Metacognition in Chemistry Education 9780841232693, 0841232695, 9780841232709

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Metacognition in Chemistry Education: Connecting Research and Practice

ACS SYMPOSIUM SERIES 1269

Metacognition in Chemistry Education: Connecting Research and Practice Patrick L. Daubenmire, Editor Loyola University Chicago Chicago, Illinois

Sponsored by the ACS Division of Chemical Education

American Chemical Society, Washington, DC Distributed in print by Oxford University Press

Library of Congress Cataloging-in-Publication Data Names: Daubenmire, Patrick L., editor. | American Chemical Society. Division of Chemical Education. Title: Metacognition in chemistry education : connecting research and practice / Patrick L. Daubenmire, editor, Loyola University Chicago, Chicago, Illinois ; sponsored by the ACS Division of Chemical Education. Description: Washington, DC : American Chemical Society, [2017] | Series: ACS symposium series ; 1269 | Includes bibliographical references and index. Identifiers: LCCN 2017055539 (print) | LCCN 2017061370 (ebook) | ISBN 9780841232693 (ebook) | ISBN 9780841232709 Subjects: LCSH: Chemistry--Study and teaching. | Metacognition. Classification: LCC QD40 (ebook) | LCC QD40 .M3945 2017 (print) | DDC 540.71--dc23 LC record available at https://lccn.loc.gov/2017055539

The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences—Permanence of Paper for Printed Library Materials, ANSI Z39.48n1984. Copyright © 2017 American Chemical Society Distributed in print by Oxford University Press All Rights Reserved. Reprographic copying beyond that permitted by Sections 107 or 108 of the U.S. Copyright Act is allowed for internal use only, provided that a per-chapter fee of $40.25 plus $0.75 per page is paid to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. Republication or reproduction for sale of pages in this book is permitted only under license from ACS. Direct these and other permission requests to ACS Copyright Office, Publications Division, 1155 16th Street, N.W., Washington, DC 20036. The citation of trade names and/or names of manufacturers in this publication is not to be construed as an endorsement or as approval by ACS of the commercial products or services referenced herein; nor should the mere reference herein to any drawing, specification, chemical process, or other data be regarded as a license or as a conveyance of any right or permission to the holder, reader, or any other person or corporation, to manufacture, reproduce, use, or sell any patented invention or copyrighted work that may in any way be related thereto. Registered names, trademarks, etc., used in this publication, even without specific indication thereof, are not to be considered unprotected by law. PRINTED IN THE UNITED STATES OF AMERICA

Foreword The ACS Symposium Series was first published in 1974 to provide a mechanism for publishing symposia quickly in book form. The purpose of the series is to publish timely, comprehensive books developed from the ACS sponsored symposia based on current scientific research. Occasionally, books are developed from symposia sponsored by other organizations when the topic is of keen interest to the chemistry audience. Before agreeing to publish a book, the proposed table of contents is reviewed for appropriate and comprehensive coverage and for interest to the audience. Some papers may be excluded to better focus the book; others may be added to provide comprehensiveness. When appropriate, overview or introductory chapters are added. Drafts of chapters are peer-reviewed prior to final acceptance or rejection, and manuscripts are prepared in camera-ready format. As a rule, only original research papers and original review papers are included in the volumes. Verbatim reproductions of previous published papers are not accepted.

ACS Books Department

Contents Preface .............................................................................................................................. ix 1.

Measurement, Theory, and Current Issues in Metacognition: An Overview .... 1 Tyler M. Miller

2.

Metacognition across the STEM Disciplines ....................................................... 17 Sharon S. Vestal, Matthew Miller, and Larry Browning

3.

A Cognitive Perspective on Chemistry Instruction: Building Students’ Chemistry Knowledge through Advancing Fundamental Literacy and Metacognitive Skills ............................................................................................... 31 Megan K. Littrell-Baez and Donna Caccamise

4.

Metacognition as an Element of the Scientific Process ....................................... 43 Mary T. van Opstal and Patrick L. Daubenmire

5.

Metacognition as a Construct for Studying How Students Learn from Molecular Visualizations ....................................................................................... 55 Resa Kelly and Jinyan Wang

6.

Promoting Metacognitive Practices in Faculty and Students ............................ 81 Anusha S. Rao, Terri A. Tarr, and Pratibha Varma-Nelson

Editor’s Biography ....................................................................................................... 101

Indexes Author Index ................................................................................................................ 105 Subject Index ................................................................................................................ 107

vii

Preface Metacognition is a critical part of the learning process for any content area, any academic subject. It is an inseparable part of the cognitive tapestry that is our learning. Further spreading the word about metacognition – how it benefits learning and how it can be fostered in classroom environments – especially in chemistry education, is a primary goal of this Series. Two colleagues, Seth Anthony and Michael Dianvosky, had the original idea for creating this Series. I am grateful to them for beginning this effort. In the pages that follow, readers will, I hope, find new or sharper insights into how metacognition and its tasks can be stitched into the fabric of classroom instruction and curricula. Tyler Miller (Chapter 1) begins the Series with various articulations about metacognition and its features and shares ideas for improving the accuracy of metacognition as well as promoting its sophistication. Sharon Vestal, Matthew Miller, and Larry Browning (Chapter 2) follow with a survey of metacognitive research in other STEM education disciplines. They go on to share tools that can be used across these disciplines to develop metacognition. Next, Megan Littrell-Baez and Donna Caccamise (Chapter 3) emphasize aspects of fundamental literacy and share characteristic features of metacognitive activities that can promote comprehension in chemistry. Mary van Opstal and myself (Chapter 4) then describe how metacognition is an essential part of the scientific process and share strategies and ways it can be engaged and fostered in the instructional laboratory. Resa Kelly and Jinyan Wang (Chapter 5) continue the Series with a detailed view of how metacognition can be used as a research tool for assessing how students understand molecular visualizations. The final chapter (Chapter 6) by Anusha Rao, Terri Tarr, and Pratibha Varma-Nelson shares “strategies to make faculty better teachers and their students better learners” – a fitting conclusion to the Series. The ultimate intent of this Series, to help reach its goal, is to offer some ways to make valid connections between what we do in our classrooms or what we pursue in our education research efforts to better helping students monitor and build their knowledge. This, I posit, remains an ongoing and necessary outcome of our education efforts. We must weave into our teaching that which helps our students.

Patrick L. Daubenmire Department of Chemistry and Biochemistry Loyola University Chicago Chicago, Illinois 60660, United States ix

Chapter 1

Measurement, Theory, and Current Issues in Metacognition: An Overview Tyler M. Miller* Department of Psychology, South Dakota State University, Brookings, South Dakota 57007, United States *E-mail: [email protected].

People thinking about their own thinking, a phenomenon known as metacognition, has a long history. Records of metacognitive thinking date back to ancient Greece, but people thinking about their own thoughts and beliefs certainly existed before then. However, actual metacognitive research in psychology only began in the mid-nineteenth century. In this chapter, I provide a very brief account of metacognition in history and present an overview of some topics in measurement, theory, and current research from my perspective as an experimental psychologist. Topics included have psychological, neuropsychological, and educational relevance. I pay particular attention to the varieties, causes, and consequences of metacognitive bias. I also include a sample of interventions from the literature used by teachers and researchers to improve metacognitive monitoring accuracy in the classroom and in the lab. Finally, the reasons why metacognitive sophistication should be promoted are found throughout the chapter.

© 2017 American Chemical Society

Suppose we try to recall a forgotten name. The state of our consciousness is peculiar. There is a gap therein; but no mere gap. It is a gap that is intensely active. A sort of wraith of the name is in it, beckoning us in a given direction, making us at moments tingle with the sense of our closeness, and then letting us sink back without the longed-for term. If wrong names are proposed to us, this singularly definite gap acts immediately so as to negate them. They do not fit into its mould. And the gap of one word does not feel like the gap of another, all empty of content as both might seem necessarily to be when described as gaps. -William James, The Principles of Psychology, 1890 (1)

Introduction Metacognition is thinking about thinking, knowing about knowing, or first in the literature as “cognition about cognitive phenomena” (p. 906; (2)). Eighty-nine years earlier, William James was writing about metacognition in his classic twovolume book, Principles of Psychology (1). In the quote above, James referred to one of the three interrelated processes that make up metacognition – monitoring. The other two are knowledge and control of cognition. The person described in the quote is in the unenviable position known as a “tip of the tongue” state. He is sure he knows the name but is unable to produce it. In other words, he has monitored his memory and determined the name is available just not accessible. Perhaps if he were given more he could retrieve the name. Or, perhaps the name would spontaneously appear in his consciousness with more time. When asked about something else, he may monitor his memory again for the longed for information, and confess he does not know the answer and will never know the answer no matter how much time passes. These scenarios both describe metacognitive monitoring. Another component of metacognition is control and refers to a person intentionally directing her thinking. A student may prepare for an upcoming exam by allocating her study time to the material that is perceived to be the most difficult, easiest, or somewhere in between. Finally, metacognitive knowledge is a person’s belief about cognitive processes. For example, an astute student will know that preparing for an exam in a distracting environment will interfere with learning. These three metacognitive processes operate in concert at each phase of learning (i.e., encoding, storage, and retrieval). John Flavell, the American developmental psychologist, is credited with the term metacognition itself (2). In his seminal works, Flavell discussed metacognition and tracked the development of metamemory in school-aged children (3). The term metacognition is a broad term that encompasses a person’s awareness of all cognitive processes. Metamemory refers to a person’s awareness of their own memory. Although all types of metacognition have received attention from researchers, the focus of this overview is considered metamemory. Even before William James, metacognitive processes were top-of-mind for researchers in psychology and other figures in history. People were very likely thinking about their own mental representations since the beginning. Evidence of 2

metacognitive behavior even exists in non-human primates (e.g., rhesus monkeys) and suggests an evolutionary pressure for this advanced form of thinking perhaps co-occurring with the appearance of humans’ earliest ancestors (4). Ancient Greek orators, like Simonides (c. 556-468 BC), exhibited metacognitive knowledge and control by recognizing the limitations of memory and devised strategies (e.g., the method of loci) to allow them to deliver long speeches and recite poetry without notes. Today, the method of loci and other strategies to scaffold memory are called mnemonic devices. The German psychologist, Herman Ebbinghaus (1850-1909) also exhibited metacognitive knowledge and used only nonsense syllables as stimuli in his classic memory research. He used nonsense syllables, rather than meaningful syllables, because he knew meaningful stimuli would be more memorable and thus contaminate his results (5). The entire system known as structuralism promoted by the English psychologist Edward Titchener (1867-1927) was based on monitoring one’s own thinking through analytic introspection. Through introspection, the structuralist’s goal was to create a periodic table of consciousness of simple characteristics like brightness, loudness, clarity and others that could be combined to form an individual’s perception (6). Today, changes in measurement, theory, and technology have significantly changed the scope of metacognitive research since the work of James, Ebbinghaus, and Titchener. Researchers in many disciplines measure a wide variety of metacognitive judgments using many research designs to answer both basic and applied questions.

Early Modern Research and Theory in Metacognition Measuring metacognition, like some other psychological constructs, is not as straightforward as measuring tangible objects. In early modern research, researchers began comparing peoples’ metacognitive judgments against an objective standard such as a person’s performance on a memory test. Joseph Hart was among the first in the modern era to evaluate a person’s metacognitive state against some basis of fact. In this research, Hart asked participants to make a feeling-of-knowing (FOK) judgment after failing to retrieve a question. For example, an experimenter may first ask a participant to name the prime minister of Great Britain. If the participant cannot name the prime minister, he would then report how likely he would be to select the correct name from a list of names. Hart then compared participants’ FOK reports to their actual ability to select the correct name. As it happened, participants were accurate at predicting their ability to select the correct name (7). Similarly, Brown and McNeill, in what may be considered a 75-year later epilogue to James’ quote to start this chapter, gave definitions of rare words (e.g., What is the name of the instrument that uses the position of the sun and stars to navigate?”) to participants and asked them to produce the term [sextant] (8). For some participants, the term was unknown, but for others the definition produced a state in which they felt they knew the term and they were close to producing it but simply could not. These latter participants were experiencing a peculiar state of consciousness known as the “tip-of-the-tongue” (TOT) state. What was most intriguing about this state of 3

consciousness was that participants were often able to produce the first letter of the term, the number of syllables, or similar sounding words. Flash forward to 1990, and a complete framework of the monitoring and control judgments that could be made at each stage of learning was proposed by Thomas Nelson and Louis Narens (9). This framework, which included the encoding, storage, and retrieval stages of learning connected researchers to a large body of literature investigating numerous types of judgments. Not only were researchers investigating participants’ FOK and TOT states, there had been intense interest in the types of monitoring and control judgments at the time of encoding (10). For example, even before learning, a student will exert control by deciding how to study by selecting a type of processing (e.g., reading, testing, highlighting). Furthermore, they decide what to study – either by starting with already learned information or information that is deemed most difficult or easiest (i.e. allocation of study time or item selection). Finally, the student decides when to quit studying. These control judgments are linked to monitoring judgments that are made at the same time – and much research has investigated the relationship between the two. The relationship between monitoring and control is exemplified in a model by Nelson & Narens (9). An important assumption of the model is that people have the ability to be self-reflective about their cognitive activity. The model also splits cognitive processes into two inter-related components. The first component is the object-level and encompasses any ongoing cognitive process. For example, memory is an object-level process. The second component is the meta-level and contains a model of the object-level cognitive process. It is also assumed there is a communication scheme by which the meta-level receives information about the object-level via monitoring processes, defined as “subjective reports about his or her introspections” (p. 127). Once updated, the meta-level can compare the object-level state to another state (such as mastery of the material) and then communicate to the object-level processes via control processes. The goal of control communication is to modify the object-level process and could include continuing an on-going process, terminating an on-going process, or changing the process (9). The Nelson and Narens (1990) model of metamemory does not assume that monitoring processes provide a veridical account of object-level processes. In fact, there are many biases that exist in metacognitive monitoring. For example, people often believe they will remember more than they actually do, a metacognitive bias known as the better-than-average effect or simply overconfidence (11, 12). This bias and others are discussed in detail in a later section. Exactly how a person monitors, or subjectively reports his or her introspections, is still the topic of debate. One early theory was the direct access theory of metacognition (13). In the theory, a person would directly assess the associative strength of to-beremembered items. In other words, the same information that is used in memory forms the basis of metacognitive judgments (14). For example, imagine a person recognizes the person standing next to them in line at the grocery store but cannot remember her name. Even though the person cannot remember her name, he is sure he knew it at one time. His metacognitive state is based on knowing the name is stored in memory, but the memory trace for that name is not strong enough to be retrieved. In this way, the direct access view claims the memory trace and 4

the metamemory state are based on the same cognitive process. The direct access theory is included here because it was influential in metacognition’s history. Today, though, researchers have dismissed the idea that people have direct access to their own memory traces . In contrast to the direct access theory, the indirect or inferential theory of metacognition claims people do not have direct access to a memory trace, and therefore can neither assess the strength of memory nor base a metacognitive judgment on that assessment. Rather, people assess proxies of the memory trace to make metacognitive judgments (15, 16). For example, if the person at the grocery store was asked to make a FOK judgment about the likelihood of retrieving her name, he may make the judgment based on recalling other information about the person, maybe, information about where she works, where he saw her last, the names of people she is with and other related information. If he were able to retrieve more related information, he would give a higher FOK judgment. If he could retrieve little related information, he would report a lower FOK judgment. Further, the sources of related information in the inferential theory of monitoring will vary depending on the type of metacognitive judgment (17). Research on the judgment of learning (JOL), a monitoring judgment people make at the time of encoding, began almost as early as any topic in the modern era (13). A JOL is a person’s report of his or her own likelihood of recalling a studied item on a later test. The exact form of the report for a JOL can vary, but essentially a person studies an item and then immediately after or sometime later reports if they will remember it on a test. Like many other metacognitive judgments, the JOL can be dichotomous – the person reports they will or will not remember it on the test – or the judgment can be made on a scale of 0-100 for example. Importantly, the JOL report is compared to performance on the test. Comparing a metacognitive judgment, in this case, a JOL, to performance can yield two types of accuracy – relative and absolute accuracy. Relative accuracy, typically measured by calculating a gamma correlation, is a person’s ability to discriminate between well-learned information from poorly-learned information within a list of to-be-remembered material. Like other correlational coefficients, “0” values indicate no relationship and extreme values indicate strong relationships. Absolute accuracy on the other hand, can be measured using a difference score by subtracting performance from one’s prediction. In this way, “0” values indicate perfect metacognitive accuracy, positive values indicate overconfidence and negative values indicate underconfidence. Using both relative and absolute measures of metacognitive accuracy can lead to different conclusions (18). While the vast majority of researchers use traditional measures of metacognitive accuracy (i.e., gamma correlations and/or difference scores), there is growing interest and work in the area of improving measurement. For example, Higham and colleagues pointed out that traditional conceptualizations of metacognitive accuracy fail to account for the variety of ways participant’s translate their subjective metacognitive experiences to overt scale values, a process they have named “mapping 1” (19). Failing to account for “mapping 1” in their view can lead to multiple problems, all of which hamper a study’s validity. Newer approaches to measurement that can attenuate these validity 5

concerns include using receiving operating characteristics and signal detection theory concepts. In another paradigm, participants rate how difficult items would be to remember later. When compared to their ability to remember the items, these difficulty ratings predicted participants’ memory ability (20). Today, participants make “ease-of-learning judgments” (21). Classic metacognitive research and most research since indicates peoples’ metacognitive monitoring judgments have above-chance predictive accuracy. While there is substantial room for improvement, people know what they know and know what they do not know. That people’s predictions are not perfectly accurate opens up investigation into the varieties and causes of inaccuracy as well as the consequences and potential interventions to improve metacognitive accuracy – these topics are discussed in detail in a later section. Research on metacognitive control has gained a significant understanding of what students choose to study if they have a goal of study. For example, if a student needs to study for an upcoming exam, she must decide if her goal is to get an “A” or perhaps just to pass with a “D”. The goal of study then, may contribute to her reasoning about how long to study and what material to study. There are two prominent models that have been proposed to explain how people decide how long to study, the discrepancy-reduction model (22) and the region-of-proximal learning model (23). In the discrepancy-reduction model, learners have a goal, which is known as the norm-of-study. If the norm-of-study is complete mastery, then learners would continue to study items until they believe they have completely memorized the material. In other words, the goal of studying is to reduce the discrepancy between what is known and the norm-of-study (22). Another model of study time allocation, proposes that, perhaps in addition to reducing the discrepancy between what is known and the goal, learners prioritize their study time from the subjectively easiest material to the hardest material. According to this model, learners terminate study as soon as they believe they are no longer learning (23). Ideally, students study for a sufficient amount of time to perform well on an upcoming test. Yet, students often do not study for enough time to perform well on tests. There may be several reasons why students stop studying prematurely. For example, they may simply run out of time, especially if they have busy schedules or if they start studying at the last minute. Students may also stop studying prematurely because they falsely believe that they are prepared for the exam (12, 24), or because they make a reasonable guess about their preparation (25). Given metacognitive control decisions, such as study time allocation, are based on metacognitive monitoring, they can only be as good as the quality of the monitoring (9).

Benefits of Accurate Monitoring and Control Metacognition refers to the understanding that a person has about his or her own cognition. It is preferable in most cases to have accurate metacognition. Certainly, in educational settings, it is preferable for students to be able to 6

accurately assess their own knowledge. If students know that they do not understand the course material they can decide to continue to study for the exam. Several laboratory and classroom studies demonstrate that accurate metacognition is associated with better performance (26–31). Conversely, metacognitive inaccuracies can lead to poor academic performance (26), under-preparedness for exams, and inefficient study decisions (32). Metacognitive accuracy is associated with and causally related to more efficient learning and improved performance outcomes. In one such study, participants studied paired words from different languages (e.g., ardhi - soil), reported judgments of learning for each word pair and took a cued-recall test (e.g., ardhi - ?; (29)). Researchers also told participants the study would conclude when the participant could recall all 36 word pairs or after a set number of trials. Following the first study-test phase, participants were able to self-select items to re-study. Participants with the most accurate JOLs and the most effective item-selection also had the highest cued-recall test performance. When metacognitive monitoring is experimentally manipulated by asking some participants to generate keywords and self-select material to re-study, the data lead to the same conclusion – more accurate monitoring leads to more effective control which, in turn, leads to better performance outcomes (30).

Biases in Metacognition When differences in monitoring accuracy emerge, one immediately wonders why there are differences. Countless studies show that people show inaccurate metacognition in the direction of being overconfident in their abilities and characteristics – and this overconfidence extends to a variety of domains. People are often overconfident about their dating attractiveness (33), driving ability (34, 35), performance on college course exams (36), humor recognition (12), and gun safety knowledge (37). Low performers in particular are prone to being overconfident about their abilities. It has been suggested that low performers suffer from a “double curse,” – they do not know the material they will be tested on and they do not know that they are underprepared (12). Indeed, David Dunning likened low performers’ inflated self-assessments to a form of brain damage (i.e., anosognosia), and suggested that “people performing poorly cannot be expected to recognize their ineptitude” and that “the ability to recognize the depth of their inadequacies is beyond them” (38). It follows from the double-curse account that if low performers lack knowledge and awareness then, in addition to making inaccurate performance predictions, they would also be unduly confident in these predictions. However, there is some evidence that people may not be entirely unaware of their metacognitive shortcomings (26). In one study, participants made performance predictions about an upcoming test and then reported their confidence in the accuracy of the prediction. For example, a participant could have reported that she would earn an 86% on the test and either high or low confidence that the prediction was accurate. Some research groups have coined this confidence judgment a “second-order” judgment—the performance prediction is the first-order 7

metacognitive judgment and confidence about the performance prediction is the second-order metacognitive judgment (39). Miller and Geraci showed that while students are sometimes overconfident, they predict that they will perform much better than they do, their confidence in the accuracy of their prediction can be low, suggesting a dissociation between the two types of judgments (26). The finding that overly high performance predictions of low performers were associated with low confidence was taken as evidence that some participants may have awareness that their judgments are inaccurate. Other possible explanations for low performers’ overconfidence are that they wish to “look good” to the experimenter (37), they are motivated to be overconfident (40), they attribute their poor performance to external factors (41), or their overconfidence is simply a statistical artifact (42). Two recent papers have provided support for the idea that students may base their predictions for upcoming exams on desired grades (43, 44). In one such series of studies, students in upper and lower level college courses reported scores they hoped to earn on an upcoming test (desired grade) and reported scores they thought they would earn (predicted grade; (43)). In another series of studies, students in upper and lower level college courses reported predictions and indicated what factors they considered when making the predictions. The factors were categorized around two major themes: 1) educational factors like exam preparation, or 2) motivational factors like desired grades (44). In both sets of studies, motivational factors explained more variance in predictions than educational factors. One clear implication is that any attempt to improve metacognitive accuracy that does not address motivational factors, like desired grades, are not likely to eliminate overconfidence. However, as some have noted, there could be adaptive reasons for people to believe they can perform better than they actually do (40, 45). Future research should assess metacognitive accuracy versus “wishful thinking” to determine their beneficial and/or detrimental contributions to performance and other outcomes in educational, occupational, social, and other important areas of functioning. Overconfidence is not the only bias in metacognitive monitoring judgments. Another systematic distortion is the hard-easy effect and occurs when people overestimate their learning on hard items and underestimate their learning on easy items (46). Participants can also show underconfidence with practice (UWP). When participants have multiple study opportunities and make multiple JOLs, their JOLs may underestimate the amount of learning that has taken place (47). In other words, people do not believe they have learned as much as they have. This fact, coupled with the fact that people believe their memories will remain accessible over time has been referred to as a “stability bias” in human memory (48).

