a length of time as much as to a coherent topic and the information and activities associated with teaching it. The instruction need not be mediated, but there was a period during the 1960s and 1970s during which self-instructional, media-based “modules” were considered an efficient, economical teaching solution. “Modules” were manufactured locally by media technicians and by businesses to reduce personnel costs, reduce instructional time, and deliver a learning experience of consistent quality. Modules provided teaching for students in situations where teaching expertise was not locally available, to make flex scheduling possible, and to provide instruction when there was insufficient demand to support delivery to a group. None of the foregoing is relevant to the discussion of modules in this chapter. The last few paragraphs describe what this chapter is not about. From this point on, the discussion of modules will refer to an abstract architectural feature of designs. Before we define “module” in a more technical way, the following examples of the evolution of a modular approach to instructional design will show how modules and layers can emerge naturally.

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Project #1: The Flight Plan Critic Three successive design projects illustrate the evolution of a modular design architecture. Each project represents a step toward increasing modularity, demonstrating that modularity need not be an all-or-none decision. One point that is illustrated in these examples is a relationship between modularity and product cost. Another point is that the evolution took place naturally, as a by-product of trying to find a more straightforward approach to implementing a more complex type of design. The first project was an exploratory effort whose aim was to create an instructional simulation to assess the ability of student pilots to create cross-country flight plans. A cross-country flight involves significant planning. For new pilots this planning is harder and more painstaking than it becomes later in their careers. The point of the simulation was to assess at an early stage how well the pilots could create the details of a safe flight plan. Following a problem in flight planning, the strategy plan included giving the learner tailored feedback based on the steps they had taken and the values they had set into their plan. The simulation design called for displaying a map to learners and then allowing them to choose any set of waypoints shown on the map. This meant that there was not just a single correct answer to the problem, but many possible correct answers, and the feedback system had to be able to tell acceptable answers from incorrect ones. At the time of the project, the typical style of implementing computer-based instructional products was to create single monolithic programs; the authoring tools used promoted this. At the time, authoring tools saw themselves in terms of one-program-does-it-all, and most designers accepted this standard because of a lack of existing alternatives. There was no capability in the authoring tool of choice for building an expert system program for the feedback-providing critic. Consequently, the function of administering the simulation was performed in a program separate from the function of providing feedback. This separated the product into two physical (software) modules, a fact that the learner could not detect because the comments of the critic were represented through the authoring system’s user interface. The interaction plan was for the learners to be given a computerized aviation map of the problem area and have them mark the flight path they wanted to follow in terms of waypoints to be reached on the way to the destination. Following waypoint marking, a number of computations had to be completed by the learner relative to each waypoint, specifying expected fuel usage and time of arrival at the point, proper course, and proper heading. Each calculation had to take into account multiple variables of wind direction, course direction, flight altitude, passenger weight, seat location of the passengers, and so forth. Every calculated value for each leg of the trip had to be entered into a table on the computer screen, which was also where the learner obtained data from manuals for making calculations. Each entry in the table represented a partial answer that potentially created fault diagnostic data, and each had to be judged for correctness. Since the learner was the one who chose waypoints in the flight path, there was no way to determine in advance all of the possible flight paths that could be chosen. A program was created, therefore, capable of solving flight plan problems, given the waypoints the learner had chosen and external data such as wind direction and weather. Online manuals supplied the learner with this data specific to the problem. A separate expert system was created that could examine the learner’s answer file and give an expert critique. The process of creating the expert critic forced the designers to be explicit about: (1) the allowable steps and calculations in flight planning, and (2) the rules for delivering a post-problem critique. This amounted to two separate knowledge bases. Until the design began to evolve the designers had not thought of it in these terms. Creating the problem-solving interface was one design problem; creating the problem solver was a second design problem; coming up with the rules for administering feedback was yet a third design problem. This last problem required that the designers consider details of feedback strategy that they had never realized existed.

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Creating the expert critic was a new kind of problem: the designers had never paid so much attention to the design of a major instructional function separate from a function they had previously considered a bundled function of the instructional strategy. The feedback critic presented a new kind of problem, because there were several dimensions to the structure of the critic’s actions that had to be dealt with in great detail, and contingency plans had to be anticipated to deal with unexpected elements of answers. For example: • When the learner’s response file was opened, it could contain many extra steps which were not critical to the solution but which did not represent errors. When solving a problem a learner might end up in a blind alley, back up, and try a new direction. In the learners’ files there was found a rich source of data the designers had never considered would complicate the parsing and judging of the learner’s performance. Which paths were acceptable? Which ones were not necessary but not incorrect? Which ones were incorrect? Which ones deserved mention? Which ones should be ignored? • The designers discovered that some answers had to be judged correct or incorrect depending on other answers already given. That is, earlier responses supported or invalidated later responses. More importantly, the designers discovered that they had to check not only whether an answer was acceptable, but whether it was obtained in the correct order. It was hypothetically possible to enter a correct (cribbed) answer and then back-fill the supporting data. • The designers discovered that the process of judging an answer acceptable or unacceptable was one thing and that giving feedback messages about different parts of the answer was another. First, the feedback messages had to take into account the feelings of the learner in response to correction. There had to be positive results noticed along with the correction. Second, the messages that were given had to be given in a particular order to make sense to the learner. The critic could not just produce a random commentary of correct and incorrect answers. There had to be a plan for constructing the feedback message. It became apparent that there is a unique structure in the order of things noticed by a feedback message. It became apparent to the designers how reliant they had been on traditional lesson patterns that failed to look beyond the traditions to see more instructor-like, more individualized, and less mechanical solutions. The designers had to deal with the message layer at a new level of detail because messages had to be generated by the critic, as it was not possible to anticipate in advance and prestore in memory all of the needed messages. This led to the creation of a typology of the feedback message elements that laid bare recurring message patterns and their structures. In the final product the handoff between the two modules worked very well. The flight plan evaluator was successful, which was good, because by the time it was finished, it turned out to be much more expensive than expected. Project #2: The Maintenance Evaluator The flight plan evaluator (Project #1) was sufficiently successful to attract a second client, who was impressed by the ability to give tailored feedback that was sensitive to the responses of an individual. The price of the first project was a problem, however. The new client was willing to pay about onetenth of the price of the original project for a new evaluator, which, like the original, would supply just one problem to the learner but give tailored feedback to unpredictable response sequences. The performance assessed by the second evaluator was the ability to find and replace faulty parts in an aircraft’s electronics. This simulation was to have the same architecture as the first—that is, separate physical software modules for problem administration and for feedback. A learner had to

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identify a problem in the electronic system of a commercial aircraft and do it within a very realistic work-like setting. This meant that the problem data had to be provided in a realistic visual form, and the learner had to know where to go and what to ask for in order to obtain it. In designing this simulation there were new challenges that led to additional insights on modularization and layers. This new design problem brought up the need for a sophisticated set of controls. The maintenance problem took place within a realistic (graphical) workplace representation. Learners navigated a three-dimensional space that included a maintenance office at the airport, the apron where the aircraft was located, and several interior spaces within the aircraft (pilot cabin, passenger cabin, electronics bay). Moreover, within two of the spaces, they had to operate aircraft electronic controls to test for the fault, and the system’s response had to correctly represent current system state. In several spaces (pilot cabin, telephone, parts room), they had to meet and converse with static avatars, obtaining information and interacting in the parts–tools room to check out spare parts and tools. The designers realized that creating a system of navigational controls for all of these functions was essentially creating a set of unique problem-space languages through which the learner could perform complex actions. For spatial navigation they devised a language for manipulating an object by turning it over, as it were, in one’s hands. This included the ability to obtain a cross-sectional view of the object. In this case, the object was the plane, and the cross-sectional view allowed the learner to gain admittance to any of the aircraft’s compartments. The designers had to devise a second language that allowed the learner to “pick things up” and perform different actions on them (electrical grounding, replacing an electronic unit, speaking to a person, etc.). This language included obtaining information from manuals, computer reports, databases, and a microfilm reader. Finally, the designers had to create a means of asking specific questions of different human characters (the avatars) in the simulation: the pilot and the cabin attendant. Most of these controls required a nonverbal interaction through learner actions at a touch panel. The net effect of this emphasis on realism was that the designers became aware of the conversational nature of problem-solving interactions in the real world. The languages that were invented constituted the substance of a control layer. The most serious problem the designers had encountered in the design of the first simulation was cost. In the post-mortem on the project they discovered that the cost was generated mainly by the primitive tools available for working with, and this turned their attention to yet another major concern of designers: the media-logic layer. How could it be, they asked, that a few missing capabilities in these tools were making it so expensive to build the simulation? On close examination, they discovered that it was not the tool in general, but two or three missing capabilities. For example, in the version of the tool they were using it was not possible to place graphical objects in a library and draw them out during instruction at the moment of need. Every graphic was “hard-wired” into the logical unit that placed it onto the display. This made it necessary to populate individual logic units (which in those days were called “frames”) with their graphics and store them in memory. This created enormous computer files. The software problems from Project #1 were solved in time for Project #2. One effect of this problem, however, was to bring into focus another major layer of the design—the media-logic layer—and its implications for the cost, skill level, and level of effort required to complete a project. Moreover, the physical and functional separation of the expert critic from the main body of the simulation logic reinforced the concept that modularity has different senses—physical and functional—and that therefore there are practical implications when it comes to actual product form. At the end of Project #2 there was another happy customer, and costs had been cut by 90 percent. However, the problem with the two simulations that had been built was that they were one-problem, demonstration-only products. Each one was only capable of executing a single problem. In order to

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be usable in everyday training settings, the evaluators had to be able to support multiple problems: it was not an option to simply “hard-wire” problems into separate software packages. Multiple problems had to be delivered by a single problem (and feedback) “engine”. Project #3: Responding to In-flight Emergencies The designers confronted the challenge of creating multiple problems building a simulation engine. Instructional events—especially those that are computer-based—have a repeating cycle as their central feature. The important simulation design task for the designers was to determine the common steps in a cycle capable of administering multiple problems. The designers created engines for both the simulation environment and the expert critic and then created data structures to feed them. In this case, since the multiple problems could take place in different locations, graphical resources also had to be stored in a database. The simulation and critic engines represented new types of module: ones pertaining to the representation and media-logic layers. Once the engines for the simulation and critic were designed, it was a matter of preparing the three kinds of data (problem, media resource, and critic rules) for each problem. The designers accomplished the design and development of seven problems for half the budget of Project #2, achieving a per-problem cost compared to Project #1 that was reduced by 99 percent. The ability to do this on time and under budget can be attributed to the modularization lessons learned from the sequence of problems and the fact that some software carried over from Project #2 to Project #3, with modifications. Summarizing: Lessons Learned about Modularity Much of the cost reduction achieved on Project #3 was the result of being more aware of modularity in designing. Project #1 separated the simulation execution functions from the feedback functions within the same large program, showing that an instructional function (feedback) could be made functionally independent. Once the project was completed, however, the designers considered whether the instructional functions could have been integrated into the simulation in a tutorial mode and whether the instructional function could still be treated as a functionally separate module. The answer to both questions appeared to be “yes”, for these reasons: • Maintainability—With instructional features isolated in one area of the program, the designers believed maintenance, change, and update would be easier during development and over the product’s lifetime. • Reusability—The designers believed that if instructional functions could be located together within the program, they could be lifted out and reused with modifications in other programs. Today’s programming styles and tools have changed considerably, so there is no question that both of these goals could be accomplished. A second modularization lesson learned from Project #1 was the emerging importance of data management layer functions. As learners interacted with the simulation, each action was accumulated into a historical file for later analysis by the expert critic. This file included navigation moves, information accesses, choices expressed, and values entered into the detailed flight plan. The enormous amount of information generated by every instructional event (information that is not usually collected and analyzed), was useful when applied by the critic to give diagnostic feedback. In future design practice, the design of the data management layer will become much more important because every instructional conversation generates this volume of data, even the most simple ones. Project #2 demonstrated that modularity had at least two aspects: functional modularity, in which different functions are designed as separate modules, even if they reside in the same physical

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program, and physical modularity, in which different functions are executed by physically separate modules. Finally, Project #3 taught the designers that if the interfaces between modules were standardized within a line of products, modules can be made substitutable. This was demonstrated in the ability to plug different problems and resources into the same software engines for execution. The designers found themselves speculating about how many of the functions of instructional designs could be made independent and replaceable in this way. This insight is echoed in the design of complex simulation systems as described by Rickel and Johnson (2000): “STEVE [a complex simulation system] is unique in having domain-independent capabilities to support task-oriented dialogues situated in three-dimensional worlds” (p. 96). Rickel and Johnson describe the key to making the STEVE system domain independent: Steve is fully implemented and integrated with other software components on which he relies (i.e., visual interface software, a simulator, and commercial speech synthesis and recognition products) . . . Steve is not limited to this [gas turbine engines] domain, he can provide instruction in a new domain given only the appropriate declarative domain knowledge. —(p. 96) The series of three projects described above provided a contrast with a project completed prior to any of them. In that project, which was also a simulation project, the design concepts had not matured very far, and the designers executed the project using a frame-by-frame approach that was common at the time. The media-logic concepts at the time were dominated by the idea of advancing from static frame to static frame. Moreover, the graphic tools used had no animation capability, so needles advancing or retreating across the face of a dial had to be individually hand-created and placed on separate, manually sequenced frames. It took sometimes nearly a hundred such frames (because of the nature of the simulation) to show a needle rising from zero to the maximum value. This points up the value of designing for generativity—the ability to generate some part of the instruction (e.g., representation, message, strategy, content, etc.) at the time it is needed.

Application Exercise The STEVE simulation (www.isi.edu/isd/VET/steve-demo.html) creates instruction that is much more sophisticated than the average technology-based tutorial. A learner can swap modes from observer to performer in an instant, and the learning companion observes and corrects mistakes. But if you were to open one of the computer files used to run the STEVE simulation, you would see nothing that looked like the instruction. That is because what happens during STEVE instruction is generated at the time of instruction, including the visuals, the audible spoken messages, the sequence of actions, and even the gestures Steve makes, such as looking at the learners when he is addressing them. • What does this tell you about the number of strategic dimensions that the designer had to take into account during design? • Is it practical to be so analytic in designs? • What possible advantage can there be in this level of analysis and designing? • How much of STEVE would you estimate is reusable? • What would reusability do to the cost of the next STEVE product? And the next? And the next?

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What is a Module? Without the aid of theory these projects evolved toward modularization because of the value of modularizing different product functions. Designers, from a wide variety of design and manufacturing fields, were during the same time period realizing the benefits of modularization in their products as a natural by-product of the goals of reducing costs, increasing functionality, and increasing reusability. The growing complexity of products and the marketplace was forcing their organizations to design strategically. Over several decades, formal technological theories of modular product organization have emerged. Today there exists a constellation of terms to describe modularization and its application, and the reach of product modularization has been sufficient to revolutionize some parts of industry (for example, the computer industry as described above). The reach of modularity has extended well beyond what may in the past have been considered the traditional bounds of product design. Today organizations and businesses themselves are being seen as things to be designed (Martin, 2009), and modular design has a new dimension of meaning that includes many types of artifact beyond simply products, as Krippendorff (2000) describes. Today modularity and its related concepts of mass customization, delivery engines, and recommender systems are leading to a reorganization of businesses, their manufacturing, their marketing, their distribution, their customer service, and their management around the modularity plans of their products. In some industries this will become a competitive edge that determines which organizations will remain viable. These ideas are treated next in this chapter. Before that, however, it is important to clarify the concept of module in the way it is used generally and the relationship of modules to design layers. The Module What is a module? A module is a set of product or service functions or features that are grouped together and treated as a unitary element during design for the purpose of controlling costs, managing manufacture, and simplifying maintenance—giving longer service life to the artifact. A module is a functional decomposition of a product or service in a way that gives strategic advantage to the product or service. The origins of modular thinking may be traceable to the World War II period and the need to maintain electronic systems during a period of rapidly escalating complexity. There was then not enough time to find the single resistor that was burned out and replace it. The average electronic system (think of your computer) contains enormous number of individual components. A breakdown in a non-modularized system becomes a costly operational nightmare. The way to avoid the nightmares was discovered early: bundle individual components into functional units that can be replaced easily. The value of keeping all of the components related to a certain function together and isolated from the others is that when something breaks you just lose the one function without losing the whole system. In the early days of modular innovation, the replaceable modules were called line replaceable units, meaning that they allowed repairs to be made by maintainers in the field with minimal tools and equipment and lower-than-engineer levels of expertise. No soldering guns, just replaceable metal boxes. Though the concept of a module may have originally been invented out of expedience to reduce the costs of maintaining breakable but critical subsystems, today modularity motivated by maintainability is a minimum industry standard. When you are sitting in a plane at the gate and the captain says “We just have one quick repair to make and we’ll be ready to go”, there is a good possibility that a maintenance person is pulling one non-functional module out of the plane right there, on the flight line (hence, the term “line replaceable unit”) and replacing it with a working module (and then testing it, of course). This is better than a technician with a solder gun and a set of schematics. Modular maintenance is used in aircraft, ships, weapons systems, air traffic control radars, all of the space

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shuttles, and the space station, not to mention your car. If your engine quits when you are driving, a mechanic may tell you that your ignition module needs replacement. Essentially, a replaceable functional module has become the heartbeat of a working automobile engine. How are modules defined? This is where the strategic element of modularity enters design. Besides making maintenance easier and cheaper, there are many reasons a designer might choose for isolating a set of functions or features into a semi-independent, replaceable module. Ericsson and Erixon (1999) propose a list of reasons for creating modules within a design: • Carryover—If some functional core of a design is likely going to carry over from version to version of the product, then it may be a candidate for becoming a module. Not every part of an auto design changes with each year’s new model. Some things carry over from year to year. • Technology evolution—If some function or feature of a design is likely to change more rapidly than others due to advances in technology, then it may need to be modularized to eliminate disruption of the whole design when the change occurs. The modularization of Internet protocols allows new media formats to be carried non-disruptively onto the Internet as they emerge. • Planned product changes—If the long-term strategic product plan calls for the evolution of the product in a certain direction, modularizing of the design can simplify the changes as they occur with different timing. • Different specification—When a product is used in different environments, it may require an adaptive interface function. For example, international electrical voltage standards vary. Some computer power supplies are equipped to detect and adjust to both standards because of their modular circuitry. • Styling—Parts of the product design which need to respond to changes in style or user preference over time can be modularized to facilitate the updating. This includes, for example, the theme applied to your e-mail software. • Common unit—Perhaps there are parts of the product design that you would like to be able to reuse or remix into other products. These are candidates for modularization. • Process and/or organization—It may be advantageous for production purposes to modularize things that must go through a common production process. According to Ericsson and Erixon, “Suitable work content, special process skills, and long lead-time processes are all organizational-related reasons for forming a module” (p. 24). • Separate testing—As products are assembled, sub-assemblies may need to be tested for functionality before they are added to the larger assembly. A module can represent an intermediate level of sub-assembly completion of the production that is testable. • Available from supplier—Some sub-assembly of the product may come ready-made from a stable supplier, or you may want to subcontract out a particular part of the production. Modularization is a no-brainer in this case. • Service and maintenance—You can see that the easy, low-cost, fast repair of products is a good reason for modularization. You can also see by now that maintenance is only one small part of the modularization argument. • Upgrading—Modularizing to extend the usefulness of your product for the customer through upgrades or added features that clip on makes sense. Modularization for this purpose can be seen as part of a larger product strategy. In the everyday context, add-ons that you have installed to your browser or e-mail software represent this strategic use of modularization. • Recycling—If there are parts of the product that can be recycled or which need to be disposed of in an environmentally safe way, then modularization may be important.

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Application Exercise Modular products are all around you. When you think about modular products, do not think about the modules as you think about the interfaces between modules. • In your car, what is the interface between the electrical system and the stereo? • If you decided to change stereos, what would be more of a constraint, hooking up the electricity or getting the stereo that fit into the space of the old stereo? • What does this say about the interface between the dashboard and the stereo? • In your computer, what is the interface between the central processor and the hard drive? How about an external memory such as a thumb drive? • What is the interface between the display on your laptop and the rest of the computer? • Which would be easier to swap out on your computer, the display or the hard drive? • What do all of these issues say about how hard it can be to modularize some functions in a design? Modularity and Layers The principle of modularity is a hedge against the destructive effects of change. Stewart Brand (1994) illustrates this by describing the effects of time on the different layers of a building. A building design is not a monolithic thing, according to Brand. It is a collection of individual layer designs integrated into a coherent whole design. Some layers, like the structural layer, age more slowly than less-permanent layers, such as the outer skin of the building, which can be replaced without disturbing the structure. This is exemplified in Figures 2.4 and 2.5 of Chapter 2 Modularization is one way a designer can incorporate layers into a design. Layer functions can be isolated into a replaceable module as exemplified in Project #3 above where different problem sets could be made pluggable into the same simulation engine. A modularization plan can also be based on any of the other purposes suggested by the list above: ease of change, anticipation of the future, reusability, and so on. This emphasizes the idea that modularization can be theory-driven or driven by practical considerations. Given the list above, you may be more aware of the extent to which modularity is already a part of the designed world around you. Look closely around your home: a modular phone system allows you to add extensions just by purchasing another handset; a modular computer allows you to add memory, improve video quality, and upgrade your audio; modular light bulbs fit into standardized sockets; a modularized phone allows you to add a memory chip; modularized power tools allow you to fit an electrical motor unit with a drill attachment or a sander attachment. Modularity has become common in every aspect of our lives, and you can expect it to become more so in the future. Everyone gains advantage from modularity: • The user Ease of use Lower purchase cost Convenience. • The designer Ease of construction Lower production cost Better team coordination Flexibility to innovation.   

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• The organization Stability of a product core More flexible product concept Ability to adapt readily to market changes Maintainability Supportability. • The community Reusability, recyclability Sustainability Greener design.     

