Understanding Girls: Quantitative and Qualitative Research 9463004955, 9789463004954

Table of contents :
TABLE OF CONTENTS
INTRODUCTION
CHAPTER 1: CAN THE DIFFERENCES BETWEEN MALE AND FEMALE SCIENCE MAJORS ACCOUNT FOR THE LOW NUMBER OF WOMEN AT THE DOCTORAL LEVEL IN SCIENCE?
WHY I CONDUCTED THE STUDY
METHODOLOGICAL DECISIONS
SCIENCE EDUCATION AT THE TIME OF THE STUDY
RESEARCH INTO GENDER IN THE WIDER FIELD OF EDUCATION
THE CULTURE AND THE TIMES
IMPACT OF MY WORK
RESEARCH IN COLLEGE SCIENCE TEACHING: Can the Difference between Male and Female Science Majors Account
for the Low Number of Women at the Doctoral Level in Science?
ABSTRACT
SEX DIFFERENCES
DIFFERENCES ACROSS MAJORS
SEX DIFFERENCES WITHIN MAJORS
DIFFERENCES AMONG THE FOUR GROUPS
IMPLICATIONS
REFERENCES
NOTES
CHAPTER 2: THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE CONFLICT ON CAREER CHOICES IN SCIENCE
WHY I CONDUCTED THE STUDY
METHODOLOGICAL DECISIONS
SCIENCE EDUCATION AT THE TIME OF THE STUDY
RESEARCH IN THE WIDER FIELD OF EDUCATION
THE CULTURE OF THE TIMES
IMPACT OF MY WORK
THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE IDENTITY ON CAREER CHOICES IN SCIENCE
ABSTRACT
INTRODUCTION
HYPOTHESES
PROCEDURE
Sample
Instruments
Analysis
RESULTS
Analysis of Variance
Discriminant Analysis
DISCUSSION
NOTE
REFERENCES
CHAPTER 3: SEX DIFFERENCES IN CLASSROOM
INTERACTIONS IN SECONDARY SCIENCE
WHY I CONDUCTED THE STUDY
METHODOLOGICAL DECISIONS
SCIENCE EDUCATION AT THE TIME OF THE STUDY
RESEARCH IN THE WIDER FIELD OF EDUCATION
THE CULTURE OF THE TIMES
IMPACT OF MY WORK
SEX DIFFERENCES IN CLASSROOM INTERACTIONS IN SECONDARY SCIENCE
ABSTRACT
OBJECTIVES
PROCEDURE
Sample
Data Collection
Instrument
Classroom Formats
RESULTS
CONCLUSIONS
NOTE
REFERENCES
CHAPTER 4: SEX DIFFERENCES IN FORMAL REASONING ABILITY:
Task and Interviewer Effects
WHY I CONDUCTED THE STUDY
METHODOLOGICAL DECISIONS
SCIENCE EDUCATION AT THE TIME OF THE STUDY
RESEARCH IN THE WIDER FIELD OF EDUCATION
THE CULTURE OF THE TIMES
THE CULTURE OF THE TIMES
IMPACT OF MY WORK
SEX DIFFERENCES IN FORMAL REASONING ABILITY: TASK AND INTERVIEWER EFFECTS
INTRODUCTION
Logical Reasoning and Success in Science
Effect of Task Content, Task Format, and Sex of Interviewer
STATEMENT OF THE PROBLEM
METHOD
RESULTS
DISCUSSION
NOTE
REFERENCES
CHAPTER 5:
LETTING GIRLS SPEAK OUT ABOUT SCIENCE
WHY I CONDUCTED THIS STUDY
METHODOLOGICAL DECISIONS
SCIENCE EDUCATION AT THE TIME OF THE STUDY
RESEARCH INTO GENDER IN THE WIDER FIELD OF EDUCATION
THE CULTURE AND THE TIMES
IMPACT OF MY WORK
LETTING GIRLS SPEAK OUT ABOUT SCIENCE
ABSTRACT
METHODOLOGICAL ISSUES
WOMEN’S DECISION MAKING
SCHOOL INFLUENCES
SOCIAL INFLUENCES
METHOD
RESULTS
SCHOOL SCIENCE
SOCIETAL FACTORS
Summary
EQUITY
Summary
CONCLUSIONS
NOTE
REFERENCES
CHAPTER 6:
EQUITY ISSUES IN SCIENCE EDUCATION
WHY I CONDUCTED THE STUDY
METHODOLOGICAL DECISIONS
SCIENCE EDUCATION AT THE TIME OF THE STUDY
RESEARCH INTO GENDER IN THE WIDER FIELD OF EDUCATION
THE CULTURE OF THE TIMES
IMPACT OF MY WORK
EQUITY ISSUES IN SCIENCE EDUCATION
HISTORICAL CONTEXT OF THE PROBLEM
Women
Minorities
NUMBER OF WOMEN AND MINORITIES IN SCIENCE
School Opportunities and Participation Rates
University and Career Participation Rates
Achievement by Gender
Achievement by Country and Ethnicity
Summary and Implications: Women and Minorities
INFLUENCE OF SCHOOLS
Achievement and the Influence of Schools
Gender
Ethnicity
International Data
Curriculum
Opportunities to Learn
Summary and Implications: Influence of Schools
INFLUENCE OF THE HOME
Socioeconomic Factors
Parental Attitudes
Summary and Implications: Influence of the Home
SOCIOCULTURAL BARRIERS
Gender Roles
Cultural Dissonance
Culture and Interaction Patterns
Summary and Implications: Sociocultural Barriers
NATURE OF SCIENCE
Science as a Male Domain
Science as a White European Domain
How Women do Science
Attitudes Towards Science
Summary and Implications: Nature of Science
INTERVENTIONS
Strategies for Gender Equity
Strategies for Cultural Equity
Materials
Policy and Program Guidelines
Strategies for the University Level
Summary and Implications: Intervention
CONCLUSION
NOTE
REFERENCES
CHAPTER 7:
AN INTERVENTION TO ADDRESS GENDER ISSUES IN A COURSE ON DESIGN, ENGINEERING, AND TECHNOLOGY FOR SCIENCE EDUCATORS
WHY I CONDUCTED THE STUDY
METHODOLOGICAL DECISIONS
SCIENCE EDUCATION AT THE TIME OF THE STUDY
RESEARCH IN THE WIDER FIELD OF EDUCATION
THE CULTURE OF THE TIMES
IMPACT OF MY WORK
AN INTERVENTION TO ADDRESS GENDER ISSUES IN A COURSE ON DESIGN, ENGINEERING, AND TECHNOLOGY FOR SCIENCE EDUCATORS
ABSTRACT
I. INTRODUCTION
II. REVIEW OF THE LITERATURE
A. Gender-Based Interests and Attitudes in K-12 Science Classrooms
B. Psychosocial Factors in Gender Issues in Science and Engineering Classrooms
C. Teachers and Design, Engineering and Technology
III. RESEARCH QUESTIONS
A. The Bridging Engineering and Education Course
B. Participants
C. Data Sources and Analysis
V. RESULTS AND DISCUSSION
A. Tinkering Self-efficacy
B. Technical Self-efficacy
C. Societal Relevance of Engineering
D. Team-member Interactions
VI. CONCLUSIONS AND IMPLICATIONS
A. Conclusions
B. Implications for Practice and Research
ACKNOWLEDGMENT
NOTE
REFERENCES
CHAPTER 8: WHAT WORKS: Using Curriculum and Pedagogy to Increase Girls’ Interest
and Participation in Science and Engineering
WHY I CONDUCTED THE STUDY
METHODOLOGICAL DECISIONS
SCIENCE EDUCATION AT THE TIME OF THE STUDY
RESEARCH IN THE WIDER FIELD OF EDUCATION
THE CULTURE OF THE TIMES
IMPACT OF MY WORK
WHAT WORKS: USING CURRICULUM AND PEDAGOGY TO INCREASE GIRLS’ INTEREST AND PARTICIPATION IN SCIENCE
STRATEGIES TO IMPROVE ACHIEVEMENT
STRATEGIES TO ENHANCE CONFIDENCE, COMPETENCE,
AND SELF-EFFICACY
STRATEGIES TO INCREASE PARTICIPATION
STRATEGIES FOR EFFECTIVE USE OF COMPUTERS
SPECIAL CONSIDERATION FOR YOUNG SCIENCE LEARNERS
A SUMMARY OF CURRICULUM AND PEDAGOGY THAT WORKS FOR GIRLS
IMPLICATIONS FOR EDUCATIONAL POLICIES
NOTE
REFERENCES
CHAPTER 9: GIRLS’ SUMMER LAB:
An Intervention
SYNOPSIS
WHY I CONDUCTED THE STUDY
METHODOLOGICAL DECISIONS
SCIENCE EDUCATION AT THE TIME OF THE STUDY
RESEARCH IN THE WIDER FIELD OF EDUCATION
THE CULTURE OF THE TIMES
IMPACT OF MY WORK
CHAPTER 10: GOOD INTENTIONS: An Experiment in Middle School Single-Sex Science and Mathematics Classrooms with High Minority Enrollment
STUDY SYNOPSIS
WHY I CONDUCTED THE STUDY
METHODOLOGICAL DECISIONS
SCIENCE EDUCATION AT THE TIME OF THE STUDY
RESEARCH IN THE WIDER FIELD OF EDUCATION
THE CULTURE OF THE TIMES
IMPACT OF MY WORK
CHAPTER 11: SUMMARY:
What Does It All Mean?
REFERENCES

Citation preview

Understanding Girls

CULTURAL AND HISTORICAL PERSPECTIVES ON SCIENCE ­EDUCATION:

DISTINGUISHED CONTRIBUTORS Volume 6 Series Editors

Catherine Milne, New York University, USA Kathryn Scantlebury, University of Delaware, USA Cultural and Historical Perspectives on Science Education: Distinguished ­Contributors features a profile of scholarly products selected from across the career of an outstanding science education researcher. Although there are several variants in regard to what is included in the volumes of the series the most basic form consists of republication of 8-10 of the scholar’s most significant publications along with a critical review and commentary of these pieces in terms of the field at the time of doing the work, the theories underpinning the research and the methods employed, and the extent to which the work made an impact in science education and beyond. Another genre of Key Works republishes the most influential research in a selected area of interest to science educators. Examples of the areas we will feature include science teacher education, science teaching, language in science, equity, the social nature of scientific knowledge, and conceptions and conceptual change. ­Collections of articles are placed in an historical context and the rationale for changing perspectives is provided and analyzed in relation to advances and changing priorities in science education. Each volume shows how individuals shaped and were shaped by the cultural context of science education, including its historical unfolding.

Understanding Girls Quantitative and Qualitative Research

Dale Rose Baker Arizona State University, USA

A C.I.P. record for this book is available from the Library of Congress. ISBN: 978-94-6300-495-4 (paperback) ISBN: 978-94-6300-496-1 (hardback) ISBN: 978-94-6300-497-8 (e-book) Published by: Sense Publishers, P.O. Box 21858, 3001 AW Rotterdam, The Netherlands https://www.sensepublishers.com/ All chapters in this book have undergone peer review. The following book chapters are reprinted here with permission from the publishers: Chapter 1: Baker, D. (1983). Can the differences between male and female science majors account for the low number of women at the doctoral level in science? Journal of College Science Teaching, 13, 102–107. Chapter 2: Baker, D. (1989). The influence of role-specific self-concept and sex-role identity on career choices in science. Journal of Research in Science Teaching, 24(8), 739–756. Chapter 3: Baker, D. (1987). Sex differences in classroom interactions in secondary science. The Journal of Classroom Interaction, 22, 6–12. Chapter 4: Piburn, M. D., & Baker, D. R. (1989). Sex differences in formal reasoning ability: Task and interviewer effects. Science Education, 73, 101–113. Chapter 5: Baker, D., & Leary, R. (1995). Letting girls speak out about science. Journal of Research in Science Teaching, 32(1), 3–27. Chapter 6: Baker, D. (1998). Equity issues in science education. In B. Fraser & K. Tobin (Eds.), International Handbook of Research in Science Education (pp. 869–896). Amsterdam: Kluwer. Chapter 7: Baker, D., Krause, S., Yasar, S., Roberts, C., & Robinson-Kurpius, S. (2007). An intervention to address gender issues in a course on design, engineering and technology for science educators. Journal of Engineering Education, 96, 213–226. Chapter 8: Baker, D. (2013). What works: Using curriculum and pedagogy to increase girls’ interest and participation in science. Special Issue Theory into Practice, 52, 14–20. Printed on acid-free paper All Rights Reserved © 2016 Sense Publishers No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work.

This book is dedicated to my husband Dr. Michael Dee Piburn. His love and support have made my career possible.

TABLE OF CONTENTS

Introduction

xi

Chapter 1: Can the Differences between Male and Female Science Majors Account for the Low Number of Women at the Doctoral Level in Science?

1

Why I Conducted the Study Methodological Decisions Science Education at the Time of the Study Research into Gender in the Wider Field of Education The Culture and the Times Impact of My Work Research in College Science Teaching: Can the Difference between Male and Female Science Majors Account for the Low Number of Women at the Doctoral Level in Science?, by Dale R. Baker (reprinted article) Chapter 2: The Influence of Role-Specific Self-Concept and Sex-Role Conflict on Career Choices in Science

1 2 3 4 6 7 9 23

Why I Conducted the Study Methodological Decisions Science Education at the Time of the Study Research in the Wider Field of Education The Culture of the Times Impact of My Work The Influence of Role-Specific Self-Concept and Sex-Role Identity on Career Choices in Science, by Dale R. Baker (reprinted article)

31

Chapter 3: Sex Differences in Classroom Interactions in Secondary Science

51

Why I Conducted the Study Methodological Decisions Science Education at the Time of the Study Research in the Wider Field of Education The Culture of the Times Impact of My Work Sex Differences in Classroom Interactions in Secondary Science, by Dale R. Baker (reprinted article)

vii

23 23 25 26 28 29

51 51 52 53 54 54 56

TABLE OF CONTENTS

Chapter 4: Sex Differences in Formal Reasoning Ability: Task and Interviewer Effects Why I Conducted the Study Methodological Decisions Science Education at the Time of the Study Research in the Wider Field of Education The Culture of the Times Impact of My Work Sex Differences in Formal Reasoning Ability: Task and Interviewer Effects, by Michael D. Piburn and Dale R. Baker (reprinted article) Chapter 5: Letting Girls Speak Out about Science Why I Conducted This Study Methodological Decisions Science Education at the Time of the Study Research into Gender in the Wider Field of Education The Culture and the Times Impact of My Work Letting Girls Speak Out about Science, by Dale Baker and Rosemary Leary (reprinted article) Chapter 6: Equity Issues in Science Education Why I Conducted the Study Methodological Decisions Science Education at the Time of the Study Research into Gender in the Wider Field of Education The Culture of the Times Impact of My Work Equity Issues in Science Education, by Dale R. Baker (reprinted article) Chapter 7: An Intervention to Address Gender Issues in a Course on Design, Engineering, and Technology for Science Educators Why I Conducted the Study Methodological Decisions Science Education at the Time of the Study Research in the Wider Field of Education The Culture of the Times

viii

69 69 70 71 72 73 74 76 89 89 89 90 92 92 93 94 127 127 127 128 129 131 132 134 161 161 162 162 164 166

TABLE OF CONTENTS

Impact of My Work An Intervention to Address Gender Issues in a Course on Design, Engineering, and Technology for Science Educators, by Dale Baker, Stephen Krause, Şenay Yaşar, Chell Roberts and Sharon Robinson-Kurpius (reprinted article)

168

Chapter 8: What Works: Using Curriculum and Pedagogy to Increase Girls’ Interest and Participation in Science and Engineering

197

Why I Conducted the Study Methodological Decisions Science Education at the Time of the Study Research in the Wider Field of Education The Culture of the Times Impact of My Work What Works: Using Curriculum and Pedagogy to Increase Girls’ Interest and Participation in Science, by Dale Baker (reprinted article)

167

197 197 198 199 200 202 203

Chapter 9: Girls’ Summer Lab: An Intervention

213

Synopsis Why I Conducted the Study Methodological Decisions Science Education at the Time of the Study Research in the Wider Field of Education The Culture of the Times Impact of My Work

213 214 214 215 216 216 218

Chapter 10: Good Intentions: An Experiment in Middle School Single-Sex Science and Mathematics Classrooms with High Minority Enrollment

219

Study Synopsis Why I Conducted the Study Methodological Decisions Science Education at the Time of the Study Research in the Wider Field of Education The Culture of the Times Impact of My Work

219 220 221 222 223 224 225

Chapter 11: Summary: What Does It All Mean?

227

References

231

ix

INTRODUCTION

The articles reprinted in this volume represent my career long interest in doing research on a topic that is rooted in my own experiences as a women interested in science. Some of those experiences were negative, some were positive, and some were puzzling. Some experiences reflected the social expectations for women in the 1950s and 1960s when my interest in science was developing. Other experiences were rooted in the open classroom movement of the 1970s during which I discovered that girls responded positively to science when it was interactive and aligned with topics that were relevant and interesting. My experiences led to many questions for which I had no answers. Every college bound student in my high school studied science so I wondered why there were so few women in my science classes at the university. When I began to teach, I didn’t understand why many elementary teachers avoided teaching science, when I  loved it. As I considered graduate school I was faced with trying to understand how  my past experiences led me to make the career choice that I did; science education rather than pure science. Studying girls and women in science was personal. It was an effort to make sense of my own experiences and those of many other girls and women and to answer the questions these experiences raised. I was driven to investigate what influenced women’s career choices. I wanted to know if there really were male and female differences in attitudes toward science. I wanted to explain the male female differences in rates of participation in science. I wanted to make sense of the conflicting data of girls’ achievement in science. And finally, I wondered if anything could be done about increasing the participation of women in science. I struggled with biological, psychological, and sociocultural theories to frame my research. Each study I conducted or read made me reconsider the relative influence of biology, psychology, and sociocultural factors on women and girls in relation to their participation in science. My struggles were fueled by the implications derived from these theories. I was uncomfortable with biology as destiny, but I was equally disturbed by the thought that girls were being socialized in ways that limited their choices. Even more daunting was the thought that biology, psychology, society, and culture all played a part in explaining attitudes, choices, achievement, and rates of participation of girls and women in science. How would I ever sort this out? Over the course of my career I have employed a variety of analytical techniques to make sense of data. Despite a degree in anthropology and experience doing fieldwork, my early science education research was quantitative. Anthropological fieldwork was hard work for which I was ill suited as a young researcher. In comparison, I found statistical analysis relatively easy. Furthermore, the questions I was asking early in my career were best answered using quantitative data analysis.

xi

INTRODUCTION

However, as I began to ask different questions it became clear that I would have to use qualitative  techniques to collect and understand my data. Fortunately, by this time, I was confident enough as a scholar to overcome the difficulties I had experienced earlier. At thirty-five plus years into my career, I am neither a proponent of the quantitative or qualitative approach. I am a survivor of the paradigm wars of the 1980s when heated battles were fought over the “positivist evils” of quantitative research and the “lack of rigor” of a qualitative approach. As such, my current position is that of a pragmatist. I use any analytical tool that helps make sense of the data. Although comparing males and females can provide useful insights, I am always mindful that such comparisons run the risk of using a deficit model where the male is the norm and the female is found lacking. Although I have done many comparison studies, my most important breakthrough was when I ignored the data from males. I did not ignore the male data out of a conviction that one should not make male female comparisons. Nor, was I ignoring the male data for strong theoretical reasons. Rather, the reason was simpler. I could not make sense of the data by comparing male and female students, so instead, I put the male data aside and began to read about possible theories to justify this decision. Analyzing only the female interviews using feminist theory was a revelation. Patterns that had been obscured became clear. This led to the publication of my most important work (Baker, D., & Leary, R. (1995). Letting girls speak out about science. Journal of Research in Science Teaching, 1, 3–27), and the most cited. It was selected as among the twelve most influential papers published in the Journal of Research in Science Teaching and reprinted in a special issue celebrating the 40th anniversary of the journal in 2003. I have conducted research in many settings and with students at many grade levels and in countries other than the United States. Some studies have looked at grade level changes from kindergarten through twelfth grade. Others have focused exclusively on high school or middle school students. Some studies have been conducted with university undergraduates and others with graduate students. There have also been studies that focused on teachers. In addition to generating my own original data, I have written reviews of research that attempted to capture what we know about equity issues world-wide and what the research tells us about curriculum and pedagogy to increase girls’ participation in science. The choice of research questions and settings was just as likely to be serendipitous as it was to be planed. A presentation to teachers resulted in an invitation to come study their school’s experiment in single sex science and mathematics classes. Another study was the result of participating in a larger project led by other researchers. A third study arose from a grant funded intervention. Other studies were initiated by me to answer a question that was intriguing. The review articles were invitations from editors. Data gathering tools have been varied and reflect the times in which the study took place. Early work employed paper and pencil assessments to quantify xii

INTRODUCTION

psychological variables such as masculinity, femininity and androgyny; personality, self-efficacy, self-concept, and formal reasoning. Test scores were used to measured mathematical, verbal, and spatial ability. Transitional work turned classroom observations of teacher  student interactions into categorical data for quantitative analysis or used clinical interviews with subjects engaged in solving tasks. Later work used qualitative data gathering tools such as long term classroom observations, interviews, and artifact analysis. After spending many years of doing research on girls and women in science, I began to work with colleagues in engineering. These colleagues were also concerned about rates of female participation as reflected in the low number of women choosing to study engineering as a university major. My engineering colleagues were also concerned that high school students and teachers had little knowledge about what engineers do. They thought that helping teachers understand engineering and teach design, engineering, and technology would increase the number of both males and females choosing engineering as a career long before we had the Next Generation Science Standards. This new line of research was closely aligned with my earlier work. Science courses (biology, chemistry, and physics) as well as advanced mathematics are part of the foundational knowledge needed to be an engineer. In addition, engineering is gender stereotyped as a male domain. The largest number of women can be found in biomedical engineering with a clear role in helping others as opposed to computer engineering. However, the overall number of undergraduate degrees award to women across all fields of engineering has remained at approximately 18% for many years despite efforts to increase this number. So, although my focus has broadened to include engineering, the questions and struggles remain the same. If there is any criticism of my research, it is that it is narrowly focused on girls and women. On the whole, race and ethnicity have not been explored. This limitation does affect generalizability but it also reflects the state of research into gender issues when I began my career. I leave it to the current and future generation of scholars to unravel the knot of intersectionalty. The articles selected for this volume are by no means all I have written. All but one article, represents work that I have contributed the most to as the first author or only author. The work where I am second author (Piburn, M., & Baker, D. (1989). Sex Differences in Formal Reasoning Ability: Task and Interviewer Effects. Science Education, 73, 101–113), is research where both authors contributed equally. This research also won an award. The articles in this volume are mostly organized in chronological order, starting with a publication that was based on my dissertation and ends with a recent review article. This chronological order is deliberate. It provides insight in to my thinking about gender issues over the span of a career. It allows me, in the commentary that follows each article, to place the work in context. The commentary explains the rationale for choosing or abandoning a particular theoretical framework, the strengths and weaknesses of the analytical techniques, and provides me with an opportunity xiii

INTRODUCTION

to evaluate the contribution of the work to science education. Best of all, it allows me to pull back the curtain and reveal the messiness behind conducting the research that is never addressed in the more linear retelling that constitutes a journal article. Sadly, two of my most interesting articles could not be included in this volume. They are Baker, D. (2002). Good Intentions: An Experiment in Single-Sex Science and Mathematics Classrooms with High Minority Enrollment. Journal of Women and Minorities in Science and Engineering, 8, 1–24 and Baker, D., Lindsey, R., & Blair, C. (1999). Girls’ Summer Lab: An Intervention. Journal of Women and Minorities in Science and Engineering, 5, 79–95. The publisher of the Journal of Women and Minorities in Science and Engineering, Begell House, does not allow reprints of articles in their entirety. That being the case, I encourage you to seek them out and read them. I will however, describe them in the next to last chapters of the book and place them in context as I do for the reprinted articles. Both studies resulted from invitations to study the phenomena and have counter intuitive outcomes. I place my work in the context of science education at the time of publication as  well as research in the wider field of education and the culture of the times. As such, I had to make decisions about what to review and search. I examined scholarly books and journal articles published around the time of each article and reliable websites that provided times lines for major cultural events. I chose not to review articles in the Journal of Science Teacher Education. Although this is an excellent journal the narrow focus on pre-service and in-service research on teachers would have skewed the larger picture of what was happening in science education. Furthermore, although I have conducted research in teacher education, the work I present does not address science teacher preparation or professional development. Despite having published in the Journal of College Science Teaching (see Chapter 1), I chose not to include studies from this excellent journal. Most of the work published in the Journal of College Science Teaching is narrowly focused on improving undergraduate college teaching, especially for non majors. It has a strong focus on disseminating ideas that contribute to college science teaching and has a practical orientation. Including studies from this journal would also have skewed the picture of what was happening in the broader field of science education which has by and large conducted less research in undergraduate settings and even less focusing on the college science instructor. I hope you enjoy reading this book as much as I have enjoyed writing the commentary that accompanies the reprinted articles. It was a wonderful journey down memory lane.

xiv

CHAPTER 1

CAN THE DIFFERENCES BETWEEN MALE AND FEMALE SCIENCE MAJORS ACCOUNT FOR THE LOW NUMBER OF WOMEN AT THE DOCTORAL LEVEL IN SCIENCE?

WHY I CONDUCTED THE STUDY

This article comes from my doctoral dissertation. I decided on this study after writing two other dissertation proposals that built on my interest in neurophysiology and my work identifying the aggression center in the brains of cats as well as my interest in studying why there were so few women in science. The first proposal I wrote is best forgotten. The second, required a pilot study in which I built a rear screen projection tachistoscope to measure verbal and spatial processing in the left and right hemispheres of the brain. I intended to use hemispheric differentiation as a way to predict who would choose science as a college major in a population of males and females. The pilot study indicated that this second proposal was not feasible due to measurement issues. After a year of data gathering, I concluded that I could not obtain the level of accuracy and consistency I needed. As a result, I set out to develop the third proposal. The questions I settled on in the third proposal arose partly from my own negative experiences in science and my need to understand these experiences. Why was I told at a college admissions interview that the department did not waste space and resources on women because they would get married? Why was there only one other female student in my undergraduate geology course? Why didn’t I have a single female professor in the sciences? Why were professors reluctant to write me letters of recommendation for graduate school even though I was an excellent student? Why was I getting messages that I did not belong? Certainly, the prevailing view when I embarked on graduate studies in the 1970s was that women did not have the spatial, mathematical, and personality traits that would allow them to be successful in science. I wanted to know if these prevailing views were correct. I knew that it would be impossible to increase the number of women who liked science, excelled at science, and chose science as a career, if we did not know the root causes for male female differences in rates of participation. I was also puzzled by research that found that females had more negative attitudes toward science than males. My own experience as a teacher showed me that females did like science, especially hands-on science. Furthermore, I liked science despite 1

CHAPTER 1

my own negative experiences. Yet, I chose the path of science education rather than lab or field science. Clearly, attitude deserved further investigation. And then there was the question of gender roles and femininity. The work of Helen Austin (1969) indicated that marriage and family were not obstacles to a scientific career for women who received their doctorates in the late 1950s. But what about women who did not choose science? Were they more traditionally feminine? Research  at this time using the Personal Attributes Scale (Spence & Helmreich, 1978) indicated that female scientists perceive themselves as having both masculine and feminine characteristics (46% androgynous) or having masculine (23%) characteristics. This was in contrast to college women in general. Women in engineering were found to be more masculine than women in home economics (Tanico, Hardin, & McLaughlin, 1978) and college women who perceived themselves to be androgynous expected to do better in mathematics and science than women who perceived themselves to be feminine (Brewer & Blum, 1978). Maybe, I thought, it was not ability but how invested in traditional gender roles a woman was that affected her career choice. METHODOLOGICAL DECISIONS

This was a quantitative study. There were three reasons for my methodological choice. First, I wanted a large sample (n = 180) in order to do multiple contrasts of males and females majoring in the physical and biological sciences and nonscience areas such as English. Second, I wanted to see if my selection of variables were predictive of a college major which required a statistical technique called discriminant analysis. I also wanted to know whether there were differences in mean scores for each group which required analysis of variance or differences in the frequency of majors by perceived masculinity, femininity, and androgyny which dictated chi square analysis. Third, except for Piagetian studies, no one in science education at this time was  doing qualitative studies. In fact, I do not recall that a qualitative inquiry course was even offered in my doctoral program. Since the prevailing analytical paradigm was quantitative, it never occurred to me to design a study that would not use statistical analysis. In keeping with the interests and questions that led to the study, I used popular measures of the time. These were the Myers-Briggs Type Indicator (Myers, 1962) a Jungian measure of personality; the Scholastic Aptitude Test as a measure of mathematics and verbal ability; the Personal Attributes Questionnaire (Spence & Helmreich, 1978) to measure masculinity, femininity, and androgyny; and a spatial rotations task taken from the Kit of Factor Referenced Cognitive Tests (Ekstrom, French Harman, & Derman, 1976). Since existing attitude measures did not assess what I wanted to know, I asked four questions I created about the degree to which the participants in the study liked science and were committed to a career in science. As I reflect on the decisions I made in the design of the study, I do not think I would make any changes. The work was embedded in the research paradigms of 2

CAN THE DIFFERENCES BETWEEN MALE AND FEMALE SCIENCE MAJORS ACCOUNT

the time and allowed me to answer the questions I was posing. A qualitative design would not have been appropriate and reviewers would probably have rejected the manuscript. And, although the Myers-Briggs Type Indicator has been criticized for being theoretically weak so perhaps another measure of personality could have been chosen, the criticisms came long after I and others used it. SCIENCE EDUCATION AT THE TIME OF THE STUDY

Although the study Can the Differences Between Male and Female Science Majors Account for the Low number of Women at the Doctoral Level in Science was published in 1983, it was conceived, conducted, and written between 1977 and 1980. The research at the time of the study was overwhelmingly quantitative. An examination of articles published in the Journal of Research in Science Teaching from 1977 to 1979 revealed only two purely qualitative studies. Nine studies used both qualitative and quantitative techniques either to compare the results of paper and pencil assessments of Piagetian levels to results from clinical interviews or to create nonclinical Piagetian tests that could be analyzed statistically. About 60% of the studies focused on the K-12 system and the remainder on postsecondary education. Among the postsecondary studies, two took place at the community college level. Students were the focus of most of the studies in science education journals, followed by teachers. Few studies looked at both the student and teacher. University faculty were rarely studied. Pre-service elementary teachers received the most research attention while studies of pre-service secondary teachers and studies of the professional development of teachers were conspicuously absent. A bit more than half of the studies had a biology content focus with Earth science and chemistry receiving little attention. Piagetian theory was the dominant theoretical paradigm and studies focused on developing paper and pencil tests for Piagetian interview tasks or increasing Piagetian reasoning levels through interventions. There was also considerable research into the National Science Foundation funded curricula developed in the late sixties and early seventies such as Elementary Science Study (SCIS) and Science a Process Approach (SAPA). Along with an interest in Science a Process Approach, process skills in general and inquiry in particular were a focus of study. Studies about attitude toward science were also popular as were studies reporting test development and the psychometric properties of tests. Some studies took sex into consideration and looked for male female differences. However, this aspect of the studies seemed like an afterthought. Sex was just one of many variables examined and not the primary question driving the research. These studies found few sex differences in performance. Two studies stand out in particular for their insensitivity to issues of gender. Overall, issues of gender were not a concern of the research community. When the research community was asked to set research priorities, sex differences in performance and the low number of females in science majors and careers did not appear on the list (Butts et al., 1978; Yaeger, 1978). 3

CHAPTER 1

RESEARCH INTO GENDER IN THE WIDER FIELD OF EDUCATION

The journals of the American Educational Research Association published little that addressed sex differences in performance or rates of participation of women in science majors or careers. There were no articles in the 1977, 1978 or 1979 issues of the Review of Educational Research investigating gender differences, science participation of females, or spatial and mathematical ability. In the 1979 American Educational Research Journal there was one article about women who entered male dominated fields (Peng & Jaffe, 1979). In the 1978 issue there was one book review for a book examining sex bias in schools (Lockheed, 1978), and in 1977 there were two articles by mathematics researchers Elizabeth Fenema and Julia Sherman examining attitude, spatial visualization, and mathematics achievement (Fenema & Sherman, 1977; Sherman & Fenema, 1977). In contrast to the field of education, the fields of psychology, sociology, and medicine were exploring a variety of biological, social, and psychological theories to examine sex difference in cognitive abilities and male female rates of participation in science. There were several books that were particularly influential in helping me conceive of this study and made an impact on the study of sex differences. These were Fair Science: Women in the Scientific Community (Cole, 1979), Sociology of Science (Gaston, 1978), the Psychology of Women (Gullahorn, 1979), Masculinity and Femininity: Their Psychological Dimensions, Correlates, and Antecedents (Spence & Helmreich, 1978), Psychology of Sex (Maccoby & Jacklin, 1974), Sex Related Cognitive Differences (Sherman, 1978), Female and Male: Psychological Perspectives (Unger, 1979), and Man & Woman, Boy & Girl: The Differentiation and Dimorphism of Gender Identity from Conception to Maturity (Money & Ehrhardt, 1975). In addition, the work of Bem on masculinity and femininity (1977) and her colleague Lenny (1976), as well as Spence and Helmreich (1978) informed my thinking. Bem (1977), Bem and Lenny (1976), and Spence and Helmreich (1978), turned to conceptions of masculinity and femininity as an alternative explanation to biological differences to understand male female differences in a variety of arenas. The measures these scholars devised had strong predictive power, especially the Personal Attributes Questionnaire (PAQ) developed by Spence and Helmreich (1978). The PAQ has masculinity (M) and femininity (F) scales. The masculinity scale measures instrumental traits and the femininity scale measures expressive traits. However, if an individual is high on both M and F scales Spence and Helmreich deemed the person as androgynous. Spence and Helmreich (1978) found that androgyny and masculinity in females was correlated with egalitarian attitudes, self-esteem, personal adjustment, competitiveness, achievement measures, mastery, and aggressiveness. Lack of emotional vulnerability, another androgynous characteristic, was correlated with scientific success and choice of a career for women. In contrast, femininity was negatively correlated with a scientific career for women. A majority of the sample