Biological Bases of Metacognition Metacognitive research using special population samples and imaging technology has made the understanding of the brain areas associated with metacognitive processes more complete. For example, younger college student 8

participants display more overconfidence than their older student counterparts (49). Compared to older adults, some studies show younger people display more overconfidence than older adults (50) or equivalent metacognitive resolution (51). Older adults’ spared metacognitive ability has even been used to improve test performance (52, 53). Older adults with dementia attributed to Alzheimer’s disease show little insight into their cognitive impairment. In fact, lack of insight is an early symptom of dementia (54). Importantly, individuals with vascular dementia have relatively preserved insight. The difference in metacognitive insight between the two types of dementias may be explained by the differences in pathophysiology. Whereas significant neocortical, frontal lobe atrophy is common with dementia of the Alzheimer’s type and fronto-temporal dementia, frontal-lobe atrophy is not as common in vascular dementia (55). Similarly, patients with frontal lobe lesions due to surgical removal of brain tumors or arteriovenous malformation treatment exhibited greater metacognitive inaccuracy (i.e., overconfidence) than control subjects, particularly those with left frontal lesions (56). Research using imaging technology has corroborated the important role of the pre-frontal cortex in metacognitive processing (57, 58). In one such study, participant’s grey matter volume in the anterior prefrontal cortex was associated with more metacognitive awareness (59). In another imaging study, participants viewed pictures and were asked to make dichotomous predictions about their future memory performance (60). Participants reported either they would remember or would not remember the picture on a recognition memory test while fMRI data were collected. The authors reached several conclusions from the data. Among them was the conclusion that medial-temporal lobe activity, long implicated in memory formation, was not associated with metacognitive processing. Additionally, ventro-medial and lateral prefrontal cortical activity was associated with greater metacognitive accuracy. Experimentally, repetitive trans-cranial magnetic stimulation (rTMS) has been used to depress activity in the dorsolateral prefrontal cortex resulting in less insight into cognitive processes (61).

Training Metacognition Given the benefits of accurate metacognition but also the systematic biases that exist, there have been several attempts to improve or “train” metacognition in the classroom and in the laboratory. These attempts have yielded mixed results. Several interventions to improve metacognitive accuracy have used feedback. For example, studies show that providing repeated feedback to participants about the accuracy of their predictions appears to lead to modest improvements in metacognitive accuracy. In one study, participants viewed word pairs under various encoding tasks and then rated their confidence that their answers were correct on five successive days of sessions (62). Participants who received feedback after each session, showed moderate improvement in their confidence accuracy from session one to five while control condition participants did not improve. In another study, participants made retrospective confidence judgments 9

about the accuracy of their responses to general knowledge questions and received feedback about their accuracy (63). After 23 1-hour sessions, participants who were overconfident at the beginning of the training improved their calibration; although the majority of the improvement occurred between the first and second sessions. The authors wrote the training protocol for the study was “both arduous and expensive,” so much so that they completed a second experiment to determine if similar improvement would be seen with a shortened training program that was only 11 sessions long instead of 23 (pg. 166). The shortened training program did yield similar improvement. Another theme of intervention studies has been to give participants practice making predictions and practice taking memory tests. For example, when participants make one global judgment of learning, complete a practice test, and then are allowed to adjust their performance prediction, they become more accurate (64). Sometimes, these interventions improve predictions for certain participants and not others. For example, Kelemen and colleagues showed that participants’ predictions improved significantly after 5 sessions of making performance predictions, but it was only the high achieving students who were able to improve their metacognitive accuracy (65). This outcome – when an intervention designed to benefit low achieving students has a greater benefit for high achieving students – has become known as the “Matthew effect” (36, 65). There are other non-intervention approaches that have been used to improve metacognitive accuracy in the laboratory, too. For example, one way to improve accuracy is to increase the time between when the subject finishes studying and when the prediction is made (66). This improvement is known as the delayedJOL effect and is thought to occur because participants are only able to use the contents of long term memory when the judgment is made (i.e., the monitoring dual memories hypothesis), which matches what the participant will use during the test. The advantage of delayed-JOLs over immediate JOLs has recently been confirmed through a meta-analysis involving more than 40 studies (67). Methods to improve metacognition in the classroom have used interventions that include practice (68), feedback (41), incentives (24), self-reflection (69) and combinations of these interventions. Results from these studies indicate that metacognitive monitoring ability is very resistant to intervention, and in instances when the intervention does improve metacognitive monitoring it sometimes only benefits the highest-performing students.

Education and Metacognition At the same time modern metacognitive research was beginning, Ann Brown acknowledged the topic as having huge importance for educational practice (70). All varieties of study designs (i.e., correlational, cross-sectional, and longitudinal studies) have confirmed the importance of instruction using metacognitive theory beginning in very early grade-levels and continuing through secondary education to improve performance outcomes (71). On the other hand, metacognitive inaccuracy can lead to underachievement (72). Further, it has been argued that 10

flawed self-assessment has had the effect of higher rates of attrition from STEM fields (73). Not only should instructors be cognizant of and use instruction that fosters students’ metacognitive sophistication, instructors need to inform, and sometimes retrain students about effective study strategies (74). Fostering students’ understanding that monitoring processes are flawed is a key factor. For example, most students are not aware of the most effective study strategies, preferring less effective study strategies like re-reading or highlighting (75). At the extreme, some strategies used by students can even lead their monitoring accuracy and control to become more distorted. These strategies lead to so-called “illusions of competence” and can be detrimental to academic achievement (76, 77). With the publication of the current volume, enhancing learning and instruction with an understanding of metacognitive processes will continue to be an important applied research area. Some themes of this research include people’s metacognitive capabilities, causes and consequences of metacognitive inaccuracy as well as benefits of accurate metacognition, interventions to improve monitoring and control, and selection of optimal study strategies. Understanding what people know about their own cognition and how they use that knowledge to guide their future behavior in order to develop more effective interventions and teaching techniques will have a significant impact on students’ lives in the high-stakes world of education.

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29. Thiede, K. W. The importance of monitoring and self-regulation during multitrial learning. Psychon. Bull. Rev. 1999, 6, 662–667. 30. Thiede, K. W.; Anderson, M. C. M.; Therriault, D. Accuracy of metacognitive monitoring affects learning of texts. J. Educ. Psychol. 2003, 95, 66–73. 31. Rawson, K. A.; O’Neil, R.; Dunlosky, J. Accurate monitoring leads to effective control and greater learning of patient education materials. J. Exp. Psychol.: Appl. 2011, 17, 288–302. 32. Bjork, R. A.; Dunlosky, J.; Kornell, N. Self-regulated learning: Beliefs, techniques, and illusions. Annu. Rev. Psychol. 2013, 64, 417–444. 33. Preuss, G. S.; Alicke, M. D. Everybody loves me: Self-evaluations and metaperceptions of dating popularity. Pers. Soc. Psychol. Bull. 2009, 35, 937–950. 34. Knouse, L. E.; Bagwell, C. L.; Barkley, R. A.; Murphy, K. R. Accuracy of self-evaluation in adults with ADHD: Evidence from a driving study. J. Atten. Disord. 2005, 8, 221–234. 35. Williams, A. F. Views of U.S. drivers about driving safety. J. Saf. Res. 2003, 34, 491–494. 36. Hacker, D. J.; Bol, L.; Horgan, D.; Rakow, E. A. Test prediction and performance in a classroom context. J. Educ. Psychol. 2000, 92, 160–170. 37. Ehrlinger, J.; Johnson, K.; Banner, M.; Dunning, D.; Kruger, J. Why the unskilled are unaware: Further explorations of (absent) self-insight among the incompetent. Organ. Behav. Hum. Decis. Processes 2008, 105, 98–121. 38. Dunning, D. Self-insight: Roadblocks and Detours on the Path to Knowing Thyself; Taylor & Francis Books: New York, 2005; pp 1−244. 39. Dunlosky, J.; Serra, M. J.; Matvey, G.; Rawson, K. A. Second-order judgments about judgments of learning. J. Gen. Psychol. 2005, 132, 335–346. 40. Gramzow, R. H.; Willard, G.; Mendes, W. B. Big tales and cool heads: Academic exaggeration is related to cardiac vagal reactivity. Emotion 2008, 8, 138–144. 41. Hacker, D. J.; Bol, L.; Bahbahani, K. Explaining calibration accuracy in classroom contexts: The effects of incentives, reflection and explanatory style. Metacognit. Learn. 2008, 3, 101–121. 42. Krueger, J.; Mueller, R. A. Unskilled, unaware, or both? The better-thanaverage heuristic and statistical regression predict errors in estimates of own performance. J. Pers. Soc. Psychol. 2002, 82, 180–188. 43. Serra, M. J.; DeMarree, K. G. Unskilled and unaware in the classroom: College students’ desired grades predict their biased grade predictions. Mem. Cognit. 2016, 44, 1127–1137. 44. Saenz, G. D.; Geraci, L.; Miller, T. M.; Tirso, R. Metacognition in the classroom: The association between students’ exam predictions and their desired grades. Conscious. Cognit. 2017, 51, 125–139. 45. Shin, H.; Bjorklund, D. F.; Beck, E. F. The adaptive nature of children’s overestimation in a strategic memory task. Cognit. Dev. 2007, 22, 197–212.

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46. Lichtenstein, S.; Fischoff, B.; Phillips, L. D. In Judgment Under Uncertainty: Heuristics and Biases; Kahneman, D.; Slovic, P.; Tversky, A., Eds.; Cambridge University Press: New York, 1982; pp 306−344. 47. Koriat, A.; Sheffer, L.; Ma’ayan, H. Comparing objective and subjective learning curves: Judgments of learning exhibit increased underconfidence with practice. J. Exp. Psychol.: Gen. 2002, 131, 147–162. 48. Kornell, N.; Bjork, R. A. A stability bias in human memory: Overestimating remembering and underestimating learning. J. Exp. Psychol.: Gen. 2009, 138, 449–468. 49. Grimes, P. W. The overconfident principles of economics student: An examination of a metacognitive skill. J. Econ. Educ. 2002, 33, 15–30. 50. Hertzog, C.; Sinclair, S. M.; Dunlosky, J. Age differences in the monitoring of learning: Cross-sectional evidence of spared resolution across the adult life span. Dev. Psychol. 2010, 46, 939–948. 51. Hertzog, C.; Kidder, D. P.; Powell-Moman, A.; Dunlosky, J. Aging and monitoring associative learning: Is monitoring accuracy spared or impaired? Psychol. Aging 2002, 17, 209–225. 52. Dunlosky, J.; Kubat-Silman, A.; Hertzog, C. Training monitoring skills improves older adults’ associative learning. Psychol. Aging 2003, 18, 340–345. 53. Hertzog, C.; Dunlosky, J. Metacognition in later adulthood: Spared monitoring can benefit older adults’ self-regulation. Curr. Dir. Psychol. Sci. 2011, 20, 167–173. 54. Mendez, M. F.; Cummings, J. L. Dementia: A Clinical Approach. Butterworth Heinemann: Philadelphia, PA, 2003; pp 1−688. 55. Salat, D. H.; Kaye, J. H.; Janowsky, J. S. Selective preservation and degeneration in the prefrontal cortex in aging and Alzheimer disease. Arch. Neurol. 2001, 58, 1403–1408. 56. Vilkki, J.; Servo, A.; Outi, S. Word list learning and prediction of recall after frontal lobe lesions. Neuropsychology 1998, 12, 268–277. 57. Pannu, J. K.; Kaszniak, A. W. Metamemory experiments in neurological populations: A review. Neuropsychol. Rev. 2005, 15, 105–130. 58. Schwartz, B. L.; Bacon, E. In Handbook of Metamemory and Memory; Dunlosky, J.; Bjork, R. A., Eds.; Psychology Press: New York, 2008; pp 355−371. 59. Fleming, S. M.; Weil, R. S.; Nagy, Z.; Dolan, R. J.; Rees, G. Relating introspective accuracy to individual differences in brain structure. Sci. 2010, 329, 1541–1543. 60. Kao, Y. C.; Davis, E. S.; Gabrieli, J. D. E. Neural correlates of actual and predicted memory formation. Nat. Neurosci. 2005, 8, 1776–1783. 61. Rounis, E.; Maniscalco, B.; Rothwell, J. C.; Passingham, R. E.; Lau, H. Theta-burst transcranial magnetic stimulation to the prefrontal cortex impairs metacognitive visual awareness. Cognit. Neurosci. 2010, 1, 165–175. 62. Adams, P. A.; Adams, J. K. Training in confidence-judgments. Amer. J. Psychol. 1958, 71, 747–751. 63. Lichtenstein, S.; Fischoff, B. Training for calibration. Organ. Beh. Hum. Perform. 1980, 26, 149–171. 14

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Chapter 2

Metacognition across the STEM Disciplines Sharon S. Vestal,*,1 Matthew Miller,2 and Larry Browning3 1Department

of Mathematics & Statistics, South Dakota State University, Box 2225, Brookings, South Dakota 57007, United States 2Chemistry & Biochemistry, South Dakota State University, Box 2202, Brookings, South Dakota 57007, United States 3Department of Physics, South Dakota State University, Box 2222, Brookings, South Dakota 57007, United States *E-mail: [email protected].

In this chapter we provide an overview of metacognitive research in the fields of Biology, Mathematics, and Physics and how STEM teachers can utilize these metacognitive strategies in their classrooms to promote critical thinking. In the era of the Common Core Standards for Mathematical Practice and the Next Generation Science and Engineering Practices, STEM teachers need to provide classroom instruction and assessments that promote deep understanding. This chapter provides tools that STEM teachers can use to improve STEM learning for all students.

Introduction Throughout the history of our country, educational decisions have been made at the local and state level. After the publication of the book A Nation at Risk, there has been a push for national standards in several disciplines. At the center of this movement has been the need for higher expectations for all students with a focus on problem solving (1). For the STEM disciplines, the creation of the Common Core State Standards for Mathematics (2) (CCSSM) and the Next Generation Science Standards (3) (NGSS) has prompted a need for us to rethink how we teach so that we can improve students’ problem-solving skills and prepare them for careers that may not yet even exist (4). At the heart of both the CCSSM and the NGSS are practices that focus on skills that are part of all STEM disciplines. In fact, when you look at the lists below, you © 2017 American Chemical Society

will see striking similarities in the CCSSM Standards for Mathematical Practices and the NGSS Science and Engineering Practices Table 1.

Table 1. CCSSM Standards for Mathematical Practice and NGSS Science and Engineering Practices Standards for Mathematical Practice (2)

Science and Engineering Practices (3)

1. Make sense of problems and persevere in solving them

1. Ask questions (for science) and define problems (for engineering)

2. Reason abstractly and quantitatively

2. Develop and use models

3. Construct viable arguments and critique the reasoning of others

3. Plan and carry out investigations

4. Model with mathematics

4. Analyze and interpret data

5. Use appropriate tools strategically

5. Use mathematics and computational thinking

6. Attend to precision

6. Construct explanations (for science) and design solutions (for engineering)

7. Look for and make use of structure

7. Engage in arguments of evidence

8. Look for and express regularity in repeated reasoning

8. Obtain, evaluate, and communicate information

As STEM educators are tasked to improve all students’ critical thinking skills, one of the most useful tools for doing this is metacognition. While Flavell defines metacognition as “thinking about your own thinking,” later researchers explored how we can help students learn the metacognitive process (5). Fogarty states that the metacognitive process has three phases: planning, monitoring, and evaluating (6). In this chapter we will give an overview of what has been learned about studying metacognition in biology, mathematics, and physics, and how teachers can help students improve their metacognition in these STEM disciplines.

Biology Metacognitive research across biology has taken multiple approaches. These approaches include metacognitive awareness, combined pedagogical approaches, the use of technology, and writing methods. Furthermore, we believe that these categories of metacognitive research provide methods to promote the NGGS practices in STEM classrooms. Metacognitive Awareness Several studies have monitored student awareness regarding their abilities and performances. While each study showed that student awareness of metacognitive concepts leads to improved performance, there is evidence of significant differences between metacognitive abilities of students. This is relevant according 18

to Baird and White as they describe the learning process as dependent on student understanding of their own learning process. Students must make decisions about their learning and therefore must be provided the necessary knowledge to make appropriate decisions (7). Snyder, Nieetfeld, and Linnenbrink-Garcia studied a semester-long high school biology course monitoring the difference in metacognition between gifted and typical students. Student metacognition was measured using student self-reports of awareness. Data from the study supports the idea that gifted students have a metacognitive advantage over typical students (8). Bissell and Lemon developed an assessment where both content and critical thinking skills were expected as part of the answer. Questions were carefully composed to include conceptual knowledge as well as critical thinking processes, with each component carrying a significant measure of student performance. Since each portion of the assessment was weighted equally, students were encouraged to reflect on and to improve their critical thinking abilities (9). Finally, Carpenter, Lund, Coffman, Armstrong, Lamm, and Reason studied a large enrollment biology course to compare the impact of retrieval on student learning. Retrieval methods require students to identify personal prior knowledge for use in course work. In this study students had two choices: they could complete an in-class activity by retrieving prior knowledge or by simply copying the information from a different source. Their choice was monitored to determine if there was an impact on a follow-up quiz. The data showed that high-performing students benefited more from retrieval than from copying, while middle- or low-performing students benefited more from copying (10). In all the metacognitive awareness studies, the outcomes were similar. Making students aware of expectations helped them to think about their approach toward learning, which, in turn, enhanced their metacognitive skills. Additionally, these studies suggest that there are differences across students, specifically across varying performance levels. Finally, these metacognitive awareness studies show the importance of students’ thinking about their knowledge and the processes by which they have gained that knowledge, which is similar to the NGSS practice of “obtaining, evaluating and communicating information (3).”

Combined Pedagogical Approaches Other metacognitive studies in biology have taken a multiple pedagogical approach including Group Investigation (GI), Think Talk Write (TTW), Problem Based Learning (PBL) jigsaw, and field trip/group work. Each of these studies suggests that combining pedagogical approaches can enhance student metacognitive abilities. Listiana, Susilo, and Suarsini combined several pedagogies to encourage metacognitive activities in students in a high school biology class. Group Investigation (GI) activities were combined with Think Talk Write (TTW) activities to encourage metacognition by helping students to address what, how, and why a strategy or resource is used to understand an idea. This combined GITTW method was shown to have advantages and resulted in even higher metacognitive skills for students (11). 19

Four different teaching strategies (direct instruction, jigsaw, PBL, PBL jigsaw) were used by Palennari across four different biology classes to determine if there was a correlation between metacognition (both awareness and skills) and cognitive retention. No relationship was found between metacognitive awareness and cognitive retention, but there was a significant correlation between metacognitive skill and cognitive retention. When students were cognizant of a specific metacognitive skill, they were more likely to retain specific content knowledge. In particular, the combined PBL jigsaw strategy provided the highest correlation potentially indicating significant empowerment of metacognitive skills (12). Anderson, Thomas, and Nashon investigated learning through a field trip to a nature center. These biology students also worked in groups to complete activities making this a multi-pedagogical approach. Findings in this study indicated that when groups were successfully engaged in the content of the work, there were social issues that led to diminished academic achievement. It is important to remember that metacognitive skills may be altered by group dynamics. Therefore, group work creates another set of metacognitive skills that must be developed so that students can focus their metacognition on learning (13). These studies indicate that combining pedagogical approaches provide enhanced opportunities for metacognitive awareness and practice. However, group activities include additional social factors that can also change metacognitive practice. Thus the instructor must help students learn metacognitive skills as well as how to work in groups. In particular, the NGSS practice of “engaging in argument” can easily be seen in group work (3).

Technology The use of technology in the classroom can shape pedagogical methods that help students with metacognitive learning. A few studies in biology show how technology can be used to encourage metacognitive learning. Parker’s study involved ninth and tenth grade biology students. The students were assigned either a traditional or a shared internet learning environment while studying ecology. The internet learning environment required students to construct concept maps. The statistical analysis showed that this shared internet learning environment increased problem solving ability through increased metacognitive reflection (14). A biology study by Sandoval and Reiser showed that students not only need support as they construct ideas, but also as they reflect on those ideas. In this study, guided reflective discussions were used to discuss evolution (15). Lei, Sun, Lin, and Huang compared metacognitive strategy skills of students while conducting video searches. Students’ thinking was monitored as they searched for videos to use in the biology activity. The results showed that students with better metacognitive skills completed the video search more quickly because they were able to identify more appropriate keywords (16). Technology offers alternative methods for assessing student metacognitive ability. Again, the instructor needs to take time to help the students learn how to best use the technology. While the NGGS does not have explicit practices 20

that mention the use of technology, the practice of “obtaining, evaluating, and communicating information” can certainly be enhanced through technology (3). Writing Methods The use of writing in the classroom has long been a pedagogical approach for the classroom. In a study done by Balgopal and Montplaisir, students used reflective writing in an upper-division physiological ecology course. Students were asked to consider new concepts and connect those concepts to prior knowledge. While reflective writing is not seen as scientific, it does afford student the opportunity to admit which concepts they don’t understood and to express the ideas the do have (17). Stephens and Winterbottom used learning logs in a biology class during the study of digestion, respiration and breathing. In a learning log students express what they believe they have learned. While the learning log did stimulate reflection, the semi-structured interviews with the students actually resulted in better reflection. This suggests that the learning log may be too restrictive to promote strong reflections. However, findings from the study do suggest that reflection improved the role of the learning log in the assessment process. In addition, the learning log increased the student’s self-concept as a biologist (18). The use of writing in the STEM classroom enhances the opportunity for students to practice metacognitive skills and develop an awareness of these thinking. These writing processes also encourage students to take on the role of the scientist. One of the practices of the NGSS suggests that students “plan and carry out an investigation.” In doing so students may recognize the utility of metacognitive skills in learning. All of these studies about metacognitive practices in biology classrooms give teachers various tools to improve their students’ learning through metacognition. As the NGGS continues to become a prominent fixture within the science classroom, teachers need to structure lessons using the NGGS practices. Teachers must specifically focus on helping students develop metacognitive skills.