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Modularity and Design Modularity is a principle of value-creation. The story of the design of the IBM computer at the beginning of this chapter is about modularity applied to a design to create new value. It holds some important lessons for instructional designers. The IBM project was an important event in the introduction of modular thinking into the computer business. Today the shock waves from that project are still being felt, and they have revolutionized how products and services are designed, manufactured, and distributed. In these areas of business, modularity has changed the nature and organization of the business itself. There are now a number of businesses and industries that are built around the concept of product modularization. Just why this is important to instructional designers is the next subject. The Modular Product If technology plays a role in improving education and training, it will not be because of the medium itself or any inherent power it possesses but because of how it is employed. The original vision at the introduction of every new instructional technology—the way the technology is sold to a general audience—has historically been its ability to reduce costs, increase access, and adapt instruction to the needs of the individual. When the computer was introduced as an instructional medium, leading researchers on this new medium claimed that adaptivity was its main benefit, probably because the cost and size of computers were prohibitive and the idea of shrinking both was not in anyone’s mind yet. Atkinson and Wilson (1969b), leaders in the emerging field of technology-based learning, gave concrete dimensions to this promise: “The ultimate computer-based instructional system is one in which the student could input free-form questions and statements which would be analyzed by the system in the sense that the system would then compose and display appropriate replies” (p. 8). Patrick Suppes (1969), a researcher and pioneer designer of computer-based instruction said: “At the third and deepest level of interaction there are dialogue systems aimed at permitting the student to conduct genuine dialogue with the computer” (p. 44, emphasis in the original). Somehow, despite the glowing vision, the main use of the new medium has been to mass deliver instruction to larger and more distant audiences for mostly economic motives. The quality of the instruction remains about the same: old educational practices are perpetuated. This is a mass production mindset. With the introduction of the computer there was special promise because of the high degree of interactivity, the computer’s ability to remember, and its ability to make decisions insofar as they were programmed into the computer. Stolurow (1969) realized that the power of this new medium was not inherent in itself. After naming some of the instructional interactions the computer made possible, he said:

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These elements of instruction are some of the units with which we must work to develop rules or concepts that permit the separate, or joint, manipulation of each event in ways that optimize a student’s performance. These rules also can be called organizing rules; they are the rules of an instructional grammar. Eventually we should develop generative grammars for instruction. —(p. 76) Stolurow and others at the time stressed the need to think more ambitiously and more analytically about instruction and to identify ways to customize instructional plans for the individual. In the nearly half-century since Stolurow’s counsel, however, no coherent approach for doing this has emerged. The purpose of this chapter is to examine the concept of mass customization that is gaining wide acceptance in many non-educational areas of business to see how it might apply to education and training uses. Mass customization, in which some aspects of a product can be tailored to the individual, stands in contrast with mass production, in which all product decisions have been made beforehand unchangeably—representing a one-size-fit-all philosophy. Particularly, this chapter examines how a designer might begin to implement some principles of mass customization, with the conviction that doing this will represent a major value-added for the instructional design profession, and that it will better align the interests and practices of the instructional designer with client and sponsoring organization goals. What is Mass Customization? Mass customization is a way of organizing product design, production, marketing, and support in a way that goes a step beyond mass production. It is based on the principle of personalizing one or more aspects of the product to the user while still achieving the benefits of relatively low cost and wide distribution. The alternatives to mass customization in a large market like education and training are either small-volume craft production by hand or mass production: the two prevailing paradigms used today by instructional designers. The Principle that Connects Mass Production to Mass Customization The ability to mass produce product has been attributed to the invention of the assembly line by Henry Ford, but this perception is wrong on two counts. First, the moving assembly line concept existed long before Henry Ford. Second, the assembly line was not the secret of the greatest increase in productivity in Ford’s shop, according to James Womack and his associates. In the book The Machine That Changed the World: The Story of Lean Production (1990), Womack and his coauthors reveal a little-recognized fact about the innovation that revolutionized auto manufacture that began at Ford, turning auto manufacture from a hand-made craft market into a mass market product: The key to mass production wasn’t—as many people then and now believe—the moving, or continuous, assembly line. Rather, it was the complete and consistent interchangeability of parts and the simplicity of attaching them to each other. These were the manufacturing innovations that made the assembly line possible. —(Womack et al., 1990, pp. 26–27, emphasis in the original) Prior to fully interchangeable parts, assembling a car in Henry Ford’s shop—in any auto shop— required the services of a fitter. A fitter is a person who makes things fit. Before the interchangeable parts problem was solved, in Henry Ford’s shop each part had to be filed to fit a specific car. It went something like this:

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Ford’s first efforts to assemble his cars, beginning in 1903, involved setting up assembly stands on which a whole car was built, often by one fitter. In 1908, on the eve of the introduction of the model T, a Ford assembler’s average task cycle—the amount of time he worked before repeating the same operations—totaled 514 minutes, or 8.56 hours. Each worker would assemble a large part of the car before moving on to the next. For example, a worker might put all the mechanical parts—wheels, springs, motor, transmission, generator—on the chassis, a set of activities that took a whole day to complete. The assembler/ fitters performed the same set of activities over and over at stationary assembly stands. They had to get the necessary parts, file them down so they would fit . . . then bolt them in place. —(p. 27) The need for fitting stemmed from the fact that parts were made using soft iron, because machine tools of the day—the tools used to shape the parts—were not sufficiently hard to work and shape hardened steel, which the car parts required for strength. Therefore, the soft iron parts had to be formed while soft and then heat-treated to harden them, but in the process they heat-warped slightly so that they didn’t fit on any car. Ford . . . benefited from recent advances in machine tools able to work on pre-hardened metals. The warping that occurred as machined parts were being hardened had been the bane of previous attempts to standardize parts. Once the warping problem was solved, Ford was able to develop innovative designs that reduced the number of parts needed and made these parts easy to attach. Taken together, interchangeability, simplicity, and ease of attachment gave Ford tremendous advantages over his competition. —(p. 27) Interchangeable parts revolutionized everything:  . . . around 1908, when Ford finally achieved perfect part interchangeability. He decided that the assembler would perform only a single task and move from vehicle to vehicle around the assembly hall. By August of 1913, just before the moving assembly line was introduced, the task cycle for the average Ford assembler had been reduced from 514 to 2.3 minutes. —(p. 28) Only then did the moving assembly line add more productivity in Ford’s shop, and it was not that much proportionally: Ford’s stroke of genius in the spring of 1913 at his new Highland Park plant in Detroit was the introduction of the moving assembly line, which brought the car past the stationary worker. This innovation cut cycle time from 2.3 minutes to 1.19 minutes; the difference lay in the time saved in the worker standing still rather than walking and in the faster workplace, which the moving line could enforce. —(pp. 27–28) The math tells the tale of which of the two innovations—the interchangeable part or the moving assembly line—made the bigger difference. It was the interchangeability of hardened parts that made it all possible. Prior to achieving interchangeability, once one part was filed to fit one car, it did not fit on another: everything was essentially hand-made. The difference lay in the ability to create a standard interface between hardened parts that would allow them to fit together the first time,

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without the need for filing. What Henry Ford achieved was modularity—a standard component definition for parts that could be assembled quickly (hence the ability to move the assembly line) into a functioning whole with other parts. Ford’s modularity innovation, coupled with the moving assembly line can be seen as one of the great symbolic events in the rise of mass production. Ironically, modularization is also the main principle underlying mass customization. Though Ford took glory in the mass-ness of mass production (“You can have the Model T in any color so long as it is black”), we can wonder now, with the benefit of hindsight, whether Ford stopped too soon, missing a much more powerful innovation that would have allowed him to deliver Model Ts in Candy Apple Red and Titanium Grey as well. This is facetious, of course. But we can ask the question: Why didn’t mass customization arise alongside mass production since both are grounded in the same principle—modularity? Application Exercise The assembly line has been given credit for Ford’s ability to churn out more cars, but it is clear from the numbers that the real advantage came from the interchangeable part. • What other industries might there be that depended more on the interchangeable part than on mass production as the source of prosperity and growth? • To what extent can the rapid growth of the cell phone industry be attributed to corporate standards that supply uniformity? • Are there still corporate barriers that make it difficult to use cell phones interchangeably? Do all phones work on all networks? Why?  

Organization for Mass Customization The answer is that as mass production principles became widely adopted it changed the organization of the companies that adopted it, and the new organization was centered on the assembly line. Engineering, production, marketing, and distribution—all of the functions of the organization—were organized around the idea of mass production of a single model or a handful of models that were assembled on a constantly moving line. All roads in the mass production organization, so to speak, led to the assembly line. This included the design department whose much-celebrated geniuses designed the “cars of tomorrow” out of their own heads, the tooling department that took the design and built the tools to make the new models, the marketing department which promoted the models designed by the geniuses, the parts supply chain, which created an inventory of parts to feed the hungry production line, the network of dealerships anxious to have a lot full of shiny new cars symbolic of prosperity, and the customer, who had been taught that the designers knew best and that the dealers were the people to talk to. The organization of the company was framed to push pre-set designs out to the consumer, making the consumer hungry for today’s new style (and tomorrow’s and tomorrow’s, etc.). This trend was not typical of just the auto industry. It was typical of any industry that had adopted mass production, the assembly line, and frequent changes in fashion as its basic business proposition—which included just about everyone. Organizations in this competitive atmosphere had to grow big and become competitive, meaning that they could not put themselves into a position of depending on or cooperating with other companies. Moreover, they had to control their supply lines, grow huge marketing organizations, and spend large amounts of money advertising their new products.

378 • The Designer’s Value-Added

A mass customizing organization works differently. The main activity of the company is centered on building a modular concept of the product based on knowledge of the customer, providing an ordering system which allows the customer to specify their choices of modules, building the customer’s order rapidly, and shipping it to them, not to a dealer. There are no inventories in such a system, no lots full of unsold cars. And there is not a large inventory of parts on the manufacturer’s shelf. Parts to be assembled are ordered according to current need. Some things typical of the mass production organization are missing from this description, and some important new features are added: • The mass customized organization uses factors in addition to manufacturability to design a product in modular form. Factors that may be used are suggested in the list of modularization principles given earlier and may be selected to protect long-lived core modules, anticipate future changes, place style elements on the surface for quick change, and allow multiple uses as well as reuse of the same module. • The mass customized organization depends on the participation of the customer in the design of the product and in the enumeration of its modular choices. • The mass customized organization prizes knowledge of the customer and the product advantage points that will attract customers and for which customers will be willing to pay a slightly, but not much, higher price. • The mass customized organization forms its relationship with the customer through a sophisticated online ordering and distribution network, without the help of dealers, brokers, or other mid-level agents. • The mass customized organization gathers detailed information from the customer that allows it to tailor the product to the exact specifications of the customer. • The mass customized organization rapidly builds customer orders that are placed over the network using a flexible manufacturing capability consisting of an assembly line specially configured for mass customization. • The mass customized organization outsources some assembly to cooperating organizations who are part of a larger, integrated supply and manufacturing system. • The mass customized organization minimizes the inventory of warehoused parts and materials through cooperative relationships with other-company suppliers. Parts and materials will be in inventory for less than one working day. • The mass customized organization accepts returns freely, using the customer’s feedback as data to improve future designs and offerings. The principle that makes a mass customization organization different from a mass production organization is that the mass customization organization is built around quick fulfillment of the customer’s tailored order. All parts of the organization are focused on this process, and all departments—from design, to marketing, to support—act in cooperation within a silo-less atmosphere to serve the customer’s needs and gather additional information that will allow them to improve the modular product design to better serve the customer in the future. Application Exercise Some computer companies do not build your computer until you order it online. Some car manufacturers are moving to mass customization in this way also. That is, they do not build the car until you order it.

Layers and Modularity • 379

• How many car companies can you name that have moved or are moving in this direction? • What other industries can you name where some of the companies are moving toward mass customizing? Can you name a clothing manufacturer that does mass customization? Can you name a shoe manufacturer?  

How Would Mass Customization Work in Education and Training? Mass customization has shown itself over several decades to be capable of providing custom-ordered products and services in a wide variety of industries. Recall, for example, the IBM 360 computer project, which was carried out in the mid-1960s. It produced an entire family of computer configurations that for years dominated their market. Other successes of mass customization range from personal computer retailing to furniture sales, from clothing to online radio stations, and from industrial equipment to call-processing systems. Large segments of the entertainment and communications industry today—notably music, film, and computer apps—are rapidly becoming mass customized. Instead of going to a record store to buy an existing CD or to a movie house to see “What’s playing”, we tend increasingly to order the products and services we want when we want them over the Internet. We build our own playlists and order the movies we want when we want them. Do education and training fit the profile of a mass customizable industry? The first, most obvious observation is that there exists a remarkable alignment between the long-held goal of individualizing instruction and the market-sensitive innovation of mass customization. Second, there have been successful attempts at mass customization in the field of instructional design, but they have tended to work from the designer’s side of the equation and do not include the learner in making decisions. Diagnose-and-prescribe designs administer tests to learners before instruction to determine whether there are lessons the learner can skip. This has tended to be a matter of determining which pre-produced lessons the learner still needs. What is being customized is the learner’s path through a pre-defined sequence of everyone-gets-the-same lessons. It is customization, but it operates at a high level of granularity—the lesson. Are there other elements of instruction that can be mass customized? Consider, as an example, one innovation from the 1970s which operated at a finer level of granularity and which involved the learner in more moment-to-moment decisions of consequence. It is called the TICCIT system, an NSF-funded research project in computer-based instruction conducted in the early 1970s. The TICCIT project was co-directed by the MITRE Corporation and by Dr. C. Victor Bunderson, one of the leading thinkers in CAI at the time. The strategy component of the system was designed by M. David Merrill, then a leading innovator in analytic instructional strategy. What was made modular in the TICCIT system, and how was the learner involved in making choices? There were two degrees of modularity in the TICCIT system. One was at the level of lesson choice, and the other was at the level of content and strategy within a lesson. Lesson choice: In the TICCIT system, lessons were organized hierarchically into Units, Lessons, and Segments, each represented by a “map”, which consisted of a hierarchical diagram showing the arrangement of its contents from bottom to top, with the top representing mastery of the Unit, Lesson, or Segment. Selecting an item on a map opened up a more detailed map at the next level down. The boxes on each map symbolized progress by giving the border of each map box a color: red, yellow or green. Boxes on each map were connected by lines representing the designer’s decision about prerequisite relationships. Figure 15.1 shows a typical TICCIT lesson-level topic map. Content and strategy choice: Within a segment there were no set presentations for the learner, and there was no set order of instruction. Instead, the learner was supplied with a specialized keyset

380 • The Designer’s Value-Added

INTRO

TEST 1 3

2 4

5 6

4.1 Pronoun – Referent Agreement 4.2 Agreement with Compound Referent – OR/NOR 4.3 Agreement with Compound 4.4 Agreement with Collecve Referent

7 8

Press ENTER to see Addional topics

PREREQUISITES UNIT 5 LESSON 4 MAP Figure 15.1 A typical lesson-level map used in the TICCIT system. (Adapted from Merrill et al., 1980.)

that occupied the right side of the TICCIT system keyboard. Figure 2.2 in Chapter 2 illustrates this set of keys. In order to obtain instruction the learner was required to ask for it by kind, using the specialized keyset that contained the following types of keys: • Administrative keys: Attention, Exit, and Repeat. • Menu navigation keys: Go (downward), Map (upward). • Strategy keys: For scope: Objective For type of content: Rule, Example, Practice For version of content: Easy, Hard. • Assistance/Orientation keys: Advice, Help.   

Using these keys, a learner could navigate a structured information space in any order of choice. Figure 15.2 shows the complete set of pathways this provided to the learner. The essence of the TICCIT system was its concept of structured strategy, which was based on Merrill’s then-radical idea of component display theory (1983), a direct-instruction approach. According to this view, for any given subject-matter, within the scope of an objective, there were certain kinds of information display that pertained, regardless of the specific content, whether it was math, writing, or English usage. The RULE display consisted of the main idea—the definition of a concept, or the statement of a rule that the learner was to learn. The EXAMPLE display provided multiple examples of the concept definition or the rule being applied. The PRACTICE display provided practice problems for the learner in applying the rule to unsolved examples. EASY and HARD keys allowed the learner to ask for either more or less challenging examples or practice items. What is important about the TICCIT example is that it succeeded in placing into the hands of the learner important sequence-strategic choices that would normally be retained by the designer. Learners in TICCIT were not able to settle back and enjoy the ride. Full participation was required

Layers and Modularity • 381 6 RULE HELP

TO PRACTICE

TO EXAMPLE

5 RULE HARD

20

RULE EASY

RULE

4 PRACTICE HELP

2 SEGMENT OBJECTIVE

MINI LESSON

TO PRACTICE

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4

PRACTICE FEEDBACK

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17, 19, 21

18, 22

+ -n

PRACTICE EASY

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LESSON MAP

7 ,9 , 10 *

TT PRACTICE HELP

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24, 26 PRACTICE FEEDBACK

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ID

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O K—

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35, 36, 37, 38 LESSON TFST

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23, 25

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13, 15

EXAMPLE HARD

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(H)(3)

Figure 15.2 The pathways made available to learners through the specialized TICCIT keyset. (From Merrill et al., 1980. Copyright by Educational Technology Publications. Reproduced with permission from the publisher.)

or nothing happened, and the learner was required to make decisions instead of complying with designer choices. Advice was available, but nothing happened until the learner ordered it. The caveat to this is, of course, that the learner could order anything that was on the menu. Merrill’s design anticipated what were thought to be important choices within the paradigm of direct instruction within which TICCIT was operating. The TICCIT keyboard and the displays related to each key are an example of modularity applied in the cause of mass customization. What was modularized was the message structure related to a single strategic scope (objective). Instructional material was created according to this

382 • The Designer’s Value-Added

modularization for both mathematics and writing instruction. This required design assumptions about the kinds of objective that could be pursued and the kinds of message appropriate in the pursuit of that kind of objective, and makes it obvious that customization and the modularization that makes it possible is not an all-or-none principle. A designer decides what can be made available to choice by the learner. This would indicate that TICCIT is only one example of mass customization applied to instructional design—the tip of an iceberg that demonstrates the principle within one paradigm. Many alternatives are possible that apply other paradigms and other assumptions about what can and should be placed in the hands of the learner at any given time. In terms of mass customization, TICCIT exemplified two key factors: (1) a modular product concept, and (2) a manufacturing organization structured around the custom ordering and rapid delivery of the customized product. In this case, manufacturing took place in two stages: first, the manufacturing of the display contents by a design team according to a prescribed formula, and second, the combination of the displays into specific instructional sequences by the learner. Educational delivery is uniquely suited for mass customization. It is perhaps better suited than other industries that have already employed it: • Instruction is an experiential, not a physical, process. Though physical objects from flasks to flight simulators are used in instruction, these are not the instructional product, but merely the physical implements that make possible an instructional experience. Technology can create virtual worlds in which physical objects can be represented through simulation. Pilots take their final check ride for a new aircraft in a simulator. With simulations there is no stock room, no inventory of flasks, no breakage, no lab space to build and maintain, and no clean-up. • Because there is not necessarily a physical product involved in it, instruction can be made up of non-physical experiences that can be generated (manufactured) in real time, on demand. This is true of live and technology-based instruction. The materials costs and repetitive manufacturing costs normally associated with physical products can in most cases be shifted to one-time programming costs and nearly free computing costs. When full generativity is not possible, creation of the raw materials from which generation can occur can be much simplified because standard component types can be used to guide the work of authors. Conclusion Because instruction is not a physical product, it has advantages for mass customization that are not available to manufacturers of shirts and boats: more of the product can be generated on the spot in an instant. There is no shipping charge and no wait. Additionally, the units of generation can be much more fine-grained, depending on the structures made manipulable by the designer to the learner. Mass customization is relative to the number of dimensions and qualities of the product that can be varied. A shirt has a certain number of variables that can be the subject of tailoring, mass or not. Some of these are important to consumers and some are not. How many variables of instruction can be the subject of mass customization? How many variables of instruction should be the subject of mass customization? Experimentation in the past has nibbled at both ends of this question. During the 1970s and 1980s artificial intelligence and tutoring research attempted to subject large numbers of variables to customization, only to learn the insurmountable complexities of comprehensive solutions. Diagnose-and-prescribe instructional solutions, on the other hand, subjected very few variables to customization, and without any participation of the learner in choice-making. There is a middle ground in which selected variables can be opened to negotiation and choice by learners. The question that remains is which variables can and should be placed under choice to

Layers and Modularity • 383

achieve a desired outcome. According to the experience of successful mass customization efforts in many fields, the answer is that the consumer—the one for whom the customization is being made— should be a reliable source of that information. One question that will be resolved through research and experience is the level of granularity that is practical and useful in mass customizations. With the multiplication of options and increasing levels of detail, there is a proportional increase in the number and complexity of choices the learner must make. Experience in mass customization has shown that eventually this becomes a burden rather than a benefit for the consumer. Instructional customizations have so many potential variables that the most important ones must be determined and capitalized upon, while others are omitted. Experimentation is sure to find that different variables have value in different contexts and different stages of learning. A more important practical question remains regarding the organizational changes that are necessary for mass customization. Many successful businesses have considered or undertaken mass customization projects only to find that they were not organized properly. One of the major keys to success of mass customization appears to be the organization of the organization. Instead of a focus on producing unitary, bundled, engineered, and monolithic designs, there must be an organizational emphasis on determining the modularity of the product through a cooperative dialogue with the consumer and then not deviating from the modular plan in ways that involve further engineering for individual customers. Many of the most successful mass customization organizations began as mass customization organizations or were in a position to make major changes in the way they design, produce, market, and distribute products. Given the history of educational change, one wonders whether it will be fleet-footed commercial enterprises or ponderous educational institutions that are able to negotiate this change most successfully.

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16

Adding Value to the Organization

The most valuable assets of a 20th-century company were its production equipment. The most valuable asset of a 21st-century institution, whether business or non-business, will be its knowledge workers and their productivity. —(Peter Drucker, 1999) Ten years from now you’re going to be doing something that you weren’t trained in at college because it’s not a career that existed ten years ago. So you’re going to need continual training. —(Peter Norvig, quoted in Vanderbilt, 2012) Instructional designers are knowledge workers who create strategic value for organizations with creative solutions to performance problems; a designer today must be adequately prepared to do this within a rapidly changing work environment. Several trends discussed in this chapter will impact the future workplace and practice of the instructional designer. Some trends will open exciting opportunities; other trends signal the decline of traditional niches for the practice of design. Preparation with skills and understanding for working in this new world is of paramount importance. This chapter surveys changes that are taking place in the designer’s world. References to a wide range of topics provide an overview of the subjects and a beginning point for further inquiry. These are only beginning points for growing an understanding of a constantly changing landscape. The business world has awakened to the value of the instructional designer and the value designers can add to an organization. Education, training, and performance assessment have been drawn over the past two decades toward the center of organizations large and small, industrial and service, public and private. The training function has in many organizations been assigned to a new organizational leader for education and training, the Chief Learning Officer (CLO). This officer manages the value-adding of education and training to the organization, ranging from designing and producing training programs to managing careers and retaining talent. This phenomenon is not just corporate. Universities have for a long time had chief learning officers, usually holding positions such as Provost or Dean. Though these positions are not normally considered strategic in a business sense, the students who enroll in a prestigious university must feel that they are, because a university or a college within a university competes for superior students, living or dying by its academic reputation—a reputation that confers value on the learner and then grows as learners themselves become successful and notable. 385

386 • The Designer’s Value-Added

The Designer Within a New Landscape Organizational oversight and focus is only one factor that will determine the designer’s future working environment. Figure 16.1 depicts the traditional relationship among major stakeholders in the education and training environment. The circles in Figure 16.1 represent the major functions carried out to create and deliver education and training. A provider is any entity that delivers instructional experiences or services to consumers—a school, a university, a corporation, a military group, a government agency, or a private vendor. A consumer is a person or group that purchases or otherwise obtains the use of instructional products or services from a provider. That use most often comes as a result of being employed by a provider. A producer is a designer/developer. Producers produce and then distribute either through providers or directly to consumers. Historically, these functions—producer, provider, and consumer—have for the most part tended to belong to the same organization: a corporation, the military, a government agency, a public school, a service organization, or a university. Producers worked in a training department. Providers ordered the development of training from producers and then supplied instructors to deliver the training to the consumers, who were generally employees, enrolled students, or the public. In some cases, commercial courses would be subscribed or purchased from outside organizations. Recently, there has been a tendency for the organizational affiliations of the producer, the provider, and the consumer to fragment and separate. The designer traditionally worked for either a provider or a producer, who then distributed the product to the consumer. The designer today still acts as a producer, but has also branched out in many cases as an entrepreneur, becoming a provider by selling or providing products through new kinds of outlets: app stores, online markets, cooperatives, repositories, and through new kinds of sponsorships by organizations themselves. The new consumer has also become more independent, tending to self-educate using products from entrepreneurial producers or from a new kind of for-profit provider: the educational broker. This new relationship of the designer to producing organizations, providers, and consumers is illustrated in Figure 16.2. Major Trends Influencing the Environment of Instructional Design The sections that follow identify major trends that influence the future instructional designer’s practice, skill, and knowledge requirements. These trends redefine the roles of the producer, the provider, and the consumer, describing a new marketplace within which the designer can add value. These sections suggest topics for the professional development of instructional designers. They highlight

Producer

Provider

Consumer Figure 16.1 The stakeholders in the professional environment new instructional designers will enter. New economic opportunities are redefining these and the relationships among them.