4

CAN THE DIFFERENCES BETWEEN MALE AND FEMALE SCIENCE MAJORS ACCOUNT

of women scientists Spence and Helmreich studied were found to be androgynous (46%) or masculine (23%). Cole (1979), addressed the issue of female participation in science from a different perspective using the lens of the sociology of science. He asked whether science is really a meritocracy given that women held lower rank than men and women were more likely than men to be in postdoctoral positions than tenure track positions. To answer his question, he examined sex based discrimination in science, accumulated disadvantage of females, social and self-selection in and out of science, scientific productivity, IQ, sex differences in ability, work family priorities, teaching focus, and reputational standing among peers. He concluded that there was no difference in ability, marriage and children did not account for lower rates of publishing, and that productivity and quality were not correlated with rank since women were not promoted as often as men with equal publication records. He also speculated on the causes of women’s lower scholarly productivity but had no clear cut data to explain this finding. He suspected that subtle factors more difficult to measure than those he examined, such as socially structured motivation, mentor/student relationships, and access to old boy networks were the possible causes. Another sociologist of science (Reskin, 1978) reviewed the research about women in science and somewhat surprisingly found that … the term discrimination rarely appeared in titles, and the debate was carried out circumspectly in the articles’ concluding pages. (Reskin, 1978, p. 7) Despite the covert nature of the debate, Reskin found that discrepancies in collegial roles (e.g. networks, collaboration, division of labor), with women at lower status, accounted for sex differences in productivity and the full integration of women into the scientific enterprise. He concluded that the status inequality of women outside of scientific roles and traditional sex roles in society were models for male/female interactions within science. The data for biological theories of male superiority was also examined carefully, especially in mathematics and spatial ability, since these abilities are very important to success in science. Scholars looked at the performance gap between males and females in mathematics and spatial ability using theories of brain lateralization, hormones, sex linked traits, and rates of maturation (Gullahorn, 1979; Money & Ehrhardt, 1975; Sherman, 1978). These scholars concluded that overall, biological theories were disconfirmed. As a further note, some of the work of John Money on the biological origins of sex differences, which was very influential at the time, has been discredited because of misrepresentation of data (Switzer, 2005). Despite the disconfirmation of biological theories, scholars have found persistent but small differences in performance between males and females. Females have better verbal skills than males and males have better spatial visualization skills, dis-embedding skills, and mathematical skills than females (Maccoby & Jacklin, 1974; Unger, 1979). Despite the differences, Sherman (1978) in particular, 5

CHAPTER 1

concluded that the differences were meaningless. They were too small, and in some cases contradictory, to account for the large difference in the rates of participation. Almost a decade later, Marcia Linn and Janet Hyde (1989) were also arguing that the differences were small, did not explain career choices, and could be attributed to situational and cultural effects as well as course enrollment and training. THE CULTURE AND THE TIMES

Despite both male and female ambivalence to women working and an endorsement of traditional sex roles in families where women expected to enter and leave the work force because of childbearing (Komarovsky, 1979), the roles of women were changing. It was the end of the second wave of feminism (Gamble, 2001) and women such as Golda Meir and Indira Gandhi (Pogrebin, 2009) were leading nations. In the United States, Robin Morgan, Kate Millet, Gloria Steinem, Betty Friedan (Langston, 2002), Betty Ford (National First Ladies Library, n.d.), Shirley Chisholm (Office of the Historian, n.d.), Bella Abzug (Cook, 2009), and Elizabeth Holtzman (Lederhendler, 2009) were advocating for women’s rights through writing, lecturing, demonstrating, and crafting legislation. Even the United Nations was cognizant of the need to acknowledge women’s rights and established the International Year of the Women with a conference in Mexico City in 1975. In the United States, President Gerald Ford signed an executive order in response to the International Year of the Women establishing a commission to promote equality between men and women. This was followed by legislation introduced by congresswomen Bella Abzug and Patsy Mink to support a National Women’s conference. President James Carter then appointed Bella Abzug to head the commission on the observance of International Women’s year resulting, in 1977, with the first United States National Women’s Conference held in Houston, Texas. The conference was chaired by Bella Abzug and created an action plan with 25 resolutions on women’s rights, including elderly, minority, disabled, sexual orientation, child care, and reproductive rights. Subsequently, President Carter fired Bella Abzug as commission chair under political pressure (Cottrell, 2010). Legislation to support the rights of girls and women was also passed in the 1970s. In 1972, title IX was signed into law. However, it was not being enforced. In response to lax enforcement, the National Coalition of Women and Girls in Education was formed in 1975 (2015). The American Association of University Women also saw that the regulations were not being enforced and published Monitoring title IX: A Guide to Action for the Volunteer Organization (American Association of University Women, 1977). This was a how to manual that was used when making campus visits to monitor implementation of the regulation. American Association of University Women campus visitors, using the guide, were met with compliance to outright defiance. Despite defiance there were large increases in girl’s and women’s participation in sports. Before passage of the legislation, only about 300,000 (one out of every 27 girls) nationwide participated in high school sports but by 1977 there 6

CAN THE DIFFERENCES BETWEEN MALE AND FEMALE SCIENCE MAJORS ACCOUNT

were 2,400,000 or one out of every three girls participating in high school sports. Between 1971 and 1977 the increase in female college athletes was approximately 35% (Cahn, 2001). The Equal Rights Amendment (ERA) was also introduced in congress in 1972 but by the late 1970s it was running out of time for ratification by the states. This spurred the National Organization of Women to sponsor a march in 1978 to support the Equal Rights Amendment (Barakso, 2004). One hundred thousand people came out to march and the time limit for ratification was extended from 1979 to 1982. However, the amendment was never ratified. The early seventies also saw the establishment of women’s studies departments and by the mid-seventies there were 80 women’s studies departments nationwide. The increase in women’s studies departments led, in 1977, to the founding of National Women’s Studies Association (Napikoski, 2015). The 1970s also saw the of rise of the conservative movement with neoconservatives, the religious right, and Reagan Democrats joining the movement supported by think tanks like the Heritage Foundation. The Moral Majority and Jerry Falwell represented  the new right in politics. The conservative movement was fueled by social change embodied by the women’s movement, abortion rights, and sexual freedom as well as a resurgence of evangelical Christianity. Opposition to the Equal Rights Amendment by traditional values groups and Phyllis Schlafly was particularly strong. Religious conservatives feared that the Equal Rights Amendment would result in the reduction of privacy rights, gay marriage, abortion rights, and women in combat. Businesses opposed the Equal Rights Amendment for financial reasons because insurance rates would go up. States saw the Equal Rights Amendment as an incursion on states’ rights (Girr, 2001; Gross, Medvetz, & Russell, 2011). IMPACT OF MY WORK

My work contributed to the body of research that made biological explanations for the low rates of women’s participation in science unconvincing. Furthermore, the data indicated that mathematical ability was not as much of a barrier to choosing a science undergraduate major then was previously thought. Males and females within majors were alike, refuting the idea that sex was the determining factor. Males in non science majors were self-selecting out of science based on the same set of variables as women. Rather than sex, there were clear personality characteristics such as attitude; perceptions of masculinity, femininity, and androgyny; and approaches to decision making that were strong predictors of who would or would not choose science as a major. The data clearly supported socialization and the power of traditional gender roles rather than biology. Despite the small to non-existent sex differences in abilities, the culture at large, even until very recently, found biological explanations more compelling than socialization explanations. Data, is often unconvincing in the face of strongly held beliefs. However, this work in concert with the work of others, contributed to changes in perceptions, dispelling the myths 7

CHAPTER 1

of innate female inadequacies, and led to the development of programs aimed at increasing the participation of women in science. A look at the most recent survey of Women, Minorities and Persons with Disabilities in Science and Engineering (National Science Foundation, 2012a) indicated that there is strong evidence that few people feel that biology is destiny. Women now comprise 50.5% of all science and engineering undergraduate majors. If we look at just science majors, that number jumps to 56.6% with biology majors at 59.3%. However, only 18.2% of women majored in computer science. Thus, the overall picture is good but there are still majors in which men and women have not reached parity.

8

DALE R. BAKER

RESEARCH IN COLLEGE SCIENCE TEACHING1 Can the Difference between Male and Female Science Majors Account for the Low Number of Women at the Doctoral Level in Science?

ABSTRACT

One hundred and eighty (180) subjects were tested to determine which factors related to success in science were present among biological, physical science, and nonscience majors. Factors examined were mathematical and spatial ability, personality, masculinity, feminity, and attitude toward science. The subjects were given Cube Comparisons, The Personal Attributes Questionnaire, and the MyersBriggs Type Indicator. SAT quantitative scores were used to measure mathematical ability and a questionnaire measured attitude. Results indicate that the personality of males was different from females primarily in terms of decision making. Males had higher mathematics scores than females and science majors had higher mathematics scores than nonscience majors. Science majors had a “scientific” personality while nonscience majors did not. Male and female physical science majors were “masculine.” Female nonscience majors were “feminine” and female biology majors were distributed between the masculine and feminine categories. Science majors had a positive attitude toward science and nonscience majors a negative attitude. There were no differences in spatial ability. Approximately half of all undergraduate majors in science are female [24] yet only 10.6 percent of the doctoral degrees in the physical sciences and 23.4 percent of the degrees in the biological sciences are awarded to women [18]. Why do so few females who major in science go on to do graduate work at the doctoral level? When they do go on, why do more females choose the biological rather than the physical sciences? Are female undergraduate science majors somehow different from their male counterparts? Some researchers would have us believe that they are different, especially in the area of mathematics [1,19], while others dispute the extent of such findings [7,14]. Spatial ability is another area in which most researchers report male/female differences [5,10], the magnitude of which depends upon the degree to which an individual is stereotypically masculine or feminine [5], the type of test [3], and experience [21]. Attitude toward science has clearer sex differences: From an early age, girls think that science is a male-only field [9,21]. 9

CHAPTER 1

Since attitude toward science is strongly related to achievement, as is mathematical and spatial ability [9,21], any differences between males and females in these areas could account for the small number of women choosing graduate-level work in science. A further explanation may be sought in personality theory. Holland suggests that individuals choose careers because their personality characteristics fit the characteristics of the career [8]. The poorer the match, the greater the likelihood that the individual will be unsuccessful and dissatisfied, and leave the field. Research with the Myers-Briggs Type Indicator suggests that individuals who choose science and do well in science have a specific personality type. They are likely to be intuitive and introverted, preferring to base decisions on logical analysis. They prefer to remain open and curious to understand events. In contrast, individuals in the helping professions and the humanities tend to be fact-minded, realistic, and extroverted, preferring to base decisions on values and feelings. They like to order and control events [12,13]. In addition, the scientific personality type has been found to outperform other personality types on science achievement tests [16]. Females, perceiving science as a male field and perceiving themselves as having female characteristics, may be correct in not choosing science. Most characteristics associated with the scientific personality are exaggerated masculine characteristics such as dominance, aloofness, detachment, and taciturnity [4,17]. Spence and Helmreich [20] found that female scientists were more “androgynous,” that is, exhibited both stereotypical masculine and feminine characteristics, and were more “masculine” than college women in general. Women engineering majors have also been found to be more “masculine” than women in home economics [23], and androgynous women in college expect to do better in math and science than do “feminine” women [2]. In general, women found in male-dominated occupations have traits that are stereotypical of men [11]. However, within science, biology is seen to be less masculine and less likely to conflict with the female personality characteristics than the physical sciences [25]. This would account for the greater number of female Ph.D.’s in biology than physics. Subjects were given two personality tests: the Myers-Briggs Type Indicator, based on Jungian typologies [15], which measures the scientific personality, and the Personal Attributes Questionnaire, which measures the degree to which individuals perceive themselves as having stereotypical masculine, feminine, or androgynous characteristics [20]. The scales on the Myers-Briggs Type Indicator are as follows: The extroversion-introversion scale measures a preference for relating to the outer world of people (E) or the inner world of ideas (I). The sensing-intuition scale measures a preference for working with known facts (S) or possibilities and relationships (N). The thinking-feeling scale measures a preference for basing judgments on impersonal analysis and logic (T) or personal values (F). The judging-perceiving scale measures a preference for a planned, orderly life (J) or a spontaneous, flexible life (P). 10

CAN THE DIFFERENCES BETWEEN MALE AND FEMALE SCIENCE MAJORS ACCOUNT

The Personal Attributes Questionnaire allows an individual to have masculine and feminine characteristics simultaneously. An individual is classified as androgynous if his or her score is above the median on both the masculine (M) and feminine (F) scales. Subjects scoring below the median on the M and F scales are “undifferentiated.” A score above the median on just the M scale classifies the subject as masculine, and a score above the median on the F scale classifies the subject as feminine. There is also a bipolar scale (M-F) on which individuals can rate themselves as closer to the masculine or feminine end of the scale for a given trait. Subjects were also given a spatial-rotation task, based on Thurstone’s cubes [6]. The Scholastic Aptitude Test was used as the measure of mathematical and verbal ability. Attitude was measured by the response to four questions that assessed the degree to which the subject liked science and the degree of commitment to a scientific career the subject showed. This study addresses the question of whether or not males and females majoring in the biological and physical sciences really are different in terms of mathematical, spatial, attitudinal, and personality characteristics. One hundred and eighty (180) juniors and seniors in college were tested: 30 male and 30 female physical science majors, 30 male and 30 female biological science majors, and 30 male and 30 female nonscience majors. The nonscience group was used as a control against which the other groups were compared and contrasted. Mechanics of the Study. Subjects were recruited through class announcements and advertisements in the school newspaper. Each subject was screened for major and class rank (junior or senior). Qualified students were then paid five dollars for their participation in the study. Declared majors at the junior and senior levels were selected on the assumption that they represented the pool of potential graduate students in science. This assumption was based on several observations. First, entrance to the university at which this study was undertaken is highly competitive. Normally, only those students in the top 10 percent of their high school graduating class are accepted. Second, the combined SAT scores for all groups in the study was above 1,000, except for female biology and female nonscience majors; their scores were above 900. These scores suggest that this was a group of students with the potential to do rigorous academic work. Third, these students had obtained sufficient competence in their majors to enroll in upper-division courses. Finally, this was a group of students who had persisted in taking courses in science for all or almost all of their college careers. That is, they did not just indicate an interest in studying science, as perhaps a freshman might; they had acted upon their interest in science and maintained that interest beyond lower-division courses that frequently act as filtering mechanisms for those who lack the ability or real interest to go on. SEX DIFFERENCES

Although there were sex differences between males and females when all groups (science and nonscience) were combined, the differences were few. 11

CHAPTER 1

Males did have  higher mathematics scores than females. They also differed in one personality  aspect of the Myers-Briggs Type Indicator: Males preferred a logical, analytical approach to  decision making (the scientific mode) while females preferred to base decisions on personal values. Table 1 is the analysis of variance for mathematics and the thinking-feeling scale. Significant differences for the main effects and sex indicate that the differences are between males and females, not between majors. Examination of the means for the male SAT math scores (579.43) and female SAT math scores (509.45) indicate the direction of the significant difference. There was no significant sex-by-major interaction. The female mean for the thinking-feeling scale was 102.73. The male mean for the thinking-feeling scales was 91.73. There was no sex-by-major interaction on the thinking-feeling scale; and there were no sex differences for spatial ability, verbal ability, or attitude toward science. Table 1. Analysis of Variance for Sex Differences in Mathematics and Personality. Source

SS

df

Ms

F

Main Effects

303879.880

3

101293.250

6.82a

Sex

298284.000

1

202284.000

14.03a

95595.880

2

47797.940

3.22b

2951.430

5

1475.720

.10

Main Effects

7620.219

3

2540.073

6.23a

Sex

5205.688

1

5205.688

12.76a

Major

2414.533

2

1207.267

2.96

268.047

2

134.230

.33

Mathematics

Major Two-way Interaction Sex by Major Thinking-Feeling

Two-way Interaction Sex by Major a b

p < .001 p < .05

Males, as one would expect, were most often classified as stereotypically masculine and females as stereotypically feminine (see Table 2). Table 2 is the chisquare analysis of the distribution of males and females in the masculine, feminine, androgynous, and undifferentiated categories. The fairly equitable distribution of males and females in the androgynous and undifferentiated categories as compared with the distribution of males and females in the masculine and feminine categories indicates the significant differences between males and females are in these latter two categories. 12

CAN THE DIFFERENCES BETWEEN MALE AND FEMALE SCIENCE MAJORS ACCOUNT

DIFFERENCES ACROSS MAJORS

Table 3 is the post hoc analysis of the analysis of variance presented in Table 2. Table 3 indicates that physical science majors had higher SAT mathematics scores than either biological science majors or nonscience majors. In fact, the mathematics scores of the biological science majors were not statistically different from nonscience majors. Both science majors had better spatial ability than nonscience majors and a more favorable attitude toward science and a scientific career. Majors grouped in the same subset have means which are not statistically significantly different from each other. Groups found in different subsets do have means that are statistically significantly different from each other. Table 2. Chi-Square Analysis of Males and Females by Personal Attributes Questionnaire Categories. Androgynous Undifferentiated Absolute

%

Absolute

%

Absolute

%

Absolute

%

Row Total

Female

23

25.6

25

27.8

12

13.3

30

33.3

90

Male

26

28.9

21

23.3

32

35.6

11

12.2

90

Column Total

49

Sex

46

Masculine

Feminine

44

41

180

x2 = 18.43. df = 3.00. p < .001.

Not surprisingly, the science majors had the personality characteristics associated with science. They were intuitive, analytical, and logical, with the ability to impose order on and come to conclusions from data. Female nonscience majors, in general, preferred to base decisions on personal values, while the male nonscience majors preferred a logical and analytical approach to decision making.2 Table 4 is the chi-square analysis of the distribution of females with biological, physical science, or nonscience majors. It indicates that female nonscience majors were more often classified as feminine than female science majors. Within science, the female physical science majors had more individuals classified as stereotypically masculine than the biological science majors, and the biological science majors had more individuals classified as androgynous (having high scores on both the masculine and feminine scales) than the physical science majors. SEX DIFFERENCES WITHIN MAJORS

Within the physical and biological sciences, there were few sex differences. Both males and females had the scientific personality, liked science, and were planning a scientific career. There was no difference in verbal, spatial, or mathematical ability. 13

CHAPTER 1

Table 3. Duncan Post Hoc Analysis of Mean Scores Mathematics, Spatial Ability, and Attitude. Homogeneous Subsets Subset 1

Mathematics (SAT)

Group

Nonscience

Biology

Mean Score

525.00

528.67

Subset 2

Spatial Ability (Spatial Relations Task)

Group

Physical Science

Mean Score

575.62

Subset 1 Group

Physical Science

Biology

Mean Score

30.28

30.03

Subset 2

Attitude (Questionnaire)

Group

Nonscience

Mean Score

26.53

Subset 1 Group

Nonscience

Mean Score

1.30

Subset 2 Group

Biology

Physical Science

Mean Score

2.85

3.08

Female biological science majors were equally distributed in the stereotypically masculine and feminine categories. More female physical science majors were classified as masculine than feminine, and all nonscience females were classified as stereotypically feminine (see Table 4). Males of all majors were most often classified as stereotypically masculine or androgynous (see Table 2). DIFFERENCES AMONG THE FOUR GROUPS

Table 5 is the discriminant analysis for male and female biological, physical science, and nonscience majors. It indicates that the four groups can be differentiated on the basis of eight variables. Attitude is the most important discriminating variable, followed by the M and F scales, SAT mathematics, the JP and TF scales, SAT verbal, and the M-F scale. One would be most likely to correctly classify females, especially biological science majors, on the basis of these variables, and least likely 14

CAN THE DIFFERENCES BETWEEN MALE AND FEMALE SCIENCE MAJORS ACCOUNT

to correctly classify males, especially biological science majors, on the basis of these variables. Summary profiles of each group are presented in Table 6. Table 4. Chi-Square Analysis of Females by Major and Personal Attributes Questionnaire Categories. Androgynous Undifferentiated Absolute

%

Absolute

%

Absolute

%

Absolute

%

Row Total

Nonscience

8

26.7

6

20.0

2

6.7

14

46.7

30

Biology

11

36.7

10

33.3

2

6.7

7

23.3

30

Physical Science

4

13.3

9

30.0

8

26.7

9

30.0

30

Column Total

23

Major

25

Masculine

12

Feminine

30

90

f 2 = 12.86. df = 6.00. p < .05 IMPLICATIONS

It is clear from summary Table 6 that the differences between males and females in the sciences were few. More to the point, the size of the differences that do exist are not large enough to account for the differences in number found at the doctoral level. Although it is true that females in the physical sciences were more often classified as stereotypically masculine than females in the biological sciences, this difference is slight and can hardly account for the difference in the number of Ph.D.’s in each field.  The same can be said for the nonsignificant differences found in the mathematical abilities of females in the biological and physical sciences. Why then do so many able women with characteristics no different from their male counterparts foreshorten their scientific careers at the undergraduate level? If the answer is not cognitive ability, personality, or attitude, it must be socialization. Our society as a whole is still telling young women that careers requiring years of schooling are more appropriate for males. Even some women at the most prestigious schools in the nation see careers as something to do before children are born or after they are grown, or as a way to put a husband through graduate school [22]. Many feel uncertain that the roles of wife and mother are compatible with a career as demanding in terms of time and effort as science. Until we both accept and encourage young women to consider the rewards of a scientific career, we cannot expect more than a few of our female undergraduate science majors to pursue a doctorate in science. However, the mood of the country does not seem to be moving in that direction. Legislative action to amend Title IX, the defeat of the ERA, and a return to more traditional values are not likely to encourage young women to choose nontraditional careers. 15

CHAPTER 1

Table 5. Discriminant Analysis of Male and Female Biological, Physical Science, and Nonscience Majors. Order of Variablesa

Rao’s V

Canonical Function 1

Coefficients Function 2

Attitude

69.52

−.88

.43

M

95.55

.40

−.40

F

121.80

.16

.21

SAT Math

143.80

−.35

−.87

JP

159.00

−.28

−.35

TF

173.60

.24

.32

SAT Verbal

186.90

.04

.63

M-F

198.90

Group Centroids

.35

−.05 Function 1

Function 2

Female Biological Science Major

−.16

.88

Female Physical Science Major

−.64

.54

Female Nonscience Major

1.40

.40

Male Biological Science Major

−.78

−.40

Male Physical Science Major

−.46

−.71

Male Nonscience Major Correct Classification

.64

−.90 Percentages

Female Biological Science Major

69.2

Female Physical Science Major

66.7

Female Nonscience Major

66.7

Male Biological Science Major

35.7

Male Physical Science Major

48.3

Male Nonscience Major

53.3

a

Nonsignificant variables were not included.

Further research should focus on the doctoral student in science, especially in the area of mathematics and affiliation needs. Some researchers have concluded that the reason fewer women than men choose to study science is that they inherently are poorer in mathematical ability than men. However, if we find sex differences in mathematical ability still exist at the doctoral level, then this differential in abilities can be discounted as a factor in preventing women from studying science. Also, if women pursuing the doctorate in science were found to have lower affiliative needs, then one could conclude that they were less affected by or disagreed with socialization pressures that suggest that scientific careers are incompatible with the female role. 16

x s.d.

%

Science

Biological

Male

x s.d.

%

Science

Biological

Female

Group

F

J

P

97.13 23.97

101.13 33.73

23.30

F a

5.97

23.46 22.16 3.94 6.13

6.70

6.23

19.23 23.83

6.70

M

46.7 48.3 51.7 36.70

92.53 22.74

53.3

105.66 18.43

51.7 48.3 62.1 37.9

T

50.00

43.30

17.73 3.68

56.70

18.56 4.97

50.00

Female Male Traits Traits

M-F

0

0

0

2

3

Math

Verbal Description

(Continued)

Prefers logical analysis in decision making. As likely to prefer orderly life 0 23.3 76.7 or spontaneous life. 2.96 560.66 510.00 More masculine than 1.22 85.01 81.87 feminine; positive attitude toward science; good math and verbal.

Prefers logical analysis in decision making; planned orderly life. More 0 20.7 79.3 feminine than b 3.20 496.66 478.67 masculine ; positive 1.63 115.17 127.84 attitude toward science; good math and verbal.

1

Attitude

Table 6. Profiles of Male and Female Biological, Physical Science, and Nonscience Majors.

CAN THE DIFFERENCES BETWEEN MALE AND FEMALE SCIENCE MAJORS ACCOUNT

17

18

x s.d.

%

Science

Physical

Male

x s.d.

%

Science

Physical

Female

Group

F

J

P

M

F a

87.26 31.26

19.33 22.50 5.93 3.81

88.53 24.41

104.30 24.49

21.46 21.70 5.08 4.25

60.00

16.70 4.26

43.30

19.30 3.45

66.7 33.3 43.3 56.7 33.30 23.30 56.70

95.93 19.04

60.0 40.0 72.4 27.6 18.20 22.00 40.00

T

Female Male Traits Traits

M-F

0

0

0

Table 6. (Continued)

3.13 1.14

7.1

2

Math

Verbal Description

Prefers logical analysis in decision making; planned orderly life. More 92.9 masculine than 545.23 499.19 feminine; positive 196.91 191.82 attitude toward science; good math and verbal.

3

Prefers logical analysis in decision making; spontaneous life. As likely to 3.7 29.6 66.7 have masculine 2.56 606.00 516.33 characteristics as .73 88.53 84.06 feminine. Positive attitude toward science; excellent math; good verbal.

0

1

Attitude

CHAPTER 1

103.60 25.32

94.8 20.31

106.00 41.92

21.50 21.20 4.69 4.95

6.70

64.50

40.00

16.96 3.39

60.00

19.00 3.74

46.70 35.50

18.13 25.66 3.94 3.16

6.70

66.7 33.3 30.8 69.2 36.70

106.53 19.85

32.3 67.7 41.9 58.1

Prefers logical analysis in decision making; spontaneous 36.4 9.1 42.4 12.1 life. More masculine 1.23 564.66 491.67 than feminine. As 1.04 69.32 77.33 likely to dislike or be indifferent to science as to like science; good math and verbal.

1.36 1.79

44.8 6.9 41.4 6.9

Prefers basing decisions on personal values; spontaneous life. 485.33 477.00 More feminine than 131.04 118.27 masculine. Dislikes or indifferent to science; poor math; good verbal.

b

a

Percentages don’t add to 100% because percent androgynous and percent undifferentiated are not shown. “Masculine” was determined by adding the percentage classified as M and the percentage having masculine traits on the M-F scale. “Feminine” was determined by adding the percentage classified as F and percentage having feminine traits on the M-F scale.

x s.d.

%

Nonscience

Male

x s.d.

%

Nonscience

Female

CAN THE DIFFERENCES BETWEEN MALE AND FEMALE SCIENCE MAJORS ACCOUNT

19

CHAPTER 1

REFERENCES   1. Benbow, C., and J. Stanley. “Sex Differences in Mathematics Ability: Fact or Fiction?” Science, 210:1262–1264; 1980.   2. Brewer, M., and M. Blum. “Sex Role Androgeny and Patterns of Causal Attribution for Academic Achievement.” Sex Roles 5:783–796; 1976.   3. Burnet, S., and D. Lane. “Effects of Academic Instruction on Spatial Visualization.” Intelligence 4:233–242; 1979.   4. Cattell, R. The Sixteen Personality Factor Questionnaire. Champaign, Ill.: IPAT, 1975.   5. Connor, J., and L. Serbin. Mathematics Visual Spatial Ability and Sex Roles. Final Report to the National Institute of Education, Washington, D.C., 1980.   6. Ekstrom, R., J. French, H. Harman, and D. Derman. Manual for Kit of Factor Referenced Cognitive Tests. Princeton, N.J.: Educational Testing Service, 1976.   7. Fenema, E. “Mathematics Learning and the Sexes: A Review.” Journal of Research in Mathematics Education 5:126–139; 1974.   8. Holland, J. Making Vocational Choices. Englewood Cliffs: Prentice-Hall, Inc., 1973.   9. Kelly, A. “Girls in Science: An International Study of Sex Differences in School Science Achievement.” International Educational Monograph Studies, 1978 (Serial No. 9). 10. Kohl, S., M. Malone, and M. Fleming. Sex Related Difference in Pre-College Science: Findings of the Meta-Analysis Report. Paper presented at the annual meeting, American Educational Research Association, New York, N.Y., March 1982. 11. Lemkau, J. “Personality and Background Characteristics of Women in Male Dominated Occupations: A Review. Psychology of Women Quarterly 4:221–240; 1979. 12. McCaulley, M. “Psychological Types in Engineering: Implications for Teaching.” Engineering Education 66:729–736; 1975–1976. 13. ——, and M. Rowe. “Personality Variables: Model Profiles that Characterize the Various Fields of Science and What They Mean for Education.” Journal of College Science Teaching 7:114–120; 1977. 14. Maccoby, E., and C. Jacklin. The Psychology of Sex Differences. Palo Alto, Calif.: Stanford University Press, 1974. 15. Myers, I. B. The Myers-Briggs Type Indicator. Palo Alto, Calif.: Consulting Psychologists Press, 1962. 16. Reynolds, R., and A. Hope. “Typology as a Moderating Variable in Success in Science.” Psychological Reports 26:711–716; 1970. 17. Roe, A. “The Psychology of the Scientist.” Science 181:456–458; 1973. 18. Science Education Databook. Washington, D.C.: National Science Foundation, 1980. 19. Sherman, J. “Mathematics Spatial Visualization and Related Factors: Changes in Girls and Boys, Grades 8–11.” Journal of Educational Psychology 72:476–482; 1979. 20. Spence, J., and R. Helmreich. Masculinity and Femininity. Austin, Tex: The University of Texas Press, 1979. 21. Steinkamp, M. Sex Related Differences in Science Attitude and Achievement: A Quantitative Synthesis of Research. Paper presented at the annual meeting, American Educational Research Association, New York, N.Y., March 1982. 22. “Survey of Metropolitan Area College Women on Career and Graduate School Goals.” The New York Times, December 28, 1980. 23. Tanico, B., S. Hardin, and K. McLaughlin. “Androgeny and Traditional Versus Non-traditional Major Choice Among College Freshmen.” Journal of Vocational Behavior 12:261–269; 1978. 24. Vetter, B. “Degree Completion by Women and Minorities in Science Increases.” Science 212:3; 1981. 25. Vockell, E., and S. Lobonc. “Sex Role Stereotyping by High School Females in Science.” Journal of Research in Science Teaching 18:209–219; 1981.