Mathematics While research on metacognition in mathematics is fairly recent, many people attribute the start of using metacognition in mathematics to George Pólya. In his book, How to Solve It, he gives four steps to use to solve a mathematics problem: “1. Understanding the problem; 2. Devising a plan; 3. Carrying out the plan; and 4. Looking back (19).” Comparing Pólya’s list to the three phases of Fogarty, it is easy to place steps 1 and 2 into the planning phase, step 3 into the monitoring phase, and step 4 into the evaluating phase (6). It is clear that Pólya was on to something even before Flavell defined metacognition. In the 1980’s, Alan Schoenfeld from the University of California-Berkeley, started to research mathematically thinking and metacognition. He discusses the variations of what problem solving is and what it means to do mathematics. His belief is that knowing basic mathematical facts or algorithms does not encompass 21

mathematics. Schoenfeld characterizes “learning to think mathematically as developing a mathematical point of view and developing competence with tools of the trade (20).” When the National Council of Teachers of Mathematics published Everybody Counts, they emphasized that teachers need to help students go beyond memorizing math facts and using algorithms to “express things in the language of mathematics (21).” These ideas from the 1980’s helped shape the Standards for Mathematical Practice in the CCSS. Schoenfeld made it clear that there needed to be more research on metacognition in mathematics education. Over the last four decades, several research studies have been conducted on the relationship between metacognition and mathematics achievement. These studies were split into two types: those focused on correlation between metacognition and mathematics achievement and those that offered interventions to improve metacognition in mathematics (30). Metacognition and Mathematics Achievement In 1994, Carr, Alexander, and Folds-Bennett looked at the role of eight year olds utilizing metacognition in their strategies to solve mathematics problems. The results showed that using internal strategies (adding numbers in one’s head) were related to metacognitive knowledge and motivation while external strategies (using one’s fingers to add numbers) were not related (22). The idea that internal strategies improve metacognitive knowledge can be tied to the mathematical practice of “reason abstractly and quantitatively (2).” The other correlational study was conducted in Germany and was an extension of a 2003 Program for International Student Assessment (PISA) study. The researchers developed a test on metacognition associated with mathematics, based on an existing instrument created for reading. When 5th graders took both the metacognitive test along with a mathematics curriculum exam, the results indicated that higher performing students also knew more metacognitive approaches. Then comparing genders, the girls didn’t do as well on the mathematics content exam, but they were equally as good as boys on the metacognition instrument. In addition, a similar study was done with 15 year olds in Germany. Both studies showed a correlation between mathematics performance and metacognitive knowledge. The importance of this finding is that students’ metacognitive knowledge can grow and then improve their knowledge in mathematics (23). This ties in nicely with Boaler’s research on the importance of a growth mindset in mathematics (24). Interventions A couple of studies mentioned in Schneider and Artelt’s paper were intervention studies, ones where an intervention was used to attempt to improve metacognition and then data was analyzed to see if there was an improvement (25). Cornoldi, Lucangeli, Caponi, Falco, Focchiatti, and Todeschini trained children at two academic levels on the monitoring and control pieces of metacognitive knowledge. The results of the first study indicated that average children were able to improve their mathematical ability by improving their metacognitive 22

awareness and control. The second study was done on children with learning disabilities and those that had difficulty learning mathematics. These results were more dramatic indicating that these children benefitted more from metacognitive training than children of average ability did (26). Another intervention program that was developed in Israel by Mevarech and Kramarski is called Introducing new material, Metacognitive questioning, Practicing, Reviewing, Obtaining mastery on both higher and lower cognitive tasks, Verification, and Enrichment (IMPROVE) (27). In 2006, Mevarech, Tabbuk, and Sinai conducted a study using IMPROVE with eighth graders. This study indicated that IMPROVE was helpful in increasing students problem solving in mathematics. Even more interesting was that IMPROVE combined with working in groups increased students’ performance even more (28). The IMPROVE strategy easily ties in with the mathematical practices of “make sense of problems and persevere in solving them,” “look for and express regularity in repeated reasoning,” and “construct viable arguments and critique the reasoning of others (2).” What strategies can teachers use to improve students’ metacognitive abilities? In the next two sections, we will focus on two strategies, “orchestrating productive mathematical discussions (29),” and using formative assessments that focus on thinking.

Mathematical Discussions When Smith and Stein wrote their book 5 Practices for Orchestrating Productive Mathematics Discussions, their premise was that learning mathematics is a social endeavor. The five practices are: Anticipating, Monitoring, Selecting, Sequencing, and Connecting. Before the teacher enters the classroom, the teacher must anticipate what strategies students might use to solve the mathematical task. Once the students start working on the problem, the teacher needs to walk around the room and monitor the problem solving methods that the students are using. When the teacher encounters a specific or unique problem solving method, she may select that student to present their solution. However, the teacher doesn’t want to send students to the board randomly so it is important that the teacher sequence the order in which the solution methods are presented. As the students are presenting the various methods, the teacher helps the students connect the methods to each other so that they can see that all are valid solutions to the task (37). These five practices and Fogarty’s three stages of metacognition can be connected by placing the first practice into the planning stage, the next three practices into the monitoring stage, and the last practice into the evaluation stage. While the upfront planning for teaching in this manner is time-consuming, the classroom becomes completely student-centered and forces students to think about their thinking – they use metacognition (6, 29). A classroom in which a teacher uses these five practices essentially uses all of the CCSSM Standards for Mathematical Practice. 23

Formative Assessments As math teachers work to incorporate the mathematical practices into their classroom, it is a good time to rethink the way they assess students’ understanding. When math instruction was focused on regurgitation of facts and memorization of procedures and algorithms, these skills were easy to assess through quizzes and exams. As we shift the focus to authentic problem solving, we need to change the way we assess students’ understanding. Having students present their solutions and connect their methods to other students’ solutions in mathematical discussions involves the CCSSM practices of “construct viable arguments and critique the reasoning of others,” as well as “look for and make use of structure (2).” Below we will look at other types of formative assessment that can be useful in the mathematics classroom to promote metacognition. In the context of the following examples, formative assessment is used to provide feedback to students and to improve instruction (30). End of class questions: At the end of class, ask students to answer the following questions on a half-sheet of paper: What did we do? Why did we do it? What did I learn today? How can I apply it? What part of it don’t I understand? These questions really get at the heart of metacognitive awareness (30). Traffic lights: Use colored cups or colored paper or notecards with a green dot, yellow dot, and red dot. On their desk, students place the cup, paper, or notecard of the color that corresponds to how well they understand the topic. Green indicates “I know this.” Yellow indicates “I may know this” or “I understand part of it.” Red indicates “I do not understand.” While some students may be intimidated by letting everyone know how well they understand a mathematical topic, others might be relieved to see that they are not the only one that doesn’t get it yet (30). 3-2-1 Exit slip: On a small sheet of paper at the end of class, have students indicate three things that they learned today, two things that they found interesting, and one thing that they have questions about. As you are reading through these exit slips, you can plan for the next day of class and address their questions. Again, this forces students to think about what they don’t know (30). My Favorite No: Give students a problem to solve at the beginning of class. Students solve the problem on a notecard and put their initials on the back. The teacher then sorts the cards into three piles: correct, correct strategy but small error, and incorrect. The teacher selects a favorite from the middle pile (correct strategy but small error). The teacher shows the solution to the students and gives them a couple minutes to figure out the error and discuss it with a partner. Then the teacher leads a whole class discussion to clear up any misconceptions. This assessment emphasizes the fact that we learn from our mistakes (30). Watch body language and ask clarifying questions. In general it is very important as a teacher to make frequent eye contact with your students, and watch their body language (30). It is usually fairly easy to tell which students are confused. While these formative assessment techniques were presented in the context of mathematics, they can certainly be used in other STEM disciplines. We can see that these metacognitive strategies tie in nicely with the CCSSM Standards for Mathematical practice as well as the NGSS Science and Engineering Practices. 24

Physics The introduction of metacognitive practices in physics parallels that of other subjects. It typically focuses on problem solving practices either as a method of better instruction or as a subject of a research study. In chapter 3 of Teaching Physics with the Physics Suite, Redish discusses the hidden agenda of physics courses which is “A Second Cognitive Level” that he identifies as executive function which has three parts: expectations, metacognition, and affect. In Redish’s model, metacognition is only one of the executive functions and is separated from the expectations of a student about a course, as well as the emotional, motivational, and self-image of a student (the “affect” function). Thus metacognition is associated with how a student “thinks about thinking” and allows for both knowledge and control of their learning (31). Gok did an exhaustive review of metacognition and its application to physics problems. He attributes general problem solving strategies to Dewey, Polya, and Kneeland (19, 32–34). These step by step processes are listed in Table 2. Notice the parallels with techniques discussed in Mathematics.

Table 2. General Problem Solving Processes Dewey (33)

Polya (19)

Kneeland (34)

1. Location and Definition

1. Understanding the Problem

1. Awareness of the problem

2. Possible Solution

2. Devising a Plan

2. Gathering of relevant facts

3. Develop Solution

3. Carrying out the Plan

3. Definition of the problem

4. Further Verification

4. Looking Back

4. Development of solution options 5. Selection of the best solution 6. Implementation of the solution

Much of the earlier work in studying problem solving in physics was to attempt to understand the differences between novice and experienced problem solvers. Schoenfeld concluded that both used the same steps, but that experienced solvers spend more time understanding the problem initially and reflecting on the solution at the conclusion while novice solvers tend to spend time finding a solution plan and calculating (35). Kohl and Finkelstein concluded that experienced and novice solvers often used the same representations for physics problems (i.e. pictures and free-body-diagrams) but the diagrams had more meaning for the experienced solvers (36). Ultimately Gok concludes that the problem solving approaches in physics can be categorized as three steps with similar aspects to metacognition. Step 1 is to identify the fundamental principle in the problem (planning); step 2 is solving 25

the problem (monitoring); and step 3 is checking the solution (evaluating). His results suggest that instruction in metacognition promotes structured knowledge and guides students toward science expertise (32). Much of the research in physics on metacognition has been done with “think aloud” protocols. Unfortunately, taxonomies to classify metacognition have been somewhat elusive and are not generally agreed upon, at least in detail (37). Thus, researchers will often have to develop their own assessments of metacognition or focus on one or two aspects that are easily measured (32). One of the measures that has been studied in physics metacognitive research is whether or not gender plays a role. A study of 746 Serbian students by Bogdanović, Obadovic, Crjeticanin, Segedinac, and Budic showed higher metacognitive awareness in 15-year-old females but no gender difference for physics achievement (38). In contrast, a study of 172 university students in Petra, Jordan, showed no gender correlation for metacognition, but did find the metacognitive skill of “fault picking” was highly correlated with students’ ability to solve both mathematics and scientific problems (39). Metacognitive Strategies in Physics In the early 1990’s, Mazur at Harvard University decided to try a new approach to teach physics to non-science majors. This approach is called Peer Instruction. Peer instruction is an active-learning method, where the teacher gives short focused lectures on important concepts from the reading. After each short lecture, students are given a ConcepTest with one or two questions over the presentation. Students are given a several minutes to think about their answer and then share their answer with their teacher. Then they are told to discuss their answer with their classmates trying to convince them that their answer is correct. During the group discussions, the instructor circulates around the room, listening to the students’ conversations. After the group discussions, students may change their answer to the ConcepTest. The ConcepTests are not graded for correctness, they are just used for a participation portion of the grade (40). Mazur’s teaching method includes the science and engineering practices of: “constructing explanations,” “engaging in argument of evidence,” and “obtaining, evaluating, and communicating information (3).” One key to the success of peer instruction is that students read their textbook prior to the lecture. Harvard used multiple methods of encouraging and checking that students had completed the reading. They first used reading questions, then required students to write short summaries of the reading, and finally they used a web-based assignment with three free-response questions. The first two questions cover part of the reading that may be difficult to grasp. The third question is: “What did you find difficult and confusing about the reading? If nothing was difficult or confusing, tell us what you found most interesting. Please be as specific as possible.” This reading assignment closely resembles the 3-2-1 exit slip strategy in mathematics, except it is done prior to class rather than after class (40). Even though the lecture emphasized concepts rather than problem solving, students in the peer instruction course were equally as good at quantitative problem solving as students in the traditional course. In addition, peer instruction courses 26

included a structured discussion sessions focused on cooperative problem solving activities. During these sessions, the instructor shows the students how to solve a problem correctly. Then students worked in groups on homework problems (40). About the same time that Mazur was experimenting with peer instruction at Harvard, Reif was researching and trying to improve physics instruction at Carnegie-Mellon. He observed that students who had earned good grades in physics courses did not actually have a good grasp on physics concepts. This led him to ask the following two questions: 1. “Can one understand better the underlying thought process required to deal with a science like physics?” and 2. “How can such an understanding be used to design more effective instruction (41)?” Reif divided the cognitive knowledge to understand science into two categories, Basic Abilities and Problem Solving. Basic abilities include interpreting, describing, and organizing, while problem solving includes analyzing problems, constructing solutions, and checking. His goal was to create instructional practices that gave students skills in problem solving. In science it is important for students to interpret a concept correctly. Instructors can help students in this process by presenting a concept in a manner that is needed for correct interpretation, having students apply the interpretation method with various cases, particularly those that may cause difficulty, and asking students to summarize the results of these special cases so they can increase their knowledge of the concept (41). It is also essential that students can correctly explain physics concepts. How can instructors improve students’ ability to improve knowledge description? Reif suggested that we should teach description methods and spend significant time on both qualitative and quantitative description. He emphasizes that physics cannot be taught as a series of formulas but rather needs to taught for conceptual understanding (41). This idea mirrors exactly what is promoted in the NGSS science and engineering practices (3). Much of what has been researched about physics and metacognition focuses on problem solving. Reif emphasizes that students go through three stages in problem solving: analyzing the problem, constructing the solution, and checking the solution (41). His stages are very similar to those of Polya from How to Solve it (24). These techniques of Mazur and Reif and others (Modeling Instruction (42) or Just-in-Time Teaching (JiTT) (43), Tutorials in Introductory Physics (44), PRISMS (45), Flipped Learning (46), use of Learning Assistants (47), etc.) are designed to help students confront misconceptions and develop metacognitive knowledge (40, 41). Students with a highly developed sense of metacognition are better equipped to deal with new and unexpected situations, are able to use multiple representations of problems meaningfully, and will spend more time understanding the problem and reflecting on the process and less time calculating a solution. Metacognitive awareness by students will enable them to overcome initial failures and better deal with new problems and situations in physics and other STEM fields.

27

Conclusion As we look at the various research on metacognition in biology, mathematics, and physics, there is certainly more commonalities than differences. In all subjects, we see strategies involving students working in groups, students reflecting on what they understand and do not understand, and students talking about how to solve problems. If we can develop these metacognitive skills in our STEM classrooms daily, all students may not only better understand math and science, but also enjoy these subjects more. Metacognition is the key to successfully implementing the CCSSM Standards for Mathematical Practice and the NGSS Science and Engineering Practices. We need to go beyond teaching basic knowledge and skills and teach critically thinking. This is the key to moving students towards 21st century skills and to increasing the number of students pursuing STEM careers.

References National Commission on Excellence in Education. A Nation at Risk: The Imperative for Educational Reform. A report to the nation and the secretary of education, Washington, DC, 1983. 2. National Governors Association Center for Best Practices, Council of Chief State School Officers. Common Core State Standards (Mathematics); National Governors Association Center for Best Practices, Council of Chief State School Officers: Washington, DC, 2010. 3. NGSS Lead States. Next Generation Science Standards: For States, By States; The National Academies Press: Washington, DC, 2013. 4. Wixson, K. K.; Dutro, E.; Athan, R. G. The challenge of developing content standards. In Review of Research in Education; Floden, R. E., Ed.; American Educational Research Association: Washington, DC, 2003; pp 69−107. 5. Flavell, J. H. Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. Am. Psychol. 1979, 34, 906. 6. Fogarty, R. How To Teach for Metacognition; IRI/Skylight Publishing: Palatine, IL, 1994. 7. Baird, J. R.; White, R. T. Improving Learning through Enhanced Metacognition: A Classroom Study; Paper presented at the 68th Annual Meeting of the American Educational Research Association, New Orleans, LA, 1984. 8. Snyder, K. E.; Nietfeld, J. L.; Linnenbrink-Garcia, L. Giftedness and metacognition: A short-term longitudinal investigation of metacognitive monitoring in the classroom. Gifted Child Quarterly. 2011, 55, 181. 9. Bissell, A. N.; Lemons, P. P. A new method for assessing critical thinking in the classroom. BioScience 2006, 56, 66. 10. Carpenter, S. K.; Lund, T. J. S.; Coffman, C. R.; Armstrong, P. I.; Lamm, M. H.; Reason, R. D. A Classroom study on relationships in retrieval enhancement. Educational Psychol. Rev. 2016, 28, 353. 11. Listiana, L.; Susilo, H.; Suwono, H.; Suarsini, E. Empowering students’ metacognitive skills through new teaching strategy (group investigation 1.

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Chapter 3

A Cognitive Perspective on Chemistry Instruction: Building Students’ Chemistry Knowledge through Advancing Fundamental Literacy and Metacognitive Skills Megan K. Littrell-Baez* and Donna Caccamise University of Colorado, Boulder Institute of Cognitive Science, 594 UCB, Boulder, Colorado 80309, United States *E-mail: [email protected].

This chapter focuses on scaffolded metacognitive activities that promote successful knowledge-building and comprehension in chemistry, with an emphasis on fundamental literacy. Theoretical perspectives and evidence-based strategies are presented to explain how metacognitive instruction should be implemented to best support learning along with the development of fundamental literacy and metacognitive knowledge.

Metacognition involves key processes that students must develop in order to become better and more efficient learners. To be successful in chemistry, as well as other science domains, students need to develop a capacity to evaluate and govern their own learning and thought processes as they encounter new information from a variety of sources (e.g., text, class presentations, hands-on projects, and visual representations). These metacognitive processes are deemed crucial for learning, knowledge transfer, and mastery of chemistry (1, 2), and are correlated with higher academic performance and better problem-solving skills (3). They also help students to build domain knowledge, which is positively correlated with higher performance on academic measures in the fields of science, technology, engineering, and mathematics (STEM; (4, 5)). In this chapter, we present a view of chemistry instruction and student knowledge-building through a cognitive science lens. Our focus is on how © 2017 American Chemical Society

instruction that combines literacy skills and knowledge of how to engage in metacognition helps students to build the cognitive knowledge structures that serve as a foundation for learning. Three types of literacy are important for science education, particularly in chemistry: (a) scientific literacy; (b) visual Literacy; and (c) fundamental literacy. Scientific literacy refers to an understanding of practices and procedures involved in carrying out science and may differ depending on the domain, e.g., chemistry vs. biology (6). Visual literacy refers to one’s ability to interpret, generate, and draw external representations of physical and molecular models (7, 8). There is no question that scientific and visual literacy are important for understanding chemistry and making contributions to the discipline. However, in this chapter, we focus on a type of literacy that is also important but often overlooked in STEM education – fundamental literacy. Fundamental literacy represents students’ abilities to read and deeply comprehend informational texts in a way that enables them to build knowledge within a discipline (9). This is critical for success in chemistry as well as other scientific disciplines because it is key to students’ learning and ability to retain information from text, as well as for integrating information from other class activities into a developing core knowledge. As Okanlawon explains, Chemistry students who may be skilled readers of narrative texts often encounter difficulty in reading scientific and mathematical texts. This is because narrative texts deal with a broad theme and convey information in a story form which is easier for readers to understand, while scientific texts are densely loaded with important information and minutely detailed logical arguments which render them difficult to understand. In such texts, if one part of an argument is skipped or misunderstood, the remaining parts become incomprehensible ((10), p. 215).

The Role of Metacognitive Processes in Comprehension In addition to developing literacy skills for scientific texts, students must also be able to engage in metacognitive processes in order to recognize and repair gaps in understanding. These processes are critical to both skilled reading and for integrating knowledge from other sources such as lab exercises. Metacognitive proficiency in conjunction with fundamental literacy is at the cornerstone of how scientists develop, communicate about, and advance scientific theories and procedures (9). In other words, it is necessary that students develop fundamental literacy skills in order to build their domain knowledge and also to comprehend and communicate successfully in scientific fields. The authors of this chapter have created theory and evidence-based approaches for the instruction of fundamental literacy skills that include metacognitive components in order to help students learn from science texts (11–15). This chapter focuses on the metacognitive activities that support comprehension and knowledge-building. We highlight theoretical perspectives on how and why metacognitive instruction can be implemented to support learning in chemistry, and provide a model for scaffolded instruction that incorporates both fundamental literacy and development of 32

metacognitive knowledge to improve students’ success and comprehension in chemistry.

Metacognitive Skills and Knowledge Metacognition has often been described in terms of two major categories, metacognitive skills (MS) and metacognitive knowledge (MK). Figure 1 illustrates these divisions with detail on how each category is subdivided. MS represent one’s procedural knowledge for carrying out metacognitive tasks. As shown in Figure 1, MS are broken down into procedures involved in monitoring learning (reflecting on or checking understanding) and self-regulation of learning (e.g. planning, organizing information, and re-reading; (16–18)).

Figure 1. Model of the Components of Metacognition. Let us consider the case of a hypothetical student, Peyton, who is taking a college introductory chemistry class. Peyton is reading the textbook in preparation for the next class lecture. After reading a few paragraphs, Peyton stops to think about what the information in the text means and whether it makes sense. Peyton is feeling a bit lost and does not really understand how the concepts are connected or how they relate to the problems in the class homework assignment. This is metacognitive thinking that average to poor readers and learners do not employ. Peyton is demonstrating a metacognitive skill—monitoring. This skill alerts Peyton to notice gaps in her understanding, but does not provide clues to what to do to improve understanding. Assessing one’s comprehension of the material and understanding when it is good or faulty is the first step in the metacognitive practices that good readers and learners employ on the road to building knowledge (19). However, most students lack the insight to adequately engage in this sort of meta thinking that is critical to the learning process, let alone possess the metacognitive knowledge to recognize what to do to fix the problem with their understanding (20, 21). Although studies have shown that well-developed metacognitive skills (e.g., accurate monitoring and use of self-regulation activities) are often associated 33

with better learning outcomes (22, 23), many students with strong metacognitive skills do not put these skills into practice in an effective way (24–28). We have observed this in students who struggle with reading comprehension, and we would argue that it more broadly impedes students’ recognition of important learning strategies when needed. Essentially, students demonstrate a lack of metacognitive knowledge (MK). MK is comprised of declarative and conditional knowledge about metacognition. Declarative knowledge involves explicit understanding of what metacognition is and how it may be useful for learning, including knowledge of the resources and skills needed to complete learning tasks such as reading a text chapter, studying for an exam, or completing a homework assignment. Conditional knowledge represents an understanding of how, when, and why metacognitive skills can be used to improve understanding and learning (16). Thus, a successful instructional model should be scaffolded to develop metacognitive skills needed to monitor and self-regulate learning, as well as explicit instruction that allows for development of the metacognitive knowledge necessary to know when and how to utilize these skills to improve learning.