Adding Value to the Organization • 387 Producer

Provider

Designer

Consumer Figure 16.2 Potential relationships of the instructional designer in the changing marketplace for education and training.

trends the designer should research independently in more depth. What follows in this chapter is not a complete review of the literature on any of the topics, but a description of trends and a few search terms and entry points into the literature. A satisfying design career can be ensured through continuous, independent, self-directed study. It is difficult to incorporate experience with all of the topics discussed below into the time frame devoted to Masters or even PhD studies. This indicated a need to push some of the training of designers into undergraduate years and broaden the scope of subjects included in the curriculum. Failing that, the best way for a designer to pursue continuing professional studies is through membership in a standing study group meeting regularly to sharpen the professional saw. Designers should constantly add new topics to the list in this chapter that can confer strategic knowledge and skill advantage. The New Consumer and the New Product The rise of mobile personal media has changed habits of communication and, with them, expectations and patterns of media consumption. This is impacting the preparation and work of instructional designers in several ways. The Social Media Generation The social media generation of today is not so recent as much of the literature would indicate. It is not just a product of new media devices and software. “Social media” is a two-part term. The social aspect of the new media movement had its beginnings in the early 1960s. Turner (2006) describes a radical and critical shift in the perception of the computer by youth disaffected by the Vietnam War and other social issues: For marchers of the Free Speech movement, as for many other Americans throughout the 1960s, computers loomed as technologies of dehumanization, of centralized bureaucracy and the rationalization of social life, and ultimately, of the Vietnam War. Yet, in the 1990s the same machines that served as the defining devices of cold war technocracy emerged as the symbols of its transformation. Two decades after the Vietnam War and the fading of the American counterculture, computers somehow seemed poised to bring to life the countercultural dream of empowered individualism, collaborative community, and spiritual communion. How did the cultural meaning of information shift so drastically? —(p. 2) Perhaps it was that before long the computer came to be seen by the countercultural group as a tool for building its own community: “[The] vision of benevolent man–machine systems, of

388 • The Designer’s Value-Added

circular flows of information, would emerge as a driving force in the establishment of the militaryindustrial–academic complex and as a model of an alternative to that complex” (p. 21, emphasis in the original). The initial style of this counterculture was to live independently in a way that shunned the prosperous post-war and cold war style that seemed that it would to turn humans into servants of the “system”. Jenkins (2008) describes the manner in which increasingly easy information production and sharing—which later became multi-media production and sharing—promoted self-expression and encouraged a folk culture that found the Internet and the Web convenient tools for reaching a social and like-minded audience: The story of American arts in the twenty-first century might be told in terms of the public reemergence of grassroots creativity as everyday people take advantage of new technologies that enable them to archive, annotate, appropriate, and recirculate media content. Once you have a reliable system of distribution, folk culture production begins to flourish again overnight . . . Some of what amateurs create will be surprisingly good, and the best artists will be recruited into the commercial entertainment or art world. Much of it will be good enough to engage the interest of some modest public, to inspire someone else to create, to provide new content, which, when polished through many hands, may turn into something valuable down the line. That’s the way the folk process works and grassroots convergence represents the folk process accelerated and expanded for the digital age. —(p. 136) The final stage in the creation of today’s digital social culture—which is unlike the original—was the creation of a succession of popular and easy-to-use software products for mailing, messaging, posting, and publishing aimed at making self-expression not only easy but—by making it free and ubiquitous—necessary if one was to stay in touch, keep up with friends, conduct a vigorous social life, and keep current moment by moment. The motives for using the social media system have changed from joining the counterculture to joining the common culture. The habits and expectations of the new media generation have become a part of the professional environment of the instructional designer. Learning, which has become part of the social fabric, has taken on new places, forms, and structures. Learners have formed new habits and expectations of information seeking and sharing. They have become used to “instant-ness” and have a “right now” attitude. Moreover, they expect information to be useful in making choices and acting: they do not seek information for information’s sake. This shapes preferred modes, formats, and structures of media and communicating. It drives the pace of interaction and shortens the length of time learners are willing to listen before they act. The social media generation is used to doing rather than listening, and their communications have taken the form of frequent conversations with both humans and computers where the conversation is a prelude to and a part of action. Rising Quality Expectations Bonk (2006) notes that “bored students are dropping out of online classes while pleading for richer and more engaging online experiences”, also asserting that “online learning environments are facing a ‘perfect e-storm’ ” (n.p.). Oblinger and Oblinger (2005, p. 2.5) describe the “hypertext mind” and note how learners have come to deal with information differently in their skills for: • Ready interpretation of visual images • Visual–spatial integration • Skills for inductive discovery

Adding Value to the Organization • 389

• Ability to shift attention rapidly between tasks • Rapid response. In addition, they note that the new type of learners tend to prefer experiential learning, and are “prolific communicators”, preferring working in teams, and structure rather than ambiguity. They suggest that these learning preferences may be age-independent: The differentiating factor may not be so much one person’s generation versus another: the difference may be in experience [with interactive technology] . . . They don’t think in terms of technology: they think in terms of the activity technology enables . . . What we consider “new technology” . . . are not thought of as technology by students . . . The activity enabled is more important to the Net Gen than the technology behind it. —(p. 2.10) The rising quality expectations, according to the Oblingers’ analysis, do not pertain to surface features as much as the social dimension and functionality created by the technology. Earlier generations of instructional designers have tended to see instructional experiences in terms of the technology and its workings. A younger generation of consumers wants that to become transparent; the designer and the technological mechanics are expected to stay in the background. There are several implications for designers in this set of observations. The most obvious is that designers need to develop a new sensitivity to the dynamic, fluid quality expected of the instructional experience. Many designs claim to be based on “interactive” experience, but their designs reveal a strong tendency toward “tell-first” and “interact-later”. Verbalizations and non-dramatic experiential structures govern most designs. In contrast, Parrish describes both instruction (Parrish, 2008) and the design of instruction (2006) in terms of the narrative arc, which is structured in terms of emotion management rather than information management. Another implication of Net Gen learner preferences is that social interaction patterns and unscheduled and unschedulable initiatives for learning have to become elements in the design—not because the designer’s theory says so, but because it is what the learner will expect. Any college professor will tell you how disconcerting it is to have students sitting behind their laptops and tablets, fact-checking the presentation and uncovering new, troublesome sources that update the teacher. Today’s media are smaller, more powerful, more visual, mobile, and ubiquitous. Alexander (2004) describes today’s wireless devices as “prosthetics for information, memory, and creativity” (n.p.). Designers need to learn to tell the story of knowledge in a fractured way, where the dominant structural factor is the emotions related to participating socially in solving a mystery rather than the elegance of the exposition. The Net Gen learner desires being part of the discovery process with a group of peers who are also involved in the chase. This emphasizes learning as a shared, unfolding experience. Though much has been written about the mobility of learning, what is probably most important is temporal mobility that allows learning within a socially connected group to overflow the boundaries of the class period, taking place within its own time frame. This means designers need to step out of their privileged position as exposers of knowledge and see themselves as designers of dramas and mysteries, thought games, and projects. Tailoring for the Individual Rothrock (1982) describes the growth, peaking, and decline of an individualized instruction movement with origins in the early 1960s, peaking in the mid-1970s, and then subsiding in the 1980s. However, the concept of individualized instruction is as old as two people sitting on a log. Weber’s (1977) comparison of individualized systems shows that in general the concept of individualized

390 • The Designer’s Value-Added

instruction was not well defined early on and that as a concept it supplied more of an umbrella function than specific guidance. However, Glaser (1977) provides this clear definition of its characteristics: Quality and equality in education does not mean offering the same program to all, but offering a program which reaches out to every person to maximize intellectual and social growth. An educational system that is adaptive to the individual has three essential ingredients. It provides a variety of alternatives for learning and many goals from which to choose. It attempts to utilize and develop the capabilities that an individual brings to these alternatives and to adjust to the learner’s particular talents, strengths, and weaknesses. Also, an adaptive educational environment attempts to strengthen an individual’s ability to meet the demands of available educational opportunities and develop the skills necessary for success in the complex world. —(Glaser, 1977, p. v) Glaser’s description of individualized, adaptive instruction was unique in its time because it viewed value from the learner’s point of view rather than from the point of view of economic value, efficiency, or techno-methods. The concept of individualized instruction has not gone away, though its popularity under that name has waned. What has replaced it is the concept of adaptive instruction, which ironically also had its origins during the 1960s and 1970s, but on a parallel research track. Glaser’s influence helped stimulate the concept of “adaptivity”, which is evident in the above quotation. Shute and Zapata-Rivera (2012) describe the resurgence of interest in adaptivity. Recent compendia by Nkambou et al. (2010) and Woolf (2008) demonstrate an ongoing research commitment to the principles of adaptive systems. Traditionally, adapting instruction to the individual has been seen as the mechanical problem of how to get a computer program to act in a certain (human-like) way. Less often, the approach is taken of finding out what learners want. Consider this list of qualities from a survey published in Educational Leadership (2008, p. 48–51). Learners ask: • • • • • • • • • •

Take me seriously. Challenge me to think. Nurture my self-respect. Show me I can make a difference. Let me do it my way. Point me toward my goals. Make me feel important. Build on my interests. Tap my creativity. Bring out my best self.

Delaney et al. (2010) report a similar “Top 9” list of qualities desired of live and online instructors derived from a survey of 17,000 university students in Canada. (See Table 16.1.) The implication for instructional designers is that the design of adaptive instruction must provide solutions for both a technical problem and one of dealing with human communication values. Glaser’s vision of adaptive instruction is within our technical reach. However, whether that instruction is palatable or not to learners will depend on remembering what they themselves see as the key qualities of the adaptivity they desire.

Adding Value to the Organization • 391 Table 16.1

A “Top 9” List of Qualities Preferred in a Survey of Canadian University Students

In Online Instruction

In Face-to-face Instruction

1.

Respectful

1.

2.

Responsive

2.

Knowledgeable

3.

Knowledgeable

3.

Approachable

4.

Approachable

4.

Engaging

5.

Communicative

5.

Communicative

6.

Organized

6.

Organized

7.

Engaging

7.

Responsive

Respectful

8. Professional

8. Professional

9. Humorous

9. Humorous

Note: Bold entries draw attention to interesting differences between media forms in two of the qualities Source: After Delaney et al., 2010

The Changing Nature of the Instructional Product One change that has already begun to have impact on instructional designers is a morphing of the image of instruction itself. What is a designer creating when the focus shifts from the media product itself to the experience that is produced by a combination of the media product, the timing, the place, the setting, the social interactions, and the possibilities for performance and feedback? Designers in the mid-1900s tended for a period to think of instruction in terms of “packages”. That image is still valid in some applications today, but the lines of the package concept are rapidly blurring, and a new image is forming that requires the designer to “see” the experience potential of combining multiple functions—most of them invisible to the learner—modularly (see Lucas et al., 2012, Chapters 2 and 7). Chapter 3 in this book described Krippendorff ’s “trajectory of artificiality”—what Krippendorff described as a trajectory of kinds of design problems (Krippendorff, 2000). Each waypoint along the trajectory describes a different level of social obligation for the user, and the design of each type of artifact (a brand of product, an interface, a network, a project) requires a different kind of structural thinking from the designer. The concept of the instructional event is changing, and with it the conceptual basis for the architectural structures that support events. Young designers will not have as much problem crossing this conceptual barrier as more mature designers who have in the past designed mainly self-contained products. Krippendorff ’s trajectory is but one view of how different concepts of artifact structuring create new design targets. His is only one approach to reconceiving the “product”. Consider the impact of the modularity concept explained in Chapter 15. That concept leads us to conceive of the physical packaging of the product apart from the structure of its effective or functional parts and the structure of the experience itself. Working with a modular concept, a designer may see a single instructional experience in terms of multiple, remixable modular elements called together from different sources and assembled into a functional unit at the time of instruction, bringing even more meaning to the term “blended” learning. A useful exercise for instructional designers is therefore to look beneath the surface of instructional experiences they encounter in the wild and reverse engineer them until they can see their inside workings in the same way an architect, a structural engineer, and the other members of a building design team can “see” inside a building plan.

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Trends in Assessment of Outcomes Increasing emphasis is sure to be placed on the assessment of performance for certification and professional advancement purposes. Earlier chapters stressed the notion that assessment lies at the heart of instruction that can adapt to the learner. This places greater emphasis on assessment instruments and practices that measure with greater accuracy, validity, and reliability. Assessment may, over time, become a specialty area within instructional design due to an increasing emphasis on intermingling instruction and assessment (Pellegrino et al., 2001). Three areas of assessment deserve special attention: (1) the measurement of performances (as opposed to objective item assessments), (2) the reliability of assessments, and (3) the validity of assessments. Performance Assessment, Reliability, and Validity Moss (1992) describes performance assessment in terms of “extended discourse, work exhibits, portfolios, or other products or performances” (p. 229), noting “the role that multiple-choice assessments have played in narrowing the curriculum to reflect the form and content of . . . tests” (p. 229). Increasing emphasis is placed and will in the future continue to be placed on performance assessment. Bunderson et al. (1988) describe four generations of computerized testing. One of the generations—computerized testing—has been in general use for some time since their writing, though at that time it was still a novel idea. A second generation—computer-adaptive testing—has also since become common. A student or post-graduate designer reading this book will probably have been tested by a computer-adaptive test multiple times. The third and fourth generations of testing described by Bunderson and his associates—continuous measurement and intelligent measurement— were laboratory concepts at the time that will emerge into use in intelligent tutoring systems like those that are moving into commercial use (Razzaq et al., 2005; Van Lehn and Chi, 2012). Bunderson’s first generation—computerized testing—is common today as a typical part of an instructional design. However, with the exception of standardized tests, instructional designers typically do not concern themselves with issues of adaptivity (Bunderson et al., 1988), validity (Moss, 1992; Mislevy, 2009), and reliability (Wigdor and Green, 1991). The validity and reliability issues of test construction will demand increasing attention in the future as tests and commercial testing services become increasingly designed by one organization to provide unbiased assessments of instructional outcomes of services provided by another—the usual pattern of certification testing (Coscarelli et al., 1998). Testing (and certification) services will in the future be marketed on the basis of test validity and reliability credentials and on their validity with respect to real performance. Designers must be especially familiar with the negative, sometimes serious consequences of overlooking these factors of test building (Flippo and Riccards, 2000). The Granularity of Performance Assessments Assessment of performance always takes place within a defined scope that is of momentary interest. Chapter 12 described techniques for varying performance scope at a given moment and creating instruction and assessments relative to that scope. Burton et al. (1984) describe a concept of increasingly complex microworlds (ICM) that provides additional perspective on scope adjustment: In this paradigm, the student is exposed to a sequence of environments (microworlds) in which his tasks become increasingly complex. The purpose of an individual microworld [is] to provide the student with a task that he can perform successfully using a simplified version of the final skill that is the goal. This allows the student to focus on and master one aspect of the skill in a context that requires related subskills. As a result, the student learns when to use

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the skill as well as how to use it. The purpose of the sequence is to evolve the simplified skills toward the goal skill. The ICM framework focuses both on what is learned in any particular microworld and how to choose the next microworld in the sequence. —(p. 139) Bunderson and his associates (Bunderson et al., 1981; Gibbons et al., 1995) refer to these as work models, since they are models of work the learner is trying to achieve. An approach for clustering instructional objectives into work model groupings for targeted practice and assessment is described in an earlier chapter. Van Merriënboer and Kirschner (2012) describe a detailed process for isolating and sequencing the elements of skilled performance. Heritage (2008) proposes that “teachers need to have in mind a continuum of how learning develops in any particular knowledge domain so that they are able to locate students’ current learning status and decide on pedagogical action to move students’ learning forward“ (p. 2). She criticizes teaching units organized only around topics that “are often not connected to each other in a coherent vision for the progressive acquisition of concepts and skills” (p. 3). It is doubtful that there exist single right trajectories of skill and knowledge development. This means that the construction of trajectories is itself a skill to be learned by a designer and practiced at various levels of competence. This skill begins with intuitive orderings or orderings that employ a formal method like the ones just mentioned for analyzing component skills. Over time, empirical data can be used to further discipline the ordering of steps for populations, but the ultimate goal should be to determine orderings dynamically for a given individual, based on rate of progress. Designers should acquire the skills and techniques that will lead to insightful, individually tailored, orderings of learning experiences and assessments. Adaptive Instruction Shute and Zapata-Rivera (2008) give a general description of an adaptive instructional system that involves the use of learning analytics and recommendation: An adaptive system adjusts itself to suit particular learner characteristics and needs of the learner. Adaptive technologies help achieve this goal and are typically controlled by the computational devices, adapting content for different learners’ needs and sometimes preferences. Information is usually maintained within a learner model, which is a representation of the learner managed by an adaptive system. Learner models provide the basis for deciding how to provide personalized content to a particular individual and may include cognitive as well as noncognitive information. Learner models have been used in many areas, such as adaptive educational and training systems (e.g., intelligent tutoring systems), help systems, and recommender systems. —(p. 279) Figure 16.3 (Shute and Zapata-Rivera, 2008, p. 281; see also Shute and Zapata-Rivera, 2012) illustrates a four-process adaptive cycle that represents the learner (black symbol) responding (1) during instruction to create data, which is captured and (2) analyzed. The results of the analysis are used to (3) update a learner model (white symbol), which then is used as the basis for (4) selecting instructional events (and sub-events). Events are (5) executed, (6) involving the learner in further data-producing interactions. The details of the Shute and Zapata-Rivera model define a complete self-contained adaptive (stand-alone) application, but also they describe how through a modularized, distributed architecture the functions of an adaptive system (the capture, analysis, selection, and presentation functions) can reside at different locations in different modular arrangements. This makes it possible to define alternative path configurations of the cycle in which:

394 • The Designer’s Value-Added

4

3

Analyze

Select

9

2

5

7

8

Capture

Present

1

6

Figure 16.3 A four-process adaptive instruction cycle that provides multiple pathways to making adaptations. (From Shute and ZapataRivera, 2008, p. 281. Copyright, Oxford University Press. Reproduced by permission.)

• A complete cycle is executed (path: 1, 2, 3, 4, 5, 6). • The learner interacts with the learner model (path: 1, 2, 3, 4, 5, 6, 9), perhaps exercising a greater degree of self-direction after studying the model. • Data is gathered on the learner, but only used to refine the learner model (path: 1, 2, 3). • The learner model is cut out of the cycle and only the most recent interactions are used in the selection process (path: 1, 7, 5, 6). • The learner follows a predefined curriculum path that involves data capture and analysis but no update of the learner model (path: 1, 2, 8, 6). One additional path configuration might include a traditional diagnose–prescribe version in which the learner model was short-circuited but selection was accomplished by a fixed remedial– prescriptive rule (path: capture, analyze, select, present, omitting the student model). The combined learning analytics–recommender function is a disruptor by Christensen’s definition (Christensen et al., 2004). The principles of such systems are important for instructional designers to understand. The New Producer The producer of instruction today must stay apprised of constant changes in tools, mechanisms, standards, infrastructure, processes, and competencies.

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Evolving Tools and Techniques Evolving tools and techniques are continually setting off shock waves that rock instructional design practice. It used to be a new kind of slide projector that would send technicians scurrying to buy the latest model. Today’s new ultra-fast smart phones and tablet computers will soon be as retro as those systems. Occasionally technological innovations come along, however, that are true game changers—what Clayton Christensen et al. (2004) would consider innovative disruptors. Computers were once (and continue to be) innovative disruptors because once the computer entered the media lab it gobbled up all of the other media forms. Now new innovations continue to press forward, hoping to be the next disruptive innovation. One pair of innovations almost certain to change the face of instruction—and with that the practice of the instructional designer—is the combination of learning analytics and recommender systems. Data Analytics and Learning Analytics The analytics-and-recommender combination fits several of Christensen’s criteria for a disruptive innovation: • It makes learning more convenient by supplying what the learner needs, very possibly in a form congenial to the learner’s chosen style (more convenient, more customized). • It is able to accommodate overshot learners (the impatient, the discouraged, the unconfident) by meeting their needs in a manner not before possible at an individualized level. • It can possibly expand education to former non-consumers (drop-outs, academically challenged). • It potentially allows instruction to be carried out in new contexts: non-classroom locations such as at home, in transit, at the office, and while on travel. Data analytics involves sorting masses of data for hidden patterns. Learning analytics sorts through data created by user choices, in search of otherwise hidden patterns of behavior and learning preferences. The burgeoning field of analytics grew from the application of data to marketing decisions but has been applied since to decision-making in virtually every aspect of organizational operations (Ferguson, 2012). In the context of business marketing, Kaushik (2010) describes what he terms Web analytics 2.0, wherein he names levels of analysis that represent increasingly intensive examination of data patterns: • The level of What includes “collecting, storing, processing, and analyzing your websites clicklevel data” (p. 7). According to Kaushik, clickstream is related to “visits, visitors, time on site, page views, bounce rate, sources, and more” (p. 7). • The level of How Much “means connecting customer behavior to the bottom line of the company” (p. 8). • The level of Why involves finding out why something works or doesn’t work. “You can run experiments live on your site with various ideas and let customers tell you what works best” (p. 8). As Kaushik explains, “failing online is cheap and fast” (p. 9). The level of Why also includes “surveys, lab usability testing, remote usability testing, card sorts, and more”, which yields “direct feedback from customers” (p. 9). This, says Kaushik, is necessary because “your Web analytics tool can report only what it can record. What your customers wanted but did not see was not recorded” (p. 9). • The level of What Else, Kaushik says, includes knowing about competitors by accessing data online about competitor site performance.