20

CAN THE DIFFERENCES BETWEEN MALE AND FEMALE SCIENCE MAJORS ACCOUNT

NOTES Originally published as Baker, D. (1983). Can the differences between male and female science majors account for the low number of women at the doctoral level in science? Journal of College Science Teaching, 13, 102–107. Reprinted here with permission. 2 See Table 6 for the means, deviations, and percentages for those variables that distinguish among male and female biological, physical, and nonscience majors. 1

21

CHAPTER 2

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE CONFLICT ON CAREER CHOICES IN SCIENCE

WHY I CONDUCTED THE STUDY

When I began conceiving this study there was little research in the science education literature about role-specific self-concept and sex-role identity as factors influencing career choices in science for women. Cognitive rather than sociological theories drove science education research. However, at the time of this study there was a great deal of evidence that women were not choosing science careers but instead selecting more traditional careers despite evidence that women had the capacity to do well in science careers. Why then, were they not choosing science? Earlier work that colleagues and I conducted in mathematics (Peterson, Baker, & Burton, 1983) found that role-specific self-concept influenced women’s mathematical career choices. Other research in mathematics was also helpful in my decision to look at both rolespecific self-concept and future mathematics course taking (Chipman, Brush, & Wilson, 1985). Since role-specific self-concept was important in mathematics, I felt that role-specific self-concept might also be a factor in science career choice as well. There was also strong interest in sex-role stereotyping in the psychological literature at the time of this study as an explanation for why so few women chose science careers. I had found, in an earlier study, that the more stereotypical traits a women perceived herself to have the less likely she would choose a career that she perceived as nontraditional for a women or more appropriate for males (Baker, 1983). However, how the two factors of role-specific self-concept and sex-role identity worked together to influence women’s career choices had not been investigated. Thus, I thought looking at the relative contribution of each factor as well as the impact of both factors in combination on career choices in science would be worth studying. METHODOLOGICAL DECISIONS

Role-specific self-concept and sex-role identity were operationalized by the instruments I chose to use. Role-specific self-concept was defined as having the traits of either an ideal science learner or lacking the traits of an ideal science learner. Role-specific self-concept is different from general self-concept in that an

23

CHAPTER 2

individual can have an overall good self-concept but still think that they are not good at specific  tasks such as learning science or playing tennis. Role-specific self-concept in science was measured by the Peterson Yaakobi Q-Sort (Peterson, Kauchak, & Yaakobi, 1980). The Q-Sort consisted of 20 cards with statements such as concerned about grades or reads about science outside of class. To obtain data about role-specific self-concept, students do two sorts: one for their perception of the ideal learner and one that reflects themselves as real learners. First students place the cards in order from most to least like an ideal science learner. The second sort is based on their perceptions of themselves ordering the cards from most like themselves to least like themselves. This provides a way to examine the differences in ranking of ideal and real traits. It provides insight into the degree to which students think they are different from or like an ideal science learner. This technique was chosen because it used the students’ own perceptions of an ideal science learner rather than the perceptions of researchers or a group of students unrelated to the population under study. Thus, the instrument does not have to be renormed when used with different populations. It is also a motivating format because it is different from a paper and pencil assessment and allows students to revised the order of the cards easily until they are satisfied that the ordering reflected what they thought was the ideal learner and what order described themselves as science learners. The Bem Sex-Role Inventory (BSRI) (Bem, 1974) was used extensively, at the time of this study to examine sex-role identity in relation to many factors including career choice. It was one of two instruments available to measure sex-role identity and had high reliability. Since I had already used the lesser known Personal Attributes Questionnaire (Spence & Helmreich, 1978) in a previous study, I chose the more widely used BSRI for this study. Since I was still tied to my Piagetian roots, I included a test of logical thinking. The TOLT (Tobin & Capie, 1981) was used to determine the effect of reasoning ability on career choice. Reasoning was and still is an important component of success in science. The TOLT was also a way to confirm or disconfirm if the perceptions of the students themselves as a real science learner were accurate. It would also help to determine whether sex-role identity was immaterial, if one did not have the logical ability to succeed in science. The final instrument used in the study was a simple questionnaire to again determine students’ perceptions of their ability in science (basic, intermediate or advanced), the number of science and mathematics course they intended to take in the future, and career preferences. Since this study was conducted in Australia where school leaving age was tenth grade, students who were not going on to 11th and 12th grade were dropped from the study. Students in the study were seventeen years old and in grade 10. Less than 1% were nonwhite. Only data from the 356 students in the mathematics and science course who intended to stay in school for grades 11 and 12 were used. Mathematics and science courses were assigned a number to reflect the level of difficulty in the 24

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE

Australian school system with higher numbers indicating greater difficulty. Career preferences were grouped according to categories that took into consideration the gendered nature of careers in Australia. The five categories were science, non science, allied health, nontraditional non science, and traditional. Both male and female students were included in the study since I was still at the point where I was comparing male and females across the same characteristics. I had not yet come to the realization that such comparisons might not provide me with the information I was seeking or that such comparisons might reinforce a deficit model for women and girls. I was still using statistical analysis in my work and sought a large sample. However, there were not enough students indicating they wanted a science career to do a regression analysis given the number of variables in the study. Consequently, I conducted both an analysis of variance and a discriminant analysis. At the time, discriminant analysis was my statistical tool of choice. I liked it because it allowed me to see if the variables I had chosen as important to career choice successfully predicted group membership (e. g. students choosing science or non-science careers). Since this study was still influenced by a positivist perspective, I generated hypotheses based on the previous research literature to predict the outcomes of the study. SCIENCE EDUCATION AT THE TIME OF THE STUDY

Role-specific self-concept, sex roles, and science career choices of women and girls were not topics of study in science education when I undertook this research. The question of why so few women were choosing science careers was still the purview of psychology. Jane Butler Kahle, a science educator, was one of the few exceptions. She wrote Women in Science (1985). The chapters of her book focused on women and girls, scientists and science teachers, and took an international and historical perspective. Kahle identified obstacles for girls and women, the double problem of race and femaleness, and described pedagogy that teachers could use as well as interventions to increase female participation. As with the research on women and girls, there were few studies that examined other under-represented groups such as African Americans or Latino students. Intersectionality was not a concept or an analytical framework. Although more scholars were including gender as a variable in their studies, they did so in order to compare the performance, attitudes, and interests of males and females. The most prevalent topic for research in the science education journals was the examination of student achievement in content areas. Biology was the most frequently studied content area followed by physics/physical science, and chemistry. It was rare to see a study focusing on achievement in Earth and space science. Technology was a concern and there were two special issues of the Journal of Research in Science Teaching (24, 4 and 24, 5) addressing research on the cognitive consequences of technology in science education. 25

CHAPTER 2

A Piagetian theoretical framework still held sway but change was coming. Driver, Guesne and Tiberghien (1985) in Children’s Ideas in Science examined how children’s ideas about natural phenomena develop by looking at their misconceptions/ alternative  conceptions of scientific phenomena foreshadowing the larger body of research on misconceptions to come. Siegler (1986) also was interested in the development of children’s thinking and examined perceptual, language, memory, conceptual, and academic skill development through both a Piagetian and information processing lens. Both Driver et al. (1985) and Siegler (1986) were part of the beginning of a shift away from a Piagetian theoretical perspective. Researchers also continued to examine the impact of the alphabet soup curricula such as SAPA (Science a Process Approach). There were many studies that looked at students’ ability to engage in science processes such as controlling variables and  problem solving. Some scholars were also beginning to use large data bases such as the NAEP (National Assessment of Educational Progress) and High School and Beyond to determine the status of science achievement across the United States. A brief look at what was published in the Journal of Research in Science Teaching at this time indicates that most studies focused on students at the high school level followed by studies of university, elementary, and junior high students. Few of the studies had samples of community college students. Studies of pre-service teachers, classroom teachers, or teacher education comprised approximately 20% of the research despite the fact that most science educators then and now are engaged in teacher education at some level. Publications in Science Education were grouped into areas of learning, science teacher education, science education generally, current issues and trends, and international science education with similar grade level and content area emphases. Researchers were most likely to use quantitative analysis and there were few truly qualitative studies. Quantitative studies occasionally used supporting evidence from qualitative data such as interview data to support paper and pencil assessments. However, these studies were not true mixed methods as we currently understand this technique. RESEARCH IN THE WIDER FIELD OF EDUCATION

Mathematics educators had been grappling with the low level of participation of women in mathematics and were ahead of science educators in their concern about female participation. Chipman, Brush and Wilson (1985) in Women and Mathematics: Balancing the Equation undertook a systematic review of the research that examined a variety of factors (cognitive, affective, spatial, hormonal, school and home experiences, early intervention programs, self-perceptions, math task perceptions) to account for sex differences in achievement in mathematics. They concluded that the achievement gap was actually small and did not explain male female differences in rates of participation. Furthermore, they found that a student’s sex was twice as important as math scores for selecting majors. From the research, they identified a set of predictive variables such as previous mathematics achievement, the perceived 26

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE

utility of mathematics, teacher and parental encouragement, perception of the utility of mathematics, aspirations for a mathematics career, and confidence in ability. Not all of these variables proved to be predictive. They found that more females than males believed that mathematics was open to both males and females but there were no sex differences in liking or interest in mathematics, or teacher or parental encouragement. Other fields were also moving away from a Piagetian theoretical framework. In the field of the psychology of women Carol Gilligan challenged the Piagetian research on moral reasoning and the female deficit model that it supported. She wrote In a Different Voice (1982) in response to studies that found women were at lower stage in maturity of moral development as compared to males, as measured by Kohlberg’s tasks. She concluded, based on her research, that females were not inferior to males but rather, that males and females see a different moral problem when given the same moral dilemma. As a consequence, males and females used different reasoning to make decisions. Females used the preservation of relationships and the impact on people as a basis for decisions and males used logic and justice arguments. Gilligan concluded that women’s morality was based on caring and a sense of self in a network of making and maintaining relationships. Counselling psychology and vocational behavior was another area in which there were studies that examined sex-role orientation and self-efficacy in relation to choosing nontraditional and masculine careers that included STEM fields (e.g. Fassinger, 1985; Foss & Slaney, 1986; Gianakos & Subich, 1986; Post-Krammer & Smith, 1986). An examination of the work published in the American Educational Research Journal and Review of Educational Research revealed some interesting patterns. In 1985 there were a few studies in the American Educational Research Journal that looked at self-concept but none that addressed the variables in my study of science careers, logic, role-specific self-concept as a science learner, or femininity and masculinity. One study did conclude that the relationship between self-concept and academic achievement depended upon the area of self-concept that was considered (Marsh, Parker, & Barnes, 1985). Research that looked at sex differences focused on mathematics and the role these differences played in career choice for high ability students as well as the role of pedagogy in achievement (Boli, Allen, & Payne, 1985; Peterson & Fenema, 1985). The 1986 and 1987 issues of American Educational Research Journal also published a few studies exploring sex differences in mathematics (Benbow & Minor, 1986; Brandon, Newton, & Hammond, 1987) but Review of Educational Research publish no review articles on the topic of sex differences in science. However, Carl Grant and Christine Sleeter (1986) argued for examining gender, race and social class together in educational research without specifically addressing issues in science or other content areas. Also noteworthy is that this paper was among the first to call for studying intersectionality, although Grant and Sleeter did not use the term intersectionality in their review. Research published in Review of Educational Research on self-concept and sex-role identity was also scare at the time of my study. 27

CHAPTER 2

While science education and education in general was neglecting women in science, female scientists were not. Many books were written during this time by female scientists such as Evelyn Fox Keller, a mathematical biophysicist; and Ruth Bleier, a neurophysiologist and physician. These books were decidedly feminist and challenged the masculine nature of science and reflected the women’s movement of the time. Evelyn Fox Keller described her book Reflections on Gender and Science (1985) as examining the historic conjunction of science, masculinity, and the equally historic disjunction between science and femininity. (p. 5) In a similar vein, Ruth Bleier in Science and Gender: A Critique of Biology and its Theories on Women (1984) wrote This book is concerned with the role of science and the creation of an elaborate mythology of women’s biological inferiority as an explanation for their subordinate position in the cultures of Western civilization. (Bleier, 1984, p. vii) These books written from a feminist perspective rejected divisions of public/ private, impersonal/personal, masculine/feminine and especially the dichotomy that labels objectivity, reason, and the mind as masculine and subjectivity, feeling, and nature as feminine. They refuted theories of sociobiology, biological determinism, brain and human nature, hormones, and sex differences, man the hunter as evolutionary explanations, sexuality, ideology and patriarchy, and patriarchal science. Vivian Gornick an essayist and social critic also wrote about women in science in Women in Science: Portraits from a World in Transition (1983) and documented their challenges and decisions and how they made a place for themselves within science. Based upon interviews with women scientists, she explored the impact of the women’s movement on careers, and the differences in experiences by generation. She concluded that women do not do science differently than men and despite difficulties, they loved their life in science. THE CULTURE OF THE TIMES

From 1981 to 1989, the conservative Reagan administration was in power in the United States. Ronald Reagan had restored confidence in the presidency and contributed to the end of the cold war by asking the leader of the Soviet Union, Mikhail Gorbachev, in a speech at the Berlin wall, to tear the wall down. President Reagan was an adept politician who was able to put together a political coalition of religious, national security, and economic/libertarian conservatives (Hunter, 2012) based on fears that the liberals would gain control of government. These fears did have some basis in reality. The right to life movement was slowing and women’s rights supporters (feminists) were the majority of voters, opposition to a constitutional amendment banning abortions was increasing (62%) as was support 28

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE

for the Equal Rights amendment (75%). Eighty-four percent of respondents to a Harris poll supported equal pay for women and 68% supported affirmative action in employment (Feminist Majority Foundation, 2014a, 2014b). Buoyed by some of the 1986 election victories and changing public sentiment, national women’s groups presented congress with an agenda addressing the eroding rights of women and minorities. They advocated for the passage of (1) the Civil Rights Restoration Act, (2) a Federal pay equity bill, (3) the Family and Medical Leave Act, (4) welfare reforms, (5) increases in the minimum wage, (6) funding for reproductive health care for women, and (7) funding for affordable and accessible child care and dependent care. In addition, women’s groups advocated for the reintroduction of the Equal Rights Amendment to the constitution. There was, as expected, a backlash from conservative groups. Abortion clinics were bombed, anti-abortion legislation was introduced by Congressman Henry Hyde (R-IL), Kiwanis international rejected the admission of women, and a 20 million dollars sex discrimination case against SEARS was dismissed (Feminist Majority Foundation, 2014a, 2014b). IMPACT OF MY WORK

This study added to the small but growing body of evidence that women who choose science careers do so for different reasons than men. It also added to the studies that concluded that ability was not the determining factor in career choice. However, the definitions of masculinity and femininity used in the study reflected an earlier time and current students have less stereotypical views of themselves and the appropriateness of careers. Using the Bem Sex Role Inventory as an instrument, although appropriate for the time, is probably not as appropriate an instrument to use today. This is good news and is an indication of the progress we have made toward increasing girls and women’s participation in science. This study helped increase awareness of the problem of the low numbers of women in science and STEM fields more broadly and paved the way for programs to increase women’s participation in STEM fields. A quick look at the statistics will reveal how far we have come since 1987. Clearly, there are more women in STEM fields now than when this study was conducted with the exception of computer science. The average percentage of women holding a doctorate for the years surrounding this study (1986, 1987, 1988) was as follows: 12.4% computer science, 6.4% engineering, 35.8% life sciences, 16.5% mathematics, and 16.7% physical sciences (Burelli, 2008). In contrast, in 2012 the percentage of female doctorates in life science was 55.6%, physical sciences, 28.5% and engineering 22.3% (National Science Foundation, 2012b). The number of bachelor’s degrees in STEM fields awarded to women in the mid 1980s was: biological science 45%, chemistry 36%, mathematics 45.5% physical science 14.6% engineering 14%, and computer science 35% (Hill, Corbet, & Rose, 2010). The percentage of bachelor’s degrees awarded to women in 2012 (Digest of Educational Statistics, 2012) was computer science 2%, engineering 13.0%, physical science 30%, life sciences 50%, and mathematics 45%. 29

CHAPTER 2

Despite the increases in women in some STEM fields, role-specific self-concept and the concept of self-efficacy which is closely related to role-specific self-concept still remain powerful predictors of career choice and may help us understand the decline in women’s interest in computer science.

30

DALE R. BAKER

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE IDENTITY ON CAREER CHOICES IN SCIENCE1

ABSTRACT

Despite much effort on the part of educators the number of females who choose science careers remains low. This research focuses on two factors which may be influencing females in their choice of careers. These factors are role-specific selfconcept in science and self perception in terms of stereotypical masculine and feminine characteristics. In addition logical ability and mathematics and science courses were also examined as factors in career choice. Females preferring science related careers and females preferring nontraditional careers such as police, military and trades were found to have a positive role-specific self-concept and a masculine perception of themselves. Females preferring traditional careers such as teacher or hairdresser had a poor role-specific self-concept and a more feminine perception of themselves. Males as a group were found to have a more positive role-specific selfconcept than females. Logical ability was also related to a science career preference for both males and females. Males expected to take more higher level math courses than females, while females preferring science careers expected to take the most higher level science courses. INTRODUCTION

The factors leading to a career choice are complex, but the results are startlingly straightforward. Males aspire to higher levels of careers (Marini, 1978, Saha, 1982) and express interest in a wider range of careers than do females (Currie, 1982; Marini, 1978). Those careers chosen by females are for the most part restricted to traditional areas that are often seen as an extension of the housewife/mother role, such as teaching, nursing, secretarial, social work, and physical therapy (Howe, 1977; Marini & Greenberg, 1978; Currie, 1982). Despite recent advances females continue to be underrepresented in the sciences and their attrition rates are higher than those for men (National Science Foundation, 1982 a, b). Dunteman, Wisenbacker, and Taylor (1979) in developing a predictive model for science majors found that females had lower scores than males on four major variables: a preference for working with things or people, mothers educational 31

CHAPTER 2

aspirations for her child, mathematics ability, and number of science courses taken. However, even after these variables were taken into account there still remained a discrete negative effect of being female upon the selection of a college science major, and this effect was twice as important as their mathematics ability. Chipman and Thomas (1985) in reviewing the research suggest that the mathematical requirements of a college major or the precollege mathematical preparation of a student is not the crucial factor for women when making a career choice. Rather, they believe that majors with direct vocational implications are most likely to be chosen according to their sex-role stereotyping. In other words, women are avoiding majors such as engineering because it is a man’s job, not because it requires mathematics. Barnett (1975) has suggested that external barriers established by peers, teachers, and cultural norms eventually become internal attitudinal barriers so that females come to believe that they can not or should not engage in male dominated occupations. Two of these internal attitudinal barriers that may be affecting females in their career choices are role-specific self-concept and sex-role conflict. Rolespecific self-concept is the perception individuals have of their competence when engaged in a particular task. Sex-role conflict is the feeling individuals have when they are engaged in activities they believe to be inappropriate for their sex. Research indicates that mathematically gifted females, a group one would expect to choose mathematical or scientific careers, do not always do so. Their choice of a career in these areas depends upon their role-specific self-concept as competent in mathematics (Hollinger, 1983). Other mathematically competent females express sex-role conflict over their success in mathematics (Armstrong, 1985). Average 7th and 8th grade females who do well in mathematics have also been found to have poor role-specific self-concepts: they did not enjoy mathematics, did not plan to take more mathematics, nor use it in the future. The mathematically achieving males in this sample had a high role-specific self-concept and planned to take more mathematics because of its long term usefulness and enjoyment of the subject (Peterson, Burton,  & Baker, 1983). In 7th and 8th grade science classes, other researchers (Peterson, Kauchak & Yaakobi, 1980) have found no sex differences for role-specific self-concept. In general the more masculine characteristics a female perceives herself to have the more likely she is to choose a nontraditional career, including careers in science (Baker, 1984). Nontraditional careers for women are those that have historically employed mostly men and have entrance requirements associated with male characteristics such as mathematical ability or physical strength. The more feminine characteristics a female perceives herself to have the more likely she is to choose a traditional career (Baker, 1984; Lyson & Brown, 1982). Traditional careers for women are those that have historically employed women and are viewed as supporting and helping. Many of these careers are extensions of the wife and mother role such as teaching or nursing. Shann (1983) found that women in nontraditional 32

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE

professions rejected culturally defined sex roles and exhibited some of the same needs, motives, and values as men in masculine fields. Both sex-role identity and role-specific self-concept appear to influence career choices for females. However, the relative importance of these factors or their combined influence is unclear. Consequently this research focuses on the influence of both of these factors on male and female career preferences in Australian high schools, particularly in the area of science. HYPOTHESES

Females preferring a scientific career will have both a good role-specific self-concept and a masculine sex-role identity. Females preferring traditional careers will have a poor role-specific self-concept and a feminine sex-role identity. For males a good role-specific self-concept will be the determining factor for career choice. Sex-role identity will have little influence because it is unlikely that adolescent males will report a sex-role identity that is not masculine. A good role-specific self-concept is defined as evaluating one’s behavior, in the context of learning science, as similar to what is believed to be the behavior of an ideal science learner. A poor role-specific self-concept is an evaluation of one’s behavior as dissimilar to what is believed to be the behavior of an ideal science student. A feminine sex-role identity is defined as evaluating one’s self as having more stereotypical feminine traits than stereotypical masculine traits. A masculine sexrole identity is defined as evaluating one’s self as having more masculine traits than feminine traits. PROCEDURE

Sample Two schools were chosen from a list provided by the Western Australia Department of  Education. Selection was made on the basis of the presence of an Equal Opportunity Officer in the school, agreement to participate, and location. The schools were located in two different middle class suburbs of a large metropolitan area. There were 177 females and 179 males in the study. Less than 1% of the sample was nonwhite. Although the entire tenth year population was tested in each school only the data from those students who indicated that they were remaining in school for years 11 and 12 were used. Instruments Four instruments were used in this study, an open ended questionnaire which asked students to respond to questions about perceived science ability, career choice, and 33

CHAPTER 2

future course taking in mathematics and science, the Bem sex-role Inventory (BSRI), (Bem, 1974), the Peterson Yaakobi Q-Sort (PYQS), (Peterson, Kauchak & Yaakobi, 1980), and the Test of Logical Thinking (TOLT), (Tobin & Capie, 1980). The open ended questionnaire was an instrument devised to gather specific information for this study. It was scored in the following way. Question 1 required students to assess their ability level in science on a scale of 1 to 3. One was basic, 2, intermediate, and 3, advanced. Questions 2 and 3 required students to indicate what kind of mathematics and science courses they expected to take in years 11 and 12. The responses to this question do not necessarily reflect the true number of courses students need in order to pursue the careers of their choice. Neither does it take into consideration that the students were already enrolled in a science course. Students who indicated they were leaving school in response to this question were dropped from the study. The courses were assigned the following values; (1) applied or practical mathematics, (2) level 1 math, (3) level 2 math, (4) level 3 math, (1) general science, (2) human biology, (3) biology, (4) physical science (5) chemistry, (6) physics. The scoring system takes into account the difficulty level of the course, with easier courses being assigned lower numbers and the number of courses expected to be taken. The student’s score was the total of the numbers assigned to the courses they expected to take. Career preferences were grouped into five categories of science, nonscience, allied health, nontraditional and nonscience careers for women, and traditional careers for women. There were no nontraditional nonscience careers for men and most of the male career choices fell into either the science or nonscience category. These categories were defined in the following way: (1) Science: careers in the physical, biological or earth sciences, nursing, physiotherapy, x-ray technology, engineering, computers (2) Nonscience: careers in business, trades, service fields, military, law, arts, government (3) Allied health: when this distinction was made careers preferences in nursing, physiotherapy, and x-ray technology were removed from the science category to make a new category called allied health (4) Nontraditional nonscience: careers which do not require science, but have traditionally employed few women in Australia such as law, building and mechanical trades, and certain aspects of the military and police (5) Traditional: careers dominated by women such as secretary, hairdresser, nursery school teacher, store clerk, mother, airline stewardess, and receptionist These categories were then used to determine membership in the groups formed for the discriminant analysis. The BSRI assess stereotypical masculinity and femininity and has a reliability of 0.90. Students rate themselves using a seven point Likert scale for sixty items. The scale ranges from 1, never or almost never true to 7, always or almost always true. The items consist of adjectives or short phrases such as, cries easily, or strong. 34

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE

A  masculinity and femininity score is obtained by summing the ratings of those items designated as masculine and those items designated as feminine. The PYQS assess role-specific self-concept in science and has a reliability of 0.87. The individual items on this instrument can be found in Table I. In order to obtain a score for each item two sorts are made. For the first sort students are asked to rank the items as if they were describing themselves. The item ranked first during the first sort is most like themselves, the item ranked second is the next best descriptor and so on. The item ranked twentieth is least like themselves. The second sort requires the student to rank the items according to the behaviors of the ideal science student. Again, the first item is most like the ideal science student and the last item is least like the ideal science student. The discrepancy scores between the real and ideal rankings were then used in the analysis. Each of the 20 items was analyzed separately. Many mean scores for the items on this instrument are negative. This is the result of calculating the discrepancy scores in which the ranking of the ideal science student was subtracted from the real rankings. Thus a negative score indicates that the ideal science student is more likely to engage in the behavior than TABLE I Items on the Peterson-Yaakobi Q-Sort   1.  Reads about science outside of class   2.  Thinks up own problems to investigate   3.  Never asks own questions   4.  Memorizes many new words and terms   5.  Doesn’t read the textbook   6.  Knows the main ideas of science   7.  Is able to apply what is learned   8.  Doesn’t follow teacher directions completely   9.  Helps other students having difficulty 10.  Works and shares with other classmates 11.  Not self confident 12.  Concerned about grades 13.  Competes with classmates for teacher attention 14.  Respects teacher’s opinion 15.  Learns only what the teacher asks for 16.  Completes all homework 17.  Keeps room and equipment neat and clean 18.  Takes part during discussions 19.  Criticizes other students 20.  Disrupts class

35

CHAPTER 2

the students in the sample. A positive score indicates that the students in the sample are more likely to engage in the behavior than an ideal student. The TOLT was used to determine the influence of ability on career choice. It is considered a measure of formal reasoning and has a reliability of 0.80. It is a ten item instrument. All four of the instruments were administered in a single 90 minute session by tenth grade science teachers during regularly scheduled class time. The author was on hand to handle any unexpected problems. Analysis The data was analyzed using analysis of variance (ANOVA) and discriminant analysis. Regression analysis could not be performed because of the small number of students indicating a preference for a science related career as compared to the number of variables in the study. RESULTS

Analysis of Variance Mean scores for the significant analyses of variance are found in TABLE II. The peterson-yaakobi q-sort.  Analysis of variance was used for each of the 20 items on the q-sort. To do so discrepancy scores were calculated for each of the pairs of items by taking the differences between the rankings of the real and ideal sorts. The ANOVA (TABLE III) indicated that the discrepancy between actual behavior and that of the ideal science student was smaller for males than for females in every case where a statistically significant difference was found. Females believed that their behavior in science classes differed from the behavior of the ideal science student to a greater extent than males did. This pattern indicates that females’ role-specific self-concept was poorer than the males’ role-specific self-concept. Specifically, females reported that they did not read about science, think up their own science problems, ask their own questions, apply their knowledge or work and share with other classmates as often as the ideal science student. The open ended questionnaire.  There was no difference in male and female self assessment of ability in science, nor in the science courses they planned to take in grades 11 and 12. However, males did plan to take more higher level mathematics courses than females (TABLE III). The test of logical thinking.  There was a significant difference between male and female scores on the TOLT in favor of males. This indicates that the males were more likely than females to be capable of abstract logical thought (TABLE III). 36

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE

TABLE II Means and Standard Deviations for Significance Analyses of Variance Males PYQS

X

Females S.D.

X

S.D.

Reads about science

7.11

6.94

9.32

6.84

Thinks up problems

5.38

6.56

7.39

7.11

Never asks questions

−.79

5.75

−2.59

6.69

Apply what is leamed Works & shares Competes for attention Criticizes o thers

.88

5.38

2.14

5.59

−.80

5.58

−3.00

5.68

1.40

5.84

2.79

6.80

−1.15

6.22

.42

5.95

TOLT

4.37

2.95

2.65

2.36

Math courses

7.70

16.37

4.03

7.36

Discriminant Analysis Three discriminant analyses were performed. The first analysis looked for variables that discriminated between all students choosing science and nonscience careers and no attempt was made to look at differences resulting from sex. The second analysis looked for variables that discriminated among four groups; males choosing science and nonscience careers and females choosing science and nonscience careers. The third analysis was performed on only the females in the sample. This analysis looked for variables that discriminated among four groups of females; those preferring traditional, allied health, nonscience, and nontraditional nonscience careers. The description of the open ended questionnaire in the instrument section contains the specific careers subsumed by the broader categories of the groups used in the three discriminant analyses. The variables used in the discriminant analyses were the 20 items on the PYQS, masculinity and femininity scores from the Bem, the score from the TOLT, and responses from the open ended questionnaire concerning math and science courses and ability in science. Analysis one.  The first analysis looked for variables that discriminated between students choosing science and nonscience careers (TABLE IV). These two groups could be distinguished on the basis of three factors; the number of science courses, the number of mathematics courses, and self-confidence in their ability to learn science. This last was one of the 20 items on the PYQS. Not surprisingly, mean scores indicated that students with science career preferences planned to take more science and math courses and were more self-confident about their science ability than students who preferred nonscience careers (TABLE V). 37

CHAPTER 2

TABLE III Significant One Way Analyses of Variance PYQS

DF

SS

MS

F

SIG

Reads about science Between groups Within groups Total Thinks up problems Between groups Within groups Total Never asks questions Between groups Within groups Total Apply what is learned Between groups Within groups Total Works & shares Between groups Within groups Total Competes for attention Between groups Within groups Total Criticizes others Between groups Within groups Total TOLT Between groups Within groups Total Math Courses Between groups Within groups Total

38

1 369 370

453.6 17529.36 17982.96

453.6 47.5

9.55

.002

1 369 370

373.33 17251.87 17625.20

373.33 46.75

7.99

.005

1 369 370

297.80 14262.64 14560.44

297.80 38.65

7.7

.006

1 369 370

146.89 11087.74 11230.63

146.89 30.40

4.89

.03

1 369 370

450.69 11684.15 12134.84

450.69 31.66

14.23

.001

1 369 370

177.22 14252.09 14429.31

177.22 38.62

4.59

.03

1 369 370

22.09 13705.88 13934.95

22.09 37.14

6.17

.01

1 369 370

276.03 2676.09 2952.12

276.03 7.21

38.27

.001

1 369

4223.13 60736.06

4223.13 165.49

25.5

.001

370

64959.19

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE

TABLE IV Discriminant Analysis Between Science and Nonscience Career Preference Groups x2

DF

Sig.

0.67

139.49

27

0.001

0.35

372.03

78

0.001

Function Eigen Value % Variance Cann. Cor. Wilk’s Lambda 1

0.49

100.00

0.57

0

Discriminant Function Coefficients Variables

Function 1

Not self confident

−0.33

Science courses

0.70

Mathematics courses

0.41 Group Centroids

Group

Function 1

Science

1.34

Nonscience

−0.36

Percentage of Groups Correctly Classified Group Science G1 Nonscience G2

G1

G2

%

N

%

N

43.6

34

56.4

44

4.8

14

95.2

276

Using theses variables 43.6% of the students with science career preferences and 95.2% of the students with nonscience careers preferences were correctly classified. Notice that cognitive ability as measured by the TOLT, masculinity and femininity, self-assessed ability and all but one of the items on the q-sort were not important in the discrimination of the two groups. Analysis two.  The second analysis looked for variables that discriminated among four groups; males choosing science and nonscience careers and females choosing science and nonscience careers. Differences among these four groups were attributed to masculinity and femininity scores, the score on the TOLT, the number of science courses, and the number of math courses (TABLE VI). Mean scores for each group indicated that males who prefer science careers were the most masculine group and females who prefer nonscience careers were the most feminine group. Both males and females preferring science careers estimate their ability in science as higher than males and females preferring nonscience careers. Males preferring science careers had the highest cognitive ability scores. As expected the greatest difference among groups was in course taking behavior. Males and females preferring science careers expect to take more higher level science 39

CHAPTER 2

and within this group females expect to take more science than males. Both groups of males expect to take more higher level mathematics courses than both groups of females with males preferring a science career taking the most mathematics (TABLE V). TABLE VI also shows the percentage of each group correctly classified or misclassified on the basis of the variables in the analysis. It appears that this set of variables is moderately good at discriminating among those who would not choose a science career but has little predictive value in determining who would choose a science career. Analysis three.  The third analysis looked for variables that discriminated among the females in the sample. The groups under examination were traditional, allied health, science, and nonscience nontraditional career preferences. Differences among these groups was attributable to masculinity and femininity scores, cognitive ability, course taking behavior, and a number of role-specific behaviors (TABLE  VII). These behaviors include: asking their own questions, memorizing words and terms, applying what is learned, helping others, lacking self-confidence as a science learner, criticizing others and disrupting class. Mean scores for each group indicate that females preferring science and allied health careers perceived themselves as more masculine than did the other two groups. Females preferring nontraditional, nonscience careers perceived themselves as the least feminine of the four groups followed by females preferring science, then allied health, and lastly traditional careers. Females preferring science careers also had the highest cognitive scores, followed by the group preferring nontraditional, nonscience careers. The groups preferring allied health and traditional careers were indistinguishable on this variable. The science career preference group expected to take the most higher level science courses followed by the allied health group. However, the difference between these two groups was quite large. The traditional career group planned to take the least mathematics. The group preferring nontraditional nonscience careers had the best overall rolespecific self-concept. The groups preferring science and allied health careers also had good role-specific self-concepts, but not quite as strong as that of the nontraditional group. Students who preferred traditional careers had the poorest role-specific selfconcept. In this analysis the variables were moderately successful in discriminating among all groups except the allied health group (TABLE V). DISCUSSION

Looking at the sample as a whole, males believe that their behavior in science class is  more like that of the ideal science student than females do. This indicates that males have a better role-specific self-concept than females. However, despite a poorer self-concept in terms of classroom behaviors, there is no differences in male 40

152.62 148.61

Male science

Male nonscience

140.70

Nontraditional

b

118.35

142.24

136.03

125.39

5.12

3.24

2.53

2.06

5.22

TOLT

1.35

0.94

3.24

0.22

Q7

3.83

6.24

2.55

5.22

TOLT

4.75

6.72

Matha

science and math refer to the courses students intend to take. Q 3,4,7,8,10,11,19,20 refer to questions on the PYQS.