Theoretical Perspectives on Metacognition and Literacy A leading theoretical perspective on comprehension and learning, the Construction-Integration (CI) model of cognition and supporting research suggests why this type of instruction benefits students (29). Specifically, the CI model indicates that in order to achieve deep, long-lasting learning and build domain knowledge, students must construct a textbase from the information presented to them (e.g., idea units in a textbook, lecture, or problem) and integrate the textbase with background knowledge to build a situation model of the topic. According to Kintsch and Kintsch, a situation model is “a mental model of the situation described by the text…[that] requires the integration of information provided by the text with relevant prior knowledge and goals of the comprehender” ((30), p. 73). Development of a strong situation model helps students to understand better the theories and concepts that they are learning about in class because it gives them a cognitive structure for organizing and connecting information. This skill is a critical component for students to develop domain knowledge as they iteratively build upon their understanding of the topic with each incoming piece of new information from text and classroom activities. Only when a learner has processed information deeply enough to create a situation model is their knowledge in a state that is durable and re-usable. Consider another student example: Jace is working on a homework assignment for an introductory college chemistry class, involving solving a series of problems. The problem prompt leads Jace to activate the stored situation model for that unit, which provides a framework for solving the problem, using known problem-solving approaches and background knowledge about related concepts and theories. This problem-solving task might be quite challenging if he has not developed a strong situation model for the content and types of problems in that unit. Being new to college and chemistry at this level, it is quite likely that Jace’s situation model is still incomplete and perhaps includes 34

misconceptions. Although he may have the metacognitive skills to alleviate the challenge of learning from the course materials to build and improve a situation model, Jace may not know how to use those skills appropriately. Thus, students like Jace need to learn how to engage metacognitive strategies to check their understanding at this deeper level.

The Situation Model Approach: Utilizing Effective Monitoring Cues In order for students to self-regulate their learning, they must be able to adequately monitor their understanding and identify gaps or weak points. Unfortunately, studies have demonstrated that most students often misjudge the depth of their learning or engage in poor regulation of learning (21, 31). This applies to learning broadly, but also to metacomprehension—students tend to misjudge their understanding of the content they are reading about in their textbooks (32, 33). Rawson, Dunlosky, and Thiede (34) suggest that poor monitoring results from the use of shallow retrieval cues that fail to provide accurate feedback about the quality of the student’s knowledge of the topic. In other words, as a student is considering the level of their understanding, they rely on information that is not diagnostic of actual learning, such as the length of the text or interruptions they experienced while reading. However, the accuracy of monitoring improves when students use cues that give them feedback about their situation model (35–39). Strategies that tap into these types of situation model-level cues include, but are not limited to, summarizing the text after a delay, generating concept maps to connect ideas, making inferences or connections that go beyond what is stated in the text, and taking breaks while reading to self-explain the meaning of the text and how ideas are connected across sections of text - within a chapter or unit (40). These strategies give the reader insight into how well they know the topics in the text and how well they are able to connect ideas across sections of text and between text explanations and visual materials such as models or graphs. However, to build metacognitive knowledge, it is important that chemistry instructors explicitly point out this notion to their students. In other words, explain to them why the strategy should help them comprehend the material. Instructional interventions should encourage students to rely on monitoring cues that give them an accurate picture of their level of knowledge and provide recommendations for self-regulation to be carried out when knowledge gaps are identified. Research also indicates that successful comprehension is best supported by engaging in metacognitive activities not just during reading, but also before and after reading (19, 41, 42). Activities conducted before reading may include considering the goals or objectives for learning and previewing the chapter headings and subheadings as well as figures, diagrams, and tables to get a sense of what one expects to learn from the chapter. After reading, students may find it helpful to engage again in self-explanation of what they have learned, summarizing and connecting this information with the overall learning objectives 35

for the assignment. A more detailed example of this type of instruction is provided at the end of the chapter under “Guidelines for Chemistry Instructors.”

A Model for Metacognitive Instruction in Chemistry Metacognitive instructional interventions in chemistry have primarily focused on teaching general study skills and learning strategies and providing prompts to remind students to monitor learning (43, 44). Other researchers have taken a different approach by engaging students in metacognitive activities before and after a learning activity such as an exam or lab assignment (45, 46). While these approaches have demonstrated success in improving student learning outcomes, evidence also indicates that general instructional approaches may only benefit students who are already performing well or have some prior metacognitive knowledge (47, 48). These students already have an awareness that they must monitor their performance and enact some behavior to improve learning as necessary. For those low-performing students who may struggle with metacognition, though, simply prompting them to engage in metacognitive study strategies may not be enough. In fact, several studies in STEM classrooms have demonstrated poor metacognitive monitoring in low-performing students compared to high-performing students (35, 49–53). Low-achieving students grossly overestimate performance when engaging in metacognitive monitoring, whereas high-achieving students slightly underestimate performance (49–53). For example, Pazicni and Bauer (52) found that chemistry students in the lowest quartile greatly overestimated their ranking, whereas students in the highest quartile underestimated their ranking. This pattern was consistent across four exams throughout the semester, suggesting that students did not learn to monitor performance from simply observing the discrepancy between their monitoring and their performance. Although students are provided with guidelines and prompts to engage in metacognitive processes in existing interventions such as these, they do not necessarily build metacognitive knowledge to help students know how to implement the strategies they are learning. For instance, students may know that reading their textbook and thinking critically about the material are important for learning at the college level. However, they may not have a sense of how to carry out these tasks. This is particularly the case when we consider students’ reading comprehension in chemistry and other science fields. Many college students have difficulty with learning from their textbooks, even if they have good standardized reading scores and GPAs (41). Metacognitive interventions need to include information on how to read and comprehend the text in a way that connects new information with students’ background knowledge in order to build an in-depth situation model. To address this, we suggest explicitly teaching students evidence-based strategies to actively engage with their textbook and employ metacognitive skills to monitor and self-regulate their learning. It is important to scaffold this instruction to help students build metacognitive knowledge that may allow them to apply metacognition to a variety of contexts. The guidelines below present an evidence-based instructional model to help students build 36

metacognitive knowledge and skills that they may employ before, during, and after reading their textbook (Text examples from reference (54)). Guidelines for Chemistry Instructors Before the text is assigned, give students the key objectives that they should learn for that chapter or unit. Emphasize that they should pay attention to these objectives as they complete their reading assignment (and any associated problems or questions for homework). To ensure that your students do this, you may want to ask them to think about and write down how the reading connects to the objectives. To scaffold students’ learning, model metacognitive behaviors in a brief class demonstration to show what this might look like before, during, and after reading their text. Note that this does not necessarily need to occur for each lecture or unit. Here is an example script: Before Reading [Address the students] “I am a student today. As I sit down to read the text chapter assigned for next class, I am going to do some extra thinking about how I am going to study, what I need to learn, and how well I am learning as I go along. This practice is called metacognition and it helps me to understand what I know and don’t know so that I can stay on track in class. I would normally do most of this thinking silently, but I am going to think out loud today so that you can follow along.” [Speaking to self with the class as the audience] “I really need to know what this chapter is about and what my instructor wants me to learn. How should I figure that out?” [Flip through notebook.] “Okay, I have a note here that says in this unit, we are going to learn about ionic and covalent bonding next week.” [Open the text.] “I’m going to look ahead in the text to see where it talks about these topics. I’ll start with reading the chapter headings to see what topics will be covered.” [Show students on overhead or projector screen.] “Okay, in Chapter 4, section 4.1, I see, ‘Ionic Bonding.’ It looks like there are some subsections, too. These should help me focus on what I’m going to read about.” [Read subheading names aloud.] “The Formation of Ionic Compounds; Electronic Structures of Cations; Electronic Structures of Anions… Okay, I think I’ll go back to the beginning and see what the section objectives are. It says ‘By the end of this section, you will be able to: (a) explain the formation of cations, anions, and ionic compounds; (b) predict the charge of common metallic and nonmetallic elements, and write their electron configurations.’” “Now I think I will look at the diagrams and tables in the text.” [Go through these and think aloud about how they connect to the learning objectives.] “I will come back to these while I am reading.” During Reading Next, show students what they might do during reading. This is important because many metacognitive interventions focus solely on activities conducted 37

before and/or after a task (e.g., metacognitive wrappers). When students are reading their text, however, they need frequently to monitor their comprehension to make sure they are encoding the information and connecting it to their background knowledge in a manner that creates lasting learning (i.e., developing a situation model). [Addressing students] “Metacognition is also important while you are reading. There are a few techniques you can use to keep track of what you are learning and to better connect ideas.” “Read a short section of text (i.e., a section with a subheading or number breakdown like section 4.2 of chapter 4). Take a few minutes to do the following tasks, without looking back at your text or notes:” • •

•

“Self-explanation: Describe to yourself what you just read. What did it mean? What details do you remember?” “Connect Ideas: How is this section related to the previous section (e.g., compare/contrast ideas; think about how concepts build on each other)? How does the information you learned address the unit learning objectives? What else might you need to know to meet that objective?” “Re-read the section to check your understanding or to think about it more deeply if you struggled to answer the questions above.”

“What should you do if you are still confused? [Open up to student suggestions for self-regulation.] Maybe you could bring specific questions to class or when meeting with your instructor.” There are a few approaches to modeling what students should do while they are reading their textbook. This is only one example. If time permits, however, you could also read a section of the text aloud and stop a few times to think out loud as you go, similar to the “Before Reading” example. After Reading [Address the students.] “After you finish reading the chapter, you can use metacognition to reinforce your learning and again check your comprehension. These are a few strategies:” •

•

•

“Summarize, self-explain, and connect ideas: go back to the learning objectives. For each, summarize what you know and explain what it means to you. For example, for the learning objective ‘explain the formation of cations, anions, and ionic compounds,’ what do you know about the formation of each? What is still unclear? Write down any questions that you have to discuss with your instructor.” “Create a concept map: another way to connect ideas is to draw a map, connecting concepts from the chapter. You can also connect these concepts to what you learned in previous chapters.” [Show an example to the class.] “Practice testing yourself: If there are practice questions or problems in the chapter, go through these to test your understanding. Then check your 38

answers to confirm. When possible, try not to look back through your text as you complete these. This will give you a better idea of how you might perform on a test in class.” In summary, this chapter has introduced an often overlooked set of learning processes including metacognition and fundamental literacy. Research indicates that with a little scaffolding of evidence-based practices by instructors and teaching assistants struggling students, who are not activating these processes in STEM classrooms, can be provided with better opportunities for success.

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13. Caccamise, D.; Friend, A.; Kintsch, E.; Littrell-Baez, M. K.; Groneman, C. BRAVO: Boulder Reading Intervention; Ecology; ICS no. 2012-02; Boulder, CO: University of Colorado, 2013. 14. Caccamise, D.; Littrell-Baez, M. K.; Okochi, C. A Secondary Reading Comprehension Curriculum: How FOI Impacts Student Performance; Paper presented at the Twenty-Second Annual Meeting of Society for the Scientific Study of Reading, Porto, Portugal, 2016. 15. Littrell-Baez, M. K.; Friend, A.; Caccamise, D.; Okochi, C. Using retrieval practice and metacognitive skills to improve content learning. J. Adol. Adult Lit. 2015, 58, 680–687. 16. Veenman, M. V. Alternative assessment of strategy use with self-report instruments: a discussion. Metacognition and Learning 2011, 6, 205–211. 17. Flavell, J. H. Metacognitive aspects of problem solving. The Nature of Intelligence 1976, 12, 231–235. 18. Nelson. T. O.; Narens, L. Metamemory: A theoretical framework and some new findings. In The Psychology of Learning and Motivation; Bower, G. H., Ed.; Academic Press: New York, 1990; Vol. 26, pp 125–173. 19. Caccamise, D.; Snyder, L. Comprehension instruction in the 21st century. Persp. on Lang. and Lit.y (Special issue) 2009, 35, 23–26. 20. Dunlosky, J.; Lipko, A. R. Metacomprehension: a brief history and how to improve its accuracy. Current Dir. Psych. Sci. 2007, 16 (4), 228–32. 21. Dunning, D.; Heath, C.; Suls, J. M. Flawed self-assessment implications for health, education, and the workplace. Psych. Sci. Public Interest 2004, 5, 69–106. 22. Azevedo, R. Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educ. Psychol. 2005, 40, 199–209. 23. Graesser, A. C.; McNamara, D. S.; VanLehn, K. Scaffolding deep comprehension strategies through Point & Query, AutoTutor, and iSTART. Educ. Psychol. 2005, 40, 225–34. 24. Azevedo, R.; Cromley, J. G. Does training on self-regulated learning facilitate students’ learning with hypermedia? J. Educ. Psych. 2004, 96, 523. 25. Azevedo, R.; Guthrie, J. T.; Seibert, D. The role of self-regulated learning in fostering students’ conceptual understanding of complex systems with hypermedia. J. Educ. Computing Res. 2004, 30, 87–111. 26. Azevedo, R.; Hadwin, A. F. Scaffolding self-regulated learning and metacognition–Implications for the design of computer-based scaffolds. Instructional Sci. 2005, 33, 367–379. 27. Bannert, M.; Hildebrand, M.; Mengelkamp, C. Effects of a metacognitive support device in learning environments. Computers Human Beh. 2009, 25, 829–835. 28. Girash, J. Metacognition and instruction. In Applying the Science of Learning in Education: Infusing Psychological Science into the Curriculum; Benassi, V. A.; Overson, C. E; Hakala, C. M., Eds.; 2014; pp 152−168. 29. Kintsch, W. Comprehension: A Paradigm for Cognition; Cambridge University Press: New York, 1998. 40

30. Kintsch, W.; Kintsch, E. Comprehension. In Children’s Reading Comprehension and Assessment; Paris, S. G.; Stahl, D. S., Eds.; Erlbaum: Mahwah, NJ, 2005; pp 71−92. 31. Dunning, D. The Dunning-Kruger effect: On being ignorant of one’s own ignorance; Advances Exper. Soc. Psych. Elsevier: San Diego, CA, 2011; Vol. 44, pp 247−296. 32. Rawson, K. A.; Dunlosky, J. Improving students’ self-evaluation of learning for key concepts in textbook materials. European J. Cog. Psych. 2007, 19, 559–569. 33. Theide, K. W.; Griffin, T. D.; Wiley, J.; Redford, J. S. Metacognitive monitoring during and after reading. In Handbook of Metacognition in Education; 2009; pp 85−106. 34. Rawson, K. A.; Dunlosky, J.; Thiede, K. W. The rereading effect: Metacomprehension accuracy improves across reading trials. Mem. Cog. 2000, 28, 1004–1010. 35. Anderson, M.; Thiede, K. W. Why do delayed summaries improve metacomprehension accuracy? Acta Psychol. 2008, 128, 110–118. 36. Dunlosky, J.; Rawson, K. A. Why does rereading improve metacomprehension accuracy? Evaluating the levels-of-disruption hypothesis for the rereading effect. Discourse Processes 2005, 40, 37–55. 37. Thiede, K. W.; Anderson, M. Summarizing can improve metacomprehension accuracy. Contemporary Educ. Psych. 2003, 28, 129–160. 38. Thiede, K. W.; Anderson, M.; Therriault, D. Accuracy of metacognitive monitoring affects learning of texts. J. Educ. Psych. 2003, 95, 66. 39. Thiede, K. W.; Dunlosky, J.; Griffin, T. D.; Wiley, J. Understanding the delayed-keyword effect on metacomprehension accuracy. JEP: LMC 2005, 31, 1267. 40. Wiley, J., Griffin, T. D.; Thiede, K. W. Improving metacomprehension with the situation-model approach. In Improving Reading Comprehension through Metacognitive Reading Strategies Instruction; Mokhtari, K., Ed.; Rowman & Littlefield: Lanham, MD, 2017; pp 93−110. 41. Isakson, R. L. & Isakson, M. B. Preparing college students to learn more from academic texts through metacognitive awareness of reading strategies. In Improving Reading Comprehension through Metacognitive Reading Strategies Instruction; Mokhtari, K., Ed.; Rowman & Littlefield: Lanham, MD, 2017; pp 155−176. 42. Pressley, M. Metacognition and self-regulated comprehension. What Research Has to Say about Reading Instruction 2002, 3, 291–309. 43. Cook, E.; Kennedy, E.; McGuire, S. Y. Effect of teaching metacognitive learning strategies on performance in general chemistry courses. J. Chem. Educ. 2013, 90, 961–967. 44. Zhao, N.; Wardeska, J. G.; McGuire, S. Y.; Cook, E. Metacognition: An Effective Tool to Promote Success in College Science Learning. J. College Sci. Teach. 2014, 43. 45. Lovett, M. C. Make exams worth more than the grade: Using exam wrappers to promote metacognition. In Using Reflection and Metacognition to Improve Student Learning: Across the Disciplines, Across the Academy; Kaplan, M.; 41

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Chapter 4

Metacognition as an Element of the Scientific Process Mary T. van Opstal*,1 and Patrick L. Daubenmire*,2 1Chemistry,

Harper College, 1200 West Algonquin Road, Palatine, Illinois 60067, United States 2Department of Chemistry and Biochemistry, Loyola University Chicago, 1032 West Sheridan Road, Chicago, Illinois 60660, United States *E-mails: [email protected] (M.T. van Opstal); [email protected] (P.L. Daubenmire)

The operational functions of metacognition parallel scientific thinking. We ask questions. We collect information. We evaluate that information. We find gaps in that information, and look to fill those gaps. This chapter shares ideas for how these two ways of thinking run in tandem to one another, and how such processes can be engaged and activated in learners in the instructional laboratory setting. Through the instructional venue of several inquiry-based approaches, students can develop and use these skills both during and outside of laboratory classroom environments. Many of these approaches have demonstrated increased metacognitive awareness and use by students as well as improved academic performance.

Introduction The idea that metacognition is an element to the scientific process suggests that it is foundational and essential. When combined with the other elements of the scientific process, such as asking questions or making claims with evidence, metacognition helps lead to more fully functional processes that assist students in their learning. Since a major task of science educators is to ready students to do science, fostering metacognitive skills is a necessary companion in the science curriculum. This may involve becoming autonomous and collaborative at the same time, but always focusing on the question, “What are we really doing here?” A © 2017 American Chemical Society

thorough answer to that question must engage the metacognitive processes that know, monitor, regulate and identify areas of knowledge and skills that need to be enhanced, changed or refined.

What Is the Scientific Process? Over the years many textbooks have introduced the view of “the” scientific method as a linear process with a specific starting point – a research question – and an end point – say, a conclusion. Scientists then follow sequential steps hypothesis/predictions, methodology, data collection and analysis - in between. Presenting a linear view of “the” scientific process fosters some misconceptions about how science is actually conducted. This linear viewpoint, we contend, does not convey the true nature of enacted scientific thinking, which actually is more of a spiral process with alternate entry and exit points. One version (Figure 1 below) that has been presented reveals the more dynamic ebb and flow of scientific endeavors that have a much different geometric pathway than a line.

Figure 1. The Scientific Method (1). This notion of the scientific process that includes a clear presence of preconceptions and previous knowledge reveals for the need for an active set of metacognitive skills to evaluate what is already known and what yet needs to be known. Recall that Mendeleev made a few bold claims and left blanks in his version of the periodic table – he was willing to leave gaps because he knew things did not fit, but one day there might be a fit! He recognized that there was knowledge yet to be gained. This is a central tenet of metacognition, knowing what is known and seeing there is more yet to be learned. Without metacognition as a function to recognize gaps in our knowledge and to frame strategies to fill those gaps, the scientific process would, indeed, be reduced to two dimensional endeavors that have a certain beginning and end. We know, however, that science learning is ongoing. 44

Metacognition as an Element of Inquiry Metacognition is actually innate in humans although children and young adults often are not aware of their knowledge and skills in this area (2). Students at the secondary and post-secondary level are novices and may recognize and use some metacognitive skills such as asking themselves, “Do I really know this?”, but they often lack the skill use to monitor whether they actually learned it. Experts are aware of the knowledge they have and know how to find resources to access and gain new knowledge. They are able to determine quickly that they do not know something and then how to solve their problem (3, 4). Experts use their metacognitive skills and knowledge implicitly and gain metacognitive knowledge with experience. In order for students, novices, to learn some of these skills more quickly, they need to be taught metacognitive skills explicitly. An environment that explicitly teaches these skills may help to move from novice and long the continuum to expert thinking (4). This underscores the critical need to require students to use and develop their metacognitive strategies within the chemistry curriculum and classrooms. Students can become more efficient and life long learners through the development of metacognitive knowledge and skills (5). An impact of having a cognitive toolbox that includes learned metacognitive skills, such as tools for planning, monitoring and evaluating allows a learner to transfer strategies and knowledge to new learning situations in ways that mimics what experts do (6, 7). Different forms of metacognitive skills are likely necessary when solving a science problem or writing a book analysis for a writing class. “Successful science learners are consistently found to be adaptively metacognitive for the demands of their learning environments” (8). The type of context or environment, such as learning in a traditional classroom or an online environment, a lecture, or a research-based lab may also affect the types of metacognitive strategies students use.

Metacognition in the Laboratory Metacognitive skills are essential to the scientific process and almost two decades of research indicate that metacognitive skills can be elicited during student learning in the learning laboratory (9–12). In order to elicit metacognitive skill in students, certain practices are essential to the proper learning setup in laboratory courses. Any one of the following instructional practices can afford students opportunities to engage in metacognitive skills: (1) inquiry approaches to instruction, (2) collaborative social environments, (3) reflective prompting, and (4) writing. It is desirable to provide as many opportunities for students to use metacognitive skills, and this can be done by incorporating any one or more of the best practices into laboratory instruction. These practices provide the instructor with methods to explicitly incorporate metacognitive skills into the classroom. Several established approaches to laboratory instruction incorporate one or more of these practices into the learning environment. Examples include the Science Writing Heuristic (SWH) (13) , Argument Driven Inquiry (ADI) (14), Cooperative Chemistry Laboratory (15), The MORE framework (16), The 45

Competency Tripod (17), The Inquiry Laboratory (18), and Process Oriented Guided Inquiry Learning (POGIL) (19). Research on these inquiry-based approaches indicates that in addition to improving metacognitive skills and problem solving skills (20), students also gain improved critical thinking skills (21) and better understand some science concepts (22). The SWH has been shown to be successful from elementary to post-secondary level science. For middle school students the SWH facilitates students’ use of planning, monitoring and reflective skills while performing their experiments and writing their of reports (23). Another study found that the MORE framework prompted students to revise their molecular understanding of solutions in general chemistry at both research institutions and community college (16). In upper level undergraduate laboratories, POGIL labs for physical chemistry are set up for students to engage in the scientific process using data-think cycles that are likely to engage students’ metacognitive skills (19).