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Perhaps Kaushik’s most useful idea for instructional designers is that analytics is more than purely numerical analysis. In order to make strategic and tactical decisions, using analytics is necessary to include additional data from multiple sources, including the user personally and the competitor. The data must be interpreted carefully, seeking out user motives and rationales by triangulating sources. Kaushik uses the instance of trying to understand why the user left a page or why the user stayed engaged. Different answers to the “why” questions lead to different data sources and different analyses on the designer’s part. Data analytics is set to become a powerful tool in education and training, used to tailor instruction to the individual. Evidence of growth in this area includes newly established professional organizations, annual conferences, and workshops that are increasing in popularity (Conole and Gasevic, 2011; Shum et al., 2012; Yacef et al., 2012). Horizon reports for 2011 and 2012 (Johnson et al., 2011; Johnson et al., 2012) cite learning analytics as an innovation that is maturing toward general use rapidly. Long and Siemens (2011) show that this is consistent with the advance of analytics and other professional fields: Notable is the shift from clinical practice to evidence-based medicine in health care. The former relies on individual physicians basing their treatment decisions on their personal experience with earlier patient cases. The latter is about carefully designed data collection that builds up evidence on which clinical decisions are based. Medicine is looking even further toward computational modeling by using analytics to answer the simple question “who will get sick?” and then acting on those predictions to assist individuals in making lifestyle or health changes. —(p. 32) They note that: Higher education, a field that gathers an astonishing array of data about its “customers”, has traditionally been inefficient in its data use, often operating with substantial delays in analyzing readily evident data and feedback. Evaluating student dropouts on an annual basis leaves gaping holes of delayed action and opportunities for intervention . . . Something must change. For decades, calls have been made for reform in the efficiency and quality of higher education. Now, with the Internet, mobile technologies and open education, these calls are gaining a new level of urgency. —(p. 32) Ferguson (2012) reviews trends that have contributed to learning analytics since the mid-1900s. Tracing history from the emergence of data analytics as a technical innovation, she notes three major audiences for analytics outputs: (1) an educational policy audience, (2) an educational administration audience, and (3) a pedagogical audience. The audience of most interest in the present discussion is the pedagogical audience—the instructor, the learner, and the instructional designer. Of these, the designer is the one who must take the lead in defining and exploring the possibilities. Long and Siemens (2011) describe cumulative levels of analyses that can lead to increasingly fine-grained recommendations during learning support: • Course level—This includes gathering data at the course level related to “learning trails” using a variety of techniques. As a learner progresses through learning using a blended, instructor-led, resource-based, socially based, and computer-based means, a trail of breadcrumbs can be captured using diverse input portals that describes the choices, patterns, preferences, and performance of the learner.

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• Educational data mining—From this mass of data, patterns can then be discerned for populations of learners, for subgroups with common profiles, and for individual learners. These represent dynamic models of populations and individuals (see Koedinger et al., 2012) maintained and refined through further data gathering and analysis and used as the basis of recommendations. • Intelligent curriculum—These analyses create models not only of populations and individuals but of content, instructional strategies, and curricula as well. Clusters of meaning emerge in each of these areas that reveal hidden structures not previously perceived. • Adaptive content and adaptive learning—These new structural categories represent a kind of new, dynamic knowledge about instructional elements and qualities. Experiments with these new structures lead to new framings of content, new representations, new strategic patterns, new messaging conventions, and even new architectural arrangements of the delivery system— new modularization plans, and new patterns of recommendation. This progression over time depends on an underlying system for making recommendations to the learner that are either optional—in the form of a menu—or imperative—in the form of the traditional fixed strategy. In either case, the choice will be more tailored to the individual than the classic strategy patterns in general use today. Systems for data capture and collection are becoming more diverse and more sophisticated (Woolf, 2010; Conati, 2010; Bull and Kay, 2010; Baker, 2010), particularly when electronic media are involved (see for example Parry, 2012). Learner models are likewise maturing (Kay and Kummerfeld, 2012). Recommender Systems Recommender systems are already a part of everyday life in many areas. Our day is so full of them that we are scarcely aware they are at work. For example, we rise in the morning and read news articles selected for us by an online news source. We drive to work in a car, which, before we bought it, we studied through Internet reviews. At the office we meet at the water cooler and swap information and recommendations on great movies or books. Then we pull out our smart phones to look at critics’ reviews and check out how many stars the social net has awarded them. While we are at it, we look at a book the online bookstore has recommended. Recommender systems—informal and formal, live and technology-based—have become part of the fabric of our lives. The recommender systems of interest to the instructional designer are the formal ones, whether automated or instructor-implemented. These recommenders work with the aid of databases, personal profiles, and models to generate recommendations that are personalized to an individual or to a profiled group. They work through statistical means. There are many types of recommender system. Jannach et al. (2011) define types of system in terms of how they go about answering our questions: • Recommenders that depend on a history of shared interests among users are called collaborative. • Recommenders that match the content of an item with a profile of the user are called content-based. • Recommenders that match the preferences and behavior of a user with the features of an item are called knowledge-based. • Recommenders that combine one or more of the above types are called hybrids. Some recommenders have simple logic and are relatively easy for beginners to build; others involve complex statistical manipulations and require expert design and programming. Some recommenders require relatively little knowledge of the user; others require a detailed model of user preferences and/or behavior. Some recommenders require no database; others require small databases; yet others

398 • The Designer’s Value-Added

require enormous masses of data. The characteristics of a particular recommender system depends on the circumstances of the application—the data that is available, the number of users, the kinds of items to be managed, how much is known about users and items, and the question posed to the system. For instructional designers, the focus of a recommender system is on the kinds of items (events) to be matched to the user. Recommenders are not new in education and training, as anyone knows who has consulted with a high school or college counselor. Counselors recommend courses or course sequences (events). Perhaps the counselor will recommend an instructor as well. Within the course, the instructor will make strong—even imperative—recommendations (event assignments) as well as weak—optional—recommendations (events for extra credit, enrichment, advanced preparation, or credit toward certification). These human recommender systems have existed for a long time, but one of the criticisms of education and training today is that they have reached the limit in their ability to gather and process data leading to better, more personalized and knowledge-based recommendations than are presently possible using human assistance alone. Critics point to: • Students placed into classes for which they lack prerequisites. • Capable students who have unrecognized, sometimes very small but nonetheless disabling gaps in their knowledge, leading to learning difficulties. • Students who are bored because classes geared to the average student fail to offer sufficient challenge. • Students who are failing but who are not receiving remediation that could bring them back out of danger. • Students who cannot keep up with the pace of classes that cannot wait for them. In sum, the argument is that the system at present cannot collect and use sufficient data on individual students’ progress and learning abilities to make recommendations for instruction at a fine enough level of granularity. Product Standards The information technology infrastructure owes its existence not just to the creative minds and bold adventures, but even more to industry standards. Without standards and protocols for computer, software, and communications design, computers would be unable to talk with each other. Users would be stranded, as was originally the case, on a single disconnected computer. Over time, global standards have been evolved for portable software, replaceable computer parts, and the connection of computers into networks and for the communication of data. A similar evolution of multi-media product standards is occurring, making possible the brokering and exchange of technology-based instructional resources. A designer should be aware of these standards, their organizations, their goals, and their audiences: IMS global (http://en.wikipedia.org/wiki/IMS_Global), the open knowledge initiative (http://en.wikipedia.org/wiki/Open_Knowledge_Initiative), the AICC (http:// en.wikipedia.org/wiki/Aviation_Industry_Computer-Based_Training_Committee), ADL (http:// en.wikipedia.org/wiki/SCORM), IEEE/LTSC (http://www.ieeeltsc.org:8080/Plone), and LETSI (http://en.wikipedia.org/wiki/LETSI). Knowledge of the history of these organizations is also useful (see pp. 355–357). Sources that integrate standards information are helpful (see, for example, Ready-Go! (http://www.readygo.com/aicc/); Chew, 2008; and Richard, 2010). Rapidly Changing Infrastructure Some instructional designers during training wonder if they can find positions that do not require technological knowledge. There are places where it is possible, but they are fewer and fewer in

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number because even stand-up instruction is becoming blended with technology resources. Instructional design today demands an understanding of the technological ecosystem (Lucas et al., 2012). What many years ago was a mainframe computer with hundreds of terminals connected over telephone lines has become today a powerful tablet or smart phone operating wirelessly, pulling its content from a cloud. It is as important today to understand “clouds” and “tablets” as it was then to understand “telephone lines”—both technically and in economic terms. This kind of knowledge tempers the tendency to produce highly imaginative but technically impractical designs. The relationship between the organization (business, public school, university, military, government) and its technological infrastructure is fundamental. Today, organizations of any size depend on their information technology infrastructures. At one time the computer and its connections were peripheral to the operation of an organization: a word processor was a novelty. But that time is gone. Today the technological infrastructure of an organization is a main means of carrying out daily operations. When the technology system fails, the organization cannot function for long. The modern organization is becoming a core of information technology services, and business is conducted on the periphery of the software. The message for the instructional designer is that the organization’s information technology infrastructure has become a central strategic element of the organization. Therefore, the deepest goals of the organization will become ingrained in its technology infrastructure. If one of the main goals of the organization is education and training of its employees or its clients, then the instructional designer’s function will have an impact on the use—and in many case the design—of this system. Designers have to be prepared to make intelligent contributions to the evolution of this system when they are invited to the table. A key principle to understanding infrastructure configuration is to see it as the resolution of at least four sets of (sometimes conflicting) factors: (1) the organization’s goals and business plan, (2) the resources and constraints of the information technology group, (3) the limitations of the current technology, and (4) the flow of time and technology that creates new solutions and moves today’s system into obsolescence, even while it is still being used. An organization’s goals and plans have to be nimble in today’s competitive environment: The hierarchical structures and organizational processes we have used for decades to run and improve our enterprises are no longer up to the task of winning in this fastermoving world. In fact, they can actually thwart attempts to compete in a marketplace where discontinuities are more frequent and innovations must always be ready to face new problems. Companies used to reconsider their strategies only rarely. Today any company that is not rethinking its direction at least every few years—as well as constantly adjusting to changing contexts—and then quickly making significant operational changes is putting itself at risk. —(Kotter, 2012, p. 46) The larger and more successful an organization is, the more likely its customs, ways of thinking, and habits will become comfortable and entrenched. This is also true in the information technology department. The prosperity and daily operation of an organization depend on its technology, so the information technology group usually has considerable influence. These vested interests create the environment that the instructional designer enters: a growing organization, a strong business plan, IT department influence, limited resources, creeping obsolescence, and the hardening of the organizational arteries Designers enter the complex organizational environment with all of its vested interests as change agents in their own right. The designer’s work influences the culture of the organization and its

400 • The Designer’s Value-Added

ability to respond to changing conditions as Kotter described above. The instructional designer exercises cultural influence by speaking between the lines of instructional experiences to each of the five disciplines of the learning organization described by Senge (1990): • Personal mastery—“Personal mastery is the discipline of continually clarifying and deepening our personal vision, of focusing our energies, of developing patience, and of seeing reality objectively” (p. 7). • Mental models—“Mental models are deeply ingrained assumptions, generalizations, or even pictures or images that influence how we understand the world and how we take action” (p. 8). • Shared vision—“The practice of shared vision involves the skills of unearthing shared ‘pictures of the future’ that foster genuine commitment and enrollment rather than compliance” (p. 9). • Team learning—“The discipline of team learning starts with dialogue, the capacity of team members to suspend assumptions and enter into a genuine ‘thinking together’ ” (p. 10). • Systems thinking—“The systems perspective tells us that we must look beyond individual mistakes or bad luck to understand important problems. We must look beyond personalities and events. We must look into the underlying structures which shape individual actions and create the conditions where types of events become likely” (pp. 42–43). Senge quotes Meadows (1982), saying, “You begin to see that the system causes its own behavior”. Instructional designers influence individual behaviors and competencies, but they also influence organizational thinking patterns, visions, and ways of doing things in subtle and important ways. The vehicle for achieving influence is through instruction, which in the future will be increasingly supplied through an information technology infrastructure, whether it involves human or solely technological contact. A designer who is more knowledgeable about the principles of information technology infrastructures and how they relate to and use the global information technology infrastructures will feel more at home in this environment and have greater impact. Changing Design Competencies Because product architecture is maturing in the ways described earlier, the product is becoming more complex technically. This is due not to the over-sophisticated ideas of instructional designers but to the realities of the technical infrastructure within which the designer operates. Design today is a team process requiring the skills, architectures, and technical theories of specialists and more. A team’s organization may include technical specialists, since job categories on teams and layer design functions often correspond. But in addition, it will include specialists in the kinds of soft skills that move designers into the knowledge economy. Kelley (2005) describes a design team in these terms, naming roles that adhere to no particular technical category but that are important in the operation of a productive team: anthropologist, experimenter, cross-pollinator, hurdler, collaborator, director, experience architect, set designer, storyteller, and caregiver. Some of these categories have names that readily suggest the content of the role; other names provide surprises. A team leader-designer who is not conscious of these roles will still see many of them naturally occurring within a team, and just as with the use of theory in design, knowing about them gives the team leader a purchase on using them. The absence of one or more roles can hamper team function. An instructional design team is a team of people, often led by a designer, but design skills are hardly enough to lead a team. The value of the interpersonal skills of an instructional designer for the coming decades is hard to overstate: communication, ability to work with people, ability to listen and understand, sense of people, intercultural attitudes, openness to new ideas, persuasion skills, negotiating skills, and how to make confidence-inspiring presentations.

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Leadership skills will be increasingly important: the ability to work within an organization without being overcome by its bad habits and inertias, managerial skill, the ability to lead change, the ability to bring opposing factions together, the ability to plan, flexibility under changing circumstances, resourcefulness in solving problems, knowing how to represent a group idea to management, and a sense of directing without the need to be dictatorial. Visioneering is also a key: the ability to stimulate constant innovation without leaving the group behind, the ability to inspire people with ideas, the ability to participate in innovation sessions with a creative group. An instructional designer is also an increasingly important member of the organization itself because of the growing strategic value of education and training. The designer of education and training is increasingly being offered a place at the table where strategic options are considered. This means that a designer should be attuned to the dynamic strategic posture of the organization, whatever level of participation is offered (for an example of the dynamic nature of strategy, see Radjou et al., 2010). In the past, the training of instructional designers has not normally included a full range of topics such as these. The result is that unsuspecting and naïve designers sometimes have to drink in how to function within the organization from a figurative fire hose during their first critical year(s). Many of these topics can be absorbed on the job or during internships and apprenticeships. Project experiences, group experiences, and either courses or readings in organizational behavior and management can be helpful. Familiarity with how businesses work is useful knowledge (remember that even universities and public schools are becoming more like businesses); how funds flow through an organization and how values are calculated is important to understand. A designer-in-preparation may not be able to master this knowledge while in training, but being aware that it will be a future success factor can help one know where to look. The New Provider The provider is the channel through which instructional product makes its way to the consumer. Traditional channels of delivery are rapidly changing as education and training are increasingly influenced by entrepreneurship. Commercialization of K-12 and Higher Education Hess (2011) declares: “This is the era of educational entrepreneurship, to an unprecedented degree” (p. 1). He asks: What is educational entrepreneurship and what does it look like—both inside and outside school districts in the profit and not-for-profit sectors? Who are the educational entrepreneurs and what motivates them? What tools do entrepreneurs need to be successful, and what policies or practices enable or impede entrepreneurship? What would it mean to open up the educational sector to more entrepreneurial activity? What roadblocks and risks lie ahead if we take that course? —(p. 2) Hess’ book, Educational Entrepreneurship: Realities, Challenges, Possibilities shows a clear bias toward the advance of entrepreneurship in education and represents an opinion that is widely held, born of a dissatisfaction with present practices that some have described as “manufactured” (Berliner and Biddle, 1996), but which many in the public support through their continued migration to alternatives. One could observe that if there were no public opinion in that direction, there would be no market for the other choices.

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Smith and Petersen (2006) describe educational entrepreneurs as: Visionary thinkers who create new for-profit or nonprofit organizations from scratch that redefine our sense of what is possible. These organizations stand separate and independent from existing institutions like public school districts and teacher colleges; as such, they and the entrepreneurs who start them have the potential to spark more rapid, dramatic change than might otherwise be created by status quo organizations. —(pp. 21–22) The case for entrepreneurial education is advanced by authors like Christensen et al. (2010), whose credentials are in business. Christensen’s arguments for the disruption of traditional educational patterns are based on the premise that the delivery system for instruction itself is outmoded and will be replaced by a newer technology: not just one that consists of hardware and software, but one that is based on meeting needs of the individual learner in a way that the old technology of classroom and blackboard could not. Meeting individual needs is not a new concept in education. Glaser (1972) describes it as a primary goal for the latter half of the twentieth century: a goal that he began to champion in the 1960s that is still unmet as we advance through the early twenty-first century, Whether or not one feels comfortable with the new, rapidly changing commercial approach to education, it is a reality—one that strongly influences the future working environment of the instructional designer and implies areas of designer preparation that will be needed in the future. This will include an understanding of the new economics of education, the nature of the new educational product, and the leverage ideas that give the designer cognitive tools for adding value within this new marketplace. Educational Brokering by Corporations Whereas funding for public education has traditionally been supplied from the local level, there has been a growing trend of supplementation and special-purpose funding from state and national governments. This trend is now being joined by a new business plan that could be termed “educational brokering”, a commercial twist on an idea suggested by Meyer as “knowledge brokering” (Meyer, 2010). Brokering places a new kind of provider—the broker—between the producer and the consumer (see Figure 16.4). This gives the traditional provider more than one source of educational product and widens the gap between producers, providers, and consumers.

Producer 1

Provider 1 Consumer 1

Broker 1 Producer 2

Provider 2

Consumer 2

Broker 2 Producer 3

Provider 3

Figure 16.4 The organizational separation of the producer, provider, and consumer and the brokering relationship that is being inserted between producers, providers, and consumers, creating an entrepreneurial educational marketplace.

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An example of educational brokering is exemplified by an educational publisher’s offering of free learning management system services and hosting, which bundles the free services with access to other Web services in its offering to schools. The publisher’s offer removes a load of IT administration from the user organization (a school or district). According to Fischman, a publisher would do this “with the hopes of upending services that affect just about every instructor, student, and college in the country” (2012, n.p.). The publisher might perhaps hope that by providing an easy connection to LMS services it could broker its own for-profit educational resources, which would play through the LMS, providing as well the ability to play resources created by others, who may or may not have to pay a toll fee, either way giving wider exposure to the publisher’s educational resources. The publisher could also provide a service for the customization of textbooks that include mixed-supplier content (Azevedo, 2012). Additionally, the publisher could provide a proctored testing service that operated through local licensees to ensure the security of tests and standard administration conditions (Gaber, 2012). The publisher’s overall strategy would seem to be to supply a connected family of “open” (to other-developer products) modular services that add value to its own existing stock of educational materials. The combination of services that communicate with each other that previously required fragmented software management would be attractive. This is just one of the marketing approaches that will appear as entrepreneurial education becomes more common. Open Resources Khan Academy represents another form of educational entrepreneurship. It also matches the Smith and Petersen (2006) profile of those “who create new for-profit or nonprofit organizations from scratch that redefine our sense of what is possible” (pp. 21–22). Khan offers a library of thousands of educational video resources, at present charging nothing for their use (Noer, 2012). The overwhelming majority of videos have interesting and colorful presentations, often employing a visiting lecturer interviewed by Khan. However, recently offered courses in computer programming have begun to provide interactivity that includes writing and running programs interactively (Finley, 2012). Those familiar with Christensen’s description of how disruptive organizations overtake organizations that are slow to change will find this seemingly slight improvement in the Khan offering significant in the longer strategic view, because it plays out one step in the disruption process described by Christensen. Repositories Open repositories of no-cost, ready-to-use educational resources have been growing for many years, including associated recommender systems for selecting the right resource (Ariadne, 2005; Manouselis et al., 2009). Government sponsorship has supplied support for this trend in the past. Higher Education Coalitions Universities are banding together into brokering collaborations such as Coursera (https://www. coursera.org/; http://en.wikipedia.org/wiki/Coursera), edX (https://www.edx.org/; http://en.wikipedia.org/wiki/EdX), and Udacity (www.udacity.com/; http://en.wikipedia.org/wiki/Udacity) that offer either regular courses or MOOCs (massively open online courses) for free to anyone with the necessary broadband connection. Young (2012) explains that the vehicle for monetizing course offerings in the future is not settled for Coursera, and we may assume that the business plan for all of the collaborations will have to evolve: some sustainable position will have to be found. For now, establishing the brokering relationship between the largest number of top-rank universities and the largest number of learner audiences seems to be the priority. Whether the learners that are attracted will eventually be guided to paying for services from specific universities, a mega-university, or a third party university broker remains to be seen.

404 • The Designer’s Value-Added

At present the most popular product form for the coalitions is the MOOC (Hyman, 2012; Meyer, 2012; Papano, 2012). Who pioneered MOOCs is not as important as the fact that now institutions, including traditional schools and for-profit educators are experimenting with offering them. Schools may have been inspired and emboldened by the success and level of demand for the open learning courses offered by a number of universities to non-registrants. Both MOOCs and open courses represent universities trying out new business propositions in an effort to counter challenges from forprofit institutions on all sides that are growing in size and profitability, nibbling away at underserved and alternative populations of students, but also venturing into new offerings that begin to compete on the lower end of the traditional university population. This pattern of eating away the lower-end business products of an established industry is the one identified by Christensen in his description of disruptive innovation. Corporate Universities, Institutes, and Academies Corporate universities, more recently also referred to as institutes or academies, are a relatively recent development that has implications for instructional designers. In the 1950s, General Electric opened an executive training facility at Crotonville, New York, to provide new learning experiences to top employees, to expose them to outside opinion leaders in business and industry, and to get to know talented up-and-coming leaders within the company. Over time, Crotonville grew to be recognized as the prototypical corporate university (Knowledge@Wharton, 2012). Soon after Crotonville’s success, a number of corporations followed suit and established their own internal “university”. Most often the purpose of corporate universities was to meet employee training needs in a way that could not be met using outside sources. Early on, university consultants were used to provide training. Enthusiasm was high in the 1980–1990 time frame, but it lessened in the 2000s as corporations encountered unsupportable costs and what they felt was a too-academic approach to the curriculum (Paine, 2008). The form of the corporate university has evolved in many cases into a service organization that supplies company-specific training and development services from a central source tied closely with organizational goals. Corporations are experimenting with different ways to institutionalize their training function, and there is a great deal of variety in how this is accomplished. (For an example of the changeability, compare Bersin, 2006 with Bersin, 2008.) The willingness of corporations to expend funds on experiments with education indicates the importance of the instructional function, which is being moved toward the core of virtually every large institution. The use of learning management systems in some form will grow as talent management and career-long development of employees continue to increase in importance. New Learning Themes Our understanding of learning processes and how instructional processes interact with them is today much different from only twenty years ago. The recent publication How People Learn: Brain, Mind, Experience, and School (Bransford et al., 2000) summarizes the findings of learning research in multiple disciplines over the last thirty years. It concludes that: “A new theory of learning is coming into focus that leads to very different approaches to the design of curriculum, teaching, and assessment than those often found in schools today” (p. 3). The picture of learning and instruction presented is less like the monolithic learning theories of the past and more like a family of theoretical insights that relate theory to practice. These conclusions are supported by work originating in experimental psychology, child development,

Adding Value to the Organization • 405

anthropology, social psychology, neuroscience, and applied instructional design practice and field trials in classrooms. Theories of learning have moved out of the laboratory and into the place of learning. With this shift in understanding has come an understanding of what is useful to learn. Information and knowledge are growing at a far more rapid rate than ever before in the history of humankind . . . The meaning of “knowing” has shifted from being able to remember and repeat information to being able to find and use it . . . More than ever, the sheer magnitude of human knowledge renders its coverage by education an impossibility; rather the goal of education is better conceived as helping students develop the intellectual tools and learning strategies needed to acquire the knowledge that allows people to think productively. —(p. 5) The new premise is that people “construct new knowledge and understanding based on what they already know and believe” (p. 10). The implication is that “teachers need to pay attention to the incomplete understandings, the false beliefs, and the naïve renditions of concepts that learners bring with them to a given subject” (p. 10). This places the topic of improving assessments described earlier into perspective, along with the topic of analysis (of learner needs and interests) and recommendation. The current state of a learner’s knowledge and belief can only be tracked through assessment. It is significant that this statement about learning includes the notion of beliefs and ties them in with learning. How People Learn emphasizes that learners come to the learning occasion with preconceptions and beliefs about how the world works. These beliefs are gained through experience, and sometimes they are at cross-purposes with empirical knowledge gained through observation and experimentation by scientists. In such cases, the research shows that learners are capable of learning the new knowledge presented to them, but then revert to their prior native knowledge after instruction and testing are over. The strong influence of belief on learning would indicate that instruction is an appeal to the heart as well as the mind. A later section considers the need to consider the role of emotions during instruction. How People Learn relates instructional theory with learning theory; this is one of the emphases of the new approach to learning theory—it brings theory out of the laboratory and closer to where it is applied. It also places questions of instructional technique that are hotly debated in the literature into new perspective. Consider the many possible teaching strategies that are debated in education circles and the media . . . lecture-based teaching, text-based teaching, inquiry-based teaching, technologybased teaching organized around individual versus cooperative groups, and so forth. Are some of these teaching techniques better than others? Is lecturing a poor way to teach, as many seem to claim? Is cooperative learning effective? Do attempts to use computers (technologyenhanced teaching) help achievement or hurt it? This volume suggests that these are the wrong questions. Asking which technique is best is analogous to asking which tool is best—a hammer, a screwdriver, a knife, or pliers. In teaching as in carpentry, the selection of tools depends on the task at hand and the materials one is working with. —(p. 22) Two emerging issues in learning are: (1) the central role of emotion in learning, and (2) the extent of personal knowledge that is inaccessible to conscious management.