149.53

Allied health

a

135.78

Traditional

Masculinity 148.72

Femininity

–0.91

Nontraditional

Science

4.65

–1.65

Allied health

7.20

–3.33

Traditional

4.35

–2.26

Q4

122.31

123.53

133.48

125.22

Femininity

1.62

8.69

Sciencea

Science

Q3

137.96

Analysis 3b

148.72

Masculinity

Analysis 2

Female nonscience

−1.38

Female science

−2.71

Nonscience

Not self-confident

Science

Analysis 1

2.28

2.47

2.00

2.76

Ability

–1.02

0.65

–2.50

–1.50

Q8

2.22

2.71

2.12

2.56

Ability

0.86

2.69

0.34

10.34

Science

0.65

–1.94

–3.46

–1.72

Q10

1.46

7.36

0.64

10.34

Science

TABLE V Mean Scores for Discriminant Analyses Groups

4.76

4.76

3.00

4.30

Math

–0.50

–3.88

–1.87

–2.20

Q11

6.26

9.14

3.76

4.30

Math

0.26

–0.47

0.04

–0.22

Q19

0.19

–2.12

–1.99

0.65

Q20

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE

41

42 0.40 0.14

2

3

−0.64

Femininity

0.48

Male nonscience

−0.97

Female nonscience 1.7

−0.22

Female science

Male science

0.41 Group Centroids

−0.55

0.70

−0.001

1.5

0.73

Math courses

0.36

Science courses

0.5

−0.31

Function 2

Self-assessed ability

0.34

0.71

TOLT

0.88

0.68

Wilk’s Lambda

Discriminant Function Coefficients

0.35

0.53

6.70

Cann. Cor.

Function 1

100.00

89.64

60.11

% Variance

Masculinity

Variable

0.81

Eigen Value

1

Function 45.88

163.58

x2 24

50

DF

0.23

0.62

0.24

−0.71

0.64

−0.45

0.33

−0.44

Sig. 0.005

0.001

Function 3

TABLE VI Discriminant Analysis Among Female Science and Non-science, and Male Science and Non-Science Career Preference Groups

CHAPTER 2

36.1 1.4 7.1 1.3

Female nonscience G2

Male science G3

Male nonscience G4

%

Female science G1

Group

G1

2

3

2

13

N

18.1

2.4

75.2

41.7

%

G2

27

1

106

15

N

9.4

50.0

2.1

2.8

%

Percent of Groups Correctly Classified G3

14

21

3

1

N

71.1

40.5

21.3

19.4

%

G4

106

17

30

7

N

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE

43

CHAPTER 2

and female self assessment of ability. This would lead one to believe that the females are basing their judgement on factors other than those that are listed on the q-sort. Also, despite a poorer role-specific self-concept, females intend to take similar kinds of science courses. This may be the result of compulsory courses needed for entrance into tertiary institutions or trade schools. However, this is not the case for mathematics where males intend to take more higher level courses than females. This difference in mathematics course taking may be the result of many more males than females indicating a preference for careers in business and accounting related fields. Lastly, males performed better on the TOLT than females. This indicates that they are more likely to be capable of abstract logical thinking than females. This low female score is attributable to all the females except those preferring a science career. Sex differences in favor of males on tests requiring formal or logical thinking occur most consistently in the area of proportional reasoning and sometimes in the areas such as seriation, combinations and permutations (Mehan, 1985). Differences in the experiences and socialization of males and females have been offered as an explanation of this persistent difference. Whatever the source, it is a critical difference because abstract logical ability has been shown to be a necessary, but not sufficient condition for success in science (Enyeart, Baker, & Van Harlingen, 1980). The first discriminant analysis yielded both expected and unexpected results. As expected the group preferring a science career expected to take more higher level science courses and were more self-confident in their ability to learn science than the group that preferred a nonscience career. Surprisingly, masculinity and femininity, logical ability, and all but one item on the PYQS were not significant in discriminating between groups. In fact the variables in this analysis were better at predicting those who did not prefer a science career (95.2% correctly classified) than those who did prefer a science career (43.6% correctly classified). Subsequent discriminant analyses with finer group distinctions indicates that masculinity and femininity, logical ability, and role-specific self-concept are indeed very important factors in career preferences. The simple dichotomy of groups masked differences by sex and differences within sex. Thus it was too crude to pick up the subtleties involved in career preferences which were revealed in the subsequent discriminant analyses. The second discriminant analysis was performed on four groups; males who preferred science and nonscience careers and females who preferred science and nonscience careers. In this analysis masculinity, femininity, logic, self-assessed ability and science and math courses were discriminating factors. The misclassification of females who prefer science careers as females who prefer nonscience careers and the misclassification of males who prefer science careers as males who prefer nonscience careers indicates that unlike older students training for a science career (Baker, 1983) the subjects in this study are more like their sex group than their career preference group. 44

–0.41 –0.29 0.39 0.32

Not self-confident

Criticizes other students

Disrupts class

Masculinity

0.26

0.25

0.31

Math courses

0.46

0.66

Science courses

0.86

0.29

0.37

–0.3

0.28 0.41

(Continued)

0.72

0.01

0.001

Sig.

Function 3

–0.47

24

50

78

DF

Self-assessed ability

–0.44

–0.40

–0.32

0.3

Function 2

19.65

74.58

220.48

x 2

TOLT

Femininity

0.29

−0.27

Function 1

Help others

Doesn’t follow teacher directions

Applies what is leamed

Memorizes new words & terms

Never asks own questions

Variable

0.89

Discriminant Function Coefficients

0.53

0.39

2

93.45

0.89

0.64

Wilk’s Lambda 0.27

0.76

Cann. Cor.

73.0

% Variance

1

Eigen Value

0

Function

TABLE VII Discriminant Analysis Among Female Science, Traditional, Allied Health, Non-Science and Non-Traditional Career Preference Groups

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE

45

46 1.78

Science

1.5 11.8 5.6

Allied Health G3

Nontraditional G4

G1

Group 60.9

0.07

Nontraditional

Traditional G2

−0.19

Allied Health

Science G1

−1.23

Traditional

%

Function 1

Variable

3

2

1

28

N

22.2

35.3

74.6

8.7

%

G2

12

6

50

4

N

%

3.7

35.3

3.0

6.5

Percent of Groups Correctly Classified

−0.95

0.54

0.35

0.41

Function 2

Group Centroids

TABLE VII Continued

G3

2

6

2

3

N

68.5

17.6

20.9

23.9

%

−0.04

−1.05

0.19

0.16

G4

Function 3

37

3

14

11

N

CHAPTER 2

THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE

An obvious self-selection is taking place, both males and females preferring science careers have the logical ability and high self-estimates of their own ability necessary for success. In the case of the females preferring a science career there is also the high masculinity score and the expectation to take the most higher level science courses. These females are exhibiting the drive, self-confidence, ability and stereotypically masculine characteristics typical of females in science and other nontraditional careers whereas females preferring nonscience careers are more feminine and lacking in confidence and cognitive ability. If we return to the original hypotheses we can see that the evidence in this analysis does not support them. Role-specific self-concept is not a determining factor in career choice for males. For females, the additive effect of sex-role identity and role specific self-concept is also missing. However, masculinity among females preferring science careers and femininity among females preferring nonscience careers does remain as an important variable. Thus for females as well as males, other factors in this study such as ability are much more important than role-specific self-concept as it is defined by the PYQS. This is not to say that the concept of rolespecific self-concept is not important, but that the items on the PYQS which defines role-specific self-concept as classroom behaviors may not be factors that are taken into consideration when students make career choices. Since increasing the number of females choosing science as a career is a critical problem the final discriminant analysis examined only the female portion of the sample. In this analysis my hypotheses were supported. When discriminating among females preferring traditional, nontraditional, science and allied health careers both masculinity and femininity, and role-specific self-concept are critical variables. Eight of the 20 items on the Q-sort were significant. These can be roughly divided into two categories of items; academic behaviors, e.g. applies what is learned, and social behaviors, e.g. disrupts class. Among the four, the nontraditional and science groups had the strongest role-specific self-concept followed by the allied health and lastly the traditional group. The very strong role-specific self-concept of the nontraditional group may be a reflection of an overall good self-concept. This seems reasonable in light of the types of careers chosen by the females in this group. Many careers in this group were in the military, police, and building trades. Females choosing such careers must necessarily have a very strong overall self-concept given that institutional barriers to many aspects of these careers have only recently fallen. The masculinity and femininity scores of these groups are not so easily interpreted in all cases. The science and nontraditional group are less feminine than the allied health and traditional groups, with allied health being the most feminine. This is perhaps due to the helping nature of these professions which deal with nurturing and restoring health to the sick. The science, nontraditional and allied health groups also have high masculinity scores. This can be interpreted as reflecting the masculine nature of the careers chosen by the science and nontraditional groups, but provides no explanation for the 47

CHAPTER 2

allied health group. The allied health group does expect to take more science in the future and this may be enough to elevate the masculinity scores. Logical ability is also a factor in career choice with the most able choosing science (x = 5.22). The allied health group did approximately half as well as the science group (x = 2.53). One might speculate that choosing an allied health career is the result of wanting to help people, and interest in science but with lower aptitude. This study is descriptive and focuses on the influence of several factors on career choice, especially the cumulative effect of sex-role identity, and role-specific selfconcept and as such did not identify causes. However, it raises a number of causal questions such as the one mentioned above concerning the reasons for a career choice in allied health fields and suggests that further investigation should focus on indepth interviews with students. Of particular interest would be to determine parental, teacher, and peer influence on career choice, and the role of ability, especially for very bright females or those that fall into the traditional and nontraditional groups. Lastly it also raises technical and ethical questions concerning the difficulty and desirability of changing students’ perceptions about themselves, especially in the area of sex-role identity. NOTE 1

Originally published as Baker, D. (1989). The influence of role-specific self-concept and sex-role identity on career choices in science. Journal of Research in Science Teaching, 24(8), 739–756. Reprinted here with permission.

REFERENCES Armstrong, J. (1985). A national assessment of participation and achievement of women in mathematics. In S.F. Chipman, L.R. Brush & D.M. Wilson (Eds.), Women and mathematics: Balancing the equation. (pp. 59–94). Hillside, NJ: Lawrence Erlbaum Associates. Baker, D. (1984). Masculinity, femininity and androgeny among male and female science and nonscience college majors. School Science and Mathematics. 84, (5) 459–467. Baker, D. (1983). Can the difference between male and female science majors account for the low number of women at the doctoral level in science? Journal of College Science Teaching. 13, 102–107. Barnett, R. (1974). Sex differences and age trends in occupational preference and occupational prestige. Journal of Counseling Psychology. 22, 35–38. Chipman, S., & Thomas, V. (1985). Women’s participation in mathematics: Outlining the problem. In S.F. Chipman, L.R. Brush, & D.M. Wilson (Eds.), Women and mathematics: Balancing the equation, (pp. 1–24). Hillsdale, NJ: Lawrence Erlbaum Associates. Curie, J. (1982). The sex factor in occupational choice. Australian and New Zealand Journal of Sociology, 18, (2) 180–195. Dunteman, G., Wisenbaker, J., & Taylor, M. (1979). Race and sex differences in college science program participation, (contract No. SED77–18728). Washington, DC: National Science Foundation. Enyeart, M., Baker, D., & Van Harlingen, D. (1980). Correlation of inductive and deductive logical reasoning to college physics achievement. Journal of Research in Science Teaching, 17, (3) 263–267. Hollinger, C. (1983). Self-perception and the career aspirations of mathematically talented female adolescents. Journal of Vocational Behavior, 49–62. Howe, L. (1977). Pink collar workers. New York: Avon Books.

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THE INFLUENCE OF ROLE-SPECIFIC SELF-CONCEPT AND SEX-ROLE Lyson, T., & Brown, S. (1982). Sex-role attitudes, curriculum choice, and career ambition: A comparison between women in typical and atypical college majors. Journal of Vocational Behavior, 20, 366–375. Marini, M. (1978). Sex differences in the determination of adolescent aspirations: A research review. Sex Roles, 4, (5) 723–753. Marini, M., & Greenberger, E. (1978). Sex differences in occupational aspirations and expectations. Sociology of Work and Occupations, 5, 147–178. Mehan, A. (1984). A meta-analysis of sex differences in formal operational thought. Child Development, 55, (3) 1110–1124. National Science Foundation. (1982a, January). Women and minorities in Science and engineering (Report No. 82–302). Washington, DC: National Science Foundation. National Science Foundation. (1982b). Science and engineering education: Data and information (Report No. 82–30). Washington, DC: National Science Foundation. Peterson, K., Burton, G., & Baker, D. (1983). Geometry students’ role-specific self-concept: Success, teacher and sex differences. Journal of Educational Research, 77, (2) 122–125. Peterson, K., Kauchak, D., & Yaakobi, D. (1980). Science students role-specific self-concept: Courses, success and gender. Science Education, 64, (2) 169–174. Saha, L. (1982). Gender, school attainment and occupational plans: Determinants of aspirations and expectations among Australian urban school leavers. The Australian Journal of Education, 26, (3) 247–263. Shann, M. (1983). Career plans of men and women in gender dominant professions. Journal of Vocational behavior, 22, 343–356. Tobin, K., & Capie, W. (1981). The development and validation of a group test of logical thinking. Educational and Psychological Measurement, 41, 413–423.

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SEX DIFFERENCES IN CLASSROOM INTERACTIONS IN SECONDARY SCIENCE

WHY I CONDUCTED THE STUDY

The inspiration for this study came from my experience working on a project with the Far West Laboratory in San Francisco. The project was called Portraits of Intermediate Life Science Classes (Lash et al., 1984) and was part of their Secondary Science and Mathematics Improvement Program. The goal of the project was to document how eleven 7th grade life science teachers in California and Utah taught science. Documentation included teacher beliefs, activities, pedagogical techniques, and classroom climate. There was no evaluation as to whether the instruction was effective or not but rather the purpose of the study was to paint of picture of what a 7th grade life science classroom looked like. The research team for the project spent many hours in training watching tapped lessons to insure that we could create rich profiles of teaching. After the training, I made 10 classroom observations, interviewed the teacher, did a qualitative analysis of the data for themes, and wrote my profile. Although there was no evaluation of effectiveness in my profile, I could not help thinking about what I saw and heard, and making my own judgments about the effectiveness and appropriateness of what I saw and heard. Among the many things that disturbed me was a pattern of teacher student interactions that I found inequitable. Not all students had opportunities to participate and others were always in the spotlight. Since I suspected that there were inequities based on students’ sex, I felt that this needed further investigation. My Far West Laboratory study experience became the impetus to look more carefully at student teacher classroom interactions and resulted in Sex Differences in Classroom Interactions in Secondary Science. METHODOLOGICAL DECISIONS

Based on my experience with the Far West Laboratory study, I knew I wanted a broader picture of what was happening in classrooms across more content areas of science. I wanted to see if there were differences in teacher student interaction patterns as a function of content areas. I suspected that the ratio of male and female students, which was skewed in favor of males at the time of this study, might be an important factor. Furthermore, I suspected that interactions might be different in a laboratory setting than in a lecture setting where teachers asked questions and students answered them. Because I had been in many of the local schools observing 51

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student teachers, I was able to approach the schools to recruit teachers to participate in my study. I knew that I had to make more observations than I did for the Far West study because I was observing three teachers rather than one. I intended to make 24 observations, eight per teacher, but time and scheduling resulted in 20 observations. I also knew that I wanted to narrow the focus of the observations to a specific set of teacher student interactions rather than trying to observe and script everything that was happening in the classroom. I was particularly interested in who was called on, the nature of feedback, and disciplinary and social interactions. These factors led me to use an observation protocol rather than trying taking notes about what happened in the classroom in narrative form. After searching for existing classroom interaction protocols, I selected and used a slightly modified form of Brophy and Good’s Dyadic interaction system (1970). This observation protocol had been used widely in classroom observation studies generally but had never been used to document teacher student interactions in science classrooms. Since I was no longer working as part of a large team such as we had for the Far West  Laboratory study, I made the observations alone and a second observer periodically joined me in the classroom as a check on the reliability of the observations. The form I used allowed me to make a tick mark across 18 categories of interactions. This reduced the errors and allowed the second observer to check on the reliability of my observations easily by comparing his tick marks to mine. The form also allowed for additional remarks or descriptions of anything unusual. In the end, this information did not add to the count of the various types of teacher student interactions but was useful in describing the differences in the way the classrooms were organized and how students worked. Since the data collected was categorical, I used chi square as my analytical tool. This reduced the error that might arise from interpretation of narrative data but also reflected my preference for statistical analysis. In all honestly, I felt that my Far West Laboratory study experience writing pages and pages of observations, trying to capture everything happening in the complex system of the classroom, and then trying to make sense of the observations was a negative experience. I was not yet persuaded of the value of a strongly qualitative approach. SCIENCE EDUCATION AT THE TIME OF THE STUDY

Science education, at the time of this study, was just beginning to consider the effects of teacher student interactions but not in terms of sex differences. Mary Bud Rowe (1974a, 1974b, 1986) had introduced the concept of wait-time in teacher questioning and the effects of wait-time was explored across many content areas. As a consequence, in 1987, Kenneth Tobin conducted a review of research studies that looked at teacher student interactions for wait-time. He concluded that wait time had an impact on achievement. In science, a wait-time effect on achievement was found at the elementary, middle, and secondary level and also in mathematics but 52

SEX DIFFERENCES IN CLASSROOM INTERACTIONS IN SECONDARY SCIENCE

not in other content areas. These studies did not explore whether there were gender differences in teachers’ wait-time. Also in 1987, Tobin and Gallagher published a study that looked at target students in science classrooms. They concluded that males were more likely to be target students than females. Furthermore, males raised their hands more than females, initiated more questions than females, were called on more than females, and the teacher elaborated more on male students’ responses than female students’ responses. Some studies looking at classroom interactions across multiple classrooms included science as one of the content areas. A study published in Educational Studies in Mathematics (Lerder, 1987) compared teacher student interactions in mathematics and science classrooms and found that wait-time was longer for males than females in science classes. Another study compared teachers’ interactions toward males and females in science and language classes and found that males were favored in science and females in language classes (Worral & Tsarna, 1987). Becker (1981) found a similar biased interaction pattern favoring males over females in mathematics classes where males asked more questions and received more sustained feedback. RESEARCH IN THE WIDER FIELD OF EDUCATION

Although classroom observations had been made in many content areas and there was and still is a journal called The Journal of Classroom Interactions, science classrooms were rarely, if ever, the focus of the studies published in that journal. In fact my article was the only one addressing science classroom interactions in The Journal of Classroom Interactions in all of the 1980s. On the other hand, there were many studies in the 1980s that investigated differences in interactions with male and female students with little concern about the context of the content areas studied except for grade level. For example, Stake and Katz (1982) and French and French (1984) examined gender imbalance in primary classrooms, Boersma, Gay, Jones, Morrison, and Remick (1981) examined sex differences in college teacher student interactions, and Irvine (1986) looked at teacher student interactions as a function of race, sex, and grade level. In addition, there were many publications by the husband and wife team of Myra and David Sadker about sexism in the classroom (e.g. 1985a, 1985b, 1986). Interest in the topic of bias in interactions was so great that in 1985, an entire section in the Phi Delta Kappan was devoted to women in the classroom. Across studies, there was a pattern of female neglect or invisibility. Females received less teacher attention and feedback of all kinds than males, males called out more than females and initiated more negative and positive interactions with teachers, males received more reprimands from teachers than females, and sex segregation occurred in seating and group work. In 1982 the influential report The Classroom climate: A Chilly One for Women (Hall  & Sandler, 1982) was published by the Project on the Status of Women, 53

CHAPTER 3

Association of American Colleges. This publication was widely cited and spurred increased concern about male female inequities in classrooms K-16. This publication was followed by a call for a research agenda on sex equity in education for the divisions and SIGs at the American Educational Research Association (Tuttle, 1985) and a closer look at classrooms by feminist scholars in a book called Gender Influences in Classroom Interaction edited by Wilkinson and Marrett (1985). THE CULTURE OF THE TIMES

Since this article was published in 1987 the same year as The Influence of RoleSpecific Self-Concept and Sex-Role Identity on Career Choices in Science (Baker, 1987), there is not much more to add about the culture of the times that has not already  been discussed. Overall, the 1980s saw a growing realization that biased views of race and gender were unacceptable. In 1983 (Berkley Center for Religion, Peace and World Affairs, 2015), the Internal Revenue service denied tax exempt status to private schools that discriminated on the basis of race (e.g. Bob Jones University). This realization also led to the Senate’s rejection of Robert Bork’s nomination to the Supreme Court in 1987 (The Leadership Conference on Civil Rights Education Fund, 2001) because of his views on race and gender. Women were breaking in to politics in significant ways in the United States. In 1984 the Democratic Party nominated Geraldine Ferraro for vice president (Oakland Museum of California, 2015). She was the first women ever nominated for this position. However, we were behind countries like Norway and Yugoslavia which elected a woman as Prime Minister and Iceland, Malta, and the Philippines which elected women as President in the 1980s (International Women’s Democracy Center, 2015). This was also the time when Feminists of color began writing about sex and race (e.g. bell hooks, Angela Davis, Anzaldua and Cherrie Moraga). But women were still experiencing sexual harassment on the job and receiving less pay than men for comparable work (Oakland Museum of California, 2015). IMPACT OF MY WORK

This study added to a growing body of research that documented that a classroom was a different educational environment for male and female students. It identified inequities, conscious or otherwise, in teacher student interactions despite the belief of teachers that they treated all students equally. It identified another set of reasons why females might not be choosing science. Seeing that males are called on more often than females and receive more feedback for their answers, while being ignored by the teacher even when your hand is raised, is not going to motivate females to take more science. Nor, are interactions that focus on social rather than academic interactions encouraging because they send a message that the teacher thinks academics are unimportant for girls. I believe that this study was one more nudge at the science education community to attend to sex as an important variable in 54

SEX DIFFERENCES IN CLASSROOM INTERACTIONS IN SECONDARY SCIENCE

research. Now 28 years later, attention to equity in the classroom is a given and teachers actively encourage females to take more science and pursue science careers.  By contributing  to the research that made teachers and teacher educators aware of inequitable classroom practice, I have done my part to increase the diversity of STEM fields.

55

DALE R. BAKER

SEX DIFFERENCES IN CLASSROOM INTERACTIONS IN SECONDARY SCIENCE1

ABSTRACT

Two classrooms each of high school biology, chemistry, and physics were observed using a modified form of Brophy and Good’s classroom observation instrument. One set of classrooms used a lecture format with little laboratory activity and the other set of classrooms used a self-paced laboratory format. The observations focused on teacher and student initiated academic and procedural questions, teacher feedback, disciplinary and social interactions. Males were asked more academic and procedural questions than females and were disciplined more. Feedback was minimal, but males received more than females, and the feedback terminated the interaction rather than sustained it. Females had more social interactions with their teachers than males and initiated more academic questions than males. Kahle and Lakes (1983), in their analysis of the 1976-1977 NAEP data, report differences in the number of classroom and extracurricular science experiences for males and females. Their data indicates that at age nine females want science experiences, but they are not getting them. However, by the time females are between the ages of 13 and 17 they no longer feel this way, perceiving science as boring and a male career. Kahle and Lakes suggest that this difference in science experiences and the subsequent change in attitude toward science by females is based in cultural and social factors. One of these factors may be student/teacher interactions within the classroom. Getzels and Thelen (1960) view the classroom as a unique social system and Fox (1976) believes that it is one area in which social roles in science and mathematics are learned through interactions with teachers and peers. It is the place where students are first introduced to formal science, and as the research in classroom interactions indicates, it is the place where very strong social and cultural messages are transmitted. Research has consistently found that teachers have more interactions with males than females; they talk to males more, ask males more higher order questions and discipline them more (Brophy & Good, 1970 a, 1974; Hall & Sandler, 1982; Cooper & Good, 1983). Males, for their part, initiate more contact with teachers than females by calling out answers and raising their hands to answer questions (Brophy, 1985; Brophy & Good 1970 b; Fennema, Reyes, Perl, Konsin, & Drakenberg, 1980; Hillman & Davenport, 1978; Morse & Handley, 1985). 56

SEX DIFFERENCES IN CLASSROOM INTERACTIONS IN SECONDARY SCIENCE

Despite extensive research on sex differences in classroom interactions, little is known about specific factors that might affect these differences. Good and Brophy (1984) have suggested that the differences in male/female teacher/student interactions may be influenced by the subject matter being taught. This is a crucial statement and represents a glaring deficiency in classroom interaction studies in general and studies in science education in particular. Few studies exist that compare differences in teacher interactions with male and female students across subject areas including science, or specifically look at science. Tobin and Gallagher (personal communication, September, 1986) in their study of Australian science classes found that teachers relied on a small number of male students (targets) to keep lessons moving along. The teachers elaborated on the males’ answers more than the females’ and called on males more often than females. On the other hand, males did initiate more questions than females and volunteered more often to answer questions. Whyte (1984) conducted a study that attempted to redress the imbalance in male/ female student/teacher interactions. She trained science teachers to provide equal numbers of questions and amount of feedback to both male and female science students. However, teachers were so used to unequal interaction patterns that when the interactions approached parity most teachers felt that they were giving 90% of their attention to the female students. Morse and Handley (1985) in a longitudinal study of interactions in science classrooms followed students through years seven and eight. They found that females initiated fewer interactions with the teacher as they moved from grade seven to eight while male initiated interactions increased. Males also gave more unsolicited responses than females and received more feedback from the teacher. Overall, males experienced more teacher interactions of all kinds than females and the amount of teacher attention increased from grade seven to eight. In the related field of secondary mathematics, females also have fewer interactions with teachers than males. In geometry females are called on less frequently and are asked questions at a lower cognitive level than those directed at males. Females receive less praise, criticism, or help from their teachers than males and have fewer social interaction with their teachers such as joking or conversations. As is found in other subject areas and grade levels (Brophy, 1985), female and male geometry teachers do not differ in their behavior toward their students (Becker, 1981). Stallings (1979) observed high school algebra and geometry classes and found similar, but weaker patterns to those found by Becker. This pattern seems to begin at the elementary level where males have more academic contacts with teachers and receive more instructional time in mathematics than females (Leinhardt, Seewald, & Engel, 1979). On the other hand, Parsons, Adler, Futterman, Goff, Kaczala, Meece, and Midgley (1980) observing fifth, sixth, seventh, and ninth grade mathematics classes found few sex differences in interactions. Sex differences were limited to criticism, in which females received less criticism about the quality and form of their work 57

CHAPTER 3

than males. Differences among these studies may be the result of differences in grade level, type of mathematics, and the number of classroom visits. McDermott (1983) found that teacher expectations for success also affected interactions in mathematics classes. While males received more feedback than females and were expected to be more successful, teachers provided more feedback to females from whom they expected less in the way of achievement than for females they expected to be successful. OBJECTIVES

Most classroom interaction studies have been of a global nature. There is a scarcity of research which examines classroom interactions within the content areas of science and none which contrasts interactions in laboratory versus lecture settings. Bossert (1981) has suggested that a clearer picture of the effects of differential treatment will only come about as the result of examining teacher behavior in the context of specific subjects, activities, and instructional modes. Consequently this research undertook the examination of pupil/teacher interactions in secondary biology, chemistry, and physics classes as a function of lecture and self-paced individualized instructional strategies. The null hypotheses are: 1. There will be no difference in the number or type of interactions for males or females receiving either lecture or self-paced laboratory instruction. 2. There will be no difference in the number or type of interactions in biology, chemistry, or physics classes. PROCEDURE

Sample The sample consisted of 196 white secondary school students at three middle class suburban high schools. Students were enrolled in biology, chemistry, and physics. Science instruction at School One consisted of a traditional lecture presentation with little laboratory work. Instruction at Schools Two and Three consisted of a selfpaced individualized laboratory based program. There were 64 students at School One, 22 female and 42 male, and 132 students at Schools Two and Three, 54 females and 78 males. Each school had two biology classes, two chemistry classes and two physics classes. All of the teachers were male. Data Collection One trained observer observed each of the 18 classes. The observer was present for six 50 minute periods in School Two, six 50 minute periods in School Three, and eight 50 minute periods in School One. Periodically a second observer would be present in the classroom so that a reliability check of the observations could be made. 58

SEX DIFFERENCES IN CLASSROOM INTERACTIONS IN SECONDARY SCIENCE

Instrument The instrument used was a modification of Brophy and Good’s (1970b) Dyadic Interaction System (Figure 1). The observer used a check sheet to record the number of academic, procedural, disciplinary, and personal interactions with each student. The observer also recorded who initiated the interaction, type of student response, and teacher feedback. Academic interactions consisted mainly of questions or comments which dealt with content. Procedural interactions consisted of questions or comments dealing with how to do either laboratory activities, such as the steps to be followed in staining a slide, or classroom routines, such as the proper way to store various kinds of equipment. Disciplinary interactions consisted primarily of the teacher commenting on student behavior. Personal interactions were questions or comments relating to social activities, compliments, and teasing. Feedback was coded as terminal or sustaining. Terminal feedback was brief and ended the interaction. Usually it was a “yes,” “no,” or “I don’t know” response or a negative response from the teacher such as “I see that you didn’t read the chapter.” Sustaining feedback was longer and encouraged the student or teacher to elaborate on the answer to try again if the answer was unclear, incorrect, or not understood. Teacher initiated academic interactions were restricted to those in which the teacher first called on a student before asking a question, or asked a question and then immediately afterward named a student. Situations in which the teacher asked a question and then waited for students to raise their hands were not included. In the  Brophy-Good system this is coded as a direct question (Brophy & Good, 1970b). This limitation was imposed on the data gathering because, as was noted earlier, boys raise their hands more often than girls and the opportunity for response would not be solely controlled by the teacher. Classroom Formats The classroom formats were very different and require a brief description so the patterns of data are clearly understood. The lecture format classrooms were teacher centered. The teachers lectured and asked questions. Instruction focused on the textbook and supplemental information provided by the teachers. Although these were science classrooms, experimentation was almost nonexistent. Teachers reported that students engaged in one laboratory exercise per unit of topic of study or about once every two or three weeks. More often the teachers provided demonstrations and asked questions or gave a lecture about the demonstration. Films and other visual media were also used as a starting point for questions and lecture. The students’ role was for the most part passive. They answered questions when called upon, watched, listened, and took notes. Rarely did they ask questions about the material. Students were evaluated by tests which were administered by the teacher to the whole class after a unit of study. 59

CHAPTER 3

Figure 1. Observation Instrument.

The laboratory classroom was less teacher centered. In this format, the students were required to finish a minimum number of science experiments per grading period. Students worked at their own pace, reading the textbook topic and then doing several experiments selected to accompany the reading. The teacher rarely lectured to the class as a whole; he acted primarily as a facilitator or a resource for the students as they conducted their experiments. Whole class interactions were of two types. The first type was concerned with laboratory techniques needed to perform some experiment, reviews of algorithms for mathematical calculations, safety procedures, and proper storage and maintenance of equipment. The second type was the more traditional question and answer interaction where the teacher uses questions to review, to assess student understanding or go over homework and clarify misunderstandings. Students evaluation was conducted by self-testing from a computerized item bank based on district wide objectives. Students decided when they were ready to take the test and were required to repeat the unit of study, including experiments if they did not meet minimum competency standards. The data was analyzed using the chi-square technique for sex differences in the kinds of interactions and type of instructional format across subject areas. 60

SEX DIFFERENCES IN CLASSROOM INTERACTIONS IN SECONDARY SCIENCE

RESULTS

The first group of analyses examined teacher initiated interactions. In all the lecture classes, teachers asked males academically related questions more often than females (X2 = 24.5, p < .001) (Table 1). There was no difference in academic questioning patterns in the self-paced laboratory classes. In the self-paced biology and physics laboratory classes, teachers asked males more procedural questions than females, but females were asked more procedural questions than males in the selfpaced chemistry class (X2 = 21.79, p < .001). There was no difference in procedural questioning patterns in the lecture classes (Table 2). Table 1 Teacher Initiated Interactions for Academic Questions Biology O-f

Chemistry

E-f

O-f

E-f

Physics O-f

E-f

Lecture Format* Females

13

25.5

Males

54

41.1

23

38.7

14

21.0

73

57.3

52

45.0

Laboratory Format** Females

22

18.7

33

29.0

19

16.9

Males

20

23.3

30

34.0

64

66.0

*Chi Square = 24.56, df = 2, p< 0.001 **Chi Square = 2.4, df = 2, p> 0.05 Table 2 Teacher Initiated Interactions for Procedureal Questions Biology O-f

E-f

Females

5

3.9

Males

5

6.1

Chemistry O-f

E-f

Physics O-f

E-f

Lecture Format* 8

8.1

5

7.0

12

11.9

17

15.0

Laboratory Format** Females

8

21.8

40

33.1

6

11.6

Males

41

27.2

32

38.9

51

45.0

*Chi Square = 1.35, df = 2, p> 0.05 **Chi Square = 21.8, df = 2, p< 0.001.