Inquiry Approaches to Instruction In an inquiry lab, students follow a scientific process where they ask questions or make observations first and then make decisions about the procedure and data collected while performing the experiment. Students who ask questions and define problems as part of an experiment are likely to use more declarative and procedural knowledge. Asking questions before a lab requires that the student use some factual knowledge about the process, the steps of the experiment they are about to perform. They also require knowledge about how to complete the experiment. This metacognitive knowledge includes the students’ previous knowledge about the topic, and it can be elicited in environments that allow for problem solving, cooperative learning, discussions, and demonstrations (24). Kipnis and Hofstein found that with 11th and 12th year students in Israel, the inquiry laboratory provided students opportunities to engage in their metacognitive processes. Not only did students use the regulation strategies, planning and monitoring while performing the experiment, but they also used their procedural and declarative knowledge to generate inquiry questions and procedures (25). Students are more likely to plan out an investigation in an inquiry lab, which requires going to the toolbox of metacognitive skills along with other cognitive skills such as critical thinking and problem solving. Generally inquiry lab approaches allow for more open-ended problems. An open-ended problem is a problem in which students generate their own questions or their own procedure, and the results are open-ended. The student does not know the answer, although the instructor may be aware of the lab’s outcome (26). Open-ended problems require greater metacognitive skill because students are explicitly planning. Not only have students used their planning regulation strategies before arriving to lab, but also during lab when they find out more about the lab or they have discussed ideas with their peers. In preparing their own procedure, students will be more aware of the process and likely be monitoring their progress more. In this way, they may recognize more quickly that their procedure is not working and find that they can fix it (27, 28). Students who are monitoring themselves are more 46

likely able to determine whether or not they are acquiring proper results from their procedure. Inquiry instruction like the SWH or ADI require students to produce an argument from the data they collected. Being able to write an argument requires evaluation of data as well as the process by which it is was collected. Students are reflecting, and using conditional knowledge as suggested by Kipnis and Hofstein (25). Many of the instructional strategies incorporate discussion with peers after completion of the experiment. This allows students not only to evaluate the data they have collected, but also to review the process by which it was collected. The inquiry instruction strategies mentioned here require a report in which students write an argument and often a reflection (the SWH version) which asks them to evaluate their experiment. One study found that students who engaged in SWH report style reflections had more metacognitive knowledge and procedural knowledge than factual knowledge in their reflection compared to traditional lab reflections (29). Additionally, The MORE framework states that it explicitly encourages metacognition through the process of reflection and evaluation of students’ own concept models (16).

Working with Peers Engages Metacognitive Skills A welcoming and open learning environment allows students to comfortably acknowledge what they do not know, helps them develop a role with their peers and gives time to personally reflect on learning (30). Peer interactions promote metacognitive awareness. This likely occurs because when students engage in peer learning, they use socially constructed processes like planning or monitoring. When students have the opportunity to discuss the experiment with peers, ask each other questions, and even reflect upon what types of mistakes or issues that arose during lab, students can take these social processes and begin to internalize them. As they grow as a learner in a social environment, they will begin to use more of these metacognitive skills and may become more aware of them (31). In addition to having students work together in the laboratory in groups and as a whole class, there are benefits to having students use their peers in reviewing their reports. In ADI, students are encouraged to read peers’ reports, give feedback and then make revisions to their own report (32). In this way students have the opportunity to read how another student performed and analyzed the data from an experiment. This gives students a chance to reflect and critique their peer’s work and their own. Students engage in reflective activities, once while reading a peer’s report and then again when they revise their report after peer review. Peer learning is not only effective for supporting metacognitive skill use in the classroom, but also for improving problem solving skills (20), exam scores (33), positive perception of the learning environment (34).

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Reflective Prompting as a Means To Elicit Metacognitive Skills Novices or students in any subject may not use their metacognitive skills or even be aware of them (35). Experts, however, use their metacognitive skills during problem solving even though it may not be apparent. Experts adapt their use of skills based on experience, and it provides them the ability to adapt their knowledge as needed. Experts often can reason successfully in problems outside, but still related to, their content area. This is an important and useful ability. This supports that classrooms environments should be designed to explicitly teach students metacognitive skills. One way to practice and elicit metacognitive skills is through reflective prompting. Reflective prompting is an explicit way to encourage metacognitive use in students. The prompts can either be provided by the facilitator or written into the activity and allows students to self assess their knowledge and learning. Reflective prompting may promote the use of all metacognitive strategies including planning, monitoring and evaluating. Using properly timed prompts generally during a learning activity or after an activity, gives students a better chance to integrate their knowledge (36). Davis’ research suggests that generic prompts rather than directed prompts will likely increase students’ use of planning, monitoring and reflection strategies. In Davis and Linn’s (37) research, a generic prompt was “In thinking about doing our design, we need to…” This prompt explicitly asks students to consider planning, and students may use their declarative, and procedural knowledge to answer the prompt. In the Science Writing Heuristic, the prompts are the basis for the student template that students follow when preparing, and performing their experiment and when writing their reports. Before the experiment, students are asked, “What are my questions?” Again, they use declarative knowledge and planning. In the reflection, students are asked: “How do my ideas compare with other ideas” and “How have my ideas changed?” (13). These prompts ask students to evaluate and assess their own knowledge. If too many prompts are provided, it will likely stifle students’ idea generation, and lead to cookbook style labs where the students use the prompts as a step-by-step procedure. Too many prompts may promote procedural knowledge, and students are less likely to monitor themselves during the experiment. Students may not notice that an experiment is not working, and record incorrect data. Essentially students are focused more on completing the procedure rather than understanding what is happening in the experiment. Reflective prompting can not only written into the activities, but can also be made part of the teacher-student interaction. As students perform the experiments, teachers can make sure that students are being reflective or monitoring themselves by asking questions or providing generic prompts while the experiment is occurring. The reflective prompting allows students to check themselves or to start their thinking on a topic while learning.

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Metacognitive Skill Practice While Writing Writing is a part of the laboratory when students record data in their laboratory notebooks and when writing laboratory reports. Writing can afford students the opportunity to engage in metacognitive reflection if done right. In all of the instructional strategies discussed in this chapter, students write non-traditional laboratory reports, which are different than the common laboratory report sections of introduction, procedure, data and results, and conclusions. For example, in ADI, students are asked: “What concept are you investigating?”; “How did you go about your work and why?”; “What is your argument?”. The structure of the report provides students the opportunity to explain how and why they did the experiment and can elicit evaluation metacognitive strategies. Writing encourages reflection on content and process (38). When students write a report, they have to go back over the data they took, and how they performed the lab. In most lab reports, they have to explain why their data supports or refutes a question they asked in the beginning. These are all evaluation skills. A report style that is set-up to provide students with these opportunities urges students to work on their evaluation skills. SWH has parallel components as well. The template for the SWH is laid out in Table 1. Students are asked the following questions:

Table 1. Science Writing Heuristic Template (13) Beginning questions:

What are my questions?

Tests—

What do I do?

Observations—

What can I see?

Claims—

What can I claim?

Evidence—

How do I know?

Reflections—

How do my ideas compare with other ideas? How have my ideas changed?

Again, similar to ADI, the SWH encourages students to evaluate and reflect during report writing. The reflective writing in the SWH is shown to improve students’ critical thinking skills (21). A further study showed how the impact the SWH as a scaffold for metacognitive skills positively affects students’ use of metacognitive skills. Students in both an SWH structured laboratory class and a traditional laboratory class were asked to perform small open-ended “Chempossible” experiments. Students found that the SWH not only provided a structure to perform the open-ended experiments, but it also provided them with a structure for writing. Students in the traditional laboratory did not find a connection between their procedure and the open-ended problems. The SWH template helped to structure students’ thought process while they were evaluating the experiment. Students in the SWH laboratory reported using metacognitive strategies in their open-ended experiments, which means they were able to take the built-in metacognitive 49

strategies within the SWH and transfer them to a new experiments. When students were interviewed about report writing, they found that writing helped them to organize their understanding around the data and their process (9). As discussed above, students are not always aware of their metacognitive processes. In writing, it should be anticipated that students might struggle with these formats for writing because they may not have experienced it in a prior science class. Instructors need to provide explicit steps on how to write a report as well as a rubric that gives students a chance to check their work as they are writing and considering their experimental procedure and data. In ADI, students experience self reflection while writing their reports through the questions posed, and again when revising their reports after on peer feedback (14). The peer review provides time for students to be reflective on their understanding of the experiment as well as their process while reading their peer’s work (32). It is also suggested that TAs or instructors can help to scaffold reflective thinking in class (29). Students need to be asked to reflect explicitly in order to engage these internal processes (39).

Recognizing and Eliciting Students’ Metacognitive Skills There are several ways in which instructors can help students to recognize and use their metacognitive skills. Instructors can ask students questions and provide scaffolding to understand and use these metacognitive skills. They can also model the metacognitive skills in class. Students’ metacognitive awareness can be difficult to assess, as it is often self-reported. However, as an instructor, there are several ways to see if the instructional strategies in the learning laboratory are impacting students’ metacognitive skills. An instructor can use self-report inventories including the metacognitive activities inventory (MCAI) to focus on students’ problem solving and what metacognitive regulation skills they might use during problem solving (40). The metacognitive awareness inventory (MAI) provides a longer inventory to assess awareness of knowledge and regulation of cognition (24). Both inventories can provide the instructor with an understanding of students’ planning, monitoring and evaluating skills in lab. It is also possible to have students write reflections on their problem solving process which not only encourages evaluating of their problem solving process in lab, but also how much they planned and monitored themselves during the laboratory experiment or writing of the laboratory report. The SWH provides students an opportunity to evaluate their process in the reflection section of the report including questions (13): (a) Have I identified and explained sources of error and assumptions made during the experiment? (b) How have my ideas changed, what new questions do I have, or what new things do I have to think about? (c) How does this work tie into concepts about which I have learned in class? (d) To what can I refer in my text, my notes, or some real life application to make a connection with this laboratory work? 50

Summary Metacognition is an essential element of the scientific process and learning in general. Robust scientific investigations cannot be conducted without the use and knowledge of metacognitive skills. An environment that affords students opportunities to engage in metacognitive skill use allows students to make decisions about their learning and how they are learning. The scientific process is not linear, and metacognitive skills allow students to move among the tasks of asking questions, predicting, running experiments and identifying patterns to make claims as needed in order to be successful. An inquiry lab with reflective promoting, writing and social construction of knowledge will provide students with metacognitive scaffolding to critically examine and solve problems beyond their classroom. Students who learn metacognitive skills are more successful learners as well as more independent learners. Being explicit with students about the use and necessity of metacognitive skills during laboratory activities can lead them to be more fully functional science learners and encourage the use of these skills beyond the classroom walls. Such skills can grow and be transferred and move them to more expert-like thinking and practice!

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11. Tien, L. T.; Teichert, M. A.; Rickey, D. Effectiveness of a MORE laboratory module in prompting students to revise their molecular-level ideas about solutions. J. Chem. Educ. 2007, 84, 175–181. 12. Case, J.; Gunstone, R. Metacognitive development: A view beyond cognition. Research in Science Education 2006, 36, 51–67. 13. Burke, K. A.; Greenbowe, T. J.; Hand, B. M. Implementing the science writing heuristic in the chemistry laboratory. J. Chem. Educ. 2006, 83, 1032–1038. 14. Sampson, V.; Walker, J. P. Argument-driven inquiry as a way to help undergraduate students write to learn by learning to write in chemistry. International Journal of Science Education 2012, 34, 1443–1485. 15. Cooper, M. M.; Kerns, T. S. Changing the laboratory: Effects of a laboratory course on students’ attitudes and perceptions. J. Chem. Educ. 2006, 83, 1356. 16. Tien, L. T.; Rickey, D. The MORE thinking frame: Guiding students’ thinking in the laboratory. Journal of College Science Teaching 1999, 28. 17. Davidowitz, B.; Rollnick, M. Enabling metacognition in the laboratory: A case study of four second year university chemistry students. Research in Science Education 2003, 33, 43–69. 18. Hofstein, A.; Shore, R.; Kipnis, M. Providing high school chemistry students with opportunities to develop learning skills in an inquiry-type laboratory: A case study. International Journal of Science Education 2004, 26, 47–62. 19. Hunnicutt, S. S.; Grushow, A.; Whitnell, R. Guided-inquiry experiments for physical chemistry: the POGIL-PCL model. J. Chem. Educ. 2014, 92, 262–268. 20. Sandi-Urena, S.; Cooper, M.; Stevens, R. Effect of cooperative problembased lab instruction on metacognition and problem-solving skills. J. Chem. Educ. 2012, 89, 700–706. 21. Gupta, T.; Burke, K.; Mehta, A.; Greenbowe, T. J. Impact of guided-inquirybased instruction with a writing and reflection emphasis on chemistry students’ critical thinking abilities. J. Chem. Educ. 2014. 22. Rudd, J. A.; Greenbowe, T. J.; Hand, B. M.; Legg, M. J. Using the science writing heuristic to move toward an inquiry-based laboratory curriculum: An example from physical equilibrium. J. Chem. Educ. 2001, 78, 1680–1686. 23. Grimberg, B. I. Science Inquiry, Argument and Language: A Case for the Science Writing Heuristic. In Promoting Higher Order Thinking Skills through the Use of the Science Writing Heuristic; Hand, B. M., Ed.; Sense Publishers: Rotterdam, 2007; pp 87−97. 24. Schraw, G.; Dennison, R. S. Assessing metacognitive awareness. Contemp. Educ. Psychol. 1994, 19, 460–475. 25. Kipnis, M.; Hofstein, A. The inquiry laboratory as a source for development of metacognitive skills. International Journal of Science and Mathematics Education 2008, 6, 601–627. 26. Domin, D. S. A review of laboratory instruction styles. J. Chem. Educ. 1999, 76, 543–547.

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27. Jonassen, D. H. Instructional design models for well-structured and III-structured problem-solving learning outcomes. Educational Technology Research and Development 1997, 45, 65–94. 28. Shin, N.; Jonassen, D. H.; McGee, S. Predictors of well-structured and illstructured problem solving in an astronomy simulation. Journal of Research in Science Teaching 2003, 40, 6–33. 29. Xu, H.; Talanquer, V. Effect of the level of inquiry of lab experiments on general chemistry students’ written reflections. J. Chem. Educ. 2012, 90, 21–28. 30. Lin, X. Designing metacognitive activities. Educational Technology Research and Development 2001, 49, 23–40. 31. Kuhn, D. Metacognitive development. Current Directions in Psychological Science 2000, 9, 178–181. 32. Walker, J. P.; Sampson, V. Learning to argue and arguing to learn: Argument‐driven inquiry as a way to help undergraduate chemistry students learn how to construct arguments and engage in argumentation during a laboratory course. Journal of Research in Science Teaching 2013, 50, 561–596. 33. Lewis, S. E.; Lewis, J. E. Departing from lectures: An evaluation of a peerled guided inquiry alternative. J. Chem. Educ. 2005, 82, 135. 34. Schroeder, J. D.; Greenbowe, T. J. Implementing POGIL in the lecture and the Science Writing Heuristic in the laboratory—student perceptions and performance in undergraduate organic chemistry. Chemistry Education Research and Practice 2008, 9, 149–156. 35. National Research Council (U.S.) How People Learn: Brain, Mind, Experience, and School; National Academy Press: Washington, DC, 2000; . 36. Davis, E. Prompting middle school science students for productive reflection: Generic and directed prompts. Journal of the Learning Sciences 2003, 12, 91–142. 37. Davis, E. A.; Linn, M. C. Scaffolding students’ knowledge integration: Prompts for reflection in KIE. International Journal of Science Education 2000, 22, 819–837. 38. Wallace, C. S.; Hand, B.; Prain, V. Writing and Learning in the Science Classroom; Kluwer Academic Publishers: Dordrecht; Boston, 2004; . 39. Armstrong, N. A.; Wallace, C. S.; Chang, S. Learning from writing in college biology. Research in Science Education 2008, 38, 483–499. 40. Cooper, M. M.; Sandi-Urena, S. Design and validation of an instrument to assess metacognitive skillfulness in chemistry problem solving. J. Chem. Educ. 2009, 86, 240–245.

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Chapter 5

Metacognition as a Construct for Studying How Students Learn from Molecular Visualizations Resa Kelly* and Jinyan Wang Department of Chemistry, San Jose State University, One Washington Square, San Jose, California 95192-0101, United States *E-mail: [email protected].

In this chapter, we introduce how metacognition or thinking about thinking has been used as a research tool to uncover how students think about and process information that they learn from molecular visualizations. Examples of how metacognitive monitoring exercises were used in previous studies to examine how students understood differences between their understanding and the information portrayed in: animations, animations guided with cartoon tutors, and contrasting animations will be outlined. In addition, in a more recent study, metacognitive tasks have been employed to examine how students make sense of contrasting animations before they engage in collaborative discussion to determine the animation that best fits with evidence. Findings from this work reveal how weak chemistry understanding and lack of confidence may make students more likely to accept another students’ explanations in a collaborative setting without deep reflection, which may be a natural limit of their understanding.

Introduction The goal of this chapter is to investigate how metacognition became a useful tool for exploring what students learn when they view molecular animations, how it further evolved and how we currently use metacognitive activities in our research. Initially, this research began with exploring how students constructed their understanding of molecular level details before and after viewing animations. At that time, we were interested in how students were modifying © 2017 American Chemical Society

their understanding of sodium chloride dissolution to fit with animations of the same event (1, 2). We were concerned with how students constructed their understanding and we focused on students’ oral, written and drawn explanations of atomic level events that were portrayed in the animations. These studies helped us better understand how students developed their understanding. The results indicated that most students made significant improvements toward including more details from the animations in their explanations after they viewed the animations. However, learning was never perfect and we continued to wonder why students retained wrong information in their explanations when they were shown animations that were seemingly explicit about structural (the artistic representation of atomic and molecular level species) and mechanistic details (how molecules/atoms/ions move and interact with each other). We began to wonder how students perceived that their understanding compared to the details communicated in the animations. Consequently, our theoretical framework shifted from focusing on constructivism, in which we wanted to know how students constructed their understanding from the animations to inquiring about how students’ experiences or perceptions compared to the details portrayed in the animations. This new lens was more consistent with phenomenography in which the objective was to identify and describe variation in experiences or perceptions of a phenomenon. As we continued to explore this phenomeographical framework, a paper by Bussey et al. introduced variation theory, an adaptation of phenomenography that specifically explored how students could experience the same phenomenon, such as visualizations, differently and as a result take away different meaning from viewing the same event (3). This framework seemed the ideal lens through which to deepen our exploration into how and why students connected to or ignored information in the animations. Our aim in using this theory was to understand how an individual filters out some informational features from others to create a meaningful conception.

The Role of Metacognition in Examining Student Learning from Visualizations To examine how students’ understanding of dynamic molecular level chemistry events varied from the information portrayed in animations, the construct of metacognition was used. Metacognition, defined as “knowledge and experiences that assist learners to understand and monitor their cognitive processes (4, 5)” or “thinking about thinking (6)” was examined through monitoring exercises in which students were purposefully asked to share insights into their metacognition. Metacognitive monitoring is a strategy that enables students to observe, reflect on or experience their own cognitive processes (4, 7). In our metacognitive monitoring activity, students were asked to make judgements about their understanding before and after viewing animations. Specifically, they were asked to identify visualization features that matched with or differed from their mental model of the chemistry event. In this way, we were using metacognition as a construct for learning how students perceived their 56

understanding to differ from the information communicated in the animations. During the interviews, students were further questioned about how they made sense of critical features in the treatment consisting of several animations that varied in the complexity primarily by the number and color of water molecules that were shown in the solvent of aqueous salt solutions. Through metacognitive monitoring activity and analyzing students’ mental models by their hand drawn representation of the molecular events we studied how students made sense of the animation features. We found that when students recognized structural features that differed from their drawn representations, they made changes to their drawings that would resemble novel features from the animations that fit with their previous wrong ideas in a manner that was not entirely correct, but usually an improvement. This type of change was referred to as a transitional state of understanding (8). Additional findings revealed that there were obvious details, such as structural depictions (lattice arrangements, ions in solution) that most students recognized and incorporated into their explanations, but there were also aspects that students paid less attention to, and that they did not change. We concluded that students were entering this transitional phase and they made choices about what to change that reflected the moving, evolving nature of their understanding.

The Role of Metacognition in Examining Student Learning from Cartoon Tutorials At this point in the research journey, we understood that students missed or ignored aspects of visualizations that they might have deemed unnecessary, and we wondered if adding a cartoon tutor or guide to point out the relevance of pertinent features would assist students in better understanding the animations. Thus, cartoon tutors were designed and partnered with the molecular animations in a learning cycle approach consisting of exploration, concept development and application. In order to examine the utility of the cartoon tutor in helping students make sense of molecular animations that explored the nature of weak and strong acids, a metacognitive monitoring activity similar to the one used to explore how students made sense of only animations was used (9). Students were asked to orally describe how features portrayed in the tutorials were new to them, and they were also asked to describe the aspects that were familiar to them (9). The outcome was that students expressed having the greatest gain in understanding the particulate or structural nature of acidic solutions. They reported that the structures in the tutorials differed from how they represented the acid species and this oral confirmation was consistent with observable changes students made to their hand drawn revisions of the atomic level events. Interestingly, another event that was depicted in the tutorials was an electrical conductivity test of strong and weak acids; however, these tests were not perceived as new or different by the students as most of the students had used the testers or watched videos of the testers in lab. Despite this test being mundane to the students, most students revised their drawn representation of the mechanism. We concluded that students did not fully understanding the atomic nature of electrical conduction, yet they recognized 57

that they were familiar with the macroscopic outcome. Most students knew that acidic solutions would conduct, but not necessarily the reason why. Since they did not understand the nature at the atomic level they were able to glean new information from the tutorials to make improvements, which is consistent with the initial observation that when students recognize a difference they are more inclined to make a change. More importantly, we noted that when a cartoon tutor was used to guide the animations, students were more likely to adapt their pictures to fit exactly with what they saw.

The Role of Metacognition in Examining Student Learning from Contrasting Animations In our more recent research efforts, we wanted to alter the role of the animation as a definitive explanation and wanted to examine how students would respond if they had to decide from a pair of animations which one was best based on its fit with experimental evidence? Consequently, we began examining how students presented with a video of experimental evidence (Figure 1) were able to determine which of two contrasting molecular level animations best represented the atomic level of the experiment (10–12). One animation was more scientifically accurate or correct (depicted the electron transfer and the role of water molecules) and also quite complex or detailed in its portrayal of a redox reaction while the other animation was designed to be inaccurate or wrong (depicted the reaction as a single replacement equation with nitrate ions involved in the movement of the metal ions), but very simplistic in its design with only key species depicted. In the study, metacognitive monitoring was used to examine how students made sense of the animations in comparison to their own comprehension of the reaction event presented in the video. Students were first asked to construct atomic level pictures of the redox reaction event they saw in a video, then they constructed a handwritten list of the key features they tried to communicate through their atomic level drawings. After the students viewed each animation they also produced a list of the key features represented in each animation, and they compared the lists to their own list made before they viewed the animations. The lists were compared to examine how students noticed variation between their understanding and what they saw in the animations. Deep reflection or metacognitive monitoring revealed that students noticed mechanistic differences between the animations and their understanding, but they struggled to understand why the mechanism occurred (10). It was also observed that students sometimes ignored the mechanistic details that differed between the animations and assumed that because the wrong animation was more simplistic in its depiction that it must be a simplified version of the complex animation. Some students felt that the simplistic animation was consistent with balancing equations, while the complex animation was meant to give a more detailed and realistic account of how the reaction happens (10). Lastly, the metacognitive monitoring activity motivated several students to be more aware of the limitation of their own pictures and explanations as many recognized that their pictures were challenging to understand (10). 58

Figure 1. A screenshot of the video that shows the experimental evidence of a redox reaction between aqueous silver nitrate and solid copper. A copper wire was placed into pure water (left test tube), aqueous silver nitrate (middle test tube), and aqueous copper (II) nitrate (right test tube), individually. The color and the conductivity of the liquid in each test tube were evaluated before and 8 min after putting in the copper wire.