406 • The Designer’s Value-Added

Learning and Emotion Learning studies have for so long focused on learning as an essentially intellectual and rational process that most instructional designers consider strategies appealing to cognition to be adequate. Despite a large body of literature on the importance of appealing to the emotions, instructional theories for the most part emphasize structure, treating moves that appeal to the senses, the heart, relationships, and the enjoyments of playfulness as decorations rather than core stratagems. That view seems to be changing. Evidence of the change comes from many directions. Jerome Bruner (1983b) found insights into learning from observing children. In particular, he notes that children’s play has a strong relationship to learning. “It provides”, he claims, “not only a medium for exploration, but also for invention” (p. 61). He says that play is often “an idealized imitation of life” (p. 61). In more current terms, we might use the word “simulation” in this context to claim that much of a child’s play is a simulation, an imitation of what the child has seen others do, especially adults. Thus many of the toys played with by children are miniatures of real objects—trucks, miniaturized human forms, and play structures. Most importantly, Bruner says that “play gives pleasure—great pleasure”. He maintains that “unless we bear in mind that play is a source of pleasure, we are really missing the point of what it’s all about” (p. 61). “There is no question that the games of childhood reflect some of the ideals that exist in the adult society and that play is a kind of socialization in preparation for taking your place in that adult society” (p. 62). Play is a natural means of bringing about positive emotional states associated with learning. Rogoff et al. (2003) indicate that this positive emotional state can be created through guided participation, or what they also terms “intent participation”—learning in which the learner intends to take part: In such a tradition for learning, even very young children participate productively in their parents’ work activities, frequently on their own initiative, out of recognition of the importance to the family of what they are doing. The attraction of the activity itself provides a self-evident inherent motivation that is supported by parental expectations along with admonitions and direct indications as to what is to be done. —(p. 190, emphasis added) In an earlier work, Rogoff explains, “problem solving is not ‘cold’ cognition, but inherently involves emotion, social relations, and social structure” (Rogoff, 1990, p. 10) Other means can be used to create emotional states for learning that do not require the ancient practice of children working in a field with their parents, but these means do require an attitude of social cooperation and willing engagement in a productive activity that the learner values. It is that need for valuing—that willing participation—that underscores the importance of emotion and feeling in instruction. Damasio (1994) proposes that it is not just learning that is intertwined closely with emotion, but the entire edifice of human thinking and reasoning: I began writing this book [Descartes’ Error] to propose that reason may not be as pure as most of us think it is or wish it were, that emotions and feelings may not be intruders in the bastion of human reason at all: they may be enmeshed in its networks, for worse and for better. The strategies of human reason probably did not develop, in either evolution or any single individual, without the guiding force of mechanisms of biological regulation, of which emotion and feelings are notable expressions. Moreover, even after reasoning strategies became established in the formative years, their effective deployment probably depends, to a considerable extent, on a continued ability to experience feelings. —(p. xii, emphasis in the original)

Adding Value to the Organization • 407

One could conclude that any instructional design that did not take the sometimes conflicting and always dynamic forces of emotion into account in designs was missing an important ingredient. This was a major part of the message of Chapter 7, which attempted to show that the most crucial energies a designer influences during instruction are of an emotional nature. Tacit Knowledge Not all learning is conscious, and not all knowledge is directly accessible through conscious processes. Such knowledge has been referred to by different names, but often it is called “tacit” knowledge. Damasio (2003) proposes that knowledge, tacit and otherwise, has its origin in emotions and feelings: “Emotions and related phenomena are the foundation of our feelings, the mental events that form the bedrock of our minds” (p. 28). These feelings are related by Damasio to the educational process: In effect, one of the key purposes of our educational development is to interpose a nonautomatic evaluative step between causative objects and emotional responses. We attempt doing so to shape our natural emotional responses and bring them into line with the requirements of a given culture. —(p. 28) It is important to note that Damasio does not claim that the purpose of educational development is to extinguish emotional responses but to shape them for the benefit of the individual so that he or she can operate within a particular cultural setting. Rogoff and Gardner (1984) describe a process of “proleptic” instruction that integrates explanation and demonstration by a tutor with the learner’s execution of a performance that is being learned. That is, the learner performs within Vygotsky’s zone of proximal development in order to learn and receives guidance and help when needed by the tutor. This form of instruction is described as being appropriate in both formal and informal learning situations. The purpose of noting this form of instruction is not to promote it but to show that Rogoff and Gardner acknowledge that: Proleptic instruction functions as a deliberate but tacit process which the participants construct in the course of communication. They proceed opportunistically in the transfer of information and skills in a purposeful, flexible way, making use of the pragmatic aspects of the context to develop the means of instruction. Instruction can be conceived as a complex, tacit process developed in a particular problem situation. —(p. 103) The importance of referring to the instructional method as tacit is two-fold: first, the process itself is understood but not metacognized by the participants, and second, the knowledge that is gained is not all of the kind that can be easily verbalized or expressed by either of the parties to the instruction. One of the expected benefits of this type of instruction is that tacit knowledge is conveyed as well as explicit knowledge (knowledge that can be verbalized and dealt with at a conscious level). Miller et al. (1960) describe the progress of a piano lesson in which the piano teacher can explain and demonstrate but in which there comes a point that the teacher wishes to convey knowledge that cannot be conveyed in words or demonstrations. Perhaps the knowledge has to do with a subtle but important movement or sequence of movements or the timing of the movements, perhaps even the dynamics applied to the movements. Very often, this is the very knowledge—tacit knowledge—that leads to the rapid development of expertise, style, and fluency of performance.

408 • The Designer’s Value-Added

Polanyi (1967) describes tacit knowledge, saying “we know more than we can tell”. Amplifying Polanyi’s theme, Bereiter and Scardamalia (1993, pp. 47–48) define three kinds of expert knowledge that they call “hidden knowledge” that represent forms of tacit knowledge: • Informal knowledge—Knowledge that makes it possible to make predictions in new situations at above the chance level, but where the knowledge required would have to come from experience, not formal instruction. • Impressionistic knowledge—The knowledge that influences decisions in the absence of full factual knowledge. The knowledge that makes hunches possible. • Self-regulatory knowledge—The knowledge that makes it possible to do things that require self-mastery and self-regulation. The concept of tacit knowledge is important to the new instructional designer because it is easy to ignore the importance or even the existence of this key performance knowledge. These three new conceptions of learning—knowledge as construction, learning as a process mediated by emotion, and tacit knowledge—are important to instructional designers because they represent a departure from classical learning theory, and because they describe learning in a way that leads to design principles. They represent a category of the self-directed curriculum of the designer that will become increasingly important in the future. The Knowledge Economy Preparing learners to participate in the knowledge economy involves coming to understand the knowledge economy as an economic, social, and competence phenomenon (Kahin and Foray, 2006). As the term is used here, “knowledge economy” refers to knowledge as a kind of capital that is mobile, being the possession of an individual. The individual, as a free participant in a mercantile society, can treat the fruits of that knowledge as if it were physical capital. This makes the possessor of the knowledge a participant in an economy where capital consists of the ability to apply knowledge to unsolved problems to produce innovative solutions and additional knowledge. Drucker emphasizes the importance of understanding this phenomenon: “The most valuable assets of a 20thcentury company were its production equipment. The most valuable asset of a 21st-century institution, whether business or non-business, will be its knowledge workers and their productivity” (Drucker, 1999, p. 135). As a society’s members come to possess more valuable knowledge and skills, the society itself profits. Just as with other economies, the downside of the knowledge economy can be seen where restrictions are placed on the ability to freely market goods (Stempel, 2012). It is widely accepted that knowledge is acquired through social associations (Vygotsky, 1978; Rogoff, 1990). This suggests that the knowledge economy is also a social phenomenon. That is, intelligent problem solvers learn by associating in some way with other intelligent problem solvers, whether that is in person, through professional associations, through shared lab experiences, or only through a body of literature. A designer interested in preparing learners for the knowledge economy should understand the many different approaches to cross-fertilizing and propagating knowledge that go beyond direct instruction, including learning participation in the knowledge economy by participating in local knowledge-producing economies as a learner-contributor (Bereiter, 2002). Drucker (1999, p. 142) names six factors that determine knowledge worker productivity.

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• Knowledge worker productivity demands that we ask the question: “What is the task?” • It demands that we impose the responsibility for their productivity on the individual knowledge workers themselves. Knowledge workers have to manage themselves. They have to have autonomy. • Continuing innovation has to be part of the work, the task, and the responsibility of knowledge workers. • Knowledge work requires continuous learning on the part of the knowledge worker, but equally continuous teaching on the part of the knowledge worker. • Productivity of the knowledge worker is not—at least not primarily—a matter of the quantity of output. Quality is at least as important. • Finally, knowledge worker productivity requires that the knowledge worker is both seen and treated as an “asset” rather than a “cost”. It requires that knowledge workers want to work for the organization in preference to all other opportunities. An instructional designer needs to understand the knowledge economy from the inside, from being a knowledge worker. This implies deeper preparation than most designers receive. Designers need to become aware of themselves as knowledge producers as well as product designers. This emphasizes the importance of knowing how to apply design-based research techniques as a part of the normal design process. Organizations sponsoring instructional design in most cases already understand this process for bringing ideas from mind to market under the heading of “research and development” (see, for example, Jolly, 1997). Conclusion: Building the New Designer The diverse trends described in this chapter (and others not mentioned as well) will shape the designer’s workplace and daily practice over the coming decades. Therefore, they should shape the training needed by new designers, much of which will have to be supplied by the designer’s own initiative. The future world of design is a world of great opportunity, especially for the designer who understands design principles at a deeper level. Instructional design has become a team sport, and the designer is an important participant in the big game of creating organizational value. Whatever the designer’s future workplace may be like, a designer needs to maintain a proactive professional stance by being prepared to add specialized value in a constantly changing world. A comfortable job in a secure position is not what the future holds for most instructional designers. A designer to an increasing extent will be required to be a problem solver who understands where new value is for the provider, the producer, and the consumer, and who is constantly looking ahead for opportunities to bring value to all of them.

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Appendix A Target Population Analysis

Target Population Analysis: The Problem Instructional designs need to be informed by the characteristics of the population being instructed. Target population analysis (TPA) gathers important data about the intended learner without which it is difficult to make intelligent design decisions. There are so many dimensions on which learners vary that influence learning: • • • • • • • • • • •

Their views of themselves Their views about how to go about learning The extent and depth of their prior learning Their motivation to learn The perceived value of learning to themselves The speed at which they form new ideas and values Their willingness to engage and integrate new ideas Their physical and psychological readiness for learning Preferences, tastes, values, styles Communication patterns Patterns of responsibility-taking.

Activity during target population analysis consists of: 1. Identifying key characteristics of your learner group (the target population) 2. Gathering data from reliable sources on how your learners rate in these characteristics 3. Drawing implications from these findings and spotting design opportunities. The output of target population analysis is a list of implications for the design. This provides important input for later decisions.

References Schwen, T. (1973). Learner analysis: Some process and content concerns. Audio-Visual Communication Review, 21(1), 44–72. Smith, P. and Ragan, T. (2005). Instructional Design (3rd ed.). San Francisco, CA: Jossey-Bass.

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Target Population Analysis: The Process TPA is about focus. It is the tool the designer uses to identify learner characteristics that can confer advantage on a design. A target population may be very restricted or quite varied. TPA allows a designer to discover the mean value of each population characteristic for the purpose of designing to the mean, but it can also be used to identify variations away from the mean that point to advantageous adaptations to the individual. TPA normally proceeds in stages: Stage 1 Obtain the best available summary of target population demographics. Try to picture the group as a whole. Find information from as many sources as you can. Compare data between sources for accuracy. The list of characteristics of interest given below in this section will provide a few ideas about what kinds of information to be looking for. Keep your own list of characteristics current. Identify the characteristics where you think your learners differ from the general population. These are advantage points. Stage 2 Identify characteristics on which you think your learners are most likely to differ within their own group. Subgroup differences within your population may strengthen or weaken the case for some form of adaptivity. Stage 3 Define additional population research questions on the basis of your discoveries so far about population differences and subgroup differences. Stage 4 Find data to answer your research questions. When the answer you need is not available from an outside source, consider gathering your own. A list of methods and sources is suggested under “key principles” below. Stage 5 Document all implications using the table format given below. For each finding there may be several implications. Where do designers go to find implications? They go to theory—formal and (in its absence) informal. Designers study theories to link the causes they can devise with instructional outcomes.

Target Population Analysis: Principles What to Look For There exist an uncountable number of learner characteristics and characteristic combinations that could possibly influence instruction positively for a given target population. How does a designer know what to look for and what to pay attention to? How can you know the best questions to ask? It helps to look for obvious differences between your target population and the average person. Are your students more motivated than the average? Are they smarter? More curious? More patient? The more you look, the more you will see the uniqueness of your population. You will also begin to see subgroups within the population marked by differences. Some of these will have important design implications.

Appendix A • 413

Sources of Information about the Target Population Target population information can come from several sources. Whatever source you consult, assess its reliability and accuracy. Sources of target population data include: • Interviews and observations. Be sure the people you use for this purpose are representative of the cross-section of your population. Interviews with instructors can be useful but sometimes reveal stereotypes, assumptions, and misinformation, so choose your instructor from among the best. • Textbooks and articles. Books, chapters, and articles are published with titles like “The Adult Learner” or “The Web Learner”. Summaries of research reflect questions that researchers have asked, but not necessarily everything that could or should be asked. • Marketing studies. Large training organizations, seeing the student as their client base, conduct formal studies of their characteristics. Government reports may be available. Deriving Implications The implications you derive from your data findings will depend on: • • • •

The accuracy of your data The theories or principles of instruction you value most Your own viewpoint on instructor–learner roles and responsibilities Project resources and constraints.

Source John Doe et al. (2010) What is the source of informaon?

Mr. Wallace (instructor)

Implicaons

Finding Learners at this age are likely to process visually represented informaon beer.

Consider more graphical format for workbooks, computer displays, etc.

Aenon span tends to be shorter, and restless energy tends to be high.

Informaon should perhaps be conveyed in smaller increments with more opportunity for hands-on acon.

What did it tell you?

Most students’ families tend to be interested in the child’s learning and are supporve of the teacher.

Consider more frequent communicaon with parents.

Learners like to show off their work.

Maximize the use of posters, showand-tell, parent nights, etc.

What opportunies can you see?

Figure A.1 Target population analysis, “tools” page.

414 • Appendix A

It is impossible to identify “right” principles. Valid but opposing views lead to very different implications from the same data. Target Population Analysis: Tools TPA performs a mapping between population characteristics and their implied design alternatives. The best tool for recording these mappings is a table in the form of Table A.1. Target Population Analysis: Insights Gaining Experience with a Target Population The value of a TPA depends on your knowledge of the learners and of learning. It depends on the degree of insight you have for the particular population. Therefore, designers improve the quality of TPA over time if they have designed more than once for the same population. Each time, experience accumulates that provides insight into the specific population. New areas become apparent that didn’t register before as being important. A Competitive Edge A TPA, formal or informal, represents an important competitive dimension of your product. One designer with better insight may arrive at more effective conclusions than another. Within organizations, an improving understanding of the learners in the population and how to communicate with them becomes one of the foundations of an organizational product style. TPA boundaries are therefore broader than the individual design project. Project-specific Interests Sometimes projects have design goals that increase interest in certain population characteristics. For a project whose population places value on group action, the designer will be interested in greater detail about the characteristics of how the student population works within groups. If distance learning is the project goal, then the designer will be interested in the population’s prior experiences with distance learning. Target Population Analysis: “Tickler” List Area: Personal data Physical: age, height, weight, gender Location: residence, domicile description, atmosphere for learning, facilities for study Family: size, status, responsibilities, history, family relationships, traditions Ethnicity: race, knowledge of race history, attitudes, value placed on heritage Financial: economic level, source of funds, responsibilities, financial habits, working? Self-image: respect for self, image of self Aspirations: future plans, hopes, long-term goals Maturity: respect for self, goal-directedness, self-management, self-control Social: communication skills, sense of position in social settings, respect for others Intellectual: general interest in formal learning, theoretical–practical bias Flexibility: acceptance of change and novelty. Area: Existing skills and knowledge in target content Skills in content area Confidence in skills

Appendix A • 415

Knowledge and content area Confidence in knowledge Existing non-instructional experience with the content or skills. Area: Existing skills and knowledge not directly related to the target content Areas of expertise, breadth of interests, depth of interest, motivation for interests Technical (tool skills), cognitive skills, metacognitive skills Self-directed learning skills and attitudes Typical or favored problem-solving skills and approaches. Area: General educational history Level of education attained, attitude toward learning, enjoyment of learning Educational history, level of attainment, attitudes toward formal instruction Level of confidence placed in formal instruction Preferred modes of learning. Area: Special needs Health impairments Physical impairments Attention impairments Learning impairments Emotional impairments Learning gifts. Area: Developmental history Cognitive: cognitive processes matured, processes information Social: social processes matured, processes information Emotional: emotional processes matured, processes information Leadership: leadership abilities, desire for leadership. Area: Interests, unconstrained activities, and likes Favorite free-choice activity, hobbies, allocation of time to activities Preference for group activity, group preferences, favored group role Undeveloped interests Favorite personalities, heroes, role models Favorite entertainment Entertainment–productive activity ratio. Area: Attitudes and values Things of highest value, altruisms, commitments Antipathies Attitude toward the future—general (positive, negative) Attitude toward the future—personal (positive, negative) Feelings of efficacy, feelings of self-determination, victim feelings Attitude toward target instruction Attitudes toward learning and toward the course.

416 • Appendix A

Area: Learning preferences and aptitudes Memory aptitude, confidence in memory Representation preferences (visual, verbal, concrete) Strategic preferences, control preferences, order preferences, interaction preferences Rates of new learning, need for redundancy and review Ability to concentrate Ability to think divergently.

Appendix B Current Training and Resource Analysis

Current Training and Resource Analysis: The Problem To give your product the best chance of survival, you study as many aspects as possible of the environment that it will eventually live in, in the amount of time available. You draw implications for course design and management from this description. Current training and resource analysis (CTRA) details the environment in which the instructional system will function and draws out implications for product design and management. When you roll your product out of the design shop into regular use, it must be connected to the world through channels of supply and participate in regular cycles of preparation, administration, and maintenance within the larger organization. It must also find acceptance among those who use it—among instructors who must be enthusiastic and supportive, and among a continuing stream of students who represent an ongoing “client” base. Current training and resource analysis is where you find out about environmental, resource, and historical factors of the organization and begin to think in terms of design plans that may enhance product success and survivability. Current training and resource analysis is quite similar to target population analysis. It is a data-gathering and implication-drawing process. But the questions asked during current training and resource analysis are different. Activity during current training and resource analysis consists of: 1. Identifying key characteristics of the product environment 2. Gathering data from multiple reliable sources on that environment 3. Drawing implications from these findings and spotting design opportunities.

References Banathy, B. H. (1968). Instructional Systems. Belmont, CA: Fearon. Banathy, B. H. (1996). Designing Social Systems in a Changing World. New York: Plenum Press. Harasim, L., Hiltz, S. R., Teles, L., and Turoff, M. (1996). Learning Networks: A Field Guide to Teaching and Learning Online. Cambridge, MA: MIT Press. Havelock, R. G. (1995). The Change Agent’s Guide (2nd ed.). Englewood Cliffs, NJ: Educational Technology Publications.

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Current Training and Resource Analysis: The Process The goal of CTRA is a list of implications for the design. The goal of the process is to describe things as they are now and how things have been in the past. You would like to know what resources you have to work with, how things are done, and what mistakes have been made in the past trying to correct the problem you are solving. The process normally follows stages like these: Stage 1 List project design goals and constraints as given. These will normally come to you with the statement of the design problem. The problem may be to minimize a cost, relieve limitations of a previous product, or to make better use of resources. Constraints include upper limits on personnel, skill, time, funds, facilities, and so forth. Note these down in priority order. Stage 2 List additional goals and resources that were not given with the problem as received but which are apparent from your observations. Administrators may not understand all of the implications or causes of the problem. They may not see all of the resources that could be used. Ask where is the thorn in this lion’s paw? Stage 3 List characteristics of the organizational environment and its current training practices that will influence the design. List also the resources that the organization provides now or might provide to the instruction. Use the list provided later in this section to give you ideas on things to look for. Pick items that seem to represent the greatest challenges or opportunities for the project. Stage 4 Gather data for each item on your list from more than one source. Describe how things are done and what has been done in the past to solve the current problem. This will help you avoid recommendations that have already failed. Be aware of vested interests, local empires, and political conflicts. Stage 5 For each finding, draw out implications for the design—things that may both maximize learning and improve the use of available resources. Think also in terms of things that will make your product survivable in the environment of the organization. Stage 6 Document all implications using the table format given below. For each finding there may be several implications. Current Training and Resource Analysis: Principles Consider these principles that guide current training and resource analysis. Observing What’s Really There New designers can be so caught up in their own designer views that they fail to observe the world their product will live in with fresh eyes. They sometimes forget to ask enough about the prior products their product will replace and the reasons for their failure. They also may forget to take adequate notice of the personnel, time, funding, and physical resources available to sustain their product. The purpose of current training and resource analysis is to identify these things and to begin thinking ahead about design and management implications.