Feedback provided by teachers in response to student answers was minimal. Despite the elaborate coding system used to record the feedback within the 61

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sustaining  and terminal categories, the data had to be collapsed in order for statistical analysis to be valid because many categories had less than five observations. In addition, sustaining feedback occurred so infrequently that a chisquare analysis could only be performed with data from the chemistry class with a lecture format. In this chemistry class males received more terminal feedback and females received more sustaining feedback (X2 = 4.27, p < .03) (Table 3). However, too much should not be made of these observations because of the low number of feedback events. Table 3 Terminal and Sustaining Feedback in Lecture Format Chemistry Terminal

Sustaining

O-f

E-f

O-f

E-f

Females

17

20.3

06

02.7

Males

65

61.7

05

08.3

*Chi Square = 4.28, df = 1, p< 0.03.

In the lecture class in biology and the self-paced laboratory class in physics, no instances of sustaining feedback were observed. In the other classes, instances of sustaining feedback were self-paced laboratory biology, self-paced laboratory chemistry, and lecture format physics. The second group of analyses examines student-initiated interactions. In the lecture format classrooms there was no difference in the number of academic interactions initiated by males or females. In all of the self-paced laboratory format classrooms, females initiated more academic interactions than males (X2 = 11.4, p  0.05 **Chi Square = 11.4, df = 2, p< 0.005 Table 5 Teacher Initiated Disciplinary and Social Interactions Terminal

Sustaining

O-f

E-f

O-f

E-f

Females

7

18.0

28

17.1

Males

27

16.1

4

15.0

*Chi Square = 27.0, df = 1, p< 0.001. CONCLUSIONS

In this study of high school science classes, male and female students had different educational experiences despite being in the same classrooms together. In one crucial area, academic interactions, the female students received less attention than the males in the lecture format classrooms. Since the format of this class is one in which the teacher lectures and then asks questions based on the lecture and textbook readings, the female students had fewer opportunities to actively participate in the class. This case is not the more common situation in which the female student opts out of participation by not raising her hand (Brophy, 1985; Hillman & Davenport, 1978) because the data collected was limited to direct questions in which the teacher named the student he wished to respond. Thus, the female students’ response opportunities were directly controlled by the teachers and limited by the teachers’ acknowledgement of their presence in the classroom. In the self-paced laboratory classroom, the procedural question was very important. Students spent the majority of their time working by themselves on laboratory

63

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exercises. This format required a clear understanding of laboratory techniques and procedures on the part of the students. In this context, the teacher again favored the males with his attention, asking them more procedural questions than females. Teacher feedback also followed this pattern. Since males were asked more academic and procedural questions than females, they also received more feedback on their answers than females. Unfortunately for the males, the amount and quality of this feedback was poor, consisting of terminal responses such as “no” or “that’s not right.” Nonetheless, the majority of teacher/student talk took place between males and the teacher in the areas of importance in each of the classrooms. These important areas are academic interactions in the lecture classes and procedural interactions in the laboratory classes. Females initiated more academic questions than males in the self-paced laboratory classes, but not in the lecture classes. Why this is so is not clear. Perhaps, as Kahle and Lakes (1983) have suggested, females need more information than males to complete a task on their own and the questioning reflects the females’ poor conceptual knowledge of science. This poor conceptual knowledge may be the result of the way in which time was allocated in the laboratory classroom and the teachers’ role in that classroom. A great deal of time was devoted to independent work rather than listening to the teacher talk and the teacher functioned as facilitator rather than information giver. The initiation of academic questions on the part of the females may not have been necessary in the lecture format class because students were primarily engaged in listening, taking notes, and accurately repeating what they had heard or read. There was more academic information than the students may have wanted and they were not required to use conceptual information to complete a task. When teachers did interact with female students more than males the context was not academic, but social. The teachers were heard to ask about dates, weekend activities, cheerleader tryouts, or the school dance. Social interactions of this kind may be reflecting male female interactions in the society at large and are relatively harmless. However, the cumulative effect of who gets what kind of teacher attentions should not be ignored. A closer examination of the three content areas could not be made due to the low number of interactions per area. This is disturbing given the number of class periods observed in each content area (approximately 6.5 periods). When comparisons across disciplines were possible, teacher behavior was not found to vary. From the data one can conclude that the females in this study were not receiving the  same kind or amount of teacher attention in their science classes as males. However, a direct casual link between student/teacher classroom interactions and female career choice has not been made. One might argue that the low number of female students in college biology, chemistry, and physics is a direct result of their experiences with their science teachers in high school, but this explanation does not  take into account several other factors relating to a scientific career 64

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choice such as personality, sex-role conflict, cognitive abilities, peer pressure, and parental attitudes. However, the influence of the classroom experience as one factor in career choice in science is supported by a recent study. Kahle (1985), in a nationwide study of biology teachers who were successful in encouraging girls to study science, found that students perceived their teachers as uniform and fair in the treatment of males and females in the classroom. She also found that these teachers were aware of sexism in science and took steps to eliminate it in their classrooms. Girls were called upon to recite, assist in demonstrations, and selected to be group leaders. Many teachers reported actively working on encouraging girls in science. Kahle also surveyed former students who were science majors to determine the influence of their biology teacher on their career choices. She found that all students named their high school biology teacher (the study teachers) as the most influential person in determining their career choice. None of the former students felt that their teacher had distinguished between males and females in their classes. Eccles and Blumenfeld (1985) see most teachers’ role in career choice as more passive than active. They found that teachers do very little to change sex differentiated attitudes and beliefs that students acquire from the society at large. They believe that it is through inaction that teachers passively reinforce sex-typed academic and career choices. Therefore, it does seem reasonable to assume that differential classroom experience is one among many factors that contribute to a female’s decision to pursue or not to pursue a scientific career. Given the low number of any sort of interaction between students and teachers, it is amazing that anyone would continue studying science. The classrooms observed were not characterized by discussion, teacher probing, or spirited give and take, which are all behaviors found associated with teachers who are successful in encouraging both males and females in science (Kahle, 1985). Instead, students worked alone either taking notes or writing answers to questions or conducting an experiment by following a cookbook like set of instructions. The fact that any male or female student would continue to study science speaks more for the quality of the student than the quality of the instruction. This study describes an educational problem in secondary science classrooms that is mirrored in secondary mathematics and in various content areas at the elementary level. Given the seriousness of the problem and the large database in classroom interactions amassed over the years, researchers must now go beyond description to develop and test interventions for remediation. However, the researcher who designs and implements such a study will be faced with a number of problems. The first of these is the problem of fairness. To be fair, teachers must engage in differential behavior with male and female students. This means consciously calling on girls to respond, to participate, and to encourage girls in science. The second problem is the creation of an atmosphere in which teachers and students honestly examine their assumptions and critically evaluate their behavior 65

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with an aim towards change. This is not a simple or easy task and requires strong and sustained intervention. Many of the differences seen in male and female classroom behavior which lead to sex differences in participation are the result of sex-role socialization. This is also true for the behavior of teachers who, like students, have been under the influence of sex-role socialization their entire lives. The pervasiveness of this socialization and the unconscious nature of behavior stemming from it makes changing behaviors and beliefs which reflect sex-role expectations extremely difficult. It also means that the process of change can be very threatening to those involved. Given the magnitude of the task and the socialization forces at work outside of their classroom, change will be necessarily slow. However, professionalism on the part of the teacher demands that he or she participates in that change. As Skolnick, Langbort, and Day (1982) conclude, “Teachers’ expectations are communicated to children in myriad ways, not only through what they say explicitly but also through what they do not say, what they do, and whom they call on. Indirect or covert messages constitute a hidden curriculum which is sometimes more powerful than the lessons in the textbooks…” (p. 17). NOTE 1

Originally published as Baker. D. (1987). Sex differences in classroom interactions in secondary science. The Journal of Classroom Interaction, 22, 6–12. Reprinted here with permission.

REFERENCES Becker, J. (1981). Differential treatment of females and males in mathematics classes. Journal for Research in Mathematics Education, 12 (1), 40-53. Bossert, S. (1981). Understanding sex differences in children’s classroom experiences. The Elementary School Journal, 81 (5), 256-266. Brophy, J. (1985). Interactions of male and female students with male and female teachers. In L. Wilkinson and C. Marrett (Eds.). Gender Influences in Classroom Interactions (pp. 115-142). Orlando, FL: Academic Press. Brophy, J. & Good, T. (1970a). Teachers’ communication of differential expectations of children’s classroom performance: Some behavioral data. Journal of Educational Psychology, 61, 365-374. Brophy, J. & Good, T. (1970b). The brophy-good dyadic interaction system in A. Simon & E. Boyer (Eds.), Mirrors for behavior: An anthology of observational instruments continued. Philadelphia: Research for Better Schools. Brophy, J. & Good, T. (1974). Teacher student relationships: Causes and consequences. New York: Holt, Rinehart and Winston. Cooper, H. & Good, T. (1983). Pygmalion grows up: Studies in the expectation communication process. New York: Longman. Eccles, J. & Blumenfeld, P. (1985). Classroom experiences and student gender: Are there differences and do they matter? In L. Wilkinson and C. Marrett (Eds.). Gender influences in classroom interactions (pp. 79-114). Orlando, FL: Academic Press. Fennema, E., Reyes, L., Perl, T., Konsin, M., & Drakenberg M. (1980, April). Cognitive and affective influences on the development of sex related differences in mathematics. Symposium presented at the annual meeting of the American Educational Research Association, Boston.

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SEX DIFFERENCES IN CLASSROOM INTERACTIONS IN SECONDARY SCIENCE Fox, L. (1979). The effects of sex role socialization on mathematics participation and achievement. Washington, DC: Career Awareness Division, National institute of Education, U.S. Department of Health, Education and Welfare. Getzels, J. & Thelen, H. (1960). The classroom as a unique social system. National Society for the Study of Education Yearbook, 59, 53-81. Good, T. & Brophy, J. (1984). Looking in classrooms. New York: Harper and Row. Hall, R. & Sandler, B. (1982). The classroom climate: A chilly one for women? Project on the status and Education of Women, Washington, DC: Association of American colleges. Hillman, S. & Davenport, G. (1978). Teacher student interaction in desegregated schools. Journal of Educational Psychology, 70, 545-553. Kahle, J. (1985). Retention of girls in science: Case studies of secondary science teachers. In J. Kahle (Ed.), Women in Science (pp. 49-76). Philadelphia: The Falmer Press. Leinhardt, G., Seewald, A., & Engel, M. (1979). Learning what’s taught: Sex differences in instruction. Journal of Educational Psychology, 71, 432-439. McDermott, M. (1983, April). Impact of classroom interaction patterns on students’ achievement related beliefs and behaviors. Paper presented at the Biennial Meeting of the Society for Research in Child Development, Detroit, MI. Morse, L. & Handley, H. (1985). Listening to adolescents: Gender differences in science classroom interactions. In L. Wilkinson & C. Marrett (Eds.), Gender Influences in Classroom Interaction (pp. 37-56). Orlando, FL: Academic Press. Parsons, J., Adler, T., Futterman, R., Goff, S., Kaczala, C., Meece, J. & Midgley, C. (1980). Selfperceptions, task perceptions and academic choice: Origins and change (FinalReport, Grant NIE-G788-0022). Ann Arbor, MI: Department of Psychology, University of Michigan. (ERIC Document Reproduction Service No.Ed 186 477). Skolnick, J., Langbort, C., & Day, L. (1982). How to encourage girls in math and science, Englewood Cliffs, NJ: Prentice-Hall. Stallings, J. (1979). Factors influencing women to enroll in advanced mathematics courses: Executive summary (Final Report Grant NIE-G-78 0024). Menlo Park, CA: SRI International. Whyte, J. (1984). Observing sex stereotypes and interactions in the school lab and workshop. Educational Review, 36 (1), 75-86.

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SEX DIFFERENCES IN FORMAL REASONING ABILITY Task and Interviewer Effects

WHY I CONDUCTED THE STUDY

When I was in graduate school, teams of graduate students would conduct clinical interviews with elementary and secondary students to learn how to conduct the interviews and to help fellow students collect their data. This article comes out of that data gathering experience. Thus, the first submission of this article reflected a very traditional analysis of students by Piagetian levels and did not add much to the then current research. Consequently, it was not surprising that the first submission of the paper was rejected. The manuscript sat in a drawer for quite a while as I thought about what to do with it. After much thinking and reading I wondered if the differences I was seeing was an artifact rather than a deficit on the part of the females in the study. This led me to think about possible biases. Could it be that who was doing the interviewing and who was being interviewed might influence the outcome of the interviews? On the other hand, the findings of sex difference versus no sex differences might have been the result of differences in the skills of the interviewers. This puzzled me for a number of years and the research done by others in the years after I left graduate school did nothing to solve this puzzle. As time went by, the inconsistencies in the research literature concerning Piagetian  clinical interviews continued to accumulate with no clear answer as to why some studies of formal reasoning found sex difference and others did not. Sometimes there were differences favoring males and sometimes there was no difference between males and females. Sometimes there were differences for some tasks but not others for the same group of students. Paper and pencil tests often led to different results than clinical interviews for the same reasoning tasks. Researchers were unable to explain the inconsistencies in outcomes despite looking at many demographic and other variables. The default explanation was the deficit hypothesis. Males were superior to females in formal reasoning and thus, had the necessary cognitive ability to be successful in science. However, no one ever seriously raised the question of interviewer effects or bias with same or opposite sex of interviewer and interviewee. There were a few hints which I cited in the literature but nothing directly related to the question I wanted to investigate. As a consequence, I decided

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to reanalyze the data based on the hunch about bias I had developed in graduate school. I wanted to know whether the specific task influenced the outcome or whether outcomes varied by whether the interviewer and interviewee were of the same or different sex. Furthermore, I wanted to know whether a female interviewer was able to elicit more correct reasoning from interviewees, especially female interviewees because of a reduction in their anxiety. METHODOLOGICAL DECISIONS

My co-author and I used a sample of convenience that consisted of friends and neighbors for three reasons. First the research was conducted during the summer making access to a school population problematic. Second, the large number of tests and the number of hours of testing (8–10 per student) made conducting the study on school time unrealistic. Third, the eleven faculty and graduate students could not conduct such an intensive study in a short period of time during the academic year due to other responsibilities. We recruited males and females in three age groups to examine changes in reasoning as individuals matured. Because we wanted to clarify what was going on with clinical interviews and for comparability with other studies, we used the traditional Piagetian interview tasks as well as the commonly used Mr. Tall and Mr. Short. We also created five tasks especially for this study that were a variation of the traditional Piagetian tasks that did not tap into prior science knowledge. We took great care to insure that the logical structure of the tasks we created were the same as the original Piagetian tasks. We did not want the results to be attributable to significant differences in tasks. We also engaged in extensive training of interviewers to insure that results were not the result of poor interviewing skills and unfamiliarity with the Piagetian tasks. The interviews were audio taped as a check on the quality of the interviews and to insure that the notes of the interviewer reflected the responses of the interviewee. In scoring the interviews, we aimed for 100 inter rater reliability. We tried very hard to insure that we were testing for task and interviewer effects and not some other factor. Nevertheless, we did find sex differences in performance on the tasks. Rethinking about the data led to an analysis that was designed to reveal bias. Rather than ignoring the sex of the interviewer, we looked at dyads asking if scores for Piagetian levels on tasks was influenced by the sex of the interviewer. We asked if higher or lower scores were attained when the interviewee and interviewer were the same or opposite sex. We also asked if males or females gave higher scores to students regardless of the students’ sex. This reanalysis of data in a paper that was initially rejected resulted in a publication that received the Award of Merit for an article published in Science Education in 1988. This is a good example of the importance of the right analytical lens. Data do not speak for themselves, they are interpreted. A fruitful analytical lens can reveal hidden patterns of consequence. 70

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This paper almost did not make it into print. While we were working on it, we had a fire in our home that gutted the interior and destroyed my computer. For some reason, a hard copy of the paper was sitting on a sideboard in the dining room. After the fire was extinguished, a fireman handed me the charred manuscript! It was still readable so I retyped it and we submitted it to Science Education. SCIENCE EDUCATION AT THE TIME OF THE STUDY

At the time of this study, Piaget’s theory of cognitive development was still the dominant theory used in science education. An examination of articles published in the science education literature around the time of the publication of my research found that more than a quarter of the studies used Piaget’s theory and or examined formal reasoning and logic. Other studies that were not based on Piagetian theory were either a-theoretical or the theories were implicit rather than explicit. Other theories such as Gagne’s hierarchical learning or Fishbien and Ajzen theory of reasoned action were used in no more than a few studies and have disappeared from the literature. There was strong interest in studying the use of microcomputers, CAI, and simulations in science to improve achievement as well as research related to attitude toward science. Within the attitude realm studies also looked at intentions to study more science, science majors and careers. And, of course, many studies were interested in the effects of a variety of cognitive, psychological, and instructional, variables on achievement. Of particular note was the emergence of interest in conceptual understanding as misconceptions, preconceptions, and alternative conceptions with or without a strong theoretical framework. There was also a cluster of factors that often appeared together in studies such as locus of control, spatial ability and field dependence independence. Overall, the studies had a strong psychological orientation with little to no sociological or cultural orientation. Studies of students comprised more than half of the research conducted and high school was the most frequent context. This was followed by studies conducted in middle school/junior high and then the elementary level. Studies situated at the university were as frequent as those in elementary school but few studies took place at the community college. When pre-service teachers were studied, they were in an elementary rather than a secondary teacher preparation program. Professional development was rarely studied. When K-12 teachers were studied it was primarily for their impact on students. University faculty were rarely included in studies of their students’ achievement. A little less than a fifth of the studies looked at issues of gender including studies that had either all male or all female samples. Three quarters of the studies employed quantitative analysis. When a mixed method was employed, the qualitative portion was given a score so that the data could be analyzed quantitatively. Purely qualitative studies were rare. Most studies 71

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were conducted in a content area. Biology was most frequently studied, especially genetics. Physics/physical science was the next most popular content area examined followed by chemistry, general science, and studies conducted across multiple content areas. Earth science was conspicuously absent even at the middle school or junior high school where this content was most often found. There were studies that had some similarities to the study I conducted. For example, Poduska and Phillips (1986) using Piagetian tasks of distance, time, and speed with college students found sex differences favoring males. Burbules and Linn (1988) also conducted Piagetian interviews about volume and found a sex by treatment interaction. Boys learned the correct rule faster than girls. When girls had an opportunity to examine the testing equipment they did better than girls who had not had an opportunity to examine the testing equipment. This finding replicates our study in which familiarity with the testing apparatus eliminated sex differences. However, Burbules and Linn did not test for an interviewer effect. Jones, Lynch, and Reesink, (1987) in a study conducted by interview found sex differences favoring males for conceptions of the moon among younger children but did not attend to possible interviewer effects. In a subsequent study, Jones, Lynch, and Reesink (1989) did attend to the effect of content on gender in a study of solids and liquids, as we did, but not to interviewer effect. Stavy (1988) examined children’s conception of gas but the study did not even specify the interviewer sex nor take interviewer sex into consideration in the data analysis. RESEARCH IN THE WIDER FIELD OF EDUCATION

Beyond what was cited in the literature review of this paper, there was little in the wider field of educational research related to my study. There was a meta-analysis in the Review of Educational Research that examined sex differences in mathematical tasks (Freidman, 1989) that found that sex differences were small and had decreased over the years as well as a study of verbal and mathematical ability, as measured by the High School and Beyond data, that found small sex differences (Marsh, 1989). A study of sex differences in choice of a college major (Ware & Lee, 1988) focused on personal and family values rather than cognitive abilities. A few studies looked at outcome effects when matching for sex or ethnicity. In tutoring, similarity of ethnicity had no effect on satisfaction. However, in the studies of professors the sex of the professor and the sex of the student rating the professors did impact the outcomes. Female professors were rater lower in effectiveness than male professors and both male and female students rated male professors more effective than female professors. However, female students rated female professors higher than male students (Fresko & Chen, 1989; Basow & Silberg, 1987; Kierstead, D’Agostino, & Dill, 1988). There were also studies of reasoning. In 1986, White and Tisher wrote the chapter on research in the natural sciences for the Handbook of Research on Teaching. Their review chapter discussed the results of Piagetian interviews and the implications 72

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for science teaching extensively including difficulties arising from such interviews. Missing from their discussion were issues of gender and bias resulting from an interviewer effect. THE CULTURE OF THE TIMES

In 1989 George W. H. Bush became president of the United States and gender bias in the courts was a topic of concern. So much so, that the chairs and staff directors of 23 state Supreme Court task forces on gender bias met at the National Center for State Courts to discuss the issue. Their agenda consisted of determining what data on gender bias should be collected, how judges should be educated about gender bias, and how to implement reforms. Subsequent to this meeting, a report was published in Maryland entitled Gender Bias Exists in the Courts of Maryland, and It Affects Decision Making as well as Participants. This report prompted the Chief Justice of Maryland to appoint a committee to implement the recommendations for reform. The state of Massachusetts also issued a report of a study of its court system. This study concluded that there was pervasive gender bias throughout the court system. The findings prompted the Chief Justice of Massachusetts to appoint a committee to address the gender inequity by implementing reforms (Feminist Chronicles, 1989). In 1989, the feminist movement was still strong and had widespread support. Time magazine published a national poll that indicated that women’s perceptions of the women’s movement were extremely positive. Ninety-four percent of those polled agreed that the movement has helped women become more independent. At NOW’s national convention, a bill of rights for women for the 21st century was proposed and a study was commissioned to explore the creation of an independent political party. This was not to say that the ongoing fight for and against abortion rights had subsided. Those on both sides of the issue continued their efforts to either protect existing abortion rights or to curtail them (Feminist Chronicles, 1989). Internationally, it was a momentous year. The Berlin wall fell and former communist countries in Eastern Europe began the difficult road to democracy (e.g. the former Czechoslovakia) and in Romania, Nicolae Ceausescu and his wife, Elena were executed by a firing squad. The collapse of the Soviet Union was predicated, in part, by the reforms of the Soviet Union’s President Mikhail Gorbachev and the large debts owed by communist countries to foreign banks (Suny, 2009). In Poland, the political party Solidarity overwhelmingly won elections causing the Communist party to concede defeat (Historycentral.com, 2015). Analysts concluded that the fall of communism in Eastern Europe was as important a world event as the French or Russian revolution. However, in China things did not go so well and communism remained firmly entrenched. A student led protest for greater freedoms and democracy resulted in the Tiananmen Square massacre. The protest was the largest pro-democracy movement 73

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in history and many of those who participated have spent many subsequent years in jail or were forced in to exile (Hilton, 2009). Observing the events around the world, Francis Fukuyama (1989) wrote a piece in The National Interest, in which he stated that struggles among ideologies were at an end and the world was on a course to greater liberal democracy. He predicted more political and economic liberalism in the future. Science education was also entering an era of reform and self-examination. In 1989 the American Association for the advancement of Science published Project 2061: Science for All Americans (1989). This document was a report on literacy goals for science mathematics and technology. Note that engineering was not yet part of the conversation but the report did address the designed world. This document laid the groundwork for the National Science Education Standards (National Research Council, 1995). The title, represented a new push to educate all students regardless of their circumstances but the section on effective teaching and  learning did not address the challenges that arise from the subtle and not so subtle equity issues of gender, ethnicity, socio-economic status, and English Language Learners. IMPACT OF MY WORK

As I noted earlier, this research almost did not see the light of day because I had a fire in my home that destroyed my computer hard drive and the draft manuscript of this research was not retrievable. Fortunately, a paper copy was sitting on a sideboard. The firemen, in the midst of putting the fire out, retrieved the paper copy and gave it to me. It was burned around the edges and covered with soot but it was readable! I thanked the firemen profusely and they responded that it was their goal to save people’s possessions. It was six months before I could move back into my remodeled home but that is another story. After publication of Sex Differences in Formal Reasoning Ability: Task and Interviewer Effects, my coauthor and I received the Outstanding Paper of 1988 Award of Merit for research published in Science Education. This work was important because it revealed that sex differences can be manipulated and are not always a function of ability. Format and familiarity of context are important too. We found sex differences favoring males for clinical rather than written tasks and for tasks that were unfamiliar. Furthermore, we found that the sex of the interviewer can influence the quality of performance. Female interviewers elicited better performance from both male and female students than male interviewers. Most important, female interviewers of female students obtained proportionality scores, an area where sex differences have frequently been found, that were no different than those of male students. This work revealed that performance on clinical formal Piagetian tasks are not an unbiased measure of reasoning ability. Nor, is performance on formal reasoning tasks an explanation for why fewer females were choosing science then males. This was an important study because it 74

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laid to rest the deficit hypothesis for formal reasoning, identified bias in Piagetian clinical interviews, found that experience counts when it comes to manipulating the scientific apparatus used in Piagetian clinical interviews, and contributed to the growing body of research that indicated that women were not choosing science because of their lack of ability.

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MICHAEL D. PIBURN AND DALE R. BAKER

SEX DIFFERENCES IN FORMAL REASONING ABILITY: TASK AND INTERVIEWER EFFECTS1

INTRODUCTION

Low female enrollment is characteristic in many high school science courses (Dearman & Plisko, 1981), fewer females than males are majoring in science at the college level (Butler & Marzone, 1980), and only 13% of employed scientists and engineers are women (National Science Foundation, 1986). The question of why so few women choose to study science or to enter the scientific professions is immensely important to those concerned with equity in science and education. It is possible that the underrepresentation of females in the science courses and in the professions results from the fact that they are generally less successful than males in the kind of analytic problem solving that is tapped by the Piagetian clinical measures of formal operations. This deficit hypothesis receives substantial support from the repeated findings of sex differences in proportional reasoning, which most science teachers would rank high on their list of requisite abilities for success. An alternative hypothesis suggests that various performance factors that prevent females from exhibiting their ability in science. Variations in experience may have the effect of reducing performance in the context of unfamiliar problems. Anxiety might influence achievement on highly manipulative tasks that involve complex equipment. Even the sex of the experimenter could influence success in a society that encourages sex-role differentiation and discourages achievement by females in the sciences. In this study, we seek to shed some light on both the logical reasoning and performance factor hypotheses. Logical Reasoning and Success in Science In support of the hypothesis that logical reasoning ability may be a crucial filter for entrance into science, Piburn (1980) found significant correlations between scores on Piaget’s shadows and balance tasks and a nationally administered and scored high  school certificate (general science) examination in New Zealand. Piburn and Baker (1988) found similar relationships between scores on the Test of Logical Thinking and achievement on a state moderated high school general science examination in Australia (1988). The authors of a recent study of college biology students conclude that “lack of appropriate hypothetico-deductive reasoning skills (e.g. combinatorial, probabilistic, and proportional reasoning ability) as reflected 76

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by  performance on the test of developmental level, was the major source of difficulty, not only in solving genetics problems and in constructing classification schemes, but in interpreting text material on genetics as well” (Mitchell & Lawson, 1988, p. 33). The correlation of logical reasoning with chemistry achievement has been demonstrated in several studies, including a recent one by Chandran, Treagust & Tobin (1987) in which the authors conclude from a path analysis that formal reasoning ability, as measured by the Test of Logical Thinking, is more influential than prior knowledge in predicting chemistry achievement. Achievement in physics correlates with measures of both inductive and deductive reasoning (Enyeart, Baker & VanHarlingen, 1980). Sex-linked differences in formal reasoning develop with maturation. Graybill (1975) found the performance of males and females to be similar for nine year old subjects, but by the age of 15 males were more successful. Lawson concluded that “if it is the case that male adolescents perform more formally than females, then the shift in superiority seems to come about roughly towards the end of elementary school years and during the junior high school years” (1975, p. 403). Although there is relatively little consistency across reports of sex differences, most authors find that they occur in the case of measures of proportional reasoning ability (Karplus,  Karplus, Formisano & Paulsen, 1977; Linn & Pulos, 1983; Farrell & Farmer, 1985; Saunders & Jesunathadas, 1988). In a meta-analysis of 53 studies investigating sex differences, Meehan found that “the most reliable differences occur for proportional reasoning problems” (1984, p. 1120). Effect of Task Content, Task Format, and Sex of Interviewer Turning to the performance factors hypothesis, Golbeck (1986) suggested that sex differences on clinical tasks reflect performance, rather than competence, deficits which arise from females’ lack of experience with the physical world. She found that  males outperformed females on a measure of horizontality and verticality when the content required knowledge of physical principles, but there was no sex difference  when the content did not require such knowledge. On the basis of a correlation of age with performance on clinical tasks for males, but not for females, Hernandez, Marek and Renner (1984) concluded that the differential experiences of males and females affect their performance on Piagetian tasks. A study by Howe and Shayer (1981) appears to conflict with these interpretations. They found sex differences favoring males on clinical tasks of volume and density. After several weeks of physically interacting with materials related to the concepts of volume and density, the performance of both males and females increased, but males  continued to outperform females. A similar result was obtained in a more recent study, in which proportional reasoning problems with familiar content were easier for both males and females, but males continued to outperform females regardless of the content (Saunders & Jesunathadas, 1988). 77

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The clinical setting used in Piagetian assessment may also be a factor in sex differences. Graybill (1975) thought that females seemed more uncomfortable when asked to use equipment, and that their performance may be the result of a social role expectation that distinguishes between the sexes. Lawson and Sheperd (1979) also suggest a possible sex-bias in clinical Piagetian tasks. Farrell and Farmer (1985) found that boys outperformed girls on a written measure of proportional reasoning, but that there were no sex differences on a clinical measure. A contradictory result was reported by Lawson (1975), who found that sex differences occurred on two clinical tasks, but disappeared in the case of two out of three written measures. On the basis of principal components analyses conducted separately for males and females, he concluded that “only one psychological parameter was being measured by the manipulative tasks and the pencil and paper tasks for the males,” whereas “for the females in this sample then it appears that the manipulative tasks were measuring one parameter while the pencil and paper tasks were measuring a somewhat different parameter” (1975, p. 402). Other studies have failed to demonstrate sex differences in either format. One group of researchers attempted to demonstrate the equivalence of written and clinical measures during the validation of the Piagetian Logical Operations Task. No sex differences were found in either format (Staver & Gabel, 1979; Staver  & Pascarella, 1984; Staver, 1984). However, a factor analysis revealed that the Piagetian Logical Operations Task and clinical measures loaded on different factors in a rotated factor matrix, leading the authors to conclude that “little evidence for convergence between the two Piagetian methods is present” (Staver & Gabel, 1979, p. 359). Two other studies (Tschopp & Kurdek, 1981; Stefanich, Unroth, Perry & Phillips, 1983) revealed low order to non-significant correlations between written and clinical Piagetian measures and, when appropriate analyses were conducted, no sex differences. The sex of the interviewer and of the subject may also be a powerful influence on performance on clinical Piagetian tasks. Rumenik, Capasasso and Hendrik (1977) found, in reviewing one hundred studies, that the sex of the experimenter was a potent variable in performance studies. However, whether subjects performed better for an experimenter of the same or of different sex depended upon their age and the type of task. Children performed better on problem-solving, IQ, mathematics, and Piagetian tasks when tested by someone of the same sex. The effect of experimenter on adult performance was mixed, and the tasks reviewed were primarily assessments of verbal ability or responses on sociological instruments. McMahan (1976) found an experimenter effect with conservation tasks in which both male and female subjects were more successful with a female experimenter, but Sclafani and Labarba (1982) did not find that sex of the experimenter influenced performance on conservation tasks. Females have been shown to perform better with a female experimenter on verbal recall (Sobal & Juhasz, 1977; Joseph, McKay & McKay, 1982), but not in numerical recall (Joseph, McKay & McKay, 1982), expressions of 78

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empathy (Lennon, Eisenberg & Carroll, 1983) or the number of responses elicited on Rorschach tests (Greenberg, 1972; Greenberg & Gordon, 1983). STATEMENT OF THE PROBLEM

The purpose of this study was to examine the variables that might influence females’ success in science by experimenting with the outcomes of measures of formal reasoning ability under a number of conditions. The rationale for this approach was the strong similarity between these tasks and the demands placed upon students in academic science classes. First among these conditions is the sex of subject and experimenter. One would predict, from previous studies, that females would be less anxious, and more willing to be successful, when tested by a female rather than by a male. The second condition to be varied is the type of task. Previous research indicates that females may be more successful on written measures than in a clinical interview, and especially when the latter involves the manipulation of scientific apparatus. It also suggests that females would be more successful with a familiar than with an unfamiliar task. A finding that sex differences persist across task type and experimenter sex would tend to support a deficit hypothesis. If, on the other hand, performance factors exist that are unrelated to cognitive competence, it should be possible to vary the conditions of assessment sufficiently to eliminate sex differences in achievement. METHOD

This report results from a large summer research project at a state university in the western U.S. The design necessitated the use of many measures with a relatively small sample and, before its conclusion, nineteen instruments were administered to all subjects. Twelve of these were clinical and involved a substantial investment of time by both interviewer and subject. We estimate that the collection of the data base required between eight and ten hours of individual contact with each subject. The data were collected during the summer months because so large a group could find no other time to meet for so labor intensive an effort. The motive behind collecting the data base was to enable members of the research group to pursue separate but interrelated avenues of inquiry. This required some compromises in the design of individual research projects, but each member of the group ultimately obtained far more data than any individual could reasonably expect to accumulate working alone. One major concern of all members of the research group was the cognitive requirements of Piaget’s measures of Formal Operational Thought, and most (but not all) required information about the performance of subjects on such tasks. For the study presented here, an attempt was made to balance the design with regard to the sex of subjects and experimenters, and the format of Piagetian tasks.