Current Use of Metacognitive Monitoring Activities Our research journey continues to explore how students learn from contrasting animations that are provided as possible atomic level explanations of a macroscopic event. The purpose of this recent study was to consider how students engage in critiquing their understanding of four contrasting animations through comparison to other students. In this section, we present two cases in which two pairs of students were asked to first construct molecular level pictures of a redox reaction involving the mixing of aqueous silver nitrate and copper wire before they were presented with four contrasting animations of the same event. The students were asked to select one animation that best represented the reaction from the video. Next, they were asked to redraw their atomic level understanding, and then they were invited to share their animation selection with their partner. Figure 2 illustrates the whole process of the interview session. This section explores the nature of their understanding when they reviewed the animations independently, followed by how they shared their understanding in pairs to reach consensus.

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Figure 2. A graphic illustration of the interview session.

Research Question The following question was explored. How do students metacognitively reflect on their molecular level understanding of the redox reaction between aqueous silver nitrate and solid copper before viewing animations, during the animation viewing process and when they collaboratively work in groups to reach consensus on the animation that best fits with experimental evidence? Participants and Data Collection In the spring of 2016, twelve students were interviewed in pairs over the course of two months. First, each pair of students was interviewed individually to examine their particulate level understanding of a video of a redox reaction (Figure 1) between aqueous silver nitrate and solid copper before and after they viewed four contrasting animations of the same reaction. The students consisted of five females and seven males of diverse ethnicity (five were Hispanic, four were Asian, two were Caucasian and one was of mixed ethnicity). They were all in their first semester of General Chemistry, and they were interviewed late in the semester after they had already been introduced to redox chemistry and had completed a lab on the topic. The students ranged in age from seventeen to twenty years of age and eight of the students were majoring in engineering related fields. Many students (9 of 12) reported that they had viewed molecular animations before, 60

but not very often, while only three students indicated that they had never used molecular animations. All of the students self-reported that they had experience with YouTube videos and spent considerable time on computers. The groups were formed based on students’ availability to complete the IRB approved study. The setup worked as follows: while one student was interviewed, the other student waited in a location outside of hearing distance of the office setting where the interviews took place. After each student, completed the consent form, viewed the contrasting animations individually and selected an animation that they felt best fit with the experimental evidence, the two students that made up a group were brought together and asked to describe to each other the animation they selected as being a best fit with the experimental evidence. They were also asked to discuss the animations and together decided which animation was the best fit with the evidence. In one case, the students both chose the same animation and the collaboration served to help them solidify their reasoning. In most groups, the students chose different animations and they were asked to reach consensus on the animation that best fit with the experimental evidence. After the pairs discussed and reached consensus, a short debriefing session was held to inform the students about the right and wrong features in each animation and also to answer students’ questions (Figure 2).

Comparing Understanding to Another Student After students had the opportunity to first communicate their understanding of the molecular level of the redox reaction between aqueous silver nitrate and solid copper through molecular level drawings, each student then individually viewed a set of four animations, one at a time, that consisted of two animations (animations 1 and 3) that represented the redox mechanism similarly and correctly; however, animation 1 was most accurate as it was an adaptation of animation 3. Both showed hydrated, but separate ions of silver and nitrate ions in the solution with only the hydrated silver ions being attracted to the copper surface. Both animations also showed that electron clouds form over the neutral atoms, and that copper ions (blue in color) were extracted by water molecules. Even though both animations were designed as accurate animations, animation 1 was made after animation 3 and was meant to repair a few structural issues noticed in animation 3, thus animation 1 was the best animation of the group. In animation 1, the copper surface was designed to be less even with a divot that attracted silver ions, while in animation 3 the surface was even (Figure 3). In addition, in animation 1 a copper ion that formed at the surface was extracted, which differs from animation 3, in which a copper ion located in the center of the lattice was extracted by water molecules, which would be less likely due to the stability of its location. Lastly, the color scheme of animation 1 was meant to show that the core copper atoms were blue and did not change color. Animation 3 shows that the copper atoms were yellow and when they were removed as ions they were blue in color. There was concern that students would misinterpret this to mean that a new element was created due to the change in color. 61

Figure 3. Still images from animation 1 (left) and animation 3 (right).

Animations 2 and 4 were purposefully made to be scientifically inaccurate animations. Animation 2 was used in our previous study and it was designed to resemble the look of a single replacement reaction showing two silver nitrate molecules hitting the copper surface, breaking apart to leave the silver atoms on the surface while the two nitrate groups next extracted a copper atom into solution. This resembles the look of a single replacement reaction equation in which the reactants are silver nitrate and copper, while the products are silver and copper (II) nitrate (Figure 4).

Figure 4. A still image from scientifically inaccurate animation 2 wrongly showing the nitrate ions extracting a copper ion from the copper surface.

Animation 4 was designed to resemble how students in a previous study (11) wrongly represented the reaction as having silver and nitrate species all adhering to the copper surface (Figure 5).

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Figure 5. A still image from scientifically inaccurate animation 4 showing silver and nitrate all adhering to the copper surface. After students sequentially viewed animations 1 through 4, the four animations were reduced to all fit on the computer screen simultaneously so that the students could easily navigate and compare and contrast the animations to each other (Figure 6).

Figure 6. A screenshot of the four contrasting animations that were shown to students during the study. In the top left corner is the most accurate animation that was revised from animation 3 (animation 1). In the top right corner is an incorrect animation modeled after a single replacement reaction (animation 2). In the lower left corner is the original, scientifically accurate animation (animation 3) and in the lower right corner is an inaccurate animation modeled to show all ions adhering to the surface (animation 4). 63

Results and Discussion After students individually viewed the animations, the majority of the students (7 of 12) selected animation 1, the most accurate animation (that was a revised form of animation 3), as the animation that fit best with experimental evidence and a few students (2 of 12) chose animation 3 (Table 1). Only three students chose animation 2, modeled after the single replacement equation, as the best animation and no students selected animation 4, designed from student input that showed all ions adhering to the copper surface. Even though most students selected animations 1 and 3, many students expressed uncertainty with their selection. After the students came together in groups to discuss the animations, students who initially selected animation 2 (S5, S6 and S12) were able to convince their groups to choose animation 2, even though all of their partners chose the best animation.

Table 1. Summary of individual and group animation selection Individuals

Animation choice

Pairs

Animation choice

S1

1

1. S1 and S2

1

S2

1

2. S3 and S4

3

S3

3

3. S5 and S10

2

S4

1

4. S6 and S7

2

S5

2

5. S8 and S9

3

S6

2

6. S11 and S12

2

S7

1

S8

3

S9

1

S10

1

S11

1

S12

2

In some cases, consensus was not reached, for example students S8 and S9 disagreed on whether animation 1 or animation 3 was the best animation. They appeared to reach consensus with S8 dominating the discussion to explain that animation 1 was not stoichiometrically accurate and S9 listened and quietly agreed. However, at one point S9 indicated that he retained a preference for animation 1. Students S3 and S4 were more vocal about their lack of consensus, and they also debated between the scientifically accurate animations (Table 1). In summary, approximately half of the groups chose one of the scientifically accurate animations (animation 1 or 3) while the other groups chose the incorrect animation 2, modeled after the single replacement reaction (Table 1). 64

The Role of Metacognition During the study, the interview consisted of four metacognitive tasks: 1) a drawing activity in which students constructed molecular level pictures of their understanding of the atomic level of the reaction video before they viewed the contrasting animations. They also described their drawings; 2) A few semi-structured interview questions that asked students to reflect on how challenging the drawing task was and which pictures they were most and least confident about. Follow up questions were asked that were unique to each interview situation in order to delve more deeply into students’ understanding; 3) Reflection during animation viewing. While viewing the animations students were asked to reflect on what they liked and disliked about the animations and ultimately to select and describe the animation they felt was the best fit with the video of the experiment, and; 4) Oral communication of metacognition through collaborative task. Students were asked to reflect on their understanding to provide an overview of how and why they chose the animation that best fit with experimental evidence video and they were asked to think about the ideas proposed to mutually select the animation that best fit with the evidence. The metacognitive tasks revealed how students connected to their prior knowledge and to the experimental evidence in order to select the animation that was in their opinion the best representation. It also revealed if students were uncertain of their understanding and how this affected the animation they selected and their group interaction. To more richly demonstrate these metacognitive tasks, data from two of the six pairs of students will be presented in detail. These pairs were chosen because they both selected the wrong animation over the scientifically accurate animation and because we wish to explore the nature of student thinking through the treatment to better understand how students reason throughout these tasks. In addition, how one pair of students discussed and reflected on their understanding of the scientifically accurate choices is also examined. Pair 4: S6 and S7 Prior to coming together as a group, students S6 and S7, both male students, each constructed molecular level pictures of the redox reaction portrayed in a video that they each viewed at the beginning of their individual sessions (Figures 5 and 6). S6 found drawing the atomic level of the redox reaction to be very challenging. He reasoned about possible products that could account for the black material that formed on the wire in the video, but he struggled in his effort to recall what he had learned in previous chemistry classes that might account for the identity of the substance. For his “before they react” picture (Figure 7), S6 drew a macroscopic representation of the wire. He indicated that the silver nitrate was attracted to the wire and moving toward it and eventually it would form the rust or “brown grey-greenish” substance. He shared that the big red dots in solution were “atoms” of silver nitrate and the smaller dots were electrons associated with silver nitrate. He drew dots on the wire and indicated that those were electrons from silver nitrate that were attracted to the wire, but then later he indicated that they were electrons 65

going from the copper wire to the silver nitrates. He stated that the conductivity test measured reactivity and showed that silver nitrate was very reactive.

Figure 7. S6’s “before the reaction” representation. For his depiction of “during the reaction” (Figure 8), S6 drew a black wire macroscopically and next to it placed red dots with smaller red dots around them to represent that the electrons left the wire and they went with the silver nitrate and that’s what was causing the change of color and the added substance. He explained that the electrons were more attracted to the silver nitrate so they left the copper wire. He indicated that the silver nitrate and electrons made up the solution. He thought that the copper atoms in the wire would lose their valance electrons and the nitrate ions, because they have oxygen, would cause the wire to rust and form the black substance. When pressed to explain what made up the rust, S6 guessed it would be copper oxide or a molecule that consisted of copper and oxygen and he was not sure whether electrons would be floating about the copper oxide molecules. S6 disclosed that he was not sure what it would look like at this level with the valence electrons. He knew about neutralization and balanced equations, but he could not picture how to connect these concepts to form a picture of the reaction.

Figure 8. S6’s “during the reaction” representation. For his “after-reaction” pictures, S6 did not think the conductivity tester gave much useful information, it conducted before and it continued to do so. He drew a mixture of dots that represented silver nitrate and the blue dots were leftover copper. He noticed that in the video a test tube placed next to the test tube in which the reaction occurred was made of copper nitrate and it was blue. He reasoned 66

that the blue color produced by the reaction was due to the remaining copper in the wire or the rust, copper-oxygen molecule. As he thought more about what he was saying and noticed the blue color of the copper(II) nitrate solution, S6 decided that the copper oxide (rust) would react even more with the silver nitrate to form copper nitrate. He described this as a series of two reactions, first the silver nitrate would react with copper to form “copper-oxygen” and then the “copper-oxygen” would react with the silver nitrate to form copper nitrate. For the wire, he shared that the black substance was the copper that reacted with silver nitrate to form copper-oxygen or copper oxide (he used both names in his description) while the red was unreacted copper (Figure 9).

Figure 9. S6’s “after-reaction” representations of the solution (left) and the solid metal products (right). When S6 was asked if he found the task of drawing the molecular level of the reaction challenging he responded: S6: Yeah, I have never really thought about the chemical reaction like at that small of scale and thinking about it like that and trying to use what I have learned before it just, it kind of made it more difficult. I am trying to use something that I am not even sure is right so maybe I am adding more steps or I am making it more complicated or maybe it was simple, but I was doing too much. S6 felt most confident about his portrayal of the blue colored product solution claiming that it made sense that it turned blue because there was some copper nitrate in it and he noted the connection to the video of experimental evidence, specifically that the color was similar to the copper (II) nitrate solution that served as a control in the video. He felt that this “solidified” his belief that copper nitrate and silver nitrate were left in the solution. S7 was also challenged by the drawing task. For his before picture (Figure 10), S7 represented the copper atoms in the wire with the solution “free floating” around the wire. He represented the solution as a mix of water (blue dots) and silver nitrate (purple dots). He thought the silver nitrate solution conducted because silver was a metal which made the solution conduct.

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Figure 10. S7’s drawn representation before the reaction occurs. For his during reaction picture (Figure 11), S7 indicated that the copper atoms started to separate and release from the tightly packed portion of the solid, because of the silver nitrates. He indicated that the silver nitrates reacted with the copper atoms and they joined together as pairs which caused the black look to the wire. He shared that the copper along with the wire caused it to rust, but he was not sure if the black substance was rust. He thought that the water around the wire was not doing anything to the wire in the time that the reaction took place, but if left longer it might begin to rust. When asked what rust was, S7 said, “I guess when water breaks down the internal molecules or atoms of the metal.” He compared rusting to erosion, but did not expand on this analogy.

Figure 11. S7’s “during the reaction” representation. For the after pictures (Figure 12), S7 described the blue colored solution’s make up as being due to the copper reacting with the silver nitrate. S7 shared, “It made the water turn blue, I don’t know why it did that.” S7 further explained that when the silver nitrate reacted with the copper, it caused the copper to break down and form that black substance around it. The water got traces of the reaction as well, but then he was confused by this idea because he did not think this would cause the blue color. S7 indicated that he wished he could smell the solution, as he thought this might give him an idea of what was in solution. He decided that it 68

must be copper and silver nitrate joining and forming new substances, one product was the black substance on the wire and the other was causing the blue solution. He drew the blue solution as a mixture of water molecues (blue dots) and copper-silver nitrate molecules (orange and purple pair). For the wire, he maintained that the black substance was due to the copper and silver nitrate reacting together (orange and purple pair).

Figure 12. S7’s “after reaction” representation of the solution (left) and the solid metal products (right). S7 found the molecular level drawing task to be challenging. He reflected on his understanding and indicated that he did not feel confident about his understanding of chemistry. S7: I don’t think that I know as much chemistry as I should probably, because I didn’t know what a lot of these reactions made. So that hurt me and then, it was hard. I didn’t know what the black substance was and I didn’t know how atoms looked in their natural state, I guess and how they look when they react so I think that my lack of knowledge there hurt me. Since I didn’t know how it looked I couldn’t draw it as good as I could have or should have. He felt most confident about his drawn representation of the aqueous silver nitrate solution and the copper wire before they reacted because it was more simplistic than the reaction, for which he expressed confusion. He felt least confident about his representation of the final products. He did not know what the substance was that formed on the wire and he did not know how the chemicals reacted to form the blue colored solution. When S6 and S7 were independently asked to choose an animation that best represented the experimental evidence from the video. S7 chose animation 1 (the most accurate, revised animation), but his reasoning for his selection was connected to the length of the animation and the colors of the atom species and had little to do with the actual reaction events or the mechanism. S7: The colors make sense to me, because it showed how, what’s that called the silver nitrate and the water, how they were together because you start it up it showed them joining with the copper and kind of sticking and then it also showed how the water kind of pulled off some copper and formed the blue color. That was helpful. It shows the water being blue 69

and the copper is kind of like glowing or something. It just stands out and then how the separate like kind of yellowish color sort of serves as the black substance we saw in the final reaction. It probably felt like it was the longest one so I had time to really see everything. S7 thought that the fourth animation, which incorrectly showed the silver and nitrate ions layering onto the surface best matched how he initially pictured it, but now that he had options. He felt that the first animation made the most sense to him in how the reaction would progress. S6 chose animation 2, the inaccurate animation that resembled the single replacement equation, for its fit with his understanding and he referred to this match throughout his description. He found it confusing that two animations (1 and 3) showed water molecules more involved with the reaction. In this case, we see that he recognized the variation between his understanding and the animation, but he did not understand why the animation depicted the reaction in this way and he could not conceive it possible for water molecules to be involved in the reaction. S6: So, I liked animation 2. The main reason I liked it was because I felt like it showed the entire reaction, and it mainly focused on the silver nitrate and it turning into copper nitrate and leaving the silver on the copper wire. …I liked it the most because it showed everything and for the other ones animation 4, I remember, is like animation 2, but there was one part of it, I think the initial part of it leaving the silver or the silver nitrate going to the wire but not taking the copper…. Animation 2, the silver and the nitrate are connected (plays animation 2) so that’s why I liked it more. Animation 3 you see that it is just the silver and then the nitrate by themselves interacting with the copper so that’s why I didn’t like it and then it pulls out a copper with the water, which it doesn’t show the water initially which kind of I feel like is confusing. Animation 1 (plays it) I feel like there is a lot going on in the start and it shows like the silver is connected with the water, which, because of how I think the reaction goes, it kind of leaves the nitrate out and I feel like the nitrate and the silver are bonded and it (the nitrate) plays a bigger role in the reaction. But I like how animation 1, it does show that initial, like the silver does go to the copper wire and then it comes back out with the copper. In a sense, it’s like animation 2 shows the complete reaction. It’s just how you see it.

Collaboration and Reaching Consensus When the students were asked to defend their animation selection to each other, S6 explained why he chose animation 2, while S7 explained the limitations of his own reasoning and then indicated that he accepted S6’s choice. There was very little discussion of the evidence. S6 proposed why he chose animation 2 and although S7 reviewed each animation, he seemed to accept S6’s explanation and he did not challenge S6 further. 70

S6: It (animation 1) shows the complete reaction from start, the silver reacting with the copper wire and then it comes out and, or then they trade, the silver goes to the copper and the copper leaves so I like that, but for me, I prefer animation 2 because at the end the liquid is blue and you’ve got copper nitrate (points to a picture that has a solution of copper(II) nitrate from the video) which was blue from the start. At the end of animation 2 you see that there is copper nitrate floating around for me it like clicked. Because I see the entire reaction I see the silver nitrate going to the copper wire and then the silver is left on the copper wire, it leaves with copper turning it to copper nitrate and then that’s what gives it that blue color because copper nitrate is blue. S7: I see what you are saying. I think back to how that one changes too (points to test tube of copper II nitrate) like I was all just focused on this thing (points to the middle test tube with the reaction occurring in it). You are right how the copper turned blue and that one. I can see that. Here, I want to rewatch this thing (replays animation 2). I guess that is less confusing than that one (animation 1) because that one has a lot going on. Let me watch animation 1 again. S6: That’s also, one thing about seeing animation 1 now, you see the copper reacts with the water. It doesn’t really show why or it doesn’t really provide like how. In animation 2, you see the copper with two nitrates and it’s copper nitrate, but in the first one you just see copper with the water. S7: yeah, I like yours, that makes more sense too. To probe the students a bit more the researcher asked the students what they would do if another student picked a different animation. They responded: S6: I would have to hear what they said about it first. If they knew more about chemistry and how things react I would probably think that they are right, because I don’t know as much. But then I don’t know, animation 2 is pretty good. S7: So, for me, I see like 2 and 4 are like similar in that they have the same idea of the nitrates and the silver reacting, and 1 and 3 with water and the silver and the copper reacting. So, 4, I would say you don’t need like that full reaction, okay, that’s fine. 1 and 3, I mean, it goes back to the copper nitrate is blue so with animation 2 and 4 or at least with 2, you see that there is copper nitrate in the liquid so that helps build the proof that there is copper nitrate because it is blue. With one, you still have to think, well that copper is going to react with the nitrate and it does, but right there it is just like floating with the water. I would say that you can assume, but it is not guaranteed so like, like you can assume 71

that eventually it will react with the nitrate because they are all floating around. R: So, the nitrate has to be involved in your opinion? S7: Yeah, it’s the blue color. It’s the key to the puzzle. S6 and S7 selected animation 2, the wrong animation that looked like a single replacement equation, as the animation that best represented the experiment. To summarize, S6 convinced S7 that water molecules could not be involved in the reaction, but nitrate ions had to be involved in the reaction based on the control solution of copper (II) nitrate being blue in color.

Pair 6: S11 and S12 Prior to coming together as a group, students S11 and S12 each constructed molecular level pictures of the redox reaction portrayed in the video of the experiment that they watched at the beginning of their individual sessions. Student S11 was a male student who admitted that he did not feel comfortable with his understanding of the atomic level because he mostly remembered formulas and learning how to work problems for test preparation. Prior to seeing the animations, he was certain that silver and nitrate dissociated, but he drew a mix of some silver nitrate formulas inside circles and some free silver and nitrate ions inside circles (Figure 13).

Figure 13. S11’s “before the reaction” representation. Upon reacting, S11 explained that silver replaced the copper in the center of the wire, while copper oxide formed on the surface of the copper and copper nitrate formed in solution. He recalled from the activity series of metals that silver reacted less than copper and concluded that copper switched with it. In addition, when S11 was asked about the role of water, he indicated that it probably broke apart, but he was not sure. He drew an oxygen ion that formed from a water molecule that broke apart then reacted with copper to form copper oxide in solution and also on the wire. He also believed that copper nitrate formed in solution and this specie caused the solution to be blue (Figure 14). 72

Figure 14. S11’s “during reaction” representation of the redox reaction between aqueous silver nitrate and solid copper (top) and “after reaction” representations of the solution (bottom left) and the solid metal products (bottom right). After viewing the animations, S11 indicated that animation 1(the best animation revised from animation 3) did not make sense because the copper did not react with the nitrate and it did not replace it. He thought that the nitrate would replace the copper and the copper would be removed before the silver would go onto the wire. After viewing animation 2 that resembled a single replacement mechanism, S11 expressed that this animation made more sense to him because of how the copper nitrate formed, but he did not feel that the animation showed enough copper being removed and he recognized that water was not involved in the reaction. He noticed that none of the animations showed copper oxide being formed. S11 decided that animation 1 (the most accurate animation) was the best animation because, in his words, “the only thing it doesn’t show is the copper oxide.” He noticed that it did not show the single replacement reaction, but he liked the colors that were used in the animation better. He then stated that he thought animation 2 was probably more accurate because it showed the two nitrates bonding with the copper. When S11 was invited to make revisions, his pictures changed very little, he still believed there was a mixture of un-dissociated and dissociated silver nitrate. He thought that copper oxide formed to produce the aggregate on the wire and that copper nitrate formed to turn the solution blue. The only change that he made from viewing the animations was that water broke apart. He stated that even though the water did not break apart in the animation, it made him realize that it should dissociate because it became soluble and mostly because it was there whenever anything happened to the reaction. Student S12 was a female who felt that chemistry was her weakest subject. Prior to seeing the animations, she thought that silver nitrate adhered to the copper surface, but she admitted that the task of drawing her molecular level understanding was difficult. “In class, I learned like how to write a reaction, but I never thought about what was happening at the atomic level and how the 73

elements would interact.” For her molecular level depictions before the reaction took place, S12 noticed that the aqueous silver nitrate solution conducted, and she drew free ions of silver and nitrate floating in the space surrounding the solution. She represented copper as a cluster of atoms, with open circles, initially labeling it as charged Cu+, but then deciding that ions meant they were free so she scribbled out the positive charge and stated that the copper was made of atoms (Figure 15).