Appendix B • 419

Learning the Organization CTRA is all about taking in the big picture of the client organization, its motives, its real values, its incentives, and its political issues. The list of factors given below is suggestive of these issues. It is not a complete list, so the designer should begin to add to it and keep it as another tool in the toolbox. Gathering Data CTRA data comes from many sources. That’s the purpose of the “source” column of the CTRA worksheet given below. Data gathering usually occurs through interviews, and stories always come with a particular point of view. The designer’s job is to understand the biases in light of the organization’s stated goals. Informal contacts at lunch or social gatherings are often as valuable as formal interviews. Drawing Implications Part of the value-added of a designer is the ability to see aspects of a problem that others miss. You may discover unused resources and opportunities. There is no Complete Book of Implications to guide a designer. This is an area of designer judgment informed by experience. CTRA can be a fascinating puzzle. It is an interface between the idealistic world of conceptual design and the messy real world. Implications during CTRA should focus on the middle ground of improvements that can be tolerated by the organization, while still helping it to move into a sustainable future. Current Training and Resource Analysis: Tools CTRA creates a map between environmental characteristics and implied design alternatives. The best tool for recording these mappings is a table of the form shown in Figure B.1 below.

Source

Finding

Ms. Johnson

Training material has to be approved by department heads and the CLO’s execuve.

Consider more graphical format for workbooks, computer displays, etc.

Who is the source of informaon?

Last year’s total support for this training is esmated at $250,000 organizaon-wide.

Informaon should perhaps be conveyed in smaller increments with more opportunity for hands-on acon.

Students reported special dislike for the bulky manuals used in the previous course. They prefer portable informaon resources.

Consider new formats and possible mobile implementaon.

The CLO is planning an organizaonwide upgrade of computer support for training. This will take place in Q2 next year.

This may exceed the amount Johnson esmated for last year’s expenditure.

Implicaons

What did you learn?

Mr. Jones

Ms. Wilson

Figure B.1 Current training and resource analysis, “tools” page.

What opportunies can you see?

420 • Appendix B

Current Training and Resource Analysis: Insights Systems Analysis CTRA is really a kind of systems analysis that discovers the context in which your product will live. When the literature speaks of instructional “systems”, it means not only the instructional product as a system, but how the product communicates with the outside world and draws sustenance from it. The instructional system becomes part of the larger system that it serves, and this is made possible by analyzing the organizational system through CTRA. CTRA Benefits Benefits from current training and resource analysis include: • • • • •

Maximizing reuse of resources Improving overall cost effectiveness of training Improving design–development timescales Improving effectiveness of the instruction Bringing about sustainable change in organizations.

New designers sometimes reason that by changing the rules or the tools of an organization you automatically change the organization’s functioning and culture. It is seldom that simple. The history of instructional design is filled with examples of how seemingly insignificant changes prescribed by a designer became quite difficult to achieve and sustain. Major factors in the difficulty of changing human institutions are habit, convenience, and incentive. Change is harder than most designers realize. For this reason, it is useful for a designer to have some background in organizational behavior. Incentives for individuals within organizations can be hidden and complex. Usually a single tradition is held in place by a web of incentives. In the study of existing systems a designer has to become aware of this invisible network, and realize that designs will have to supply new incentives to replace ones that are changed by a design. Current Training and Resource Analysis: “Tickler” List The Organization • • • • • • • • • • •

Organizational chart showing reporting structure, rules Organization–behavior/processes/culture/history/response to change Personnel information for each person Authority structure, power structure, leadership structure Responsibility channels for training Funding channels for training Organizational history Organizational goals/“the business” Competing organizations/their training Future organizational trends Management/labor climate.

Training Practices • Training schedules • Use of training from outside vendors

Appendix B • 421

• • • • • • • • • • • • • • • • • • • • • • • • • •

Inter-organizational sharing of responsibility for training Other suppliers of training Clients of organization’s training Funding patterns slice/funding channels/funding procedures/funding priorities Accounting structures related to training/accounts of importance Training locations Location/operating cost/maintenance costs/availability/scheduling/layout Training equipment—hands-on Location/operating costs/maintenance costs/availability/scheduling Training equipment—media Location/operating costs/maintenance costs/availability/scheduling Training budgets Control/scheduling of training resources Training policies Training goals—implicit Training goals—explicit Degree of training emphasis within organization Corporate emphasis (cost, quality, timing) Consequences to training success/failure Measures of training success—short-term/long-term Instructor training programs/emphasis Commitment to personnel Attitude of personnel toward organization Salary scales/personnel classifications/advancement criteria Existing pools of instructional resources—libraries, etc. Standardization of training across sites

Products • • • • • • •

Product list Competing products—internal Product weaknesses and strengths Current effectiveness levels Provisions for effectiveness monitoring Overall course structure/coordination/prerequisites/schedules Reusable elements.

Product Development • • • • • • • • • •

Dimensions (footprint) Development/maintenance Formal approach Tools Management Resources Organizational support levels Volume/flow patterns Schedules Personnel roles

422 • Appendix B

• • • • • • • • • • • • •

Funding patterns and channels Delivery Preferred media Referred methods Management Resources Organizational support levels Volume/flow patterns Schedules Personnel roles Student incentives Instructor incentives Records maintained/LMS.

Appendix C Evaluation Planning

Evaluation Planning: The Problem Systems can be designed to correct their own future course and make adjustments to changing conditions. The systems that survive over time are those that can adjust to dynamic environmental conditions. Evaluation planning is the process by which a designer equips an instructional system with the ability to “sense” its environment and make adaptive changes. The problems that arise from inadequate evaluation planning include: • • • • • •

Training managers lack data for making revision decisions. Course enrollment changes, and there is no way to explain it. Designers lack feedback on whether new designs are working. There is no information on levels and patterns of course usage. There is no database for making logistical decisions about course offerings. Students find some course content errors but have no one to tell.

Evaluation planning is the design process that: (1) quality-controls the product during design and development, and (2) produces evaluation plans that go with the instructional system when it is rolled out for regular use. Evaluation planning consists of: 1. Defining questions that the system needs answered on a regular basis 2. Identifying data that can be gathered in normal use to answer the questions 3. Defining procedures, policies, and guidelines to guide regular collection of the data 4. Identifying people and responsibilities that will ensure the data gets collected.

References Kirkpatrick, D. (1998). Evaluating Training Programs: The Four Levels. San Francisco: Berrett-Koehler Publishers. Senge, P. (1999). The Dance of Change: The Challenge to Sustaining Momentum in Learning Organizations. New York: Doubleday. Worthen, B. and Sanders, J. (1987). Educational Evaluation: Alternative Approaches and Practical Guidelines. New York: Longman.

423

424 • Appendix C

Evaluation Planning: The Process The goal is an evaluation plan. This involves the stages of activity in Figure C.1: Stage 1 Define the scope and phases of evaluation. Determine: (1) the phases of evaluation, and (2) the things to be evaluated. Stage 2 Identify evaluation questions at the highest level. These general questions will focus the evaluation on the things that you selected to evaluate during stage 1. Stage 3 Refine the questions to a more detailed and answerable level. Stage 4 Identify data that can be used to answer each detailed question. Identify “countables” that can answer the question. There may be multiple countables related to a single question. Stage 5 Identify methods and instruments for gathering each element of data. This links conceptual questions to specific data gathering methods and measurements. Stage 6 Outline evaluation schedules/cycles. Define the time-cycles of activity for data-gathering. Ongoing evaluation cycles require a schedule of repeating events. Assign evaluation responsibilities. Most of the evaluation will be performed by personnel already working within the instructional system. Designate tasks and the personnel who will be responsible for them. Stage 7 Specify evaluation policies and procedures. To give your data as much interpretability as possible, specify the standard procedures to be followed while taking measurements. Also state policies to cover the recording, communication, storage, handling, access, and confidentiality of measures taken. Stage 8 Document evaluation plan as a separate document or as an annex to the management plan. Evaluation Planning: Principles Phases of Evaluation It is useful to distinguish five phases of evaluation: • • • • •

Formative evaluation—Ongoing during development to quality control the product Implementation evaluation—Intensive during implementation Normal cyclic evaluation—Ongoing during product use to monitor, guide, and correct Evaluation against external criteria—Intensive during special comparison studies Studies for data reporting and publication—During study of the product by itself.

Appendix C • 425

Questions of Evaluation • Formative evaluation—Questions related to the instruction’s accuracy, completeness, suitability for the audience, and conformance to design specifications • Implementation evaluation—Questions related to proper functioning of the product, logistical flow, adherence to instructional plans, effectiveness, and cost • Normal cyclic evaluation—Questions related to continuous operation of the product, noting fluctuations in quality, effectiveness, or cost • Evaluation against external criteria—Questions about the suitability of the product for its larger purpose, comparison with original goals, needs, other solutions, and cost • Studies for data reporting and publication—More detailed but more restricted questions to be studied in depth to determine the source of an observed fact and control it. Refining Questions If a focusing (high-level) question asks: “Does the product adhere to the design guidelines?” several refined (and measurable) questions follow: “Was the strategy implemented as outlined?” “Are graphics and text properly integrated?” “Are screen designs appropriate?” “Are prescribed interaction patterns used?” “Is the specified level of language used?” “Is the message well expressed?” Input from CTRA to Evaluation CTRA uncovers current evaluation practices. It also describes resources that may be used for evaluation purposes. The current training and resource analysis results should be reviewed during evaluation planning. Evaluation Planning: Tools Evaluation planning is a mapping process. A table like that in Figure C.2 can be used to capture the mapping of questions to procedures. Evaluation Planning: Insights Priority of Evaluation Activities Some designers do not place sufficient value on evaluation activities. Some worry that it exposes the faults in products they have created. Experienced designers place high value on evaluation for that very reason. Competitively, evaluation is essential. In organizations that place no value on honest evaluations more is spent on product development and maintenance, and products tend to have a shorter productive life. A balanced evaluation system gathers data on a wide spectrum of questions so that the designer can trade costs against benefits using data as a guide. The trick is to gather the data that tells the whole story, even if it is not what you wanted to hear. Evaluation and Needs Analysis During needs analysis (front-end analysis conducted by the organization) a problem or change was detected that moved the organization to make some decisions. These decisions concluded that

426 • Appendix C

High-level quesons

Detailed quesons

Instruments

Data types

Procedures Schedules

Data sources

Reports Responsibilies Policies

Figure C.1 Evaluation planning, “tools” page.

Queson

Detailed

Data

Instrument

Do learners find the product easy to use?

Are all learners able to complete the learning tasks?

Total compleons compared with starts

Automated data management system

Automac capture aer the event

Are there signs that some learners are having difficulty?

Number of unfinished tasks

Automated data management system

Automac capture aer the event

What is the high-level queson?

What can I count?

Procedure

How will I gather the data?

What could be used as evidence?

Do learners enjoy learning using the product?

Are there signs that some learners are having difficulty?

Number of spontaneous comments

Instructor reports

Instructor records comments

Figure C.2 Evaluation planning, “tools” page.

training in some form was needed, that a specific form of training was needed, and that a particular group or team would be given a body of resources and time to design and develop the solution. During evaluation, besides answering questions about the instruction created to meet this expressed need, the questions that simulated the project at the first should be answered.

Appendix D Management Planning

Management Planning: The Problem Designing instruction is more than designing media. It means designing a complete system. A management plan is a set of instructions that you create to sustain the use of your product, supporting it throughout its life cycle. It contains instructions for the operation and maintenance of your course. It is a book of: schedules, logistics, policy, procedures, job descriptions, personnel, standards. Management planning problems show up in the following ways: • • • • • • • • • •

Your product is not used owing to missing directions. Duplicate copies of materials are not made and distributed on time. Insufficient disk storage space is allocated for courses. Course records are lost. Student performance records are not being kept. Training sites are maintaining inadequate personnel support levels. There is inadequate learner call-in support for your course. No one knows who is responsible for maintaining the course. Some parts of the course schedule are causing bottlenecks. Students are not graduating on time.

During product implementation the management plan becomes the score from which everyone plays: it defines regular schedules of activity and responsibilities. The management plan coordinates the activities of everyone involved in some way with the administration of the product. Today the management plan is generally in the hands of the CLO of an organization. Therefore, the management plan fits the product into the organization fabric of training. For those who do not have management at the organizational level, the concepts and tools described in this section will be useful.

References Tosti, D. and Harmon, P. (1973). The management of instruction. Audio-Visual Communication Review, 21(1), 31–43.

427

428 • Appendix D Table D.1

Management Planning, “Process” Page

Management plan outline 1.0 Purpose (of the plan) 2.0 Authority (who backs the plan) 3.0 Design goals (for the product) Stage 2 4.0 Product description Identify functions to be performed, including 4.1 Courses/units/modules information flows (section 5.0). Use the Management Functions List on 4.2 Distribution method/sites Page 431 to identify functions your system will carry out. 4.3 System components (personnel, Stage 3 equipment, facilities, materials, Define processes and personnel responsibility for functions (section 5.0). software, documentation) Make a table or worksheet like the one in Figure D.1 for each system 5.0 System functions function. 5.1 Instruction and testing 5.2 Instructional administration Stage 4 5.2.1 Automated Extract management structure and organization 5.2.2 Manual (section 6.0). Extract this information from the 5.3 System administration worksheets you created. Summarize organizations, 5.4 System maintenance roles, and responsibilities information from the 5.5 Change implementation worksheets. 6.0 Organizational responsibility Stage 5 7.0 Schedules Specify system operations schedule including tasks, 8.0 Resources cycles, etc. (section 7.0). Extract these into summary tables. Stage 1 Make a detailed listing of individual components of the system (section 4.0).

Stage 6 Summarize required resource input levels (section 8.0). Show the effects of varying student loads on resource requirements. Stage 7 Summarize procedures and policies from the worksheets. Do not omit formal communications, reports, etc.

Management Planning: The Process The goal is to create a management document for your product. A management plan evolves through the following stages: Management Planning: Principles Input to Management from CTRA CTRA supplies important information to management planning: • CTRA has identified the resources (funds, personnel, etc.) available to support training. • Study of existing management procedures has revealed those that are and are not effective. If CTRA has been carried out properly, the “implications” column will contain items relevant to management planning. Input to Management from Course Design Course design also supplies important information to management planning:

Appendix D • 429

• It names instructional events to be administered to students and for which records must be kept. • It identifies forms of media that must be administered during delivery of instruction. The management plan must specify personnel, scheduling functions, facilities, and logistics to support the instruction as designed. Unlike CTRA, there is no specific part of the course design that names implications for management, so the designer must inspect the design for these implications manually. Formality of Planning Even when management planning is not performed as a formal process, it still has to be accomplished somehow. The process is often done informally for small projects, but the larger the project, the more people’s worlds it touches. Here are some guidelines for planning: • The more complex the training activity, the more likely the need to make a written version of the plan. • When others become involved in or are affected by our plans, the formality of the process increases. Then it becomes imperative to generate a formal document of some sort. Documenting A management plan document can become a standard communication tool with those outside a design group: system managers, programmers, administrators, and vendors. A standard format and content outline for the document (like the one shown in Table D.1) makes it easier to write. The key is to making it an action document—concise, but clear and complete. Management Planning: Tools The most useful tool for collecting information for management planning is the functional worksheet shown in Figure D.1. Fill this form out for each management function from the list that applies to your instructional system. Management Planning: Insights Planning in the Larger Organizational Context Larger organizations evolve a multi-level management plan, and one manifestation of the plan is a learning management system (LMS) software interface and database. The management plan is larger than the software, and it consists of a larger set of processes and personnel responsibilities, and in larger organizations, a philosophy. Specialized and shared functions (like network and database management) are normally housed in an information technology (IT) department whose concerns are larger than those of training, but training is becoming a more important aspect of IT operations, so the designer speaks with a voice that needs to be heard. The IT department’s concerns trump the designer’s concerns, however, so designers have to work within constraints shared by the whole organization. The benefit of a formal management planning process is that the designer consciously decides issues rather than defaulting them, even if many of the decisions are pre-made by the larger organization.

430 • Appendix D Name the function being described/planned. Keep a list like the Management Functions List on hand as a spur to memory.Try to standardize your list across courses.

Identify the person respon­ sible for performing this function. Identify others who will help.

Link the function to an event or to a regular management cycle.

FUNCTIO N W O R K SH E E T Function No. 1.3.2 U Function: Christmas Administration. / Personnel (Who):

/

/

Leaping lords assisted by drummers drunoumg.

Timing/Frequency (When):

/

/

every 12 days.

Describe in broad steps how the function will be carried out. This will serve later as the basis for detailing the steps.

/

General Method (Flow):

i. Load day's gift into earortruofe. p. Deliver to true love's bouse.

9

Reports or Communications: calling birds will matte ealls each day directly following g ift delivery.

Policy/Guidelines: no deliveries before e,-.oo am . SPCA guidelines for handling and housing.

Implications: (Equip, Facil, Program. Storage, Loads): A bag to put everything in. .something to etean up with.

WRITE DETAILED PROCEDURES ON OPPOSITE SIDE •

On the back, describe the details of the General Method that you outlined above. Eventually the words you place here may become part of a User's Guide.

_______

Identify memos, calls, mes­ sages, reports, agreements, or any other form of commu­ nication expected to result from or occur during the function. If the function is governed by an existing guideline or policy, identify it here. If a new one is needed, state that here. Identify implications for: -resources (funds, material) -personnel (skills, time) -infrastructure (load) -any kind of change

Figure D.1 Management planning, “tools” page.

The Management Functions List The list of instructional system functions provided at the end of this section saves time and helps avoid errors of omission. The list has evolved over years of experience, and it contains functions relevant to the individual course as well as to the entire learning organization. Keep the list current by noting down new functions not on the list as you encounter them through experience. The list will continue to be useful and grow in value over time.

Appendix D • 431

Management Planning and Change A new management plan should: 1. Save the best practices of the old system (the ones that worked well) 2. Make transitions from older systems to newer ones gradual. Management Functions List This list identifies functions carried out by mature instructional systems. Large systems execute these functions in a formal way, often using forms and deadlines. Small systems carry out most of these functions, but usually on an informal basis. As you read this list, consider a university or a large public school and ask yourself whether you have seen these functions being executed. A designer can use this list as a stimulus to memory. It will bring to mind functions for which there should be a plan, which will be incorporated into the management plan. 1.0 Conduct instruction and instructional administration 1.1 Conduct instructional administration 1.1.1 Enroll student in system 1.1.1.1 Set up a record 1.1.1.1.1 Establish historical file 1.1.1.1.2 Determine file sufficiency 1.1.1.1.2.1 Obtain additional data from external source 1.1.1.1.2.2 Conduct additional evaluations of student 1.1.1.1.2.3 Collect additional personal data. 1.1.1.2 Provide orientation to policies and facilities 1.1.1.2.1 Personal storage/working space 1.1.1.2.2 Present orientation briefs. 1.1.1.3 Provide passes/badges/ID/other administrative paperwork 1.1.1.4 Provide preliminary pre-schedule 1.1.1.4.1 Publish pre-schedule for general information 1.1.1.4.2 Conduct additional briefings/assessment. 1.1.2 Match student characteristics with instructional system characteristics 1.1.2.1 Establish student skill profile 1.1.2.1.1 Administer skill pre-tests/interviews 1.1.2.1.2 Collect previous records of skill levels 1.1.2.1.3 Record student skill profile summary 1.1.2.1.4 Match student skill profile with task listing. 1.1.2.2 Establish student at effective profile 1.1.2.2.1 Administer effect pre-tests and interviews related to effect 1.1.2.2.2 Record student at effective profile summary. 1.1.2.3 Establish student instructions/learning style 1.1.2.3.1 Determine learning strategy preferences (control, etc.) 1.1.2.3.2 Determine cognitive predisposition of practices. 1.1.2.4 Compare student profiles with minimal entry requirements 1.1.2.5 Decide to reject/remediate/accept the individual student 1.1.2.6 Match instructional system characteristics to student characteristics 1.1.2.6.1 Choose appropriate entry-level to instruction 1.1.2.6.2 Choose (“remediation only”) instruction

432 • Appendix D

1.1.3

1.1.4

1.1.5

1.1.6 1.1.7 1.1.8

1.1.9 1.1.10 1.1.11

1.1.2.6.3 Select students/instructional matching 1.1.2.6.4 Select strategy options/page options/media options, etc. Provide advisement 1.1.3.1 Provide system-initiated advisement 1.1.3.1.1 Provide periodic advisement 1.1.3.1.2 Provide panel-initiated advisement 1.1.3.1.2.1 Survey reports and progress records regularly 1.1.3.1.2.2 Determine need for advisement 1.1.3.1.2.3 Schedule advisement 1.1.3.1.2.4 Conduct advisement meeting 1.1.3.1.2.5 Conduct post-meeting communications 1.1.3.1.2.6 Complete post-meeting reports. 1.1.3.1.3 Provide event-initiated advisement. 1.1.3.2 Provide for student-initiated advisement. Execute student major event scheduling procedure 1.1.4.1 Define major course options for student 1.1.4.2 Define major instructional options for student 1.1.4.3 Determine major events to be scheduled in student career 1.1.4.4 Make schedule for student 1.1.4.5 Publish schedule for student 1.1.4.6 Publish schedules within system. Select individual instructional event for forward-moving students 1.1.5.1 Select mediated instructional event for forward-moving students 1.1.5.2 Pick equipment-based exercise for forward-moving students 1.1.5.3 Pick individual/group activities for forward-moving students 1.1.5.4 Pick major course subdivisions for forward-moving students. Determine whether to advance or to remediate student Pick remedial instructional event Issue and retrieve instructional means 1.1.8.1 Issue and retrieve instructional means for student use 1.1.8.1.1 Issue and retrieve instructional material for student use 1.1.8.1.2 Issue and retrieve academic tests 1.1.8.1.3 Issue and retrieve equipment for student use 1.1.8.1.4 Issue and retrieve media device for student use 1.1.8.1.5 Issue and retrieve supplies for student use. 1.1.8.2 Issue and retrieve instructional means for instructor use 1.1.8.3 Issue and retrieve secure/classified instructional materials and tests 1.1.8.4 Issue and retrieve adjunct study materials. Conduct student elimination Instruct students in use of instructional materials and tests Certify students 1.1.11.1 Determine certification tools to be used 1.1.11.2 Review pertinent student records 1.1.11.3 Conduct end-of-phase academic examinations 1.1.11.3.1 Schedule personnel, equipment, facilities, and materials 1.1.11.3.2 Conduct examination 1.1.11.3.3 Evaluate/score examination

Appendix D • 433

1.2

1.3

1.4

2.0 2.1

1.1.11.3.4 Record scores and test data. 1.1.11.4 Determine certification/non-certification. 1.1.12 Graduate students 1.1.12.1 Summarize records 1.1.12.2 Close files 1.1.12.3 Pass data to next school/agency 1.1.12.4 Issue notice of graduation/elimination 1.1.12.5 Retrieve all materials/equipment/ID from students 1.1.12.6 Cancel schedules/assignments. Conduct non-equipment instructions/practice 1.2.1 Instruct students 1.2.2 Record data from instructional session 1.2.3 Record instructional event completion. Conduct equipment-related instruction/practice 1.3.1 Certify students ready for exercise 1.3.2 Verify equipment availability 1.3.3 Brief students and staff on session 1.3.4 Initiate/run exercise 1.3.5 Make performance monitoring/data recording 1.3.6 Give corrective/remedial feedback 1.3.7 Conduct post-exercise debrief 1.3.8 Record exercise completion. Evaluate student knowledge/performance/attitudes 1.4.1 Schedule personnel/facilities and materials for testing 1.4.2 Administer test 1.4.2.1 Instruct students 1.4.2.2 Monitor performance 1.4.2.3 Record performance data 1.4.2.4 Score test 1.4.2.5 Assign grades/provide feedback 1.4.2.6 Record scores/grades. Maintain instructional system Maintain instructional materials 2.1.1 Store instructional materials 2.1.1.1 Store unclassified instructional materials 2.1.1.2 Store classified instructional materials. 2.1.2 Assemble (stage) instructional materials/aids for delivery to student/instructor 2.1.2.1 Determine number of assemblies needed 2.1.2.2 Inspect components for usability 2.1.2.3 Assemble according to normal procedure. 2.1.3 Distribute instructional materials 2.1.3.1 Receive requests for materials 2.1.3.2 Supply materials to students/stations 2.1.3.3 Maintain record of supplied materials. 2.1.4 Collect instructional materials 2.1.4.1 Collect materials from students/stations 2.1.4.2 Maintain record of return materials. 2.1.5 Inspect instructional materials

434 • Appendix D

2.1.5.1 Evaluate condition of materials 2.1.5.2 Route to storage/repair shop/replacement 2.1.5.3 Notify required personnel of damage 2.1.5.4 Record condition/location. 2.1.6 Repair/replace instructional materials 2.1.6.1 Repair instructional materials 2.1.6.2 Replace instructional materials. 2.1.7 Maintain material supply levels 2.1.7.1 Inventory number, condition, location of materials and copies 2.1.7.2 Compare inventory with required levels 2.1.7.3 Order additional copies of materials. 2.1.8 Maintain auxiliary material levels. 2.2 Maintain personnel readiness/effectiveness/selection 2.2.1 Administer personal training, retraining, and refresher training 2.2.2 Certify/recertify personnel 2.2.3 Monitor, evaluate and report on-the-job performance 2.2.4 Feedback to personnel resulting from monitoring/evaluation procedures 2.2.5 Replace personnel as necessary (due to transfers, etc.). 2.3 Maintain facilities for daily use 2.3.1 Contain necessary security procedures 2.3.2 Maintain adequate life-support (e.g., electricity, air, water, sanitation, etc.) 2.3.3 Maintain adequate safety conditions 2.3.4 Provide custodial functions 2.3.4.1 Supply personnel necessary materials 2.3.4.2 Schedule personnel for upkeep functions 2.3.4.3 Provide periodic custodial/safety checks/drills 2.3.4.4 Maintain auxiliary facilities (library, lounge, soda machines, etc.). 2.4 Maintain equipment (media devices, models, trainers, etc.) 2.4.1 Store equipment 2.4.1.1 Store unclassified equipment 2.4.1.2 Store classified equipment 2.4.1.3 Store replacement parts and tools. 2.4.2 Set-up for use by student/instructor 2.4.2.1 Determine equipment needed 2.4.2.2 Inspect equipment 2.4.2.2.1 Make periodic inspection of media equipment 2.4.2.2.2 Inspect equipment. 2.4.2.3 Troubleshoot and repair malfunctioning equipment 2.4.2.3.1 Troubleshoot and repair media equipment 2.4.2.3.2 Troubleshoot and repair trainers and simulators. 2.4.3 Issue and retrieve equipment for use outside classroom/carrels 2.4.3.1 Accept requests for equipment 2.4.3.2 Supply equipment to students/stations 2.4.3.3 Maintain record of supplied equipment. 2.4.4 Collect equipment 2.4.4.1 Collect equipment from students/stations 2.4.4.2 Maintain record of return equipment.