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The data were collected by seven male and four female interviewers. Two of the interviewers were male faculty members and the remainder were advanced graduate students in science education. The recruitment of the group reflected our desire to have an equal number of male and female interviewers and failed principally in  that the institution had no female faculty members in science education at the time. Every interviewer recruited 6 subjects representing three age groups, with one male and one female in each. The age groups chosen were grades 9 and 10 (Age 1), grades 11 and 12 (Age 2) and the first two years of college (Age 3). The decision to group subjects in this fashion follows in part from data (Piburn & Enyeart, 1981) suggesting a substantial change in patterns of reasoning between the 10th and 11th grade, as well as from the necessity to balance subjects between the sexes. The main considerations for recruiting subjects, beyond those of age and sex, were that individuals would be available for repeated testing and interview, and that access by the interviewer would be convenient. Thus, the majority of the subjects came from among friends and neighbors. The resulting sample was entirely white and predominantly middle class, with little or no specific background in science beyond those courses normally encountered in high school and college by non-science students. Six of the tasks used in this study came directly from the original work of Piaget: combinations of colorless chemicals, equilibrium on the balance, projection of shadows, hair color/eye color (Inhelder & Piaget, 1958), combinations of colored tokens, and proportional choice (Piaget & Inhelder, 1975). A seventh task, Mr. Tall and Mr. Short, was devised by Karplus and Peterson (1970). Five additional tasks were created for this study. In order to maintain the validity of the new tasks, they were constructed to be as structurally similar to the originals as possible. The task of writing and validation was conducted by the entire group of 11 experimenters, and there was complete agreement among them that the new tasks faithfully represented the logical structures contained within the originals. Following the collection of the data, and based upon the results of a principal components analysis (SPSS-X subprogram FACTOR with orthogonal rotation) measures were assigned to three Piagetian schemata. These were combinations and permutations, proportional reasoning, and probability and correlations. The tasks created for this study were the four coin change problem, travel between four cities, license plates, recipe, and test bat/standard bat. The four coin change problem is a combinatorial task in which students are asked to make as many different amounts of change as possible with a penny, a nickel, a dime and a quarter. Travel between four cities is another combinatorial task in which subjects are asked how many possible routes there are to connect four cities. The license plate problem asks students to construct as many license plates as possible from four letters. Because the order of letters is important, license plates is a permutations task. The Recipe problem requires subjects to expand a recipe for a cake in the ratio of two to three, and is a problem in proportional/reasoning. Test bat/standard bat is a 80

SEX DIFFERENCES IN FORMAL REASONING ABILITY

variation of the hair color/eye color task of Inhelder & Piaget (1958), where students are given the results of a series of baseball games where either a standard bat or an experimental bat is used, and asked which set of games provides the best proof for the efficacy of the experimental bat. This is a probability task. In order to avoid an unusually complex design, we attempted to vary both task format and content simultaneously in a direction that might be expected to eliminate sex differences in performance. Four written measures were chosen or created which contained as little scientific content as possible. They were the license plate and four coin change problems as combinatorial operations, and the recipe and Mr. Tall and Mr. Short as proportionality problems. With the exception of Mr. Tall and Mr. Short, all tasks contained content that should be familiar for both males and females and, in the case of recipe, more familiar for females. It is especially difficult to make the nature of correlations tasks clear, even in the clinical format, and we were unable to construct suitable written measures in this schema. Nevertheless, the clinical measures were retained in the data pool for analysis with regard to age and sex differences. The scoring and interview procedures used in this study were based upon those described by Inhelder and Piaget (1958). Subjects were scored as either formal (1) or pre-formal (0). After an initial training period, interviewers met individually with subjects each week for a period of approximately 4 weeks. Scoring sessions were held each week, at which time audiotapes and notes of interviews were reviewed. In order to meet the demands of high inter-rater reliability, any scoring difficulties were settled by the concensus of all interviewers. All statistical analyses were conducted with programs contained within SPSS-X (SPSS-X, 1986). RESULTS

Evidence from previous studies implies that sex differences in performance on logical  reasoning tasks are age related, and that they emerge approximately at adolescence (Graybill, 1975; Hernandez, Marek & Renner, 1984). There is little information available about trends that might exist between adolescence and adulthood. Thus, data analysis was begun with an attempt to determine the effects of age and sex of subject on success with Piagetian tasks. Because of earlier suggestions  that these differences might not be equally distributed across all schemas (Meehan, 1984), a two-way analysis of variance was conducted three times, using as dependent variables the total score for all measures of: 1) combinations; 2) proportions, and; 3) probability. In each analysis, age groups were defined as 9th and 10th grade (Age 1), 11th and 12th grade (Age 2) and college (Age 3), and sex groups were, of course, male and female. No significant results were found in these analyses for either combinations or probability tasks. There were no significant differences in performance across the three age groups or between the two sexes, nor were there any significant interactions between age and sex, in either of these types of task. However, this was not true in 81

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Figure 1. Mean scores of male and female subjects in three age groups on all measures of proportionality.* * Age 1 = grades 9, 10   Age 2 = grades 11, 12   Age 3 = college

the analysis of proportionality tasks. There were significant main effects for both age (F = 9.30, p < .01) and sex (F = 5.36, p < .05). The interaction between age and sex was not significant. Mean scores for male and female subjects in the three age groups are displayed in Figure 1. A LSD (least-significant difference) post hoc analysis, chosen for its power  (SPSS-X, 1986) indicates that Age 1 (grades 9–10) females receive significantly lower scores than Age 2 (grades 11–12) males or females and Age 3 (college) males. The scores of college age females are not significantly different from those of their 9th and 10th grade counter-parts. The next analyses were an attempt to identify the effect of sex of experimenter and of subject on performance on different types of task. Two separate two-way ANOVAs were conducted. In the first, using the total score for all clinical proportionality tasks, significant effects were observed both for sex of subject (F = 3.88, p < .05) and sex of experimenter (F = 3.85, p < .05). There were no significant interactions between subject sex and experimenter sex. Mean scores of male and female subjects tested by male and female expermenters are displayed in Figure 2. These reveal that female experimenters award higher scores to all subjects on clinical measures of proportionality than is true of male experimenters, but that experimenters of both sexes rate male subjects more successful that female. Inspection of this figure suggests that the gap between

82

SEX DIFFERENCES IN FORMAL REASONING ABILITY

Figure 2. Mean scores on clinical measures of proportionality of male and female subjects tested by male and female experimenters.

male and female subjects has closed slightly when interviewed by females, but the results of the ANOVA indicate that this trend is not statistically significant. A second two-way ANOVA, this time using the total for all written measures of proportionality as the dependent variable, revealed no significant main effects or interactions. There were no significant differences either in the scores awarded by male and female experimenters or in those achieved by male and female subjects. A series of factor analyses of all measures was conducted separately for males and females. Subprogram FACTOR from the SPSS-X package was used with an orthogonal (quartimax) rotation in order to maximize the loading of variables into individual factors. A two-factor solution was computed first, since the main purpose was to explore the possibility that factors might emerge which could be thought of as representing “manipulative” versus “pencil and paper” tasks (Lawson, 1975). The results were very similar for males and for females (Table I). Factor expansions were carried out until the SPSS-X default option at an eigenvalue of 1.00 was reached, ultimately resulting in four factors for males and five factors for females. None of these additional solutions revealed any more substantial differences in factor structure for either sex. The only tendency for written and clinical measures to load on separate factors was among measures of proportionality in the two-factor solution for males, as shown in Table I. This relationship was maintained even into the final five factor solution. There was no similar dichotomy between written and clinical tasks in any solution for females.

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TABLE I A comparison of principal components analyses for males and for females of measures of formal reasoning ability*

eigenvalue pct. of variance

MALES

FEMALES

Factor

Factor

1

2

1

2

4.00

1.87

2.98

1.74

33.4

Measure

15.6

24.9

14.5

Factor Loadings

Balance

.04

.85

−.20

.64

Shadows

.34

.81

.12

.85

Mr. Tall**

.74

.06

.32

.54

Recipe**

.67

−.12

.60

.06

Chemicals

.63

−.03

.78

.04

Tokens

.66

−.21

.60

.06

Cities

.73

.23

.44

−.17

Change**

.16

−.40

.28

.36

License**

.60

.41

.10

−.37

Choice

.81

.36

.67

.15

Eye-Hair

.30

−.01

.61

.10

Bats

.12

.52

.67

−.02

* after orthogonal rotation ** written tasks DISCUSSION

Mean scores (Figure 1) and the results of post hoc analysis show that the observed trend toward improved performance with age on proportionality tasks should be attributed mainly to the poorer performance of younger subjects. As had been found previously (Piburn & Enyeart, 1981), there is a relatively large increase between the 10th and 11th grades, with no subsequent improvement in the college sample. However, this effect results principally from the contribution of male subjects, since there is no significant difference in the performance of females in the youngest and the oldest age groups. We were not surprised to find sex differences confined to those tasks that required proportional reasoning, since this replicates the results of a number of earlier research studies. We also anticipated, as apparently did Saunders and Jesunthadas 84

SEX DIFFERENCES IN FORMAL REASONING ABILITY

(1988), that sex differences would be enlarged in the case of more difficult items, which might magnify the effect of expertise. That does not appear to be the case here, as subjects were more successful on measures of proportionality than they were on either combinations or correlations and probability. Sex differences were confined to tasks that are clinical, rather than written, and involve unfamiliar content. This appears to corroborate much previous evidence, and we conclude that there is a substantial bias against females in classical Piagetian measures of formal thought. We do not see in our own results any substantial confirmation of those which Lawson obtained in his effort to determine whether “manipulative tasks and pencil and paper measures were measuring different psychological parameters for the group as a whole and for the males and females taken separately” (1975, pp. 401–402). However, the question remains interesting and similar analyses should be conducted during future research that includes the evaluation of sex differences. Both male and female interviewers obtained better performance on clinical measures from males than from females, tending to confirm the intrinsic bias against females found in those tasks. One interpretation of these results is that larger sex differences occur in the case of problems that require additional background knowledge and access to specialized experimental and computational routines of the kind that arise from practice and experience. This would certainly be consistent with the results of the National Assessment of Educational Progress, in which 13 and 17 year old females report having significantly fewer experiences in science than boys in every category from watching an egg hatching to visiting a mine or quarry (Kahle & Lakes, 1983). Girls have fewer experiences using scientific equipment (balances, thermometers, telescopes and compasses) and experiment less with materials (magnets and electrical circuits). The most damning evidence of all is that science teachers usually provide boys with instructions for completing a project, but show girls how to do it or do it for them (Hall & Sandler, 1982). Even in engineering school, females receive less practical experience than males (Wittig, Sasse & Giacomi, 1984). Our study replicates the results of an earlier one by McMahan (1976). Female interviewers obtained better performance from all subjects than did male interviewers, and females interviewed by females achieved scores on clinical measures of proportionality that were no different from those of males interviewed by males. Being interviewed with a task like equilibrium on the balance, that obviously comes straight from the science classroom, can be a stressful event and female interviewers seem to create a more relaxed test environment in which subjects of either sex can be more successful. We conclude from these results that women turn away from science as avocation or profession for reasons that have little or nothing to do with their intellectual  ability.  If  women are to become more successful, it will be because they are more involved and experienced, and because social expectations toward their role in society has changed. With better backgrounds and improved attitudes 85

CHAPTER 4

should come greater success, and ultimately an increase in the number of women in science. NOTE 1

Originally published as Piburn, M. D. & Baker, D. R. (1989). Sex differences in formal reasoning ability: Task and interviewer effects. Science Education, 73, 101–113. Reprinted here with permission.

REFERENCES Butler, M. & Marzone, J. (1980). Education: The critical filter. A statistical report on the status of female students in post-secondary education (Vol. 2). San Francisco, CA: Women’s Educational Equity Network, Far West Laboratory for Educational Research and Development. Chandran, S., Treagust, D. & Tobin, K. (1987). The role of cognitive factors in chemistry achievement. Journal of Research in Science Teaching, 24(2), 145–160. Dearman, N. & Plisko, V. (1981). The conditions of education. Washington, DC: National Center for Educational Statistics. Enyeart, M., Baker, D. & VanHarlingen, D. (1980). Correlation of inductive and deductive logical reasoning to college physics achievement. Journal of Research in Science Teaching, 17(3), 263–267. Farrell, M. & Farmer, W. (1985). Adolescents’ performance on a sequence of proportional reasoning tasks. Journal of Research in Science Teaching, 22(6), 503–518. Golbeck, S. (1986). The role of physical content in Piagetian spatial tasks: Sex differences in spatial knowledge. Journal of Research in Science Teaching, 23(4), 365–374. Graybill, L. (1975). Sex differences in problem solving ability. Journal of Research in Science Teaching, 12(4), 341–346. Greenberg, R. (1972). Sexual bias on Rorschach administration. Journal of Personality Assessment, 36(4), 336–339. Greenberg, R. & Gordon, M. (1983). Sex and children’s Rorschach productivity. Psychological Reports, 53(2), 355–357. Hernandez, L., Marek, E. & Renner, J. (1984). Relationships among gender, age, and intellectual development. Journal of Research in Science Teaching, 21(4), 365–375. Hall, R. (1982). The classroom climate: A chilly one for women? Washington, D.C.: Project on the Status of Women. Howe, A. & Shayer, M. (1981). Sex related differences on a task of volume and density. Journal of Research in Science Teaching, 18(2), 169–175. Inhelder, B. & Piaget, J. (1958). The growth of logical thinking from childhood to adolescence, New York, NY: Basic Books. Joseph, C., McKay, T. & McKay, M. (1982). The effect of sex of the subject, sex of the experimenter, and reinforcement condition on serial digit learning. Journal of General Psychology, 107(1), 47–49. Kahle, J. & Lakes, M. (1983). The myth of equality in science classrooms. Journal of Research in Science Teaching, 20(2), 131–140. Karplus, R., Karplus, E., Formisano, M. & Paulsen, A. (1977). Proportional reasoning and control of variables in seven countries. Journal of Research in Science Teaching, 14(5), 411–417. Karplus, R. & Peterson, R. (1970). Intellectual development beyond elementary school II: Ratio, a survey. School Science and Mathematics, 70(9), 813–820. Lawson, A. (1975). Sex differences in concrete and formal reasoning as measured by manipulative tasks and written tasks. Science Education, 59(3), 397–405. Lawson, A. & Sheperd, G. (1979). Written language maturity and formal reasoning in male and female adolescents. Language and Speech, 22(2), 117–127. Lennon, R., Eisenberg, N. & Carroll, J. (1983). The assessment of empathy in early childhood. Journal of Applied Developmental Psychology, 4(3), 295–302.

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SEX DIFFERENCES IN FORMAL REASONING ABILITY Linn, M. C. & Pulos, S. (1983). Aptitude and experience influences on proportional reasoning during adolescence: Focus on male-female differences. Journal for Research in Mathematics Education, 14(1), 30–46. McMahan, E. (1976). The role of the experimenter in observed sex differences in conservation acquisition. Journal of Psychology, 92(4), 205–206. Meehan, A. (1984). A meta-analysis of sex differences in formal operational thought. Child Development, 55(3), 1110–1124. Mitchell, A. & Lawson, A. (1988). Predicting genetics achievement in nonmajors college biology. Journal of Research in Science Teaching, 25(1), 23–37. National Science Foundation (1986). Women and minorities in science and engineering. Washington, D.C.: Author. Piaget, J. & Inhelder, B. (1975). The origin of the idea of chance in children. New York, NY: W. W. Norton & Co. Piburn, M. (1980). Spatial reasoning as a correlate of formal thought and science achievement for New Zealand students. Journal of Research in Science Teaching, 17(5), 443–448. Piburn, M. & Baker, D. (1988, April). Reasoning about logical propositions and success in science. Paper presented at the meeting of the American Educational Research Association, New Orleans, LA. Piburn, M. & Enyeart, M. (1981, April). An error analysis of responses to a test of propositional logic. Paper presented at the meeting of the National Association for Research in Science Teaching, Grossinger’s NY. Rumenik, C., Capasso, D. & Hendrick, C. (1977). Experimenter sex effects in behavioral research. Psychological Bulletin, 84(4), 852–877. Saunders, W. and Jesunathadas, J. (1988). The effect of task content upon proportional reasoning. Journal of Research in Science Teaching, 25(1), 59–68. Sclafani, J. & Labarba, R. (1982). Sex differences and effects of sex of examiner on early conservation ability. Bulletin of the Psychonomic Society, 19(4), 191–193. Sobal, J. & Juhasz, J. (1977). Sex, experimenter and reinforcement effects in verbal learning. Journal of Social Psychology, 102(2), 267–273. SPSS-X, Inc. (1986). User’s guide. Chicago, Illinois: Author. Staver, J. (1984). Effects of method and format on subjects’ responses to a control of variables reasoning problem. Journal of Research in Science Teaching, 21(5), 517–526. Staver, J. & Gabel, D. (1979). The development and construct validation of a group administered test of formal thought. Journal of Research in Science Teaching, 16(6), 535–544. Staver, J. & Pascarella, E. (1984). The effect of method and format on the responses of subjects to a Piagetian reasoning problem. Journal of Research in Science Teaching, 21(3), 305–314. Stefanich, G., Unroth, R., Perry, B. & Phillips, G. (1983). Convergent validity of group tests of cognitive development. Journal of Research in Science Teaching, 20(6), 557–563. Tschopp, J. & Kurdek, L. (1981). An assessment of the relation between traditional and paper-and pencil operations tasks. Journal of Research in Science Teaching, 18(1), 87–92. Wittig, A., Sasse, S. and Giacomi, J. (1984). Predictive validity of five cognitive skills tests among women receiving engineering training. Journal of Research in Science Teaching, 21(5), 537–546.

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LETTING GIRLS SPEAK OUT ABOUT SCIENCE

WHY I CONDUCTED THIS STUDY

I conducted this study because I was interested in the question of why attitude research indicated that girls did not like science and were less likely to pursue a career in science. I was also puzzled about the drop in interest in science that occurred  around the time students made the transition from elementary to high school. Was it the curriculum, how girls and boys perceived science, or some other factor? So many previous studies investigated variables that seem fruitful but were found to lack explanatory power. I wanted to solve these puzzles because I felt that if I knew why girls did not like science or wanted to pursue a career in science, then I might be able to develop interventions to increase girls’ participation in science. METHODOLOGICAL DECISIONS

I began this study with a very traditional design. I would compare male and female responses. Since paper and pencil instruments had come under fire (Munby, 1983) as poor measures of attitude toward science, I opted to conduct interviews. Furthermore, since the school and social influences previously investigated did not provide a satisfactory explanation for why so few girls chose science, I started without a theoretical framework. As a qualitative study, I felt justified in looking for emergent themes in the data in the hope of identifying new factors that could explain why so few girls chose science. I decided to interview students at grades 2, 5, 8, and 11 to see how attitudes changed over time and to identify factors that could explain the dip in attitude that occurred in the transition from elementary to high school. I began at grade 2 because an earlier study revealed that kindergarten and first grade children didn’t know what science was. It was not until grade 2 that they experienced enough science instruction to form attitudes (Piburn & Baker, 1993). Since I was interviewing boys and girls, I was concerned that the students would be less forthcoming about their true attitudes when responding to questions designed to reveal gender stereotypes. To control for this possibility, I first asked the students how they would teach science to just boys and just girls. Then, I asked the girls to pretend to be a boy and the boys to pretend to be a girl. Once again, I asked them to tell me how they would teach science to just boys and just girls. After the interviews were transcribed they were analyzed using a computer program tagging sections of texts with themes. These themes were sorted into male 89

CHAPTER 5

and female groups and I proceeded to compare males and females across the themes. A month of looking at the data by comparing males and females was frustrating. Slips of paper with color coding littered my home office floor but I could not make sense of the data. I took a break, stepped back from the data, and began to read and think about what feminists theorists had written. Thinking and reading led me to put aside the males’ interviews and look at only the females’ interviews thinking I would return to the male interviews later. I tried various feminist theories as a lens to explain the data and decided that women’s decision making was the most useful theoretical framework to explain what the girls were telling me. The data suddenly made sense! The title of this article, Letting Girls Speak Out About Science, (Baker & Leary, 1995) reflects the struggles I was having making sense of my data and my decision to let the girls’ own words tell their story. However, I, like so many other science educators at this time, did not consider the effects of race or ethnicity with sex. I never did return to the male interviews which were discarded in compliance with IRB guidelines. SCIENCE EDUCATION AT THE TIME OF THE STUDY

All research takes place within context. Knowing what was happening in science education at the time of this research reveals how groundbreaking my approach was. A look at what was being published in 1993 and 1994 in science education journals reflected the priorities of the field. Science education scholars were focusing their efforts on students first, followed by teachers. When studying teachers, classroom practice predominated over teacher education programs. High school was the most frequent context for the research followed by middle school and then university and elementary settings. Studies set in community colleges were almost nonexistent. Few studies took a longitudinal approach or included samples across grade levels. Content foci were evenly distributed across biology, chemistry, and physics but Earth science was rare. Many studies did not include sex as a variable but when they did, the studies compared males to females. With a few exceptions, studies did not consider the effects of race or ethnicity or socioeconomic status. Nor, was intersectionality part of the research design. Approximately the same number of studies analyzed data quantitatively or qualitatively with a small number of studies using both approaches. However, there was no discussion of mixed methods or how the two analytical approaches informed one another. The data analysis, especially the quantitative analysis was not as sophisticated as current work. The qualitative analysis was also different from current research in that it was less revealing in terms of the researcher’s position and biases. A Theoretical framework was missing from many articles. When a theory did frame a study, it was often addressed in a cursory manner. The most prevalent theories in use were constructivism, and misconceptions and conceptual change as they related to constructivism. Studies using Piagetian theory were less prominent but still the second most commonly used followed by a variety of sociological 90

LETTING GIRLS SPEAK OUT ABOUT SCIENCE

theories (i.e. sociocultural, socio-cognitive). Feminist theory was conspicuous by its absence. The Handbook of Research on Science Teaching and Learning (Gabel, 1994) was a comprehensive review of the research in science education at the time of the publication of my article. The handbook sections reveal what the editor considered important topics of the time. They were teaching, learning, problem solving, curriculum and context. Within these sections were nineteen chapters. Only one addressed gender issues in the classroom. It was written by Kahle and Meece (1994). This was in sharp contrast to six chapters addressing problem solving in multiple contexts of elementary and middle school, Earth science, chemistry, genetics, and physics. The only other chapter in the handbook that addressed gender as a major organizational heading had a two sentence paragraph beneath the heading stating that the findings were mixed. The chapter by Kahle and Meece (1994) introduced feminist theories to science educators and criticized science education research for not breaking down samples by  race, sex, and ethnicities, and for not considering the interactions of these variables. The research reviewed was found to use a deficit model where girls lacked cognitive skills, experiences, and personal characteristics that prevented them from achieving as well as boys. Kahle and Meece also noted that when research found girls outperformed boys in mathematics or received better grades in science than boys, the research was ignored or dismissed. This was even the case when minimal and educationally insignificant differences were found in areas like spatial ability. Kahle and Meece concluded their chapter by noting that the field of science education lacked theoretical models that integrated psychological and sociocultural variables thereby limiting research on gender. The lack of attention to gender in the handbook is not surprising since it was not until the late 1980s that science education research was centrally concerned with gender with an emphasis on sex differences between males and females. At the end of the 1980s the focus on sex differences began to wane but no one was yet questioning whether the problem of girls in science was with science and not girls (Baker, 2002a). The 1990s saw the beginning of efforts to look at school science as the cause of girls’ low participation and interest in science but the practice of science itself was still not questioned. Shymansky and Kyle (1992) in their editorial, Establishing a Research Agenda: Critical Issues of Science Education Reform, criticized science education for perpetuating the social, economic, and political ideologies of the dominant culture. They encouraged researchers to consider gender, race, ethnicity, and religion together. Two prominent social critics also identified the failings of science education in 1992 in the Journal of Research in Science Teaching. Michael Apple (1992) wrote about educational reform and the educational crisis addressing economic oppression and curriculum reform. However, he never mentioned the special effect economic repression has on women. O’Loughlin (1992) in an article about rethinking science education also overlooks girls and women. He wrote about power, culture, and 91

CHAPTER 5

discourse in the classroom in relation to students of color ignoring the research on gendered classroom interactions. RESEARCH INTO GENDER IN THE WIDER FIELD OF EDUCATION

A review of articles in the 1993 and 1994 journals of the American Educational Research Association (i.e. American Educational Research Journal and the Review of Educational Research) widely regarded as the largest research organization in the world was startling. Only one article in this two year time period addressed gender specifically. This was Sex-Equity Legislation in Education: The State as a Promoter of Women’s Rights, by Nelly Stromquist (1993). While some areas of education were ignoring gender others were not. In 1993 the journal Educational Psychologist published a special issue on gender and educational achievement. The Ninety-Second Yearbook of the National Society for the Study of Education (Biklen & Pollard, 1993) was also dedicated to gender and education. Of special note was the chapter in the yearbook that specifically looked at equity issues in educational research methods (Campbell & Greenberg, 1993) revealing the biases in who did educational research and how research was done. Even philosophers were beginning to take on the traditional assumptions of research programs with the publication of Feminist Epistemologies (Alcoff & Potter, 1993). The understanding of the construction of knowledge presented in this book was far more nuanced than the way constructivist theories were used in science education. Authors focused more on the influence of social values and privileged community over the individual in the construction of knowledge. THE CULTURE AND THE TIMES

When Letting Girls Speak Out About Science was written, the government played only a symbolic and reluctant role in promoting gender equity. Despite legislation to promote gender equity, there were few efforts at enforcement (Stromquist, 1993). Myra and David Sadker (1994), in their book Failing at Fairness: How Schools Cheat Girls, came to a stronger conclusion. They reviewed the efforts of the Reagan  and George W. Bush administrations to dismantle legislation to promote gender equity; especially the Women’s Educational Equity Act (WEEA) programs. By 1992, George W. Bush had cut the WEEA budget to half a million dollars and continued efforts to eliminate the programs entirely. Since WEEA was one of the few sources of funds for gender equity research, opportunities to conduct gender research were limited (Stromquist, 1993). At the same time that the government was trying to limit funding for gender equity  research, the American Association of University Women published How Schools are Shortchanging Girls (American Association of University Women, 1992). They noted, among other problems, that there was only a slight decrease in sexism in elementary textbooks and basal readers but the problem at the secondary 92

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level was much greater. Textbooks were still giving girls stereotypical messages about what was important and appropriate for girls to study. IMPACT OF MY WORK

Letting Girls Speak Out About Science is the most cited of all my work. It was chosen for the fortieth anniversary special issue of the Journal of Research in Science Teaching (Baker & Leary, 2003) as one of the twelve most influential articles published in the forty year history of the journal. It was ground breaking for the explicit use of feminist theory that provided a strong explanatory framework for the findings and for the use of an all-female sample. It did not employ a deficit model in which males and females were compared and the male model was the norm. This decision led to the title of the article. I let girls speak for themselves providing their perspective on science. The conclusions were also important because this was the first time a distinction was made between how girls felt about school science versus science experiences outside of school. Previously, attitudes toward science results were confounded because instruments used to assess science did not distinguish between feelings for how science was experienced in school and out of school or how these contexts influenced attitudes toward science. This confounding of context led many to conclude that girls did not like and were not interested in pursuing science. I found that girls did like science and rejected cultural stereotypes. Furthermore, the study uncovered a strong affective component that promoted interest in science. Girls wanted to contribute to the greater good by helping people, animals, and the Earth through science. The findings also provided insight into why more girls were found in biology and not the physical sciences. Girls wanted to engage in science that  met their relational, affective, and moral needs. Girls did not want to be a scientist scientist, their description of one who studies the physical sciences, because they did not see physical scientists as helping or caring.