Figure 15. S12’s “before reaction” representation. S12 recognized that the reaction was a single replacement reaction in which “the copper would switch with the silver” and she thought that either silver nitrate or copper nitrate would be on the surface of the copper wire. As S12 drew an atomic level view of the copper, she first placed a black circle on the surface and labeled it Ag, but when she was pressed as to whether she thought it was silver or silver nitrate, she decided it was silver nitrate (Figure 16). She reasoned that it could not be “straight up silver, because the nitrate is in solution.” She thought that free copper ions would end up in the solution and represented it with dots, but she also recognized that silver and nitrate ions would be present, but she was unsure why.

Figure 16. S12’s “during the reaction” representation. For the product solution, S12 recognized that the blue color of the solution was due to the copper ions because copper ions are blue, and she continued to represent dots for the silver and nitrate ions to show their presence. For the wire, she continued to represent the black substance on the wire as silver nitrate, and reasoned that it reacted with the copper and ended up on the surface; however, she was not confident of this and again indicated that chemistry was not her strongest subject (Figure 17). 74

Figure 17. S12’s “after reaction” representation of the the solution(left) and the solid metal products(right). S12 thought that both animation 1(the most accurate animation, revised from animation 3) and animation 2 (the wrong animation based on the single replacement equation) were possible solutions. She liked animation 1 because the copper ions went away and the silver replaced it, but she liked animation 2 because the copper would bind with the nitrate and since the solution was turning blue, it meant that copper went into solution and silver attached to the wire. When she was asked why she liked animation 2, S12 responded: I think it was the most simplistic, I guess, because it only has the copper, the silver and the nitrate…it makes the most sense and it is less confusing…. The silver kind of attached to the copper wire and the nitrate released, took the copper and then released into solution. S12 found that the accurate animations were confusing because they showed the involvement of water molecules and while she knew that water molecules were in the solution she did not think they should be involved in the reaction. In general, S12 found it helpful to view the animations because it gave her “more solid ideas” of what was happening. “Before I wasn’t sure at all and it helped to visualize the reaction.”

Collaboration and Reaching Consensus When S11 and S12 were brought together to discuss their animation selection after they viewed the animations individually, S11 shared that he picked the first animation because it showed the copper leaving and silver ion becoming a metal. When the students were asked to describe their understanding to each other the following conversation ensued: S11: …I said that the stuff covering the wire was copper oxide, but I wasn’t sure and so that’s what made it black and the silver nitrate or the nitrate became free floating and eventually bonded with the copper to create copper nitrate. 75

S12: I was also not sure, but I put the copper wire. The stuff on the wire had silver in it, so silver nitrate maybe and the copper ions being released into the solution. S11: And then for the last two for in the solution I put that there was copper and nitrate and free floating hydrogen ions as well as water. S12: Ah yeah, I just had the copper wire and then the silver nitrate attached to the wire and then in the solution was some free-floating copper ions as well as silver and nitrate ions. S11: and for the wire I put copper oxide When asked to explain the animation that they chose and why, S11 and S12 discussed as follows: S11: So, I thought it would either be 1 or 2 (referring to animations) and 2 is more correct I think because it has two nitrates whereas this one it only shows one nitrate bonding so it doesn’t show everything, but this one shows the water interacting with the reaction more and so I like that, but I think with the experiment, animation 1 is more accurate because the copper is plus 2 I think, so it needs two nitrates. S12: I chose the second one, because I was also deciding between this and the first one, I didn’t really like factor water into the reaction, which is kind of dumb, but I completely forgot about it, also I wasn’t sure what to make of the glowing in the copper wire and the free water molecules so I thought number 2 seemed the most accurate because it was the most simplistic, also because it did show the copper leaving and the nitrate attaching to it and leaving the silver on the wire. S11: Yeah. I agree with that one. S11 and S12 shared that they chose animation 2, the wrong animation that resembled the single replacement reaction, because it showed the copper ions leaving the wire and going into the solution and it also had two nitrates attached to the copper as it left, leaving silver on the surface of the wire like in the experiment. They indicated that the formation of copper (II) nitrate was critical to their decision. S11 continued to believe that copper oxide formed on the wire. “the black stuff has to be copper oxide, but I still don’t know.” S12 shared that she thought the accumulation that formed was silver nitrate. When they were pressed to decide, what had formed on the wire, neither was certain. S11 stated, either silver nitrate or copper oxide. When asked what was making the solution blue in color, S12 stated that it was blue due to free copper ions, but then clarified that they don’t have to be free copper ions because she recalled that in lab there was “copper something” and it was blue. “Maybe there weren’t any free ions, but it was still blue or maybe not all of them are free.” At the conclusion of the 76

collaborative session, both students agreed that animations 1 and 2 had their merits. Animation 1 showed how the silver was independent of the nitrate, but animation 2 with the silver and nitrate also seemed correct. S11 stated that when the ions react they would form new molecules and then break apart so animation 2 made more sense. In general, both agreed that animation 2 fit with the reaction equation better. S11 and 12 did not seem motivated to change their understanding. They reached consensus because they were asked to do so, but neither was particularly satisfied with how the animations represented the reaction event. Apart from noticing that the way they envisioned the atomic level did not match, the students were not inclined to address why they were wrong nor did they work to challenge each other. Deliberation over Accurate Animations In the case of pair 2, consisting of students S3 and S4, both chose from the accurate animations that were presented in the study as their choices for animations that best fit with the experimental evidence. S3 selected animation 3 while S4 selected animation 1. Of interest here was that the manner in which these animations contrasted as judged and constructed by the designers. They were not deliberated upon at all by the students. Instead, the students focused on two focal points that were not designed to be in conflict with each other: the solid accumulation on the wire and the nature of the aqueous solutions. S4: Okay, so what I thought happened was… this was after a lot of deliberation and a lot of doubt, is that I know we got this layer that formed copper, which I initially thought was like some silver oxide. It reacted with the copper or at least latched onto the copper through the water. That’s also why some of the copper was dilute into the water, which is why they have the same solution (addressing the copper(II) nitrate solution in the video). So, I figured that this is the same solution, the copper nitrate and then then the residue on the coil is possibly an oxide of one of the metals which I said was probably silver. Following S4’s reasoning, S3 shared that he agreed with S4, only he thought that the residue on the copper was silver and that some of the copper dissociated from the wire and mixed with the nitrates and formed the copper nitrate solution in the test tube. Interestingly, S3’s ideas about the residue on the wire differed from S4’s but the students did not discuss this, they focused on their agreement regarding the copper(II) nitrate solution and the connection to the evidence. S3 then disclosed that he was confused by two of the animations, animations 3 and 4, because of the role of water. He noticed that in animation 4 the molecules were not dissociated and water played no role, while in animation 3 the water played a role in “getting the copper”. S4 shared that water was acting like a messenger. S3: Yeah, so I thought because they are already dissociated because the silver nitrate gets dissociated in the solution, that’s what’s happening. So, 77

the silver nitrate is already dissociated in the third one and then the silver gets attached to the copper, and the water molecules get the copper. S4 noted that S3’s observation made sense and then he asked S3, “The thing about the fourth one is that the nitrate bonds to the copper. Do you think that is happening or do you think it stays separate?” S3 responded that he did not believe that the nitrate was bonding to the copper. When the students were asked to share the features, they agreed on, the students reported that they both believed that the solution ended up being blue and that the solution was copper nitrate. They also both agreed that the residue was silver. However, S3 and S4 did not agree on how the silver was drawn toward the copper wire. S3 believed that the water molecules should not be attached to the silver and he thought this was depicted in animation 3, but that animation 1 showed that the water molecules were not attached. Interestingly, the designers did not design the animations to contrast over this detail. He also felt that electrons must be involved in the reaction, but he could not figure out how electrons were involved from the animations. S4 did not disagree with S3 over the role of water depicted in the animations, but he contended that animation 1 was better because the ion was attracted to the copper through the water. When they were asked to reach consensus, S4 admitted that he was very tentative about his animation choice and he was tempted to yield to S3’s selection. S3 reassured S4 that he was also unsure. S4 then responded, I feel like it’s very likely that the silver attracts to copper on its own, but also, I will kind of account for the residue, it appears to be some sort of oxide with a metal, which kind of makes sense because water’s oxygen could have bonded its oxygen with the silver molecules on it, but I’m also not very sure, I guess we would have to test the substance and see if there is any change in the water count. S3 continued to focus on the solubility of silver nitrate, he asked S4 if he believed that silver nitrate was soluble and dissociated. S4 responded yes. With this affirmation, S3 then stated: I believe the silver nitrate in the solution gets dissociated into silver and the nitrate and then copper is introduced. The silver gets attracted to the copper and some of the copper particles or molecules gets in the solution. The water molecules attached themselves to the copper and bring it to the solution and then, because there are nitrates and copper in the solution, it gives the color blue and it conducts electricity. Upon hearing S4’s explanation, S3 agreed and both students came to consensus that animation 3 was the best animation. From this collaborative session, we gain a sense of how students reflect on their own thinking. Both students admitted that they were not confident, but we notice that S4 was inclined to accept S3’s animation choice even though he continued to have misgivings about the makeup of the solid product that 78

accumulated on the wire. Interestingly, he conveyed his dissonance to S3, but S3 did not offer a response, instead S3 shifted the focus to the feature he was uncertain about, that of the solution. S4 gave feedback and supported the dissociated model. This helped S3 reach resolution. Even though S4 agreed with S3, his uncertainty about the solid product was unresolved. He stated, “I will yield to his (S3’s) understanding.” During the debriefing S4 inquired about the appearance of the silver and wanted to know why it appeared black. This validates that he was still trying to rectify the physical appearance of the solid with the mechanism proposed by the animation in spite of his oral agreement that animation 3 was the best animation.

Conclusions Metacognitive tasks serve a useful tool in qualitative studies as they reveal not only a richer description of student understanding, but they also help us understand the nature of alternative conceptions that lead students to select the wrong animation and sometimes the best animation. For example, some students felt very strongly that water should not be involved in the redox reaction because water does not react. In addition, some students applied prior knowledge of rusting to account for the formation of the black substance on the wire. Since the accumulation was dark and did not resemble silver, some assumed it was an oxide of some kind. The metacognitive task reveals the challenging nature of the task and the struggle students have to apply past experiences, in this case of rust, to account for what is happening submicroscopically. In addition to generating insight into how students go about choosing the animation that best represents the evidence, the collaborative task allows us to see how students use their understanding to persuade others. In the three cases, presented in this chapter, we see that one student tends to be more willing to accept the explanations of the other. For example, S7 was willing to accept S6’s explanation for choosing animation 2, most likely because his reason for selecting animation 1 was not based on chemistry evidence, but on the length of the animation and the aesthetic color. S6 connected more to the evidence and to the balanced equations and even though he chose the wrong animation, he had stronger chemical reasons for doing so, which helped him persuade S7 to change. In the case of students S11 and S12, we see that even though S11 chose animation 1, he was not entirely certain that this was the best animation. S11 and S12 seemed to settle on animation 2 but both were still uncertain, which may mean that they were confused by what they saw. Finally, S4 conformed to accept the animation that S3 chose, in part, because the animations were similar in the representation of the silver accumulation, which was the one feature that S4 continued to doubt and he felt compelled to trust S3. S3, in turn, received support from S4 and once S4 agreed with him, there was no further discussion. Confusion may entice students to further investigate the particulate nature of redox reactions, ultimately resulting in them gaining a better understanding. Metacognitive tasks done individually or through collaboration with other students, offer opportunities for insight into how students reason and take in 79

information from video sources. When students are asked to express their understanding in order to select an animation they benefit from articulating what they do not understand or what they lack confidence in understanding and they also benefit in recognizing their strengths. When they share their thoughts with others, they learn that they are not alone in questioning what they think about. The challenge is to get them to deeply discuss and question these aspects of chemistry and to not simply accept what the other student believes out of insecurity. It takes courage and perseverance to share metacognition, but doing so may help students develop their understanding of science and what it means to understand.

References 1.

Kelly, R. M.; Jones, L. L. Exploring how different features of animations of sodium chloride dissolution affect students’ explanations. J. Sci. Educ. Tech. 2007, 16, 413–429. 2. Kelly, R. M.; Jones, L. L. Investigating students’ ability to transfer ideas learned from molecular animations of the dissolution process. J. Chem. Educ. 2008, 85, 303–309. 3. Bussey, T. J.; Orgill, M.; Crippen, K. J. Variation theory: a theory of learning and a useful theoretical framework for chemical education research. Chem. Educ. Res. Pract. 2013, 14, 9–22. 4. Flavell, J. H. In The Nature of Intelligence; Resnick, L. B., Ed.; Lawrence Erlbaum Associates: Hillsdale, NJ, 1976; pp 231−235. 5. Schraw, G.; Crippen, K. J.; Hartley, K. Promoting self-regulation in science education: Metacognition as part of a broader perspective on learning. Res. Sci. Educ. 2006, 36, 111–139. 6. Rickey, D.; Stacy, A. M. The role of metacognition in learning chemistry. J. Chem. Educ. 2000, 77, 915–920. 7. Mathabathe, K. C.; Potgieter Metacognitive monitoring and learning gain in foundation chemistry. Chem. Educ. Res. Pract. 2014, 15, 94–104. 8. Kelly, R. M. Using variation theory with metacognitive monitoring to develop insights into how students learn from molecular visualizations. J. Chem. Educ. 2014, 91, 1152–1161. 9. Kelly, R. M.; Akaygun, S. Insights into how students learn the difference between a weak acid and a strong acid from cartoon tutorials employing visualizations. J. Chem. Educ. 2016, 93, 1010–1019. 10. Kelly, R. M.; Akaygun, S. Learning from contrasting molecular animations with a metacognitive monitoring activity. Educ. Quim. 2017, 28, 181–194. 11. Kelly, R. M.; Akaygun, S.; Hansen, S. J. R.; Villalta-Cerdas, A. The effect that comparing molecular animations of varying accuracy has on students’ submicroscopic explanations. Chem. Educ. Res. Pract. 2017, 18, 582–600. 12. Kelly, R. M.; Hansen, S. J. R. Exploring the design and use of molecular animations that conflict for understanding chemical reactions. Quim. Nova 2017, 40, 476–481.

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Chapter 6

Promoting Metacognitive Practices in Faculty and Students Anusha S. Rao,1 Terri A. Tarr,1 and Pratibha Varma-Nelson*,2,3 1Center

for Teaching and Learning, Indiana University−Purdue University Indianapolis, 755 W. Michigan St., UL 1125, Indianapolis, Indiana 46202, United States 2STEM Education Innovation and Research Instititute, Indiana University−Purdue University Indianapolis, 755 W. Michigan St., UL 1123, Indianapolis, Indiana 46202, United States 3Department of Chemistry, Indiana University−Purdue University Indianapolis, 402 N. Blackford St., Indianapolis, Indiana 46202, United States *E-mail: [email protected]

This chapter discusses faculty and student metacognition in the context of teaching and learning and offers strategies to make faculty better teachers and their students better learners. It provides examples of student learning outcomes that integrate metacognitive skills, the types of learning environments and pedagogies that can foster student metacognition, and ways to assess students’ metacognitive development. It then turns to instructor metacognition and addresses questions related to how faculty can become more metacognitive about their teaching and the role of faculty development in this process.

© 2017 American Chemical Society

Introduction In this chapter we suggest strategies for promoting a metacognitive approach to teaching and learning that can make chemistry faculty better teachers and their students better learners. We have organized the chapter around the following three questions: 1. 2. 3.

How can faculty members facilitate student metacognition to improve chemistry learning? What strategies are available to assess students’ metacognitive development? What can faculty do to be intentionally metacognitive about their teaching? How can faculty developers assist in that process?

Research on the science of learning has identified metacognition as one of the key constructs related to how people learn effectively (1, 2). Metacognition is often defined as thinking about one’s own thinking. Flavell (3) proposed that this process consists of two inter-related components – metacognitive knowledge and metacognitive experiences and regulation. Metacognitive knowledge includes the knowledge of one’s own cognitive processes, the task at hand, and possible strategies to complete the task. Metacognitive experiences and regulation encompass the process of self-regulated learning through planning, monitoring, and evaluation, and the associated feelings, judgments, and task-specific experiences (4, 5). Table 1 describes examples of faculty and students’ metacognitive knowledge, experiences and regulation in the context of teaching and learning. With the exception of a few students, most incoming traditional college freshman do not come equipped with metacognitive skills, unless they are explicitly taught such skills in high school. However, several studies in both K-12 and higher education have shown that metacognition is teachable and can improve students’ learning (6–10). Dunning and Kruger (11) showed that poor metacognitive ability was linked to inflated self-assessment of competence in low performing individuals. For students to be successful learners, they need to develop general and discipline-specific metacognitive learning strategies. STEM and specifically chemistry education researchers (6, 12–15) have shown that content-specific metacognitive learning strategies should be taught throughout the curriculum because as the content gets more complex the students’ metacognitive skills need to become more sophisticated as well. In the rest of this chapter, we will discuss how faculty can become more metacognitive in their teaching and encourage students to become more metacognitive in their learning.

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Table 1. Components of Faculty and Student Metacognition Component

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Faculty Metacognition About Teaching

Student Metacognition About Learning

Metacognitive Knowledge

• General and discipline-specific strategies that instructors can use to effectively teach specific disciplinary content to students. This is also referred to as pedagogical content knowledge • Conditions or circumstances appropriate for effective instruction such as prior knowledge and level of students, need for individual vs. group work, appropriate time to provide a rationale or context for implementing new instructional practices to improve student learning, etc. • One’s own beliefs and philosophy of teaching and barriers to improving teaching skills.

• General and discipline-specific learning strategies that trigger higher-order thinking skills of application, synthesis, and evaluation and how to use them. • When and which strategies are optimal for solving different types of problems and questions. • One’s own beliefs of the purpose of learning, future goals, and barriers towards achieving these goals.

Metacognitive Regulation

• Planning instructional practices not only based on the disciplinary content, but also considering one’s own and students’ feelings associated with teaching successes and challenges encountered when the content was previously taught or being taught currently. • Monitoring one’s teaching by observing students’ cognitive and affective behaviors and responses to instruction and interactions with the instructor and peers. • Evaluating the impact of teaching not only on students’ learning through appropriate assessments, but also on one’s own teaching beliefs and philosophy through documented critical

• Planning for learning (mastery of content) and performance (test grades) considering motivation, interest, and liking associated with the topic or content. • Monitoring one’s learning by identifying knowledge gaps, behaviors, feelings, and actions that promote or deter learning and improved performance. • Evaluating learning and performance within the context of a course and its impact of on one’s future goals; considering strategies that overcome barriers to learning and/or performance. Continued on next page.

Table 1. (Continued). Components of Faculty and Student Metacognition Component

Metacognitive Experiences

Faculty Metacognition About Teaching reflection and considering strategies to overcome anticipated barriers towards improved teaching. • Feelings, judgments, or estimates of instructional inputs, outputs, and the process, such as time taken to teach a topic, effect of instruction on student learning, etc.

Student Metacognition About Learning

• Feeling, judgments, or estimates of inputs, outputs, and processes associated with learning and performance, such as modifying study habits, interactions with peers and instructors, or self-assessing improvements in ability to transfer learning into various contexts.

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How Can Faculty Members Facilitate Student Metacognition To Improve Chemistry Learning? Student Learnng Outcomes Even though students may not use metacognitive skills as often or effectively as they should, faculty often focus on the content students need to learn rather than the skills and strategies students need in order to learn how to learn the content more effectively. One way for faculty to intentionally incorporate metacognitive skills training into their teaching is to explicitly include metacognitive skills in student learning outcome statements. When faculty integrate metacognitive skills into student learning outcomes, they hold themselves accountable to devise assessments and learning experiences to enhance those skills in students. Metacognition can be blended into learning outcomes by including processes needed to monitor and control learning. Ambrose (2) has described the basic metacognitive processes as those in which students: • • • • •

Assess the task at hand, taking into consideration the task’s goals and constraints Evaluate their own knowledge and skills, identifying strengths and weaknesses Plan their approach in a way that accounts for the current situation Apply various strategies to enact their plan, monitoring their progress along the way Reflect on the degree to which their current approach is working so that they can adjust and restart the cycle as needed

These general metacognitive processes can be adapted to more specific learning outcomes in a course or discipline. Barkley and Major (16) described sample course learning objectives/outcomes related to learning how to learn, such as: By the end of this course, a successful learner will be able to . . . • • • • • •

articulate a personal plan for learning new content develop a process for learning new material carry out an original investigation in the field write a report evaluating progress in completing a project conduct a self-evaluation of work and learning during a given project carry out a systematic or formal inquiry to discover and examine appropriate facts

Metacognitive student learning outcomes in a chemistry course could look as follows: Students will . . . •

evaluate their own abilities and skills in chemistry, formulating a strategy to increase lifelong learning (17) 85

• •

think critically in the design of an experiment and evaluation of the data that arise from the experiment (18). Apply the fundamentals of chemistry to the understanding of themselves and their natural and technological environments (19)

All of the above student learning outcome statements incorporate ways that students will plan, monitor, or evaluate their learning. Learning Environments and Strategies In addition to preparing students to be content experts, it is essential to promote metacognition by creating learning environments that encourage students to plan, monitor, and evaluate their learning on a regular basis. This process also involves using appropriate learning strategies to achieve desired performance and mastery in the topic. Chittleborough, Treagust, and Mocerino (20) showed that course structure, assessments, and opportunity to obtain feedback were some of the factors influencing students’ choice of learning strategies. Just about any teaching method, including lectures, can incorporate cases, problems, projects, or prompts that provide students with opportunities to be metacognitive. Pedagogies such as Problem-Based Learning (PBL), Peer-Led Team Learning (PLTL), Just-in-Time Teaching (JiTT), Calibrated Peer Review (CPR), and Course-Based Undergraduate Research Experiences (CUREs) have demonstrated improved student learning and success in chemistry and other STEM disciplines. These pedagogies, when designed and implemented appropriately, create opportunities for students to practice metacognition. Our goal is not to provide a comprehensive review of evidence-based teaching practices that promote student metacognition, but to discuss the metacognitive elements of a few exemplar pedagogies. Lecture Lecture is one of the oldest and most common teaching methods. It can be useful for conveying knowledge but may not be as effective in engaging students in higher levels of learning (e.g., analysis, synthesis, evaluation) (21). When it is the only method used, students typically have a passive role. However, instructors can provide scaffolding to engage students in metacognition during a lecture. The timing of the lecture may impact how effective it is – if there is a pre-class activity that primes students’ interests or provides needed prior knowledge, lecture may be more effective (22). Incorporating classroom assessment techniques (CATs) into the lecture, such as the ones described below (23), is another way to promote and assess students’metacognitive skills. The following are some of the most common CATs: 1.

Muddiest point: At the end of the lecture or a segment of the lecture, ask students to respond to this question: “What was the muddiest point in this lecture?” Student write their responses and turn it in.This is an easy and effective way to help students self-assess gaps in their learning. 86

2.

3.

4.

Minute paper: A variant of the Muddiest Point, in this activity, students to respond briefly to the following two questions at the end of class: “What was the most important thing you learned during this class?” and “What important question remains unanswered?” Students write their responses on a notecard and hand it in. As they complete this activity, students reflect on what they have learned and what they still need to learn. KWL: The KWL asks students to self-evaluate their learning at the beginning and at the end of instruction on a topic. Students answer two questions in the beginning of the class - what they know (K) about a topic and second what they would like to know (W) about a topic. After learning the concept or topic, students answer the third question, what they have learned (L) about the topic. A KWL chart, as shown in Table 2, can be used to frame these questions. The Pause Procedure: Stop periodically during a lecture to allow students to review their notes to look for anything they don’t understand, compare notes with their neighbor, and ask questions (24).