Appendix D • 435

2.4.5

Inspect equipment 2.4.5.1 Evaluate condition of equipment 2.4.5.2 Route to storage/repair shop/replacement 2.4.5.3 Notify required personnel and equipment usage/status 2.4.5.4 Record condition/location of equipment 2.4.5.5 Notify system manager of predicted downtime. 2.4.6 Repair/replace equipment 2.4.6.1 Repair equipment 2.4.6.2 Replace equipment. 2.4.7 Maintain equipment availability levels 2.4.7.1 Inventory number, condition, location of equipment 2.4.7.2 Compare inventory with specified on-hand levels 2.4.7.3 Order additional equipment. 3.0 Develop, revise, and implement instructional system components 3.1 Develop and revise instructional system components 3.1.1 Develop and revise development procedures documents 3.1.1.1 Develop development procedures documentation 3.1.1.2 Review and revise development procedures documents. 3.1.2 Develop and revise instructional materials and tests 3.1.3 Review system personnel structure and training provisions 3.1.3.1 Identify changes that require retraining or revision of personnel plan 3.1.3.2 Determine and report changed staffing 3.1.3.3 Determine training requirement changes from changes in staffing 3.1.3.4 Revise personnel training system. 3.1.4 Repair and upgrade facilities 3.1.4.1 Prepare facilities for use (long periodic) 3.1.4.2 Upgrade facilities. 3.1.5 Update/upgrade equipment. 3.2 Implement instructional system 3.2.1 Schedule implementation activities 3.2.2 Procure/order equipment and facilities 3.2.3 Receive/inspect 3.2.4 Set up and test equipment 3.2.5 Train system personnel 3.2.6 Rehearse/simulate system functioning.

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Appendix E Implementation Planning

Implementation Planning: The Problem Implementation planning is a special case of management planning. An implementation plan is a special set of instructions that you create for installing a training system and putting it into use for the first time. Just like management plan, the implementation plan specifies: schedules, logistics, policy, procedures, job descriptions, personnel, and standards. It describes only those functions that have to be carried out the first time an instructional system is implemented—a new course, a new location, or a new medium of delivery. The problems that arise from faulty implementation planning are different from ones that arise from management planning errors: • • • • • • • •

A new training site is unable to come up on schedule. Software upgrades for user terminals are not accomplished. Problems are encountered due to inadequate software upgrade testing. Handouts and manuals given to students are one version old. A file or directory is missing and crashes a user. The management system is still recording scores against old lessons. As a new course is installed, it erases all training records. A Web-based course requires bandwidth greater than installed systems will support.

An implementation plan defines the schedule of activities, personnel roles, and resources for bringing up a new training site or a new course. Properly planned, an implementation plan is invisible. Implementation Planning: The Process The goal is to create an implementation plan. The process of implementation planning parallels management planning. The implementation plan document becomes part of the management

References Havelock, R. (1995). The Change Agent’s Guide (2nd ed.). Englewood Cliffs, NJ: Educational Technology Publications. Rogers, E. (1995). Diffusion of Innovations (4th ed.). New York: Free Press.

437

438 • Appendix E Table E.1

Implementation Planning, “Process” Page

Stage 1 Identify implementation goals and status (3.0, 4.0). Specify the implementation deadline for each site, course, medium, etc. Set the priority of goals. Stage 2 Describe preparations required to prepare all sites. Use the list in Table E.2 to identify preparations, to all the sites. Create a function worksheet for each, as in management planning. Stage 3 Describe additional preparations for specific sites. Use the list in Table E.2 to identify functions specific to sites. Make additional worksheets for each function-site item added. Add the new site-specific worksheets under each functional area as needed. Stage 4 Assign method and personnel responsibility to functions. For this and the remaining stages, complete the function worksheets, just as during management planning.

Implementation plan outline 1.0 Purpose (of the plan) 2.0 Authority (who backs the plan) 3.0 Implementation goals/priorities 4.0 Current site descriptions 4.1 Site status by site 4.2 Site tasks by site 5.0 Implementation functions 5.1 General 5.2 Site-specific 5.2.1 Site 1 5.2.2 Site 2 5.2.3 Site . . . 6.0 Organizational responsibility 7.0 Schedules 8.0 Resources 9.0 Procedures, policies

Stage 5 Extract management structure and organization. Stage 6 Specify system operations schedule (tasks, cycles). Stage 7 Summarize procedures and policies from the worksheets. Do not omit formal communications, reports, etc.

plan document. Implementation planning draws from its own list of functions that pertain just to implementations (see under ‘Implementation Planning Tools’ below). An implementation plan evolves through the following stages: Implementation Planning: Principles Input to Implementation Planning from CTRA CTRA may supply important information to implementation planning. CTRA describes the current training situation that your designs will impact from several points of view: • • • • • • •

Current practices Current schedules Current incentives and motivations Current priorities Current support and resources Current facilities Current logistical arrangements.

Your designs are in part a response to these findings. The design will either retain old ways of doing things or describe changes to replace them. If you have completed the CTRA process with implementation planning in mind, the planning becomes much easier.

Appendix E • 439 Table E.2

Implementation Planning, “Tools” Page

Implementation planning function list Procurement functions Equipment Network connections Centralized digital storage Physical site preparation Instructional areas Support areas Instructor areas Student areas Study area Community/group areas Maintenance area Physical storage area Equipment/server area Special access/assistive tech. Power distribution Communications Environmental (A/C, etc.)

Site logistics Finances Services Reproduction/media support Maintenance services Set-up/test Hardware installation and test Network installation and test Equipment (projector) installation and test Furnishings Site personnel preparation Instructor training Non-instructional personnel training On-site promotion

Importance of Careful Implementation Planning Implementation is a period of intense and important change. In addition, it is a period of high-stakes decisions that affect the judgment of continued use of your product. Your product is not only making its first impressions on people during implementation, but it is gathering either support or censure from those most likely to determine its viability—students, instructors, and administrators. A careful implementation plan can help your product to be introduced with the best possible chance of success. Documenting If your product is implemented simultaneously at multiple sites, you cannot be in attendance. The implementation plan is a means of communicating responsibilities and schedules to others who will carry out the actual tasks of implementation. A concise, clear, and complete plan contributes a good deal to a successful launch for your product. Implementation Planning: Tools Implementation planning uses the function worksheet, just as does management planning. What changes is: 1. The list of specific functions to be planned for it 2. The fact that plans must be tailored for the needs of individual sites. The list of special implementation functions above will support site-specific planning. Its purpose is analogous to the management function list. Remember that this list is just suggestive, not exhaustive. Add to it as you encounter new list items. Implementation Planning: Insights Changing Things Implementation is one of the most interesting parts of instructional design. You see how difficult it is sometimes to bring about change within institutions that have traditions, cultures, and

440 • Appendix E

habitual ways of doing things. Just as the habits of people in organizations can resist change, over time they can also become the invisible web of influence that holds new practices in place. Everything changes; a designer can harness natural processes to bring about constructive change. Implementation is the laboratory of the designer, and planning followed by implementation is the experiment.

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Index

Adaptivity: quality of “living” designs, 12; and the design of the TICCIT system, 20 ADDIE, 94; origin of term uncertain, 99; generalizations about, 104; alignment with layers, 160 – 71 AICC, 53 Agrawala, M., 157, 159 – 60 Agency: learners as active agents during instruction, 183; degrees of agency expression by learners and learning how to learn, 183 Akera, A., 86 Alexander, B., 389 Alexander, C., 94, 138 – 9 Allen, M., 354 Almond, R., 330 Anderson, J. R., 125, 154 – 5, 285 Anderson, L., 281, 303 Andrews, D. H., 94, 98 Architecture: associated with the structure of things, ix; instructional designers likened to architects, 8; “living” architectures, 11; instructional product architecture not a mainstream topic, 29 Architectural view of instructional design (Blaauw and Brooks), 76; most abstract of the eight views of design, 76; lies at the borderline between purely conceptual and material, 76; an important source of coherence in a design, 76; architecture, implementation, and realization, 77–8; Ariadne, 403 Association for Computing Machinery Atkinson, R., 19, 343; 374 Azevedo, A., 403 Baker, R., 397 Baldwin, C., layering as the economic basis for the computer industry, 11; layering (modularization) of designs impact product economy, 28 – 9; distinguishes module-submodule as designed entities, 29; describe motives for layer (module) definition by a designer, 29; modularity as an economic factor, 46; Design Rules and design modularization, 76; 356; Ball, G., 288 Balloon frame construction: introduced new layers into building construction, 10 Banathy, B.: distinguishes system-subsystem as designed entities, 29; strongly influenced by general systems theory,

91; descriptions of the systems approach include process flavor, 91; places stress on component and function analysis, 92; underscores the need for systems thinking, 106; 107 Bannan-Ritland, B., 109, 110 Baron, J., 336 Barrows, H., 45, 344 Bass, L., 283 Bateson, G., 259 Bazerman, C., 5, 195 Bednar, A., 90 Bereiter, C., 162, 180 – 1, 279, 293, 408 Berliner, D., 401 Bersin, J., 404 Bichelmeyer, B., 134 Blaauw, G. Bloom, B., 97, 130 Bloom, H. Boling, E., 94 Bonk, C., 343, 388 Braden, R., 99 Branch, R., 220 Brand, S., 8, 10; describes the aging of buildings, 11 – 12; example of layering in the design of buildings, 24 – 5; Brand’s layer definitions compared with Schön’s, 25; compared with other authors’ choice of sub-unit terms, 29; layered design extends building lifespan, 46; 250; 373 Bransford, J., 51, 127, 130, 404 – 5 Branson, R. K., 98, 101 – 104 Brennan, J., 44 Briggs, L. J.: instigator of a new discourse (see also trajectory of artificiality), 55; associate of Gagné, 96; major influence in the establishment of the instructional design model, 96; solving the problem of teacher-media integration, 96; interest in media selection, 96; wanted to link media selection to objectives, 96; influenced by taxonomic views of objectives, 96; influential ideas, 97; shift toward design models, 97 – 8; popularized the concept of a design “model”, 98; Brooks, F. P., 76, 100 Brown, A., 118, 148, 294, 328, 345 Brown, J. S., 51 Brown, T., 108 Bruner, J., 117, 129, 406

455

456 • Index Bucciarelli, L. L.: the shared vision that is a design, 63; 139 Buchanan, B., 283; Buchanan, R. Bugos, G., 86 Bull, S., 397 Bunderson, C. V., 20, 168, 317, 379, 392 – 3 Burton, R., 128, 163, 168, 316, 392 Butler, D., 238 Buxton, B., 227, 248 – 9 Carlisle, R., 147 Carbonell, J., 218, 284 Caro, P., 19 Caswell, T., 163 Centre Pompideau: example of the reversal of layer relationships, 12 “Centrisms”: used in describing maturing designer thinking, 51 Cezzar, R, 348 Chew, K., 398 Chief Learning Officer (CLO), 14 Ching, F., 8 – 9 Christensen, C. M., 145, 152, 394, 395, 402 Christensen, T. K. Churchman, C. W., 85, 86; 109 Clancey, W., 164 Clark, R. E., 176 CMU, 53 Cognitive apprenticeship, 283 – 4 Cole, M. Collins, A.: describes learnable content under four headings, 43 – 4; 108; 109; 154 – 5; 157; 158, 192, 283; 293 Colvin, G., 292 Complexity: increasing complexity of many factors leads to new layer definition, 28 Component display theory: basis for the adaptive TICCIT system design, 20; basis for one example of a message design, 216; 380 – 2 Computers in instruction: effects of the advent of the computer, 18 – 20 Conati, C., 397 Conole, G., 396 Content layer: described, 43 – 44; not simply a textual description, 43; meaning must be seen as distinct from the prevailing usage of the term “content”, 43; content can be “captured” in a number of forms, 43; traditional views of content, 279 – 80; the conflation of content and strategy, 280 – 2; Bloom, Gagné, and taxonomies, 280 – 1; Gagné’s philosophy of taxonomies, 280; describing content in terms of performance capability, 282; computability of content, 282 – 3; semantic networks, 284 – 5; cause-effect models, 286 – 7; object models, 287 – 8; Bayesian models, 288; task structures, 289 – 90; attitudes and values, 291; skill, 291 – 2; erroneous knowledge, 292; abstract knowledge, 292 – 294; recursive nature of content structures, 294; combining different forms of analysis, 294 – 5; relating content and strategy layers, 295 – 6; content sub-layers, 296 – 7 Control layer: described, 36; second lane of a highway of communication with the learner, 227; disentangled from

the message and representation layers, 227; growth in complexity in cars levy a “bio cost”, 228; controls allow conversation with the systems we operate, 229; control systems have a limited “bandwidth” of communication with devices, 229; the semantic barrier, 229–31; some control actions require interpretation that depends on context of operation, 231; designers create a control language with each design, 31; navigational approach to control system design uses spaces, places, and pathways, 232–243; navigation, orientation, and wayfinding as the concerns of a control system, 233; Habitat Hike control system, 234–7; the TREKKER concept of control to encourage self-direction, 238–40; design decisions related to control systems from the navigational point of view, 241–243; linguistic approach to control system design takes advantage of language structures, 243–247; experientialaesthetic-semantic approach to control system design takes advantage of shared meaning, 247–251; interface and commitment, 250–1; control theories, 251–253 Conversation: instruction as conversation, 38, 117; implications of conversational instruction metaphor, 119; value of conversational instruction, 119; dimensions of a conversational design, 119; conversational objects, 121 – 2; conversations increase desire, hope, confidence, etc., 122; conversations do not require equipment, though equipment can be used in them, 133; conversations have beginning, middle, end, and dramatic structure, 182 – 3; commitment to conversation, 182 – 3; constant cycles of negotiation and assessment essential to conversation, instruction, 183; conversational tools appearing in consumer products, 185; Pangaro’s conversation model leading to commitment, 221 – 2; Winograd’s conversation for action pattern, 223 Conversation: design as conversation, 79 – 80; design as reflective conversation, 136 – 7 Coscarelli, W., 392 Coursera, 53, 403 Coventry cathederal: redesign centered on a primary generator theme, 9, 81 Cox, S. Crampton-Smith G., 249 Crawford, C., 35; describes nouns and verbs of a control design language, 36; 156; 243; Creative Commons, 163 Cross, N., 100 CTGV (1990). Damasio, A., 124, 406, 407 Darke, J.: concept of the primary generator of a design, 9; 81 Data management (DM) layer: described, 44; defined, 323; relation to adaptive instruction, 324; areas of DM planning, 324 – 28; DM for the learning companion, 325; DM for the learner, 325 – 6; DM for self-directed learning choices, 325 – 6; DM for the evaluator, 326; DM for learner analysis, 326 – 7; analytics and cost tradeoffs, 327 – 8; DM for the researcher, 328; DM and what to capture, 329; DM data validity, 330 – 33; DM and the LMS, 333 – 4; a structural basis for DM, 334 – 7; data interpretation, 337 – 38; data reporting, 338 – 9; data use by the learning companion, 339 – 40

Index • 457 Da Vinci, 145 “Default” level of a design: value not added by continued detailed decision-making, 73 – 4 Delaney, J., 390 – 1 Design: designs are organic and living and therefore must be adaptive, 11; design described from multiple viewpoints, multiple narratives 49; what designers think is being is designed is critical, 50; what is design?, 135; doctors, lawyers, accountants, and teachers as designers, 135; how designs happen, 136; design as reflective conversation, 136 – 7; design as progressive placement of constraint, 137; design as search, 137 – 8; design as the application of patterns, 138 – 9; design as a social process, 139; design as engineering, 139 – 40; design as prototyping and iteration, 140; design as copying or templating, 140 – 1; design as a process, 141; design as creative thinking, 141; Design coherence, 76, 79 Design concept: formed by the convergence of the architecture and the implementation, 78; important to be shared by a design team, 79 Design expertise: as ability to see into the inner structure of a design and the operation of forces within a design, 78 Design order: fixed vs. variable, 71 – 75; reverberation of decision-making among architecture, implementation, and realization, 79; Designer: must understand the design languages of the specialties employed on a project, 32; progression of stages in maturing designer thinking, 51 Design-based research: related to but not the same as formative evaluation, 68; the occasional need for additional data gathering during design to inform decisions, 73; design as research, 108 – 10; contrasted with traditional theory-testing research, 158; progressive refinement through design, 158; three types of theory resulting from design research, 159; example of theory development for a very narrow niche, 159 – 60 Design language view of instructional design, 193 Design languages (DL): different ones spoken by different design team members, 28, 62; name the elements, consequences, implications, and moves of a design, 80; can be grouped into clusters called “domains” (layers), 80; operational principles and their visible manifestations are given names, 193; designers draw from a stock of simple elements that combine together to form complex patterns, 194; these take on meaning and can be used for team communication, 194; DL defined, 194; variety of DL necessary for innovation, 194 – 5; how new DL terms originate, 195 – 6; DL and layers, 196 – 7; designer becomes a DL translator, 197; private DL are both a source and sign of emerging expertise, 198; DL contribute to our ability to learn about designing, 199 – 200 Design methods group, 94 Design process: tends to be the focus of design texts, 29; should lead design teams to the right design questions, 56 Design teams: solve design sub-problems and integrate their results, 28 Design theory (see also theory): designers must take control of it, not be intimidated by it, 13; as the major topic of

this book, 13; distinguished from learning theory and instructional theory, 13; cuts across disciplinary lines, 13; one of two types of technological theory of interest to instructional designers, 148; theory about how designs are made, 148; cross-disciplinary, 148; layer theory as a design theory, 149; key to improved instructional designs, 150; Di Palma, V.,12 Diamond, R. M., 101 – 104 Dick, W., 101 – 104 Domain theory (see also theory, instructional theory): one of two types of technological theory of interest to instructional designers, 148; specific to a particular domain or discipline of designing, 149; supply structuring concepts for designs of a field, 150; domains of interest to a field may overlap other fields, 150 – 1; instructional design draws on domain theories of many other fields, 150 – 1; some domain theories particular only to instructional design, 151; instructional theory as a combination of borrowed and native domain theories for instructional design, 151; Drucker P.F., 385, 408 – 9 Dubberly, H., 94, 99, 222, 228 Duval, E., 329 Dyer, D., 87 Dyson, F. Edelson, D., 158 – 9; three types of theory resulting from design research, 159, 317 – 18 Edgerton, H., 261 Educational Leadership, 390 Educational psychologist, 158 Educational researcher, 157 Educational technology, 158 Edwards, P., 86 edX, 53, 403 Eight views of design, 49, 56, 62, 69, 76, 83, 93, 173, 193 Einstein, A. 299 Ericsson, A.: 10; modularization factors enumerated, 29 – 30; modular product platforms, configurable products, 47; 372 Evaluation Plan: a s a kind of plan for learning from designing, 68 Evans, E., 46 Event: abstract structure upon which goal, time, and activity converge to create an occasion for instruction, 40 Ferguson, R., 395, 396 Finan, J., 89 Finley, K., 403 Finn, J. D.: critique of the audio-visual instructional field, 3 Fischman, J., 403 Fleming, M., 35, 37, 156 Flippo, R., 392 Fogtmann, M., 35 Fowler, M., 10; layered architecture used in enterprise software development, 46 Fox, B., 38, 185 Fuller, R. B., 49, 173