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ABSTRACT

The purpose of this study was to try to determine what influences girls to choose science. Forty girls were interviewed in Grades 2, 5, 8, 11 using a semistructured protocol. The interview focused on feelings about science, science careers, peer and parental support, and how science is taught. To determine whether their responses were based on gender, each girl was asked to respond to questions as if she were a boy. The girls were highly self-confident and positive about science. All of the girls took a strong equity position and asserted that women can and should do science. The girls liked learning science in an interactive social context rather than participating in activities that isolated them such as independent reading, writing, or note taking. Those who chose science careers were drawn to them because of strong affective experiences with a loved one and a desire to help. The interviews were analyzed through the framework of women’s affective and psychological needs. Over the years many factors have been investigated to understand why so few women choose science careers. These factors are located primarily within the realm of school and society. However, despite the large number of studies conducted, this research tells us very little about which factors influence girls to choose or reject a scientific career. Another relatively recent line of research addressing the women in science question has focused on women themselves. In particular, it focuses on women’s decision-making processes in the context of the psychology of women. This research, embedded in feminist paradigms, employs a different set of assumptions, research designs, and psychological models from those normally found in the science education literature. Ground-breaking studies within this framework suggest that feminist perspectives hold greater promise for understanding the relationship of girls and women to science than those frameworks employed in the past. Because we have chosen to investigate this promise rather than place our research in the more familiar context, we will first provide an overview of the methodologic issues involved in the feminist scholarship that influenced this study. Then we will present a brief review of the feminist literature used to interpret our data and contrast it with the lack of explanatory power of the literature within the traditional research paradigm. 94

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METHODOLOGICAL ISSUES

Feminist researchers interested in the question of women and science have moved away from paper and pencil assessments and quantitative analysis toward a more qualitative and contextualized understanding. Consequently, we chose to use indepth interviews for this study. A shift in methods appear to be justified when data collected using the two approaches are compared. For example, gender differences in knowledge of science and technology issues were found using a Likert-scale questionnaire, but were not found in the transcripts of conversations with the same girls and boys (Solomon & Harrison, 1991). The conversations, unlike the questionnaire, revealed that the girls were confident about their technical competence. Furthermore, extensive interviews by Baker (1990) and Baker, Leary, and Trammell (1992) indicated that attitudinal differences toward science between school-age girls and boys are small, and that they are more alike in what they would like to learn about in science than they are different. Holden and Edwards’s study (1987), in which women wanted more situational details than were offered with a forced choice format, and Grant and Harding’s (1987) study, in which women chose “not sure” because they said that the answer depends on the situation or on what you mean by science and technology, also emphasized the importance of context for women. Interviews provide context for researchers, too. It is the vehicle through which they hear and understand girls’ voices. Good interviews tell us not only what girls think, but why. The spoken and written word, the life stories they tell, are imbued with powerful emotions that are as important to understanding girls’ lives as less value-laden but thinner data sources (Brown & Gilligan, 1992). We have also chosen to use a sample of only girls. Feminist scholars have long advocated the abandonment of male–female comparisons in favor of looking at girls alone for two reasons. First, the approach avoids using a deficit model, or the assumption that male behavior is the norm, and allows the data to be understood in light of women’s sociopsychological reality as expressed in educational preferences, needs, and goals (Campbell, 1988). Second, attempts to compare and contrast males and females lead to noncomparable, simplistic either/or categories that do not capture the sense of the data (Brown & Gilligan, 1992). Our approach to analysis has been to place what girls are saying within the framework of women’s decision-making processes and a female identity “defined in a context of relationship …” (Gilligan, 1982, p. 163) to better understand who chooses science and why. The next section provides the rationale for choosing this framework. WOMEN’S DECISION MAKING

There is a growing body of evidence that women make decisions about their lives differently from men (Almquist, Angrist, & Mickelsen, 1980; Angrist & Almquist, 95

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1975; Arnold, 1992). These decisions arise from their expectations of multiple life roles, self-identity, and ways of interacting with people, objects, and experiences in the world. Nardi (1983) found that women make career plans in terms of personal life scripts that consist of a number of anticipated personal and professional roles. Gilligan (1982) found that highly achieving and successful women described themselves in terms of relationships. Their identity resided in their roles as mothers, wives, lovers, and children, and not in their academic or professional success. Similarly, Arnold (1992) found that academically outstanding women tended to judge success in terms of relationships, and made decisions that stressed a balance between work and family life. Younger girls and adolescents also describe their world in terms of relationships (Brown & Gilligan, 1992). Eccles (1986) concluded that women may choose less technical occupations because they are more attractive and are therefore proactive choices. She concluded that these choices are based on both short- and long-term goals, self-identity, and basic psychological needs that are, because of socialization, different from but equal to those of men. One of these needs is the inner sense of connection with others that Brown and Gilligan (1992) saw as the central organizing feature of girls’ and women’s development. Markus and Oyserman (1989) argued that women define themselves in relation to others and that their very self-concept is embedded in and arises from interactions and interpersonal experiences. Belenky, Clinchy, Goldberger, and Tarule (1986) also spoke of the importance of connections and relationships for women’s ways of knowing. In their framework, a woman who takes the epistomologic position of constructed knowledge is the woman who can construct her own knowledge from both objective and subjective experiences. Knowing is based on connections with people, ideas, objects, and the written word. Connections and relationships give rise to a moral component in attitudes, judgments, and behaviors. Thus, decisions take place in context and are evaluated in terms of their effects on others. Further evidence of the importance of relationships is found in Brown and Gilligan’s (1992) sample of school girls. Those girls with strong voices (a strong sense of self- and inner knowledge) who could deal with the need to stand up for who they were and what they believed, in the face of culturally constructed ideals of the feminine, had close confiding relationships with their mothers, who served as alternative role models. Within science, despite strong socialization to the contrary, we find that women practice their craft in a way that emphasizes relationships and connectedness to the objects of study and the members of their research teams (Sheperd, 1993). This feeling  of connectedness to nature has led to breakthroughs in fields such as primatology and genetics, but as many of the women scientists interviewed by Sheperd (1993) reported that it also leads to conflict with male mentors and colleagues, isolation, and slower rates of promotion. In extreme cases, the conflict between doing science in a related and connected way and the norms of science that emphasize hierarchy, distance, and objectivity, lead to dropping out of science. 96

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We will now briefly examine the school and social factors that comprised the body of literature within the traditional research paradigm to support our claim that this research lacks explanatory power. SCHOOL INFLUENCES

Many studies indicate that schools fail to provide environments conducive to girls’ learning. Textbooks lack positive female role models and often include sexrole stereotypes (Sadker, Sadker, & Klein, 1991). Teacher–student interactions are biased in favor of boys as early as elementary school. In the face of failure, boys are encouraged to try again and girls are allowed to give up (Oakes, 1990; Wilder & Powell, 1989). Pedagogy is often based on male learning styles, especially when competition is emphasized. Under all forms of instruction, girls have less access to science equipment, hands-on activities, and computers than boys (Kahle & Lakes, 1983; Sutton, 1991). Cumulatively, these experiences would logically seem to lead to lowered educational and career aspirations, including science, for girls. However, other nontraditional careers such as law (National Science Foundation, 1990) are attracting a large number of women, indicating that learning environments alone do not explain career choice. Mathematics ability and the number of math and science courses taken in high school are related to choosing a scientific or quantitative college major (Ethington & Wolfe, 1989; Ware, Steckler, & Lesserman, 1985). However, achievement and course-taking behavior do not explain career differences because the gender gap in these areas is not large enough to be practically significant (Oakes, 1990; Wilder & Powell, 1989). An outstanding academic record does not necessarily translate into choosing science. Female Westinghouse Award winners avoid technical careers when they go to college (Campbell, 1991), and studies of college women (Arnold, 1992; Oakes, 1990) indicated that those who are talented and academically successful drop out of science majors and change careers at a much greater rate than men, even when they are equally or better prepared. SOCIAL INFLUENCES

Social influences are even more difficult to link directly to career choice, in that factors such as attitude or self-concept are constructed from multiple experiences. In the case of attitudes some studies conclude that more girls than boys dislike science and lack interest in science careers (Hueftle, Rakow, & Welch, 1983; Maple  & Stage,  1991; Mullis & Jenkins, 1988; Ward, 1979). Other studies, especially of biology and chemistry, have found that girls have better attitudes than boys (Baker, et al., 1992; Steinkamp & Maehr, 1983). When attitude differences do exist, they are, like cognitive differences, too small to account for the differences we see in the number of males and females involved in science 97

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careers. In addition, the link between attitude toward science and doing well in science is weak for both girls and boys (Steinkamp & Maehr, 1983). However, even when girls do well and like science they do not necessarily choose science careers (Baker et al., 1992). The American Association of University Women (AAUW, 1991) concluded that as girls grow up they lose confidence in their academic abilities, expect less from life, and lower their career aspirations. Girls expect to fail at tasks that are unfamiliar,  difficult, or perceived to require high ability (Oakes, 1990). When they fail, girls internalize their failure, attributing it to themselves. This poor selfconcept  leads to taking fewer math and science courses (AAUW, 1991; DeBoer, 1984a, 1984b), but as noted earlier, taking math and science courses does not in and of itself lead to science career choices. Parents may be the single strongest negative social influence on girls’ science career choices when they hold different expectations for daughters than for sons and treat their children in ways that reinforce gender stereotypes (Campbell & Connolly, 1987). Negative influences include expecting math to be difficult, discounting the importance of higher-level math courses and home computers, and providing fewer opportunities for out-of-school science experiences for daughters (Kahle & Lakes, 1983; Oakes, 1990; Sutton, 1991). Socioeconomic status (family income, parental education, father’s occupation, and household possessions) is also an influence on girls. It is related to mathematics achievement, high school grades, SAT scores, and post-secondary plans, especially the choice of a scientific college major (Oakes, 1990). The advantage goes to the affluent, but that advantage is again very small. To illustrate the relative lack of power of these variables, we next examine the results of two causal models developed by Ethington and Wolf (1989) and Peng and Jaffe (1979). These models were attempts to determine the cluster of factors that would predict a quantitative undergraduate major for girls using longitudinal data from the High School and Beyond Study and the National Longitudinal Study. Both models included variables that we have reviewed (e.g., achievement, attitudes, SES, course-taking behavior, self-concept, and family influences). Ethington and Wolf’s model explained 8.9% of the variance in choice of a quantitative undergraduate major, and Peng and Jaffe’s model explained 6%. Neither of these models, despite the number of variables involved, provide us with good explanations as to why women are not entering the sciences. A further refutation of traditional variables comes from studies in the Scandinavian countries, especially Norway, where there is legislation against sex discrimination and for textbooks that must pass inspection for gender inclusiveness, a well-off homogeneous population, the same curriculum for all students, and role models in the form of a female prime minister and cabinet members. Yet, the number of women who choose science and engineering is extremely low and declining (Sjoberg & Imsen, 1983). 98

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METHOD

To determine what influences girls to choose science we used a volunteer sample of 40 girls in grades 2, 5, 8, and 11, who were interviewed using a semistructured protocol. They were asked to share their feelings about science, science careers, peer and parental support, how science is taught, and how they would teach science to girls or boys. To gain further insight, the girls were also asked to respond to the questions pretending to be a boy. The interviews were tape recorded and then transcribed. The transcribed texts of all the interviews were then read for emerging themes by a team of graduate students and the authors. Discussions concerning evidence for the existence of a theme and the type of evidence needed to categorize portions of the interviews within that theme took place over a summer. These discussions led to the identification of 7 themes (likes, dislikes, equity, career preparation, career choices, role models, and peers) that could account for almost all  of the students’ remarks. The interview transcripts were then reread by the authors, and the 7 categories were used to code the text of each girl’s transcript using the Textbase Alpha program (Tesch, Sommerlund, & Kristensen, 1989). Textbase Alpha is a program for the management and coding of qualitative data, especially interviews. Interrater reliability for coding the students’ statements into the theme categories was high (likes, 93%; dislikes, 97%; equity, 83%; career preparation, 96%; career choices, 93%; role models, 99%; and peers, 91%). This 7-theme system proved cumbersome and overly detailed and resulted in a Tower of Babel rather than a way to facilitate young women’s voices. Consequently, we looked for threads that were present within themes and provided links across the 7 themes that could provide insights into the relationship of girls to science. The authors again read the students’ statements theme by theme and engaged in discussions. Equity, school, and social threads were identified as the central conceptual components that were present to varying degrees within each of the original 7 themes. RESULTS

As indicated in the Method section, the categories that developed from the data revealed that girls’ relationships toward science are associated with what happens in school, the influence of society in general, and the girls’ strong feelings of equity that persisted throughout the interviews. The results are therefore presented in terms of school science, societal factors, and equity. At first, these categories sound very much like those used in more traditional research. However, the critical differences lies in examining what the girls say through the lens of relationships and connections. Although there were some grade-level differences, most views emerging at second grade remained constant across grade levels. Consequently, instead of providing quotations from students at each grade level to support the results, only those quotes

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that best represent the girls’ overall position will be presented. When grade-level differences tell a different story, as well as in the equity section, where the girls’ position becomes stronger with age, quotes from the different grades will be presented. SCHOOL SCIENCE

Girls in all four grades (2, 5, 8, and 11) were strongly positive about their school science experiences. When asked about school science they consistently responded with adjectives such as “fun,” “interesting,” “important,” and “like.” A positive reaction to science was sometimes tempered by the teacher, the instructional formats used in the classroom, the learning styles of the girls themselves, or the topic being studied. Much to our surprise, the students also enjoyed the cognitive demands of learning science. The second graders primarily identified biologic topics—for example, plants, animals, and themselves—as their favorite subject matter. Student 1 (S1): We have this science book and it’s real fun because you can learn about animals. Interviewer (I): Do you like animals? S1: Umm-hmm. I: What do you think would be your favorite thing to learn about?

S2: Animals and trees.

However, the 5th through 11th-grade girls mentioned both physical and biologic topics as being among their favorites. S1: … We learned about stuff like Newton’s Law of Motion and stuff like that … Usually it’s pretty fun. S2: I like the pH and the pOH and all that stuff, and the bonding and stuff, and how chemicals really fit together, and materials. S3: I’d like to see zoology classes, which is invertebrates, vertebrae, um. We have an anatomy class but like, maybe a close look on health or something like that. Like the nutrition class. Basically, I like all the science classes here. These girls would like to have a voice in the topics that they study instead of a curriculum generated and driven by some outside force. S: I’d, I, maybe when I came home I’d go write some stuff down that would be like good information from the kids and see what they would like to do and then out of that I’d pick different days to do what they wanted to do. 100

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Once the students got beyond the second grade, relevance became an important issue. The girls recognized that science is a part of their everyday lives, and they wanted to see that connection made in school rather than learn science out of context. S1: Umm, probably talk about more up-to-date things and things that are in most of our lives. Maybe something about the environment or something that’s a hot topic and yeah, something that matters more. That, that’ll, that’ll help us more. ’Cause, some people think that science isn’t used very much in everyday life unless you’re a scientist. But that’s not true. Science is used in, like, all different fields. The girls also had strong feelings about how science should be taught. They showed a preference for problem solving and for hands-on activities. There were some differences by grade level, with the second graders being the only group that expressed a liking for the reading and writing aspects of school science. This may be because reading was still a new skill for them and they were anxious to use it and demonstrate their new abilities. I: How do you feel about science in school? S1: Feel that it’s fun. I: It’s fun? What makes it fun? S1: Get to read and write. By fifth grade, the girls began to express a strong preference for experiments and projects in place of the reading and writing activities. They wanted to do more experiments so that they could learn for themselves and “figure things out.” I: What are the things you like? S1: When we get to actually do the experiment instead of drawing it and writing about it. I: What do you do during the labs that you think is fun? S2: How you, how the experiment is. You know, you get to see different things and how things work. The older girls—that is, the 8th and 11th-grade students, also liked science in school, especially labs. They expressed strong feelings for more interaction with their peers in their repeated requests for group work, partners, and more discussion. I: Why more activities? S1: Because lecture, you just, I mean when she lectures you, she just goes on and on and on, and I can’t stand sitting for that long and, and if I was a teacher I would understand how my students felt, that they couldn’t sit for that long. 101

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I: What kind of group, why did you say group activities? S2: ’Cause it’s fun working with different people and seeing what they do and what they like. I: What do you think kids would like to do? S3: Well, not as much like assignments and more like class work altogether or partners. Not just individuals… These girls didn’t want to passive learners and they acknowledged good student– teacher interactions when they experienced them. S: … It’s like communication back and forth instead of just sitting there. You know, her lecturing us and us just sitting here hearing it, and you know, trying to absorb it. We talk back to her so it’s more like communication and it makes it more interesting to do that so we can, you know, we don’t raise our hand. We just all, you know, answer her. It’s easier to learn that way and she’s, I think she’s the best, you know, chemistry teacher that I could have. By eighth grade the teacher had assumed an important role in the science classroom. Attitudes toward science were often dependent on whether the teacher made the subject fun or boring. I: What makes science fun? S1: Probably Mr. X. He’s like, he’s really funny and he’s smart. He’s really funny and he teaches science really good. When science was perceived as boring or irrelevant the blame was often placed on the teacher. I: What happened in sixth grade that made you decide you didn’t like science? S: Umm, my teacher was boring. She was a boring teacher and she didn’t know how to teach, so ever since that I hated it. And my seventh-grade teacher used to always yell at us and he used to give us a ton of work, so I hate it. S2: Well, it’s very structured and everything because, like, we get points taken off if we’re late or we don’t participate. We have to answer all her questions and everything, so, I kind of like that part of it but sometimes she’s a little harsh, like, if you have, like, a wrong answer, she’ll be, like, “Well, I taught that to you. I expect you to know it.” The cognitive demands of science did not result in disliking science or in negative attitudes. This was especially evident in the responses of the 8th and the 11th-grade girls. 102

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S: Well, because I pay attention, and you know, I do what I’m supposed to do. You know, the work I’m supposed to do, and I get good grades in there and the grades, you know, when you get good grades that tells part of how you, how good you’re doing. The girls expressed confidence in their ability to do science and were not afraid of the challenge of the work or of making mistakes. I: What happens to you when you make mistakes in science class? S: I make mistakes. I: What do you do? S: I correct them. I mean I don’t think anything of that. I just think, “Oohs.” Either I knew it or I didn’t and if I knew it and I got it wrong, I just thought, “God, why didn’t I think of that?” But, if I got it wrong and I did know it, I’m just like, “Well, I’ll remember that.” Although the girls were positive overall about science, they did express some dislikes. For the second and fifth graders, many of these dislikes centered on topics. These younger girls didn’t like working with reptiles, amphibians, insects, or dangerous things. S1: Well, I don’t like it when they, in our science book they showed us how, umm, eels and water snakes, umm, eat the water plants and stuff? And it’s  sort of sad and sort of disgusting also. Also, look at the maggots. Oooh. S2: Well because there’s a lot of explosives and things and there might be some that really are gross. Tests bothered some of the girls, and pedagogy that isolated the girls and forced them to work separately were not favored. Upper elementary school is the point at which tests become important in school. Not surprisingly, dissatisfaction with tests and activities that were evaluated emerged at this time. I: What kind of things happen in science that make you feel like you’re not good at science? S: Sometimes tests or when we’re like doing a paper and it’s, umm, umm, blank, weird, I don’t know. I: Do you do well on tests? S: Yeah. Pretty good. I: So why would the tests make you feel like you’re not good? S: ’Cause I’m scared that I’m not gonna pass. 103

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By eighth grade, lectures, note taking, and reliance on the textbook increase, which results in being seat-bound and socially isolated. I: What does he do that you really don’t like in science? S: I hate his lectures. Ohhh, I hate his note lectures. It’s just a pain. He always goes like, eight pages of notes, notes for every chapter, and I just hate it. It’s a pain to do all that. Several of the students expressed dislike for and confusion with topics as presented in science textbooks. I: What’s wrong with the books? S: Books. Oh. Well see, they always say, they say, Oh, well, see this guy created  this thing, all right, and he says all this stuff, but then go two chapters, three chapters down and he says, well this guy created this thing and then like, you like, and then he says, so the other guy’s wrong, but back two, three chapters it says this guy’s right, you know? Says back and forth it’s just like a little juggle back and forth, you know, this guy’s right, this guy’s wrong, this is a certain species but this can’t be in the species although it lives in this, lives with them, can’t be that species because that species is different bone structure and all this stuff. Dissection was a controversial topic for many of these girls across all grade levels.  In the earlier grades, where dissection had not yet been a part of the curriculum, societal myths dominated the girls’ perceptions. They saw dissection as cruel and gross. From second graders: I: Do you think it’s gross when you have to dissect animals and a spider? S1: Yes. I: Do you? You don’t like that part? S1: I haven’t done that part yet. I: If you had to dissect an animal, like maybe a frog? What, what part would you want to do? Would you want to do the cutting up or the writing about it? S1: The writing about it. I: How come? S1: Umm, ’cause I don’t think I would want to do the cutting up. This trend continued in varying degrees through the fifth and the eighth grades. From a fifth grader:

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S: I don’t think I’ll take biology. I: No? Why not? S: I, well, I mean zoology ’cause I mean, you have to dissect animals and I could throw up. And an eighth grader: I: How about doing dissections? Have you ever dissected anything? S1: No, it’s not in our curriculum. I: What do you think about that? S1: I don’t. Because I think it’s cruel to kill a frog just so we can see the organs. I: Okay. S1: On a computer you can just do, umm, there’s this new thing that’s called “Dissecting Frog” on the computer and it shows you all the organs and where it goes and stuff like that. I: Is that a big issue? Dissection? I mean do the people feel strongly about that? S1: There’s a lot of people who feel very strongly against it. It’s gross. The girls, especially the youngest, who were anticipating dissection, expressed many negative feelings about this activity. However, these negative perceptions became positive by the 11th grade, by which time the girls had participated in dissection activities. Many of these older girls responded that dissections were interesting and informative. I: Did you do dissections? S: Umm-hmm. I: Did you like that? S: Umm, I feel sorry for the animals, I really do, but I guess it’s, it was REALLY interesting to find out what was inside ‘em, how they were. I: What did you dissect? S: I dissected a frog and a spider and a, a little, not a scorpion, but one of those little shelled animals, I don’t know. I don’t remember what it was. I: A little shelled animal? S: Oh, I think it was a crawfish and, umm, we also dissected a peanut and stuff. That was weird. 105

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I: Umm-hmm. Okay. But you like that kind of thing. S: Yeah Overall, the number of negative statements were few in comparison with the number of positive ones, and the 2nd and 11th graders had fewer negative things to say than did the fifth and eighth-grade girls. The girls in this study were positive about science in school, as well as confident in their ability to do science despite the absence of role models. Statements by the girls suggested that schools provided few role models or activities that highlighted women’s contributions to science. I: Do kids ever do reports on women scientists? S1: Some. I: Do they do them on men scientists? S1: Yeah. Uhh, lots of times we’re doing them on, like, the Indians or the animals, the nervous system, camouflage for animals. S2: Oh there have been women scientists. I don’t know any, you know, off the top of my head. And career day in high school came too late to influence the few students who mentioned it. I: Have you been exposed to a lot of, like, career days where you’ve seen women in science and math jobs? S: Umm, we had a career day last year in science and you got to choose which areas you wanted to go visit, and I took ones like nurse, vet, and seemed like most of the girls took that more than, like, chemists and stuff. I: Do you think it would have made a difference if you had gone and seen a woman scientist that explained her job like a chemist? S: Well, no, I don’t think it would make too much difference because I don’t have a strong enough interest anyway to really want to pursue a career in science. Despite this absence of school role models, many of the girls aspired to science or science-related professions. These goals were often based on a desire to help people, animals, plants, or the earth. S1: Be a nurse and help, like, I want to be a nurse that helps, umm, animals and stuff. S2: I’d probably be a veterinarian ’cause I really like animals.

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S3: Well I’ll probably want to come and work with the earth, like, help the people to, like, go in a party to work and everything where, where it can save our earth and not die. Many of the girls, including many of the older ones, had narrow and unclear ideas  of which careers are science related and of exactly what scientists do. An eleventh grade student: S: Well, I don’t know if what I plan to do is called a scientist. I plan to study zoology, animal behavior? I want to work with animals. I want to save animals. I want to protect animals. See, what I want to do is, I want to, I’m very against animal cruelty, stuff like that, and I want to, like, help the extinct animals. I want to work them, I want to help. I want to work with the  people explaining, you know, why not to kill the elephants for their tusks and all that. Few girls at any grade level could relate the study of science in school to their personal career goals even when those goals encompassed science-related fields. The  relationship between careers and science in general, and school science in particular, was unclear to most of these girls. I: Do you think science is important? S: Well if you’re going to be a scientist, yeah, you need to learn a lot about it, but if you’re not gonna be a scientist or have anything to do with science I don’t really know. Biologically based careers were seldom seen as science because of the absence of chemicals and electricity. S1: Well, I don’t know if what I plan to do is called a scientist. I plan to study zoology, animal behavior. I want to work with animals. I: Would you like to be a scientist when you’re an adult? S2: Umm, close to a scientist, but … I: Okay, what’s the difference? S2: I would say, like a professional scientist? Well, I mean, it’s in the area of science but maybe a scientist does more with chemicals and chemicals and stuff like that than a vet would do. He does with it, you know, animals, bodies, and stuff like that. Although these girls failed to make the connection between school science and their future careers, they planned to study more science based on interest, liking science in school, or because “it’s fun” and interesting. This was particularly true for the second and fifth graders. A second grader: 107

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I: Why would you pick science? S1: ’Cause I think science is fun and I think I should learn about science more. A fifth grader: I: Do you think you’ll pick science all 4 years [talking about high school]? S1: Umm, well I’d try it the first year and if I really liked it then I would do it for the other years. The eighth-grade girls were looking forward to high school and planned to take science because that’s what you do in high school. Most were looking forward to studying biology. S1: Mostly bio, I take advance biology my freshman year and, uhh, I will take mostly all sci, I have to take 4 years of science so it’ll be biology and all the keep goin’, I’ll keep goin’ for my 4 years and then hopefully I can get into CEU with my college courses. S2: Probably science. I’m trying to, umm, go as far as I can through science, umm. I signed up for Biology 1-A next year and then I’m hoping to take science classes all through high school. I don’t want to just drop out of it. I: So in addition to the biology, what about chemistry and physics? S2: Yeah. I suppose, umm, those go along with the biology. They’re in high school and those will probably be courses I’m taking, too. In 11th grade the focus shifted to college. The girls planned to study science to get into more selective colleges, not because of an intrinsic interest in science. S1: Well, it will look good for college to have all this science background. It will probably help me in some college classes. S2: Umm, so it looks good on college applications. Only two students, an 8th and an 11th grader, expressed interest in the intellectual aspects of a science career. In addition to having a poor sense of the relationship of school science to future  careers, the girls often failed to see the relationship between science and math. I: So do you think you’re gonna need math if you’re a scientist? S1: Umm, I do okay in math and, umm, I think any math for certain parts but, umm, it’s not as important to me as it is to my parents or the science part of it, is more important to me than the math.

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Summary Science in school was perceived positively by this group of girls across grade levels. They liked science, they planned to study more science, they were confident in their ability to do science, and many were planning to pursue science-related careers. They would, however, like to see science taught differently, and many would choose different topics for study. When the girls expressed positive sentiments about school science it was because it  met their needs for relationships and connection. Good teachers really communicate, and group work lets you work with your friends. Independent work separates and isolates, and decontextualized topics are uninteresting. These girls were capable and willing. However, they did not see the link between school science and the careers they were planning, and they did not see the link between science and math. The failure to see these links probably has more do with a deficiency in the curriculum than factors within the girls themselves. For example, they were provided with few role models of women scientists in the school setting, and what they learned in school was not linked to their lives. However, these girls did not receive all of their information about science from school. Much of it came from contacts that these girls have outside of the classroom, both within their families and from the larger society in which they live. In the next section we examine some of these factors as expressed in the interviews. SOCIETAL FACTORS

What girls like or dislike about school science is often affected by social factors. Girls who did science at home, who read about science, or who watched sciencerelated television shows or movies mentioned these as experiences contributing to their attitudes toward science. However, not all of the messages received from these sources were positive. Peers were not a major influence in determining whether the girls liked science or were choosing a scientific career. Most of the girls had high career aspirations. And overall, parents were supportive of, but did not have a negative or positive influence on, the girls’ career choices unless the parents were involved in scientific careers themselves. Many of the girls had developed science-related hobbies that in turn affected their perceptions of science. I: What do you like to study? S: Rocks. I, uhh, I, uhm, I collect them and I love to look at them and see what the sand and wind and water did to them and stuff, and I love, and I have a whole big collection. There’s a, I have this big, huge case and it’s from the ceiling to the wall or the ground.

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I: Where did you get them all? S: We have a cabin, and right across the street is a creek, and it has no water in it, and we collect rocks. One girl’s choice of literature, along with her other interests, directly influenced her understanding of science. I: Well, you said you like science, tell me more about that. What do you like? S2: You see, I read this book, Michael Faraday, and he was a scientist, and I read about all the things he discovered and, you know, when he, uhm, like, like, where he made this, I can’t remember what, but he made this, well, someone that, he just kept going on, and it was element or electricity and, uhm, and I got interested in that and I’d always been interested in zoology and veterinarian ’cause I had my own ranch and a lot of animals and a colt and stuff. However, although these societal influences created positive attitudes toward science, some societal influences can send mixed messages, as one young girl demonstrated in her discussion of Mr. Wizard. (Mr. Wizard is a television show on the Nickelodeon channel. Many of the children mentioned watching it on their own, and some teachers incorporated it into their science lessons.) I: You and your mother do a lot of science at home? S: Yeah. Umm, not too much, we just watch Mr. Wizard together. I: What, uhh, types of things does Mr. Wizard do? S: He, he takes these people, umm, boy or a girl, and they do science. I: Do you think that you could be Mr. Wizard some day? S: No. I: No? Why not? S: Because I’m a girl. The girls in this study had high career aspirations. Nine girls aspired to non-science professions (attorney, journalist), 20 to science-related professions (veterinarian, physician, environmentalist), 5 to nontraditional careers (police, pilot, comedian), and 4 to traditional female careers (teacher, secretary, nurse). When the career choices were science related they were frequently based on the biologic sciences. I: Would you rather take chemistry or biology? S1: Biology. I: How come? 110

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S1: It’s, umm, let me try to explain this. Umm, you see, my mom? She died when I was seven and, umm, she loved animals just like I do and she always, uhh, she always said try to do the best you can and, you know, she says, once she said I majored, she majored in music, biology, and something else? She said that it’s, biology is different from chemistry because, it’s hard to explain. I: Do you think your mom would have been happy to know that you want to be a scientist? S1: She knew I did. I: Oh did she? When you were seven she did? S1: She was, she was, she was a zoologist. I: Oh really? So you think she would like you to be? S1: Yeah. She, she said you can be whatever you want. She said I’ll support you in what ever you want to do. I: What would your mother and your father say about being a vet? How do they feel about that? S2: My mom thinks it’s really, really great to be a vet. You know, she was gonna be a vet but she changed her major to manufacturing engineer, so, uhh, you know, umm, I think, so she would be, she really, she said, you  know, she, when you, “If you become a vet I’ll be proud of you.” So she’ll be more, she’ll be more proud of me if I became a vet than a scientist. As demonstrated in these comments, the girls often gave affective and altruistic reasons for their choices stating that they want to help people, animals, plants, or the earth. Many of the responses were emotionally charged. On the whole, laboratorybased sciences and the physical sciences in general were rejected because the girls could not make these affective links. The few instances in which the girls chose a physical science career were all based on having experienced that science with a loved one. I: An astronomer? S1: Yeah. I: Why did you pick that? S1: Oh, ’cause I like the stars, you know, it’s just neat. My grandpa, you know, he used to take me, well he still does a little bit, but you know, he takes me out on his wooden deck, ’cause he lives on the beach, you know, on the cliffs, and, you know, takes me out in the middle of the night, dresses me up 111

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in sweaters, and he’ll go, “Oh look, there’s Mars,” and boy oh boy, you know, I really used to like that. So that’s probably why I like it. S2: I know a lot about chemistry now. My, my mom’s like, she’s smart, she’s like, really smart and she like, has no trouble ’cause, like, I know I sometimes have problems in working, like, she just (snaps her fingers) does them like that. So hopefully I’ll grow out to be her. Few of the girls were aware of the conflicts that can occur when attempting to balance a science career and a family. Only one girl expressed doubts about having it all. I: Does that ever cross your mind—about the hours you’ll be working? S: Not really ’cause I don’t, I don’t really plan to get married or I don’t really think I’m gonna have kids or get married. I think it’s just … I: If you, if you weren’t planning on getting married and having kids, do you think that would be a factor? S: Yeah, I think so. Peer support for a career in science varied by grade level. Second-grade girls believed that their peers would support them if they chose to become scientists. By the fifth grade, only half the students thought that their friends would support them. In the eighth grade most thought that their friends would not be supportive. However, by the 11th grade, the girls again believed that their friends would support their choice of a science career. Eight grade: I: What would your friends say if you told them you were going to be a scientist? S: They’d probably ask me to think twice. I think there’s a stereotype scientist usually who’s always mixing chemicals and things like that and they really get under my skin and that’s … depends on which kind of scientist I would become. I: Do you have any friends who want to become scientists? S2: No. I: What would you say if a friend of yours wanted to be a scientist? S2: I’d say that’s a dumb occupation. Eleventh grade: S: They would probably support me too. They’d probably want to know more, like, why and would it, what led you to this? I mean, not that it’s bad, they would just … 112

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These girls were well aware of the negative cultural stereotypes of science and of scientists. They readily cited examples from the media, the general culture, and their homes. Most negative stereotypes were associated by them with the physical sciences. I: If you were to draw a picture of a scientist, what would you draw? S1: I would probably draw, like, they have an insect shirt on and they have, like, a cloth over so they don’t get stuff on theirself and they have glasses to help them see. I: When you think about a scientist what types of things enter your mind? What’s the picture in your head of a scientist? S2: When, like, on cartoons they show scientists doing things. Like, like, taking animals and mixing something to change them into an elephant, chicken turning into an elephant. I: Are scientists in cartoons men or women? S2: Men. However, despite their awareness of these stereotypes, or because of it, scientists were seen by these girls, especially the older ones, as normal people. S1: A scientist can look anything, be any person. It’s just someone who knows a lot about science and works on making new things. I: Could it be a man or a woman? S1: Doesn’t matter. S2: Yeah, because unlike other people they, it’s like being a scientist, you don’t, if you find something out it’s not really to help yourself. It’s to help everybody and you’re not, like, working for yourself, you’re working to, for, to, like, find out facts that would make, like, life easier for other people. Overall, the eighth graders held the strongest stereotyped views. Meanwhile, the 11th graders verbally recognized that one of the stereotypes of science is that science is a male profession. These older girls also stated that scientists are smart and curious, and that scientists work to discover new things and to help people. The girls at all grade levels frequently used the expression “scientist scientist” to distinguish between individuals working in the biologic sciences and the physical sciences. I: What’s a scientist scientist? S1: Scientist scientist is, like, the scientists that work at NASA with chemicals and stuff so, so, you know, they work with the periodic table and stuff like that. 113

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S2: I wouldn’t want to be a scientist scientist. I mean, I don’t like chemicals and if you work with chemicals you could create bombs. Bombs create wars, you know? These girls had little or no association with women in science careers who could serve as role models. Only a few were able to cite examples, and these were immediate family members. One girl’s parents are both math teachers, another has a mother who is a manufacturing engineer, and a third girl’s deceased mother was a zoologist. One student was fortunate to have had a teacher who left an impression on her life. I: Do you think a teacher is sort of a scientist? S: Well, I had, the reason I say that is cause, uhh, last year I had a teacher who was a scientist. He is a scientist but he stopped going out on all his, you know, what he was doing. He used to study, he went out, he used to go off to islands and study things to bring back information, so he really is a scientist, except he devoted his life to teaching instead of that. He’s still on to science you know, writes books about things, but, so, I guess, I mean, you have, he is a scientist but he’s just sharing his knowledge with the students now. The media, while affecting the girls’ attitudes toward science and scientists, provided few role models of women in science. The images of scientists presented by the media were both positive and negative. This mixture then required the girls to sort through the messages. I: Do you think there are more boy scientists or girl scientists? S1: More boy scientists because, you know, you always see, like, on TV or anything, TV movies it’s always, like, boys are scientists. I: Why do you think there are more boy scientists on TV? S1: Well because, umm, like, on TV they always show, like, scientists as being an adventurous job and then they just always pick, like, boys that can, well, are more masculine or something so that they can, like, work better. When positive female role models were provided they made a lasting impression. S1: Well, I’ve, I was kinda, but what really made it was when I saw Gorillas in the Mist. S1: When I saw Gorillas in the Mist. The movie? When I saw her working with the animals and saving them she just, like, became my hero. I really admired her for that and I want to do that too, so that clenched it. I, like,

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knew right then that I wanted to. But all my life I just, I loved animals, you know, but I didn’t know I wanted to make a career out of it. Most parents accepted their daughters’ choices, but few of the girls had parents who could function as role models. Science careers were rarely discussed in the home except in the cases where one or both parents were themselves engaged in such a career path. Nevertheless, most students thought that their parents would be supportive of their choice to pursue a science-related career. Girls who had a family member in a science-related career were most likely to consider careers in science. I: Do they [your parents] ever talk to you about what they’d like you to do? S1: No. They just say, “What ever you want to do.” I: Do your parents ever talk to you at home about different occupations and scientists and jobs? S2: Umm, no, they don’t, they don’t really talk about that. They, they’re pretty boring on, they’re pretty boring on science. My dad, he never talked about science. He never talks about it really ’cause when he was, when he was younger they didn’t have the kind of science that we had, not that great. I: What would your mother say if you told her you wanted to be a scientist? S3: Uhh, my mother, she’d probably like that. Umm, she is really into math because my parents are both math teachers and, umm, they’d like me to be in the subject of math, but she also knows that’s a big part of science too, and so that’s, she’d probably like that. Summary In general, this group of girls is not getting a clear message from society at large as to who scientists are and what it is that scientists do. They received mixed messages from the media, where scientists were often portrayed as strange-looking males doing bizarre things in laboratories. However, when the girls did encounter positive messages these were very influential. The girls also lacked information on the variety of scientific careers available and on the relationship of science to many careers not perceived by them as “science.” Stereotypes of science and scientists were prominent in their thinking. However, when asked to reflect, the older girls acknowledged that scientists are just ordinary people. It was clear that the mixed messages that the girls were receiving were in turn confusing their own thoughts. Only those girls who had a close friend or family member engaged in science refrained from stereotypes and presented positive images of both scientists and science-related careers. The mixed thoughts of the majority become even more apparent when the girls spoke of equity issues.