Table 2. KWL Chart What do you Know about redox reactions?

What Would you like to know about redox reactions?

What have you Learned about redox reactions/

Cook, Kennedy, and McGuire (25) suggested a different approach to integrating metacognition into a large general chemistry lecture course. They reported success with providing a 50-minute lecture on learning strategies to students after the first exam. The strategies they present encourage students to focus on understanding concepts and developing problem-solving skills rather than memorizing facts and formulas. They found that students who attended the learning strategies lecture received a final grade that was a letter grade higher than what students who did not attend the lecture received. Problem-Based Learning (PBL) In PBL (26) students first work in small groups to analyze authentic and complex real-world problems and assess what knowledge and strategies are required to solve the problem. Next, they work independently to set and meet goals to learn the required knowledge and strategies. The students then regroup, apply the new knowledge and strategies collaboratively, reflect on the outcomes and the process, and finally arrive at a possible solution. One of the goals of the PBL pedagogy is to develop self-directed learners (27). Self-directed learning requires students to use metacognitive knowledge, experience, and skills to assess problem requirements, knowledge gaps in terms of content and problem-solving strategies, and plan, monitor, and evaluate their problem-solving approaches to eventually construct a viable solution. The instructor facilitates this problem-solving process by encouraging students to critically reflect on 87

their learning and providing feedback and suggestions only as needed. Several authors (28–30) have shown that PBL promotes self-directed learning and PBL students can assess their learning needs and develop a plan to address those needs better than non-PBL students. Tosun and Senocack (31) demonstrated that PBL improved metacognitive awareness levels and increased positive attitude towards chemistry in students with weak science background knowledge compared to strong science backgrounds. Peer-Led Team Learning (PLTL) PLTL has the potential for improving metacognitive skills because of its underlying rationale of social constructivism. For example, in chemistry, students construct meaning and understanding of chemical principles by debating, defending, arguing, negotiating with each other and building consensus (32). This process involves confronting what is understood and identifying what remains to be understood to solve a problem. PLTL workshops (33, 34) consist of a group of 6-8 students led by an undergraduate student who has recently completed the course, has done well, and demonstrates good communication and leadership skills. The peer leader is neither an “answer giver” nor a substitute lecturer. The job of the peer leaders is to lead the group to answers by asking appropriate questions for which they receive weekly training in the context of the content to be covered in the workshops. Ideally the answer keys to the workshop questions are not provided. Without the answer keys and with the training of the peer leaders to ask questions rather than provide answers, students are forced to confront what they know and do not know or understand in order to solve a problem or comprehend a certain concept. Students have to rely on discussion with each other to arrive at the most defensible answer. This environment is also good for testing one’s own understanding of concepts by explaining to other students. Because a workshop unit is a set of related problems of increasing complexity the leader can be armed with a set of questions to ask the students what they need to know in order to solve the problems. At the end of the workshop a reflective activity may be used to improve metacognition. This activity includes questions such as: Reconsider your original answers in the Topics to Review section. Has your understanding of these topics changed as a result of this workshop? Summarize your modified answers in the space below. This is designed to have students reflect on their change in understanding (35). It is emphasized to the peer-leader that these reflections are particularly important. “Wrong” ideas often do not go away unless they are challenged. Students must realize that they have changed their understanding as a result of their workshop experience to replace the older incorrect thinking with the new, improved comprehension. Undergraduate Research Scientists construct meaning and understanding of scientific concepts through discussion and debate with colleagues at their own and other institutions. They also do this regularly in research group meetings by debating, negotiating, revising their 88

understanding, and building consensus. Conclusions are based on the application of logic, model building and heuristic thought to experimental observations. The process of learning science often stands in sharp contrast to how it is approached in the traditionaln classroom as it seldom incorporates such opportunities for students in STEM courses in a structured way. Many students work in isolation, with little opportunity to discuss ideas with their peers or advanced peers. Doing undergraduate research is one of the best active learning strategies. Participating in undergraduate research in a faculty member’s lab provides undergraduate students opportunities to engage in doing authentic research, which allows them to formulate a research question, design appropriate experiments, and make sense of their results by interacting with a small group of intergenerational researchers at different levels of research experiences (36). There are also numerous opportunities for students to explain what they know to others in the group. These diverse interactions force them to confront the pieces they may be missing in their own understanding of the problem and encourages them to investigate other pieces of data/explanations to fill in the gaps (36). Through this process the students learn to plan, monitor and evaluate their own learning. This intentional self-reflection on one’s own learning includes examining their perception of undergraduate research before and after and how they will use this experience moving forward (6). We recommend that this approach be applied whether the students engage in undergraduate research in a faculty member’s research lab or through a Course-Based Undergraduate Research Experience (CURE) program (37). CUREs are an excellent way of involving large numbers of students in an activity that is already proven to be beneficial to the student in STEM courses. CUREs typically involve having an entire class of students address an authentic research question or problem and therefore extend the learning benefits of doing undergraduate research to the entire class, not just a few students. Just-in-Time Teaching (JiTT) The JiTT pedagogy involves giving students web-based, pre-instruction assignments and uses those to pique students’ interest in the topic and curiosity about the answers. Instructors use insights about students’ understanding of a topic gleaned from their responses to the assignment to shape what happens during class time. Among other impacts, JiTT encourages students to self-reflect on the learning and teaching process (38). Oftentimes, the questions are focused on students’ comprehension of content, but metacognitive questions can be included to prompt student reflection about their learning and to provide the instructor with an opening to discuss approaches to studying and learning in class. For example, Draeger (39), in addition to content-related questions, included one Likert-style question in the pre-class assignment to determine how confident students were in their knowledge of a topic and a short answer question to encourage them to be metacognitive about their learning process (e.g., What was your reading strategy for this reading? What was your strategy for relating the current reading to the previous?) Other pre-instruction assignment questions that get at metacognition are: 89

• • • • •

What questions do you already have about this problem that you want to find out more about in class? How long did you spend working this problem? Was that a sufficient amount of time? What knowledge were you lacking? To what extent did you successfully accomplish the goals of the assignment? What were the strengths and flaws in your work?

Similar questions for promoting student metacognition can be found in Tanner (40). By combining questions about students’ understanding of material with questions about their confidence in their understanding of material Draeger (39) was able to look at trends to see whether students’ understanding of the material overestimated, underestimated, or matched their understanding of the material. By including questions in JiTT assignments that ask students to reflect on learning gaps revealed from pre-instruction assignments, students are encouraged to become self-directed, reflective learners. Instructors can also benefit from this process as it will provide them with opportunities to reflect on their teaching and how they can best respond to enhance students’ learning. Calibrated Peer Review (CPR) CPR is an example of a writing-to-learn strategy which has demonstrated improved student learning in chemistry and other STEM disciplines. CPR consists of three main steps. First, students write short essays on a topic in response to guiding questions that not only fosters critical thinking about the the topic but also supports the organization of their thoughts on crafting the essay response (41). Second, students review three calibrated exemplar essays and three anonymized peer essays using a grading rubric. Through this process, they learn to evaluate their own and their peers’ quality of writing and understanding of the topic. Finally, they review their own essay using the grading rubric and apply the lessons learned from reviewing the six essays in the second step.The peer and self-assessment process thus builds a strong metacognitive component into the learning process (42). Rickey and Stacey (6) have discussed examples of other instructional strategies, including peer instruction and lab experiences in chemistry, that enable students to self-assess gaps in their learning, confront and clarify misconceptions, and develop a deeper understanding of the content, thereby becoming more metacognitive about their learning.

What Strategies Are Available To Assess Students’ Metacognitive Development? If student learning outcomes include metacognitive elements, then it is essential to assess those metacognitive elements. In this section, we present a 90

brief summary of qualitative and quantitative assessment techniques and tools that are recommended to assess student metacognition. Some approaches are direct measures of students achievement of self-regulated learning that require the student to demonstrate metacognitive skills of planning, monitoring, or evaluating their own learning. Others are indirect measures that ask students their opinions or self-assessments related to their own metacognitive skills. Classroom Assessment Techniques (CATs) Angelo and Cross (23) in their classic handbook on Classroom Assessment Techniques describe techniques for assessing course-related learning and study skills, strategies, and behaviors including: 1.

2.

3.

Productive Study-Time Logs: Students keep records of how much time they spend studying for a particular class, when they study, and how productively they study at various times of the day or night ((23), p. 300) Punctuated Lectures: Students begin by listening to a lecture or demonstration. After part of the presentation has been completed, the instructor stops. Then, students reflect on what they were doing during the presentation and how their behavior while listening may have helped or hindered their understanding of that information. They then write down any insights they have gained. Finally, they give feedback to the teacher in the form of short, anonymous notes. ((23), p. 303) Process Analysis: Students focus on the process they use to do their academic work. They keep records of the actual steps they take in carrying out a representative assignment and comment on the conclusions they draw about their approaches to that assignment. ((23), p.307)

Learning Assessment Techniques Barkley and Major (16) described Learning Assessment Techniques designed to gather direct evidence of student achievement in the domain of learning how to learn such as: 1.

2.

Study Outlines: Provide students with a structure that guides them through synthesizing and organizing course information to help them prepare for an upcoming test. As part of the process they will be using key metacognitive skills such as setting goals. Instructors let students know that they will be collecting and assessing their outlines and review students’ outlines to assess how beneficial they appear to be to assist in studying for an exam. ((16), p. 364) Student Generated Rubrics: Instructor provides students with examples of outstanding disciplinary-based products such as a research paper or scientific lab report, which students analyze to determine the common characteristics and develop an assessment rubric. They then apply the rubric to test the rubric viability. This activity requires students to monitor their own progress and self-assess their performance. ((16), p. 370) 91

3.

4.

Invent the Quiz: Students write a limited number of test questions related to a recent learning unit and then create an answer sheet, model answer, or scoring sheet to accompany the test questions. The instructor uses a checklist to assess question relevance, difficulty and clarity.This technique requires students to practice anticipating and preparing for tests and monitor their learning. It allows faculty to determine how well students can understand and summarize content. ((16), p. 376) Learning Goal Listing: Students generate and prioritize a list of their learning goals at the beginning of the academic term, a unit of study, or a specific learning activity. If time permits, students can estimate the relative difficult of achieving these learning goals. Instructors will learn what students understand about the unit they are about to cover and can assess the relevance or importance of the goals students identify. ((16), p. 382)

Post-Exam Reflection Questions (Wrappers) Even though mid-term exams are intended to provide formative feedback to students and implicitly trigger self-reflection of study strategies, knowledge gaps in the content and problem-solving approaches, and motivate them to plan for improved learning, it is not always the case. Lovett (10) proposed a practical strategy to improve students’ metacognition through a post-exam reflection activity that students can complete after every mid-term to practice metacognitive skills. Multiple studies have shown that this technique, when used regularly in a single or multiple courses in a program, large and small classes, has improved students’ metacognitive skills consistently with moderate changes in student test performance (10, 43–45). The post-exam reflection activity, also known as exam wrappers, consists of completing a short questionnaire immediately when a graded exam is returned. Instructors can choose to give extra-credit for completing the wrappers. The questions focus on how students prepared for the exam, what errors they made in the exam, why they made those errors, and how they plan to overcome their errors while preparing for subsequent exams. The completed wrappers are collected by the instructor and reviewed to identify patterns in student’s study strategies and assess general metacognitive abilities. Prior to the next exam, students review their wrapper responses to the previous exam and engage in a whole-class discussion where the instructor provides feedback from her analysis of the student responses. The instructor further motivates students to implement their plans and encourages them to share effective study strategies. This discussion segment is imperative to ensure that students are reminded of their metacognitive analyses of the previous exam, have the opportunity to reflect on it as a group, and be motivated to implement new and improved study strategies for the upcoming exam. We close this section with a list of validated instruments that have been used to objectively measure various elements of metacognition (knowledge, regulation, and experience). 92

1.

2.

3.

Schraw and Denison’s (46) Metacognitive Awareness Inventory (MAI), a 52-item self-reported scale, was designed to measure metacognitive knowledge and regulation.The Metacognitive Awareness Inventory for Teachers (MAIT) (47) is a modification of the MAI that was developed to assess instructor metacognitive knowledge and regulation. Jiang, Ma, and Gao (47) designed the Teacher Metacognition Inventory (TMI), a validated 53-item self-reported scale that integrates the measurement of metacognitive experiences with metacognitive knowledge and regulation. Sandi-Urena and Cooper’s (48) Metacognitive Activties Inventory (MCAI), a 27 item self-report scale, was designed to allow faculty to assess students’ perceptions of their chemistry problem-solving skills and the activities they undertake to solve problems. We also refer to two other popular instruments that do not measure metacognition directly, but rely on measuring indicators of metacognitive development such as study strategies - Learing Assessment and Study Strategies Inventory (LASSI) (49) and the Motivated Strategies for Learning Questionnaire (MSLQ) (50). Other instruments used to assess students’ metacognition and study strategies include (51, 52)

Despite the existence and ease of use of self-reported instruments, it is to be noted that given the complex nature of metacognition and its conceptual elements, there is no conclusive evidence on the acceptable validity of these instruments (53, 54). These instruments in conjunction with other indicators such as student learning outcomes of metacognition-infused instruction could serve to assess student metacognition.

What Can Faculty Do To Be Intentionally Metacognitive about Their Teaching? How Can Faculty Developers Assist in That Process? Tanner (40) defines instructor metacognition as “thinking about how they think about their teaching”. She suggests that instructors engage in metacognition when conducting disciplinary research, but this approach does not appear to be a natural choice in their teaching practices. Just as metacognition is considered teachable to students, as faculty development professionals, we believe that instructor metacognition can be scaffolded successfully into faculty development activities with the goal of creating metacognitive instructors and students. Faculty development efforts that focus on ways for faculty to identify and build on their teaching strengths and to recognize and develop areas of weakness will support instructor’s metacognitive development (2). Short- and longer-term faculty development programming such as workshops, webinars, course design institutes, faculty learning communities, etc., are activity-based, usually with the goal of facilitating the development of an instructional and/or assessment product (e.g, a syllabus, assignment prompts, course map, collaborative student activities, etc.) that faculty can use in their 93

classes. As faculty members engage in these activities and co-construct products in collaboration with their peers and the faculty development facilitator, they are thinking critically and deeply about their own teaching practices and their students’ learning (See Table 1 for examples of behaviors and characterstics of components of faculty metacognition). According to Akerlind (55), faculty progress from being more teachercentered to more learner-centered as they develop their teaching (See Table 3). As they become more learner-centered, they become more interested in planning, monitoring, and evaluating what works and what doesn’t work for their students, and, therefore, become more metacognitive about their teaching. Keeping this in mind, faculty development programs need to consider where faculty are in their teaching development when planning professional development opportunities for them. Faculty who are confident in their content knowledge and have some teaching experience may be more receptive to learning how to become more student-centered and metacognitive in their teaching than those faculty who are just beginning to teach and feel the overwhelming need to build content expertise that allows them to be seen as expert transmitters of knowledge in the classroom. Drawing a parallel to this experience, in Table 3, we show how metacognitive elements could be incorporated into faculty development strategies and offerings that are aligned with each stage of faculty members’ teaching development. These faculty development strategies and offereing could be drawn from departments, teaching centers, professional organizations within the discipline, etc. Author Pratibha Varma-Nelson, one of the co-developers of PLTL, describes how she gave up control of her class and let her students take more control of their learning. “My first teaching responsibility as an assistant professor at a liberal arts college included teaching a two-semester sequence of chemistry courses to primarily pre-nursing students. This sequence is called GOB in the chemistry education community, which stands for a course that includes an overview of general, organic and biochemistry (GOB). This is a difficult course to teach because it covers many topics in a short time and should be assigned to someone who understands what being student-centered means and practices it as these students benefit from pedagogies that are more collaborative in nature. I was like many of the newly minted PhDs of that era who had no formal training in how to teach and I was handed a textbook and a syllabus and placed in a classroom. Needless to say, I did what was “done” to me. I was the proverbial “sage on the stage” who aspired to have an answer to every question asked of me. My goal was to impart knowledge and believed that my students’ goal was to inhale ever word of wisdom that came out of my mouth - learn it all and spit it out on the exams. Was I metacognitive about my teaching? I was focused on my own content knowledge, how good my transparencies looked, how much material I “covered”, and how well I controlled student behavior in the class. I made myself available by having office hours but not many students came until it was exam time. After a disastrous first semester, it is fair to 94

say I was focused on my own improvement and sought advice from my colleagues. Most of the advice I received led me to improve my delivery of content. My students rewarded me for this over the years by giving me increasingly better student evaluations. Better student evaluations to me meant I was a better teacher and was granted tenure. However, as my student evaluations went up the average grades of my students didn’t. Their success rate in my class judged by their grades was almost the same as when I started. This was very puzzling. And of course, I explained it by saying the “students are weaker these days”! Sound familiar? In my heart I knew something wasn’t right so I started to attend faculty development workshops and began experimenting with different ways of teaching that focused more on student learning and less on my own performance. These pursuits led to my becoming a leading contributor to the development of widely used Peer-Led Team Learning (PLTL) (56) and its online version cyberPLTL (cPLTL) (57) and executive director of the Center for Teaching and Learning at IUPUI (2008-2016). What had changed? I became more student-centered. My reflections about my teaching were now focused on whether the students learned, which questions they missed on quizzes and exams and why. I asked them which strategies worked for their learning and which didn’t. I did more formative assessments and mid-term evaluations and thus became more metacognitive about my teaching in terms of student learning. I worried more about what worked from students’ perspective. In addition to standardized course evaluation forms, I created customized evaluation questions that helped me get feedback on what worked for my students in the course. I also introduced PLTL workshops in the GOB sequence as a mandatory component. PLTL workshops are ideal for students to monitor their own learning through arguing, defending, debating and persuading each other (34). Not only did I become more metacognitive about my teaching, I helped my students become more metacognitive about their learning.” Tanner (40) provides examples of questions that faculty can ask themselves at the end of a class session or a course with the goal of planning, monitoring, and evaluating their own teaching practices and their impact on student learning. As faculty developers, we can go one step further and design individual and group self-reflection activities that encourage our participating faculty to articulate their decisions and the associated reasons for modifying their instructional and assessment practices, predict outcomes from the modifications, and their overall cognitive strategies (changes in course content, goals for students’ learning, instructional design strategies, etc.), and affective (changes in confidence, motivation, and beliefs) experiences with this process. This can give them a framework, similar to Tanner’s self-questions, for being metacognitive about their teaching. Constructing teaching philosophy statements (58, 59), teaching portfolios (60), and pursuing the scholarship of teaching and learning are other 95

instances where faculty engage in varying levels of metacognition about their teaching.

Table 3. Aligning Metacognition-Focused Faculty Development with the Evolution of Teaching Development (adapted from Akerlind (55))

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Summary Just as students need to be lifelong learners, so do faculty. Faculty often find themselves needing to adapt their teaching to respond to disciplinary changes and discoveries, different student populations, new pedagogies, or changing institutional initiatives and constraints. Strong metacognitive skills can help them efficiently and effectively make the needed changes in their teaching while still promoting student learning. We recognize that faculty often implicitly engage in the process of planning, monitoring, and evaluating their teaching practices. However, this chapter provides strategies for being intentionally metacognitive about teaching and in turn preparing students to be lifelong learners.

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Editor’s Biography Patrick L. Daubenmire Patrick Daubenmire is an Associate Professor in the Department of Chemistry and Biochemistry at Loyola University Chicago. There he acts as the Undergraduate Program Director in the Department and leads a chemistry education research group that has pursued a variety of topics and projects, among them a focus on metacognition in the laboratory. He has been an active member of the American Chemical Society for 26 years. He has served on the Division of Chemical Education Program Committee and was elected in 2017 as an Alternate Councilor in the Division.

© 2017 American Chemical Society

Indexes

Author Index Browning, L., 17 Caccamise, D., 31 Daubenmire, P., ix, 43 Kelly, R., 55 Littrell-Baez, M., 31 Miller, M., 17 Miller, T., 1

Rao, A., 81 Tarr, T., 81 van Opstal, M., 43 Varma-Nelson, P., 81 Vestal, S., 17 Wang, J., 55

105

Subject Index C

redox reaction, screenshot, 59f metacognition, role, 65 after-reaction representations, 67f collaboration and reaching consensus, 70 deliberation over accurate animations, 77 before the reaction representation, 66f during the reaction representation, 66f S7’s after reaction representation, 69f S12’s after reaction representation, 75f S12’s before reaction representation, 74f S11’s before the reaction representation, 72f S7’s drawn representation before the reaction occurs, 68f S11’s during reaction representation, 73f S7’s during the reaction representation, 68f S12’s during the reaction representation, 74f single replacement reaction, 76 metacognitive monitoring activities, current use, 59 interview session, graphic illustration, 60f research question animation 1 and animation 3, still images, 62f comparing understanding to another student, 61 four contrasting animations, screenshot, 63f participants and data collection, 60 scientifically inaccurate animation 2, still image, 62f scientifically inaccurate animation 4, still image, 63f results and discussion, 64 individual and group animation selection, summary, 64t role of metacognition, 56

Chemistry instruction, cognitive perspective, 31 comprehension, role of metacognitive processes, 32 metacognition and literacy, theoretical perspectives, 34 metacognitive instruction in chemistry, model, 36 after reading, 38 chemistry instructors, 37 before reading, 37 during reading, 37 metacognitive skills and knowledge, 33 components of metacognition, model, 33f situation model approach, 35

M Metacognition, measurement, theory, and current issues, 1 accurate monitoring and control, benefits, 6 early modern research and theory, 3 education and metacognition, 10 introduction, 2 metacognition, biases, 7 metacognition, biological bases, 8 training metacognition, 9 Metacognition, scientific process element of inquiry, metacognition, 45 inquiry approaches to instruction, 46 metacognition in the laboratory, 45 metacognitive skill practice while writing, 49 science writing heuristic template, 49t metacognitive skills, 48 metacognitive skills, working with peers, 47 recognizing and eliciting students’ metacognitive skills, 50 scientific process, 44 scientific method, 44f Molecular visualizations cartoon tutorials, role of metacognition, 57 contrasting animations, role of metacognition, 58

P Promoting metacognitive practices, 81 chemistry learning, 85 just-in-time teaching (JiTT), 89

107

KWL chart, 87t peer-led team learning (PLTL), 88 faculty developers, 93 metacognition-focused faculty development, 96t metacognitive development, 90 learning assessment techniques, 91 post-exam reflection questions (wrappers), 92

S STEM disciplines, metacognition biology combined pedagogical approaches, 19

108

metacognitive awareness, 18 technology, 20 writing methods, 21 introduction, 17 CCSSM standards, 18t mathematics, 21 formative assessments, 24 interventions, 22 mathematical discussions, 23 metacognition and mathematics achievement, 22 physics, 25 general problem solving processes, 25t physics, metacognitive strategies, 26