458 • Index Function: designs are made to provide functions particular to time, place, and expectation, 24; designer determines which functions can be addressed semi-independently, 28; physical function distinguished from operational function, 29 Functional-modular (layer) view of instructional design, 69; (see Layer View of Instructional Design) Gaber, A., 403 Gagné, R. M.: instigator of a new discourse (see also trajectory of artificiality), 55; Psychological Principles of System Development, 89; man-machine development model, 89; loss of some of the problem-solving flavor of the systems approach due to model building, 90; addition of simplifying assumptions, 90; influence on Briggs, 96; taxonomies, 97; 129; 154 – 5; 303 Galdes, D., 185 Garrett, J.: Web page design described in terms of layers, 47 General systems theory: von Bertalanffy’s dissertation, 87; applicability to systems of all kinds, natural and human-made 88; not the result of wartime research, 88; influences and influenced by systems approach and many other idea systems, 88 Generativity (see also operational principle view of instructional design): quality of “living” designs, 12; understood in the light of the architecture and implementation of a design, 78; principle that generates multiple realizations (designs), 78; Gerbman, R., 354 Gerlach, V., 101 – 104 Gibbons, A. S., 89, 90, 94, 100, 132, 143, 156, 158, 168, 183, 209, 234, 289, 295, 317, 335, 393 Gillette, M. J., 35 Glanville, R., 253, 339 Glaser, M., 227 Glaser, R., 323, 339, 390, 402 Goals (see also operational principle): learner vs. designer/ instructor goals, 39 – 40, 179 – 182; performance goals, 180; strategic goals, 181; means goals, 181; means goals facilitate conversational patterns, 182; contrast with the concept of “interactions”, 182; means goals and the message layer, 182; designer vs. learner goals emphasize negotiation, 182 Goel, V., 100 Gomery, D., 5 Gordon, J. Graham, C. R., 346 Gross, M., 137 Gustafson, K. L., 94, 99, 101 – 104 Hall, J., 354 Hamreus, D., 101 – 104 Hannafin, M. J., 156 Harel, I., 326 Harris, R., 154, 156, 258 Head, G., 68 Hearn, D., 354 Hecht, G., 86 Heims, S., 252 Heinich, R., 101 – 104 Heritage, M., 393

Hess, F., 401 Hewitt, J., 293 Heylighen, F., 252 History Shots, 35 Hitchcock, A., 255 Hmelo-Silver, C. E., 344 Horn, R., 156, 216 – 17, 258 Hounshell, D., 87 Hughes, A.: 83; 85 Hugo, 173 Hunter, D., 286 Hyman, P., 404 Inouye, D. K., 142 – 3 Instruction (see also conversation): can be seen as an extension of the everyday performance environment, 42; must be seen as the occasion for performance, 42; concept changes over time, 111; definition as conversation, 112; a model of instruction, 112; strategic acts during, 112; function of the learning companion, 113; learner as active agent, 114; definition of conversation expanded, 114 – 16; dimensions of conversation variation, 116; requires two active agents, 117; value of conversational metaphor, 118; value of testing, 118; emphasis on negotiation as part of conversation, 120; promotion of intentional learning, 121; instruction as “help”, 142, 183 – 4; Instructional design: the changing landscape of design, 4, 8; impact on cultures, 9; hampered in past by a limited understanding of theory, 151; Instructional design models: evolution from early patterns set by Gagné and Banathy, 93; terminology of the systems approach retained even when its flavor lost, 93; rapid proliferation after 1960, 94; ; increasing sameness of models, 94; trend toward simplification for novice audiences, 94; lack of critical reviews, 94; description, 94; confusion regarding science-basing, 94; grow through process decomposition, 94; influence of Silvern and Briggs, 95 – 8; aging of design models, 98 – 100; addition of theory-specific assumptions, 100; instructional design models today, 100; comparison of diverse terminologies, yet sameness of process in current models, 101 – 104; design models and design layers, 105, 160 – 1 Instructional design model view of instructional design, 93 Instructional theory (see also theory, domain theory): a body of domain theory, both borrowed and domainspecific, contributing to instructional designs, 151; supplies structures to populate design layers, 153; structures suggested by terms of theories, 153; many theories required to populate a single design, 153; no single theory sufficient for a complete design, 154; different theorists ask different questions, 154 Intentional learning environment (ILE), 293 Irving, W., 255 ISD, 94; generalizations about, 104; alignment with layers, 160 – 71 Jackendoff, R., 268 James, H., 193 Jannach, D., 397 Januszewski, A.

Index • 459 Jardini, D., 87 Jenkins, H.: impact of social media on the concept of what is designed, 52; 388 Jenn’s table: a means of coordinating imagination with structural elements, 74; a tool for keeping tabs on cost, maintainability, etc., 75 Jobs, S., 17, 227, 341 Johnson, L., 267, 396 Johnson, S.,86 Johnson, W. L. Jolly, V., 159, 409 Jonassen, D., 289, 295 Jones, J. C., 94 Journal of the Learning Sciences, 158 Jussim, E., 258, 261 Kahin, B., 44, 158, 408 Kahneman, D., 319 Kant, E., 255 Kapor, M., 249 Karlen, M., 35 Kaushik, A., 395 Kay, J.338, 397 Kearsley, G., 68 Kelley, T., 141, 400 Kelly, A., 157, 328 Kemp, J. E., 101 – 104 Kenny, R. F. Kim, D., 358 Klir, G., 109, 146 Knowledge@Wharton, 404 Knox, D., 80 Koedinger, K.R., 396 Kopstein, F., 220 Kotter, J., 399 Krippendorff, K., 14; levels of commitment and social engagement that lead to a continuum of “product” types, 53; trajectory of artificiality, 53 – 5; 197, 271 – 2; 350; 371 Kroes, P., 140 Krug, S., 227 Kruger, C., 100 Laurel, B., 189 Lave, J., 106, 127 Lawson, B., 100, 107 Layer view of instructional design: description, 69; revises traditional views of design decision order, 69, 71 – 5; cycle of design decision making using a layer approach, 72; clustering of design decisions, 73; Jenn’s table as a tool for applying layers, 74 Layers (see also sub-layers): inner structure of designs can be described in terms of functional layers, 10; layers as an entry point for theory into design, 10; in building design, 10; systems of layers evolve naturally over time as technologies mature, 10, 17, 24; represent utility, not truth, 10, 28; allow a design to change over time non-destructively, 12; invisible aspects of a design, 17; the need to “see” layers, 18 – 19; separation of layers implicit in the design of the TICCIT system but not realized, 21 – 3; layer defined as a concept, an abstract idea that exists in the designer’s mind, 24; layers appear

and disappear as design concepts advance over time, 25, 32; layers as a response to increasing design complexity, 27; related to specialty development, 27 – 8; public layers and private layers, 30; layers of instructional designs that have become highly specialized, 31; value of using layers, 32 – 3; a generic set of instructional design layers is introduced, 34; layers in the designs of other fields, 46; differences during design and during delivery, 75; different layers ask the designer to answer different questions, 154; different instructional theories align with different layers, 154; not all theories address all layers, 154; alignment with traditional design models, 160 – 71 Learner control: main concept in TICCIT system development, 20 Learning: new descriptions of learning offer the designer new options, 122; implicit/explicit learning, 124; role of emotions, 124; metacognition, 125; schemata and mental models, 125; procedural rules and semantic units, 125 – 6; skilled performance, 126; growing expertise, 127; types of knowledge, 127 – 8; dynamic models, 128; increasingly complex microworlds, 128; stories and narratives, 128 – 9; categorized behavior, taxonomies of objectives, 129 – 30; beliefs, 130 Learning environment: beyond the concept of instructional “product”, 51; characteristics of learning environments, 51 – 2; dimensions of learning environment variation, 53 – 5 Learning theory (see also theory): Leavy, M., 334 Lesgold, A., 327 Leshin, C., 101 – 104 Levy, M. Lewis, B., 253 Liddle, D., 248, 250 Lidwell, W., 258 Lifelong career learning: important goal for a designer in a today’s rapidly changing world, 142, learning to use theory in application a key skill, 153; Lima, M., 35 LoBrutto, V., 5, 31 Long, P., 396 Lucas, P., 391, 399 Luckin, R., 121; 185 Luppicini, R., 38, 220 MacKenzie, D., 86 McCloud, S., 258 McDonald, J., 79, 320 McGrayne, S., 288 Malamed, C., 35, 258 Male, A., 258 Manouselis, N., 403 Mast placement on ships: and theory, x Margolin, V., 94, 99 Markle, D. Martin, R., 371 Mayer, R. E., 35, 156, 258 Mayr, O., 147 Meadows, D., 400 Means, B., 164

460 • Index Media-logic layer: described, 45; involves directions to human instructors and computers, 45; provides execution of all other layer functions, 341; an engine, 341; convergence of logic of media devices with logic of conversation, 341; priority of media-logic concerns, 342; execution logic, 343; precision instruction, 344 – 5; blended instruction, 346; computer logic, 346 – 7; crane metaphor, 347; development tool logic, 347 – 50; media modules, 353; learning management systems, 352; spaces, places, devices, and connections, 353 – 4; the organizational training environment, 354 – 7; the organizational working environment, 357 – 8 Meister, J., 354 Melton, 91 Merrill, M. D., 20; 156, 216, 379 Message layer: described, 37 – 39; works in conjunction with strategy and representation layers, 37; most difficult of the layers to “see” because of charismatic layers it works with, 37; often conflated in the literature with strategy and representation layers, 37; separation from other layers provides the key to conversational instruction, 38; message is not a representation but an intention to send a meaning, 203; critical to adaptive, conversational instruction, 203; examples of structured messaging systems, 204; granularity of messaging units, 204; messaging serves the conversational purposes of a strategy, 204; three examples of messaging structures, 205 – 10; the use of ontology to define message units, 210 – 12; massing vs. distribution of messages, 212; non-content message structure, 213; distinction from traditional “message design, 213 – 4; everyday messaging systems, 214 – 15; message context and new information, 215 – 16; structured computerized instruction example, 216; information mapping example, 216 – 17; more sophisticated structured conversations, 217 – 18; messaging and intelligent tutors, 218 – 19; messaging by slotting and concatenation, 219; messaging and story semantics, 219 – 220; conversation theory and messaging, 220 – 22 Meyer, J., 330 Meyer, M., 402 Meyer, R., 404 Miller, G., 407 Mindell, D., 86 Misa, T. J., 5 Mislevy, R., 303, 330, 392 MIT, 53 Modularity: allows a single tool to serve multiple purposes, 33; modularity and the economics of the computer, 363 – 5; modularity and instruction, 365 – 70; definition, 371 – 2; modularity and layers, 373 – 4; mass customization, 375 – 7; organization for mass customization, 377 – 8; mass customization in education and training, 379 – 82 Moggridge, B., 229, 234, 275 Moldenda, M., 99 Moss, P., 392 Mott, J., 334 Mott, T., 275 Mystery box: as a recurrent feature of instructional design models, 69

National Park Service,17 Newell, A., 319 Nkambou, R., 29; 39, 121, 169; 185, 390 Noer, M., 403 Norman, D., 247 Norvig, 385 Novak, J., 284 O’Donnell, J., 10 Oblinger, D., 388 – 9 Ofeish, G. D., 98 Operational principle (see also: conversation, goals): operational principle (OP) view of instructional design, 173 – 93; OP defined, 173; common to design fields, 173; generative power exemplified in aircraft design, 174 – 5; represent leverage for the designer, 175; Clark’s “active ingredient”, 176; finding OP by subtraction method, 176; “conversation” as an OP, 176 – 7; acts of instruction that create OP forces, 178 – 9; instructional goals and OP, 179; goals at multiple levels owned by learner and instruction, 180; OP and dramatic structure, 185 – 193; OP for the beginning of an instructional conversation, 186; OP for the middle of an instructional conversation, 188 – 192; functions performed by the environment, 189 – 191; functions performed by the cause-effect systems, 191; functions performed by the expert performance model, 191; functions performed by the learning companion, 191 – 2; OP for concluding an instructional conversation, 192 Organizational view of instructional design, 56; and maximization of different values, 56 ; deciding to train, 57; deciding means of training, 59; deciding project resourcing, 61 OSI Model: competes with other layered models of the Internet (see TCP/IP Model), 46 OZ, 276 Paine, N., 404 Pangaro, P., 38, 221 Papano, L., 404 Papert, S., 326 Parrish, P., 182, 186, 389 Parry, M., 397 Pask, G., 220 Pellegrino, J., 337 Peterson, F. W., 10 Pirsig, R., 83 PLATO: major exploration of computerized instruction concepts, 20 Polanyi, M., 173, 408 Primary generator: Darke’s concept of, 9; the generators that discipline this book, 9; bridge between the real-world purpose of a design and design abstractions, 81; Prince, C., 273, 354 Prince, E. Radjou, N., 401 Ramage, M., 84 Ramo, S., 84 Rau, E., 86 Rawlins, G., 204, 348

Index • 461 Razzaq, L., 392 Reeves, T., 150, 158 Reigeluth, C., 153 – 4; 295; 303, 319 Reiser, R., 101 – 104 Representation layer: described, 34 – 5; operation of theory within the layer, 35; only tangible layer of a design, 35; readily assimilates other layers, 255; best considered in terms of experiences, not media forms, 255; addresses the problem of making explanations, 255; the most mature layer technologically, 256 – 7; generative representation, 257; designing representations for instructional purposes, 258; principles for representation design, 258 – 64; contrast, 259; framing, 259 – 60; structure, 260; trace, 260 – 1; symbol,261 – 2; story, 262 – 3; question, 263; access, 263 – 4; intermedia coordination, 264; understanding representation at a different level, 265 – 8; communication more than information, 265; Wenger’s continuum, 266; tuning, 267; how we process representations, 268 – 269; languages of the mind, 269; imagination and representation, 269 – 71; Darmok, 270; learner participation in representation, 271 – 2; gesture, 272 – 3; new information, 273; representation mapping and layering, 274 – 5; simplifying representation through integration, 275 – 7; representation and other layers, 277; message-torepresentation mapping, 277; media selection, 277 – 8; Resnick, L., 290 Resnick, M., 287 Reynolds, M., 84, 86, 106 Rheinfrank, J., 248 Richard, D., 398 Richey, R., 220 Rickel, J., 288, 325, 370 Rieber, L. P.: learning environments, simulations and games, 52 Rith, C., 99 Rittel, H., 99 Ritter, S. Roebling, W., 363 Rogoff, B., 117, 190, 406, 407, 408 Romiszowski, A., 156 Rosenshine, B., 45 Rosling, H., 261 Rothrock, D., 389 Rowe, P., 100 Rowland, G., 107 Russell, S., 35 Sabbagh, K. Saettler, P., 90 Salen, K., 117 Salvadori, M., 173 Sawyer, R. K., 38 Scalability: quality of “living” designs, 12; and the design of the TICCIT system, 20 Scardamalia, M., 130, 217, 293 Schank, R. C., 128 – 9, 219, 262 Schön, D.: identifies twelve layers of architectural design, 10; compared with Brand’s six layers, 25; distinguishes

domain-subdomain as designed entities, 29; design problems exist within “domains” (layers), 46; testing a cluster of tentative design decisions as a step in the design “conversation”, 73, 79 – 80; studies leading to robust descriptions of design, 99; 109; reflective conversation, 136 Schrage, M.: prototyping for innovation and learning, 68, 140, 170 SCHOLAR, 218, 284 Scientific theory (see also theory): analytic, used to construct understanding, 145; Klir’s description, 146; SCORM, 53 Scott, B., 220 Seels, B., 101 – 104, 156 Senge, P., 196, 358, 400 Sethi, R., 348 Seyrig, T. Shelton, S. M., 126, 258 Shum, S., 396 Shute, V., 324, 326, 390, 393 – 4 Siegel, D., 190 Silvern, L. C.: distinguishes system-subsystem as designed entities, 29; design models with hundreds of process boxes, 69, 95; shows shift to engineering process orientation, 95; influence of cybernetic theory, 95; graphical language of system designs, 95; relevance of his systems views, 95; relevance today, 95 – 6; 220; 253 Simon, A., 156 Simon, H. 38, 81, 94, 135; 137, 141, 147, 149, 199 Sims, P., 15 Sidnell, J., 38 Smith, K. 402, 403 Smith, K.M., 94, 99 Smith, P., 101 – 104 Smith, S., 20 Snow, R., 291 Sound layer: added to movies during the 1920’s, 5 – 7 Stamas, S. T., 94, 98 Stanney, K. M., 35, 156 Statue of Liberty: layers described in its design, 17 Steinbeck, J., 111 Stempel, J., 408 STEVE, 267 Still, D. L., 276 Stokes, P., 137 Stolurow, L. M., 156, 374 – 5 Strategy layer (see also Goals): described, 39 – 42; major concerns include goal, time, and activity, 40; these concerns converge in the event, 40; every design decision is strategic, 41; strategy lies at the heart of the instructional conversation, 41; influence on other design layers, 41; opposing views on strategies should be reconciled, 42; breaking larger goals down into smaller ones, 299; strategy related to other layers, 300; the learning companion, 300 – 301; strategy, goals, and the message layer, 301 – 302; definition of instructional strategy, 302 – 4; strategy sub-layers, 305 – 12; instructional goals, 305 – 6; assessments and performance, 306; data recording, 306 – 307; setting and siting, 307; social context, 307; initiative sharing, 308 – 9; scope dynamics, 309; scope trajectory and sequencing, 309;

462 • Index Strategy layer (see also Goals): (continued ) task and activity, 309 – 10; timing and synchrony, 310; augmentation, 310; artifacts and environments, 311; cultural design, 311; engagement, 311 – 12; documenting strategies and designs, 312 – 14; instructional goals from performance goals, 314 – 16; work models, 316 – 17; strategy design and theory, 317 – 18; combining theories in designs, 318 – 21 Structure: primary elements arranged in new combinations, 9; harmony of inner and outer structure, 9; structure of the design process, 9 Sub-layers: exemplified in building designs, 25; represent subsystems of larger systems, 27; can become the responsibility of a design team specialist, 28; natural result of technology growth, 31; represent solvable subproblems of the design problem, 156 – 7; breakdown to sub-layers keeps alignment with breakdowns in theory specializations as they advance, 157; new theory creates new sub-layers, 157; Suppes, P., 374 Systems: instructional designers as architects of systems, 8 – 9, 71 Systems approach: response to increasingly complex technologies, 83; described, 83 – 7; a toolbox of methods rather than a single method, 84; characteristics of problems needing systems approaches, 84; diminishing visibility, 86; convergence with general systems theory, 87 – 8; application in instructional design, 89 – 93; Gagné and man-machine system engineering, 89; Banathy’s influence, 89 Systems approach view of instructional design, 83 Systems thinking: challenge for new and maturing designers, 106; Banathy’s concept of deliberately designed synthetic organisms, 106; seeing communities as learning organisms, 106 – 7; designing to fit within systems contexts, 107; requires stepping out of traditional categories, 107; Banathy’s emphasis on iterative cycles, 107; learning design thinking as a team, 107; myth of the single mind, 108; designer’s stewardship, 108 Tatsuoka, K., 163 Taxonomies of objectives: Gagne, 97; Bloom, 97, Anderson, 130 Taylor, R., 94 TCP/IP Model (see OSI Model): competes with other layered models of the Internet, 46 Team process view of instructional design: designer as a leader of a team and a linguist, 62; necessary skills of organizing and managing, 63; repeating cycles of project management, 63; phases in the conceptual evolution of a design (as opposed to process waypoints), 64 – 8 Technological theory (see also theory, design theory, domain theory): synthetic, used for design synthesis, 145; described by Simon, 146; not derivative from science, autonomous body of knowledge, 146; Klir’s description, 146; impetus added by the involvement of the computer in design, 147; Simon distinguishes multiple types of technological theory, 147; distinction key to understanding the operation of theory in any

field, 152; Christensen’s theories of innovation, 152; Technology: analogy with an irrigation system, 131 – 2; defined, 131; its questions and knowledge distinguished from those of science, 132; what is instructional technology?, 132 – 3; value-added of technologies as amplifiers, 133; model of technological intervention, 133 – 5 Tenney, Y., 164 Tharp, R., 117 Theory (see also scientific theory, technological theory, learning theory, instructional theory, design theory, domain theory): layers provide entry point for layers into designs, 33; difference between scientific and technological theory, 145; Simon’s distinction, 145 – 6; Vincenti’s distinction, 146; Klir’s distinction, 146; Mayr’s distinction, 147; Brown’s designs based on theory, 148; two types of technological theory of interest to instructional designers, design theory and domain theory 148; TICCIT: pivotal project in the evolution of computerized instruction, 20; the architecture of the TICCIT learner control design, 20 – 1; as an example of message design, 216; 233 – 4; 380 – 2 Tomek, I., 52 Tripp, S., 101 – 104, 161 Tufte, E. R., 35, 156, 256, 258, 265 Turner, F., 12, 86, 387 – 8 Twelker, P. A., 94, 98 Tyler, R., 280 Udacity, 53, 403 Ulrich, K.: industrial problem of matching physical and conceptual architectures of a product, 47 Uyemura, J., 10, 46 Value-added of the designer: the designer within a new landscape, 386; trends influencing the environment of instructional design, 386 – 7; the new consumer and the new product, 387 – 394; the social media generation, 387 – 388; the changing nature of the instructional product, 391; trends in assessing outcomes, 392; performance assessment, reliability, and validity, 392; the granularity of performance assessments, 392 – 3; adaptive instruction, 393 – 4; the new producer, 394 – 401; evolving tools and techniques, 395 – 7; data analytics and learning analytics, 395; recommender systems, 397 – 8; product standards, 398; changing design competencies, 400 – 401; the new provider, 401 – 4; commercialization of K-12 and higher education, 401 – 2; educational brokering by corporations, 402 – 3; open resources, 403; repositories, 403; corporate universities, institutes, and academies, 404; new learning themes, 404 – 5; learning and emotion, 406 – 7; tacit knowledge, 407 – 8; the knowledge economy, 408 – 9 van Den Akker, J., 157, 328 van Essen, D., 268 van Merriënboer, J., 37, 126, 162, 292, 296,393 Van Patten, J., 169; 317

Index • 463 Vanderbilt, T. van Lehn, K., 392 Vincenti, W., 36, 109, 132, 139, 146 – 8, 174 von Bertalanffy, L., 87 – 8 Vygotsky, 191, 407, 408 Watson, W., 333 Weber, G., 389 Wenger, E., 29, 38, 131, 156, 169, 203, 218, 218 – 19, 265, 282, 292 Wiener, N., 339

Wiener, N., 220 Wieringa, R., 10 Wigdor, A., 392 Winograd, T., 203, 223, 248, 250, 253 Womack, J. P., 375 – 7 Woolf, B., 29, 39, 121, 185, 292, 390, 397 Wurman, R. S., 35, 156, 256, 258 Yacef, K., 396 Yanchar, S., 104 Young, J., 403

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Dr. Andrew S. Gibbons Chair, Instructional Psychology and Technology McKay School of Education Brigham Young University Provo, Utah [email protected] Dr. Andy Gibbons is a faculty member and chair of the Instructional Psychology and Technology Department in the David O. McKay School of Education at Brigham Young University. Prior to that, for ten years he taught and researched as a faculty member in the Instructional Technology Department at Utah State University. For eighteen years before that he worked in the instructional design industry, leading design projects for Courseware Inc. (now Courseware Anderson Consulting) and Wicat Systems, Inc. This involved a variety of design challenges, including high-volume design and development projects, simulation design, and creation of innovative forms of computer-based instruction. Dr. Gibbons’ current research focuses on the architecture of instructional designs and the design process. He has published a design theory of Model-Centered Instruction, proposed a Layering Theory of instructional designs, and is currently studying the use of design languages in relation to design layers as a means of creating instructional systems that are adaptive, generative, and scalable. In addition, he leads a design research team exploring a new metaphor for learning management systems that offer mentored learning experiences rather than compartmentalized traditional courses.

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