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EQUITY

Throughout the interviews the girls at all age levels expressed strong statements of equity. They repeatedly stated that liking science, achievement in science, and choosing a scientific career depended on the individual, not on one’s gender. They adamantly disagreed with the statements that girls can’t do science or that girls can’t be scientists. They repeatedly stated that girls and boys are equal. When asked to respond as if they were a boy, the girls did not alter their positions. However, despite their strong statements of equity, the girls also made stereotypical remarks about girls and boys. Some of these stereotypical beliefs extended to differences in how girls and boys should be taught. This was particularly true for the fifth and eighth-grade girls. The girls’ statements give us some clues to how difficult it is to reconcile one’s belief in oneself as a strong and capable person and notions of fairness with the cultural images of femininity and masculinity. Because the statements of equity became stronger with increasing grade, and because the patterns relating to stereotypes changed with grade, the equity statements are arranged by the girls’ grade levels. Overall, the girls did not see any differences between girls and boys vis a vis science. Second grader: I: Okay. Pretend you’re a boy. If you were a boy do you think you’d like science. S1: Umm, yes. I: Do you think you’d like it better than if you were a girl? S2: No. At this age they would not change the instructional format for boys if they were the teacher. I: How would you teach so the boys would really like it? S1: Probably the same way. If they can handle being in groups together. These young girls were aware of cultural gender stereotypes and held many such negative stereotypes themselves. S1: Girls are more quieter than the boys are. They’re more embarrassed in our class. I: To dissect frogs? Why do you think boys like to do that stuff? S2: They’re gross. They also held negative, non-stereotyped images of boys. I: Why are boys like that? Why don’t boys try as much? 116

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S: ’Cause they’re lazy. In fifth grade none of the girls agreed with the statement that girls cannot be scientists. I: Some people say that girls don’t make very good scientists. What do you think? S1: I think that girls could if they really wanted to. They can do just as good as boys. I: Okay. Umm, what makes you think that? S1: Because they’re people, too, and they’re just equal. These fifth-grade girls repeatedly said that girls are the equal of boys, that girls do science as well as or better than boys, and that doing well or liking science is dependent on the individual, not on one’s gender. I: You know, somebody told me once that girls can’t be scientists. What do you think? S1: I think that girls could be anything they really want to. Just anything, like boys. Boys are still viewed negatively and stereotypically. I: What about the boys? What do you think they want to be? S1: Well, the boys were saying some of them wanted to be professional baseball players and that kind of thing. S2: Well, ’cause they like to play, be basketball players and sports players and stuff. S3: Boys sometimes aren’t really interested and want to do sports instead of, umm, instead of science. Although pretending to be a boy still did not alter the girls career plans, these fifth-grade girls perceived boys as different from themselves when it came to choosing pedagogy. I: If you wanted to make the boys more interested, what do you think you would do? S1: Maybe we could, uhh, discuss things that boys like, like, maybe have a, uhh, you know the computer thing that you can dissect frogs or something like that? I: Umm hmm. Do boys like computers better than girls? S1: Yeah. They like Nintendo a lot. 117

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By eighth grade the equity statements became even stronger. Not only were the girls equal to the boys, but they were better. It was the girls who paid attention and who did the work while the boys goofed off. I: If I said that to you, “Girls can’t be scientists.” S1: I would say that’s stupid. What gives you the right? I: Convince me. Why? What would you say to me to convince me differently? S1: Oh, there HAVE been women scientists. I don’t know any, you know, off the top of my head, but I just think that’s being unfair. That’s like saying oh, “No men can be construction workers,” you know, that’s like sayin’ that. That’s really stupid to say that, ’cause I mean, umm, like girls are, like, supposed to be smarter than boys but, you know, then what they say is then the boys start getting smarter in high school, hopefully. Right, you know, that would, well, that’s the old, yeah, how it’s pictured to be and I think that would be, you know, stupid. These eighth-grade girls were well aware of cultural gender stereotypes and still held many such views themselves. I: Do you think girls like life science more than boys? S1: No. ’Cause we had to just dissect stuff and, you know, guys were all showin’ the eyeballs to everybody, so girls didn’t like the dissecting part. S1: ’Cause they, ’cause they think it’s gross, you know, like, “Ohhhh, it’s an eye!” you know. I: And the boys don’t? S1: No, they’re, like, “Oh let me rip into the worm,” you know. I: Do most of the girls like electricity? S2: No. It’s hard, they go, “I don’t know how to do this.” I: Do guys like it? S2: Yeah. I: Why do you think that is? S2: Oh maybe they want to be electricians and know all they’re, I don’t know. I: What about boys? Do you think they would tend to rate science about the same? S2: Well, no, higher actually.

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I: You think they’d rate it higher? S2: Yeah, ’cause, I mean, I guess it’s, like, they’re, umm, like, stereotyped or whatever to be a scientist, you know, like you, you’re a boy, you know, you’re meant to be a scientist and girls are meant to be housewives? You know I don’t believe in that, but, you know. I: Do you think most kids do? S2: Yeah, ’cause they’re, like, always going, “Aww, you’re supposed to be a housewife and I’m supposed to be this big macho construction worker,” you know, that, but, you know, I don’t want to be like that. Sit around the house, clean house, I do enough of that already. These eighth-grade girls, like the fifth graders, held negative opinions about the boys  in general. About half of these girls would teach boys and girls differently, whereas the other half would make no distinction in instructional format. And if these girls were male they would not be interested in science or science careers, but would instead prefer sports or other macho things. At the 11th grade the strong equity position continues, as do the gender stereotypes and negative opinions of boys in general. S1: Because, uhh, it’s a generalization and you can’t generalize about all girls ’cause lots of girls are smarter than guys in science and some guys are smarter than girls. It just depends on the in—the in—the individual. It’s just a generalization but … S2: It seems like boys’ brains are more geared for math and science and things like that and girls seem to do better in English and you know, different. I: What ARE girls supposed to be like? S3: Dainty and, and, uhh, petite, and they’re not supposed to do anything and everything but … I: What are boys supposed to be like? S3: Oh, the big man of the, the everything, and macho. I: Is that the way it works? S3: No, but that’s the way it’s, it’s set to work, as they say. However, the 11th-grade girls would not teach the boys and girls differently. I: Would you do anything different to make science more interesting for boys? S1: No. The same.

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Research has shown that peers can have an effect on opinions expressed by students. Therefore, the girls were asked how their friends felt about science and science careers. The answers for friends and for self were the same for the second graders. These young girls thought that their friends liked science and would choose to become scientists, and that their friends would support them if they choose to become scientists. By the fifth grade only half the students believed that their friends would support them if they decided to become scientists. They also believed that boys did not want to be scientists but intended instead to pursue careers in professional athletics. In the eighth grade most of the girls thought that their friends would not be supportive of a girl’s career choice in science. However, they believed that girls in general like science. I: What would your friends say if you told them you were going to be a scientist? S1: They’d probably ask me to think twice. I think there’s a stereotype scientist usually who’s always mixing chemicals and things like that, and they really get under my skin and that’s … depends on which kind of scientist I would become. I: Do you have any friends who want to be scientists? S1: No. I: What would you say if a friend of yours wanted to be a scientist? S1: I’d say that’s a dumb occupation. This trend reversed for the 11th-grade girls. They thought that their friends would support their choice of a science career but also believed that many girls do not like science. I: What do the girls say about science? S1: Well they just think it’s boring and stupid ’cause they have to sit and listen to lecture for so long, and there are a million other things they’d rather do. I: Like? S1: Like, I don’t know. Girl things like shopping and … I: What do you think your friends would say if you told them that you were going to be a scientist? S1: They would probably support me too. They’d probably want to know more, like, why and would it, what led you to this? I mean, not that it’s bad, they would just … 120

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Summary Even though these girls respond that girls and boys are equal, that girls can do science as well as or better than boys, and that girls can be scientists, the girls were aware of, and themselves held many, cultural gender stereotypes. When asked to respond to questions as they thought their friends would, many of the girls used cultural gender stereotypes to explain their “friends’” answers. The relationship between liking science and receiving support for pursuing a science career flip-flops between the 8th and the 11th grades. Girls at these ages held some of the strongest equity positions, but also seemed well aware of cultural beliefs. They held many of these beliefs themselves. Like the girls in Brown & Gilligan’s (1992) study, our respondents are caught in a paradox. They are struggling with establishing and standing up for who they are and the cultural feminine ideal. CONCLUSIONS

The girls in this study took a strong equity position and rejected most cultural stereotypes about women even though they may have held negative stereotypes about boys. They liked science and were confident in their ability to do well in science. They did not appear to be avoiding science. They expected to take science in high school and believed they needed science to get into college. However, the relationship between school science and scientific careers was unclear to most of the girls. These girls preferred “doing science,” especially in a group, over reading about science. They liked both physical and biologic topics, but were interested in choosing their own topics for study. They also wanted to learn about topics that had relevance to their lives. Dissection, before the girls actually experienced it, was the most problematic aspect of school science for them. Some girls perceived dissection as either gross or cruel. However, the older girls who had participated in dissection activities found them to be interesting and relevant. Teachers were able to make science fun or boring, but we did not have evidence that teachers had much of an influence on career choice. The topic of science and science-related careers was not part of these girls’ curriculum. Peers did not seem to have a large effect on a girl’s choice of a career either. Influential role models were infrequent in the school curriculum, whereas the media and society in general provided both positive and negative role models for the girls. Parents and other significant family members had little influence on the girls’ choices unless they themselves did science as an occupation or an avocation. Biologically based careers were often not seen as science because of the absence of chemicals and electricity. The term scientist scientist appeared frequently as the girls’ way of differentiating the physical from the biologic sciences. The physical sciences were largely rejected because the girls did not see physical scientists as helping or caring. When the girls chose science careers, they did so out of a 121

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desire to help people, animals, or the earth, and/or because the girl experienced or associated science with someone they loved and admired. Girls who did not choose scientific careers were choosing other challenging professional and non-traditional occupations. If we try to explain these results in the absence of a theory of girls’ psychological needs we reach a dead end. The girls seem to have all of the pieces needed to choose a scientific career, and they are not deterred by the cultural stereotypes. Many of the girls even say that they would like to pursue a scientific career. However, we know that despite interest and intentions few of these girls will end up in science. On the other hand, if we look at what the girls say through the lens of relationships and connections we have a clear picture of what draws these girls to science and what may also lead them away. Relationships, which include caring, responsibility, and affective needs, provide the standard by which these girls make judgments concerning science. Their strong equity position can be interpreted as a rejection of competition and the hierarchical ordering of individuals, both of which make positive interpersonal relationships difficult to establish and maintain. Equals are more likely to be friends and reducing competition results in working together better. The expression of negative gender stereotypes by these girls can be interpreted as the intrusion of cultural values into this expression of psychological needs. The response to how science is taught and the role of the teacher are also mediated by relational and affective needs. The girls dislike instruction that isolates them, such as reading the textbook or taking notes while listening to lectures. They prefer instruction that permits them to interact with others, such as working in groups or discussing the issues with their teachers and classmates. Dissection was a big issue because to many of the students it appeared cruel. Most of these girls want to help animals, not hurt them, and until dissection is experienced firsthand the girls do not see how dissection can be anything but hurtful. The teacher who connects with the students is a “fun guy,” and the one who does not is boring. These traits of fun or boring are then attributed to the content as well. Both physical and biologic science are interesting to study in school, but physical science careers are avoided because they seem unrelated to the girls’ concerns. Biologic science careers are perceived as helping people, animals, and the earth. It is this potential to be helpful that draws girls to these careers. The intellectual challenge of science and the puzzle solving that males often cite as reasons that draw them to science are, for the girls, replaced by relational and affective needs that have a moral component. The girls with the strongest commitment to scientific careers learned to love science through the love of a parent or grandparent involved in science. The descriptions of these experiences are highly emotionally charged and focus more on the interpersonal relationships than on the science itself. The girls do not separate their feelings about the mother who had died, the mother who “teaches me everything,” or the grandparent who explains the stars on cold evenings on the 122

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beach from their feelings for science. These feelings are one and the same. When the emotional impact is strong enough, a movie such as Gorillas in the Mist can have the same effect. Relational values such as cooperation, working with people, and helping others are characteristics of women in general (Belenky, et al., 1986), but are also important to adult women working in technical fields. Researchers involved in the Women and Technology Project at Bank Street College found that women approach computers relationally. That is, they want computers to be used to help and to make connections with people (Center for Children and Technology, 1991). These feelings are not limited to the United States. Female engineers in Norway (Sjoberg & Imsen, 1983) also want to use their jobs to benefit society and other people. However, according to Rhode (cited in Noddings, 1990), these values have been undervalued in professional cultures and are missing in professional schools and organizations. The absence of these values during the training of women scientists and during their professional lives may account for the low number of women in science. Women’s decision to discontinue science is often a consideration of multiple goals. This is especially so when women are faced with the choice of investing time in one activity over another that more effectively meets their psychological needs (Eccles, 1986). Among college women, Arnold (1992) found that dropping out of science was not related to achievement or interest, but occurred because women’s needs and goals as expressed in a desire for marriage and children were more important than their need for high-status careers. This choice was already apparent to one girl in the study who said if she were interested in marriage and children she would not be a scientist. These data speak strongly about how girls see science, and the need for science educators to address the female perspective. One such study has been conducted by Martinez (1992), who enhanced uninteresting science experiments by increasing their cognitive, mastery, and social appeal. Not surprisingly, from our perspective, he found that the girls in the study responded positively to the social aspects of the enhanced experiments, whereas the boys responded to the mastery aspects. More information is needed on how these relational and affective dimensions influence girls’ life choices, as well as information on how to integrate these dimensions into science classrooms and the scientific workplace. NOTE 1

Originally published as Baker, D. & Leary, R. (1995). Letting girls speak out about science. Journal of Research in Science Teaching, 32(1), 3–27. Reprinted here with permission.

REFERENCES Almquist, E., Angrist, S., & Mickelsen, R. (1980). Women’s career aspirations and achievements: College and seven years after. Sociology of Work and Occupations, 7, 376–384. American Association of University Women. (1991). Summary: Shortchanging girls, shortchanging America. Washington, DC: Author.

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CHAPTER 5 Angrist, S., & Almquist, E. (1975). Careers and contingencies. New York: Dunelllen. Arnold, K. (1992, April). The Illinois valedictorian project: Academically talented women ten years after high school graduation. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA. Baker, D. (1990, March). Gender differences in science: Where they start and where they go. Paper presented at the annual meeting of the National Association for Research in Science Teaching, Atlanta, GA. Baker, D., Leary, R., & Trammell, R. (1992, March). Where are the gender differences in science and what do they mean. Paper presented at the annual meeting of the National Association for Research in Science Teaching, Boston, MA. Belenky, M., Clinchy, B., Goldberger, N., & Tarule, J. (1986). Women’s ways of knowing. New York: Basic Books. Brown, L.M., & Gilligan, C. (1992). Meeting at the crossroads: Women’s psychology and girls’ development. Cambridge, MA: Harvard University Press. Campbell, J. (1991). The roots of gender inequity in technical areas. Journal of Research in Science Teaching, 28, 251–264. Campbell, J., & Connolly, C. (1987). Deciphering the effects of socialization. Journal of Educational Equity and Leadership, 7, 208–222. Campbell, P. (1988). Rethinking research: Challenges for new and not so new researchers. Washington, DC: Department of Education, Women’s Educational Equity Act. Center for Children and Technology. (1991, October). Women and technology: A new basis for understanding. News from the Center for Children and Technology, 1–4. DeBoer, G. (1984a). Factors relating to the decisions of men and women to continue taking science courses in college. Journal of Research in Science Teaching, 21, 325–329. DeBoer, G. (1984b). A study of gender effects in science and mathematics coursetaking behavior among students who graduated from college in the late 1970s. Journal of Research in Science Teaching, 21, 95–103. Eccles, J. (1986). Gender-roles and women’s achievement. Educational Researcher, 15, 15–19. Ethington, C., & Wolfe, L. (1989). Women’s selection of quantitative undergraduate fields of study: Direct and indirect influences. American Educational Research Journal, 25, 157–176. Gilligan, C. (1982). In a different voice. Cambridge, MA: Harvard University Press. Grant, M., & Harding, J. (1987). Changing the polarity. International Journal of Science Education, 9, 335–342. Holden, G., & Edwards, L. (1987). Parental attitudes toward child rearing: Instruments, issues and implications. Psychological Bulletin, 106, 29–58. Hueftle, S., Rakow, S., & Welch, W. (1983). Images of science: A summary of results from the 1981–82 national assessment in science. Minneapolis, MN: Minnesota Research and Evaluation Center. Kahle, J.B., & Lakes, M.K. (1983). The myth of equality in science classrooms. Journal of Research in Science Teaching, 20, 131–140. Maple, S., & Stage, F. (1991). Influences on the choice of math/science major by gender and ethnicity. American Educational Research Journal, 28, 37–60. Markus, H., & Oyserman, D. (1989). Gender and thought: The role of the self-concept. In M. Crawford & M. Gentry (Eds.), Gender and thought (pp. 100–127). New York: Springer-Verlag. Martinez, M. (1992). Interest enhancements to science experiments: Interactions with student gender. Journal of Research in Science Teaching, 29, 167–177. Mullis, I., & Jenkins, L. (1988). The science report card: Trends and achievement based on the 1986 national assessment. Princeton, NJ: Educational Testing Service. Nardi, B. (1983). Goals in reproductive decision making, American Ethnologist, 10, 697–714. National Science Foundation, (1990). Women and minorities in science and engineering. Washington, DC: Author. Noddings, N. (1990). Feminist critiques in the professions. In C. Cazden (Ed.), Review of Research in Education, 16 (pp. 393–424). Washington, DC: American Educational Research Association.

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LETTING GIRLS SPEAK OUT ABOUT SCIENCE Oakes, J. (1990). Opportunities, achievement and choice: Women and minority students in science and mathematics. In C. Cazden (Ed.), Review of Research in Education, 16 (pp. 153–222). Washington, DC: American Educational Research Association. Peng, S., & Jaffe, J. (1979). Women who enter male-dominated fields of study in higher education. American Educational Research Journal, 16, 285–293. Sadker, M., Sadker, D., & Klein, S. (1991). The issue of gender in elementary and secondary schools. In G. Grant (Ed.), Review of Research in Education 17 (pp. 269–334). Washington, DC: American Educational Research Association. Sheperd, L. (1993). Lifting the veil: The feminine side of science. Boston: Shambhala Publications. Sjoberg, S., & Imsen, G. (1983). Gender and science education. (Monograph No. 3). Oslo, Norway: Center for Science Education, University of Oslo. Solomon, J., & Harrison, K. (1991). Talking about science based issues: Do boys and girls differ? British Educational Research Journal, 17, 283–294. Sutton, R.E. (Winter, 1991). Equity and computers in the schools: A decade of research. Review of Educational Research, 61, 475–503. Steinkamp, M.W., & Maehr, M.L. (1983). Affect, ability and science achievement: A quantitative synthesis of correlational research. Review of Educational Research, 53, 269–396. Tesch, R., Sommerlund, B., & Kristensen, O. (1989). Textbase alpha users manual. Desert Hot Springs, CA: Qualitative Research Management. Ward, B. (1979). Attitudes toward science: A summary of results from the 1976–77 national assessment of science. (Report No. 08-S-01) Denver, CO: Education Commission of the States Ware, N., Steckler, N., & Lesserman, J. (1985). Undergraduate women: Who chooses a science major? Journal of Higher Education, 56, 73–84. Wilder, G., & Powell, K. (1989). Sex Differences in performance: A survey of the literature (College Board Report No. 89-3, ETS RR No. 89-4). New York: College Entrance Examination Board.

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WHY I CONDUCTED THE STUDY

The International Handbook of Science Education was commissioned by Kluwer Academic Publishers for their handbook series which was edited by Barry Fraser and Kenneth Tobin. These two editors invited me to take responsibility for editing one of the ten sections in the handbook. This was the section on equity. They also invited me to write the lead article for this section. As such, this article was not a study but a review of the literature in science education. METHODOLOGICAL DECISIONS

This chapter of the handbook was to be a review of the research in science education on the topic of women and minorities in science around the world. It needed to address seven broad areas of equity and to be balanced in terms of what was happening world-wide. This was a challenge for two reasons. First, as with any publication, there was a page limit to what I was to write. Second, the research literature was skewed. More was written about the topic as it pertained to North America, especially the United States, and to Australia and Europe. Less information and research was available for other parts of the world. As a check on the thoroughness and balance of the review I was fortunate to have as a chapter consultant, Svein Sjøberg from Oslo University in Norway. He pushed me to be as comprehensive as possible to reflect a global perspective. His comments were invaluable in the revisions of the chapter. As many of the science education journals that I could access as well as Science were searched for research on the topic of gender equity. These included Journal of Research in Science Teaching, Science Education, International Journal of Science Education, Journal of Research in Mathematics Education, Journal of Women and Minorities in Science and Engineering, and the Australian Science Teachers Journal. I also scoured more general journals of educational research such as the American Educational Research Journal and Review of Educational Research. International studies of achievement were helpful as were government websites, conference papers, books, conference presentations, earlier handbooks, and dissertations. Non-English publications were not included due my inability to read them.

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SCIENCE EDUCATION AT THE TIME OF THE STUDY

Science education was becoming more interested in issues of gender as well equity for other underrepresented groups such as the homeless. This interest was situated in a larger context of educational reform in science education and led to the publication of three special issues of the Journal of Research in Science Teaching. In 1998, issue 35, 7 was dedicated to an examination of policy and recommendations for the future. This was followed by issue 35, 8 which was dedicated to gender. Issue 35, volume 4 in 1998 was another special issue examining pedagogy in science education from a feminist, critical, and post structuralist perspective. Authors in this issue used theories of race, gender and class to bring to the forefront inequities. These three special issues resulted in many more review articles than usual that provided a synopsis of the research as well as a critique. Articles in Science Education also focused on minority students, culture, and language using similar theoretical frameworks. In addition to feminist, critical and post structuralist approaches and the theories that reflected these positions, science education scholars were also situating their work in a constructivist paradigm. Researchers working from a constructivist perspective were interested in conceptual change, situated cognition, and metacognition. Problem solving, reasoning, and conceptual integration as measured by concept maps were explored in various content areas and at various grade levels. Studies addressing learning in a variety of topics in biology and physics were the most common followed by studies of chemistry topics. As noted in previous descriptions of science education research at the time of a study in other chapters, Earth science was not addressed. Science education research in informal settings was featured in a 1997 special issue in Science Education (volume 8, issue 7). Studies of students predominated in the literature with equal attention given to secondary and elementary grades. Junior high school or middle school studies appeared less frequently. Studies at the university level whether examining students or faculty were scarce and studies of the community college were almost nonexistent. Studies that examined teacher preparation were predominantly studies of the impact of elementary methods courses on various outcomes. Studies of practicing classroom teachers focused on their identity, growth, pedagogical content knowledge, or the teacher as knower or researcher. Some of the studies of pre-service teachers and practicing teachers addressed feminist pedagogy and perspectives in the teaching of science. Methodologically there was also a shift from earlier times. Quantitative and qualitative studies appeared in comparable numbers although mixed methods were used less frequently. Among the qualitative studies, designs using case study and interviews predominated. Another strong indicator of the interest in gender issues in science education could be found in the Science and Technology Education Library series of Kluwer Academic Publishers. As part of the series Parker, Rennie, and Fraser (1996) edited a book called Gender, Science and Mathematics: Shortening the Shadow. Chapters

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in this book explored perceptions and attitudes, schools, classrooms, curriculum assessment, policy, and the future of science education. The National Science Teachers Association, in their efforts to help teachers apply  the National Science Education Standards (National Research Council, 1986), published one of a series of books aimed at classroom teachers. These books were called NSTA Pathways to the Science Standards: Guidelines for Moving Vision into Practice, and addressed the standards by grade level. Nineteen-ninetyeight saw the publication of the middle school edition (Rakow, 1998) and 1997 the elementary and high school editions (Texley  & Wild, 1997; Lowery, 1997). The Pathways books provided teachers with examples of classroom activities to help them understand the standards and implement them in their classrooms. These documents did not address the issues of girls’ participation in science or the under representation in science of some groups of students head on. Rather they took the position that the standards and pedagogy recommended would help all students achieve. RESEARCH INTO GENDER IN THE WIDER FIELD OF EDUCATION

Education generally was becoming more interested in the issue of under representation in science and scholars outside of the field of science education began to conduct studies. In 1994 the Journal of Women and Minorities in Science and Engineering was established with a strong discipline based perspective. The current editor and associate editors have backgrounds in higher education, business, and Latino education. Another is a faculty member at Ecole National de Ponts et Chausees, also known as Paris Tech. Thus, their editorial stance as editors is reflected in their backgrounds which are different from those in science education. Contributors to the journal are more likely to have backgrounds in sociology and higher education and use  these as lenses for their research. According to the journal’s website, it was designed as a unique and much-needed resource for educators, managers, and policymakers. The purpose of Journal of Women and Minorities in Science and Engineering is to publish peer-reviewed papers about innovative ideas and programs for classroom teachers, as well as research studies, especially studies about discipline-specific issues addressing retention of under-represented groups in science and engineering. It seeks work that addresses the entire range of education from K-12, postsecondary, graduate, post-graduate and continuing education. Studies in the past have addressed feminist teaching methods, black student/white teacher interactions, and sociocultural impacts on classroom climate. The journal encourages work that raises new questions about women and minorities in science. The journal includes book reviews and reports from business, industry, and federal and state agencies as well as from women and men of color in academic institutions (http://www.begellhouse.com/journals/journal-of-women-and-minorities-inscience-and-engineering.html). 129

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In 1997, the Journal of Women and Minorities in Science and Engineering published 12 articles related to gender and science, 15 articles relating to gender and science with some attention to race in 1998, and 4 articles relating to gender and science in 1999. Other articles published in this time span examined race exclusively or mathematics or engineering. Articles related to gender and science also began to appear in publications of the American Educational Research Association. In the Review of Educational Research (RER) and American Educational Research Journal  (AERJ) gender and racial equity was examined in the academy with reference to women and African Americans (Tierney, 1997). As was the impact of single-sex schools for girls which was, at the time, a hot topic of debate with varying results depending upon the outcomes examined and the context. Achievement in single-sex schools for girls was studied by Lepore and Warren (1997) writing in AERJ and Mael (1989) writing in RER who concluded that differences in achievement were due to pre-enrollment differences. Since this was a time of reform documents, Lee (1999b) was among several scholars who were not science educators who critiqued the equity implications of science achievement as presented in reform documents in AERJ. For example, the National Science Education Standards (National Research Council, 1996) allocated only nine pages to issues of equity in general and only two pages to gender issues. And, although How People Learn: Brain, Mind, Experience, School (Bransford, Brown, & Cocking, 1999) was an important and influential publication with many examples from science used to illustrate learning; girls, gender, equity, and sex differences did not appear in index. Several others articles appeared in AERJ at this time. Burkham, Lee and Smerdon (1997), who were known for looking at large data sets in various content areas, examined NELS data to determine learning science in high school and the impact of labs as did Jovanovic and King (1998) who mined participation in performance-based science (i.e. labs). These authors found that active involvement promoted gender equity but when girls did not participate equally there were negative consequence for beliefs about ability and attitudes. Science career plans for girls living in rural areas was explored by Jacobs, Finken, Griffin and Wright (1998) who found that previous experiences and intrinsic interest were more important to science career plans than peer support, parents’ attitudes, and activities. Other areas in which scholars examined gender included mathematics and group dynamics. Gender differences by item difficulty of multiple choice items on mathematics assessments was explored by Bielinski and Davison (1998) as was mathematics course taking (Davenport, Davidson, & Kang, 1998) where few gender differences were found. Webb, Nemer, Chizhik, and Sugrue (1998) studied the equity issues in collaborative group assessment in terms of the group’s ability composition and the effect the composition had on performance. Oddly, little was published about gender issues in the Psychology of Women Quarterly. There were no articles addressing science in 1997 or 1998 and only one 130

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in 1999 (Farmer, Wadrop, & Rotella, 1999). These three authors looked at factors early in life that differentiated women and men who chose science versus non science careers. They found that females who chose science careers valued mathematics and  science courses, expected to take more science, and had higher grade point averages. On the other hand, the American Association of University Women had a number of publication addressing gender issues for girls and women that examined issues of gender broadly but not specifically in science. Girls in the Middle: Working to Succeed in School (Cohen, Blanc, & Chrsitman, 1996) was a summarization of the experiences of middle school girls and was focused on equity and school reform but not specifically on equity in science. Separated by Sex (American Association of University Women, 1998) examined single sex education as a solution to gender inequity and concluded that it was not the solution to gender inequity. Another American Association of University Women publication that appeared around the time of my review was Gaining a Foothold: Women’s Transition Through Work and College (American Association of University Women, 1999). This publication examined how and why girls and women make educational decisions. The last American Association of University Women publication (Haag, 1999) Voices of a Generation: Teenage Girls on Sex, School, and Self explored the societal forces on girls and societal expectations for girls as well as identity formation and sexual activity with an emphasis on the role schools play. The National Council of Research on Women (1998) also published an important document called The Girls Report: What We Need to Know About Growing up Female. This report examined identity, health, sexuality, violence, and schooling. One of the most influential books of the times was written by sociologists Seymour and Hewitt (1997). Their large scale study documented the reasons why undergraduates left science majors laying the blame squarely on poor teaching and the climate of science. They found that males and females were equally affected and changed majors out of science at similar rates. An interesting book published in 1997 (Wertheim, 1997) Pythagoras’ Trousers: God, Physics and the Gender Wars did not receive as much attention as other books about women in science but provided an in-depth historical look at women in physics. Another book, published in the late 1990s, was an attempt to make the case for singlesex schooling for girls (Streitmatter, 1999). Streitmatter acknowledged that there might not be an impact on achievement but argued that the impact on attitude was just as important. THE CULTURE OF THE TIMES

Nineteen ninety-eight was an interesting year (Wider Culture, 1998) for issues of gender equity and sex. President Clinton was dealing with sexual harassment charges from Paula Jones as well as the fallout from the Monica Lewinsky affair. Gay rights was making progress on some fronts and regressing on others. For example, the 131

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Supreme Court of the United States ruled in Oncale versus Sundowner Offshore Services that federal laws banning on-the-job sexual harassment applied even when both parties were the same sex. In contrast, the legislature of the state of Maine repealed a gay rights law that was enacted the previous year (i.e. 1997). On a more positive note, Britain’s House of Commons gave first born daughters the same claim to the British throne as first born sons. Nineteen ninety-eight also saw advances in women’s sports with the first female hockey game in the Olympics in which the United States women’s team won the gold medal by defeating Canada (Wider Culture, 1998). Women also advanced in employment. By the end of the 1990s women comprised half of the labor force (United States Census Bureau, 1999) as a consequence of the Family Medical Leave Act of 1993. However, despite comprising half of the workforce women faced pay differentials receiving lower pay than men for the same work and parental leave policies that were much less generous than in other developed countries. Home grown terrorists were in the news. A women’s abortion clinic was bombed in Birmingham Alabama and one person was killed. The “Unabomber” Ted Kaczynski received four life sentences plus 30 years after he accepted a plea agreement sparing  him from the death penalty. Both Michael Fortier and Terry Nichols also received prison sentences for their roles in the Oklahoma City bombing. Fortier was sentenced to 12 years and fined $200,000 for failing to warn authorities about the terrorist plot and Terry Nichols received a life sentence. Sadly, 1998 was also the year that the War in Kosovo began (Wider Culture, 1998). Nineteen-ninety-nine saw the Columbine high school massacre and the Y2K bug had Americans scrambling to insure that their computers would work as we entered the year 2000. However, there was little impact on schools or education. Many countries of Europe finally agree to use a common currency called Euro which became the new currency (About Education, 2015). The world was on the cusp of the twenty first century and there was both high expectations and anxiety about what the new century would bring. IMPACT OF MY WORK

The opportunity to write this chapter allowed me to provide an international perspective on gender equity in science education and to put the issue of gender equity in an historical context. As a handbook chapter, the readership was greater than for an article in a refereed journal and thus, of greater impact. It was the first time an international synthesis in science education had been done and gender was examined in developed and developing countries. It provided information about countries where gender equity was far from evident as well as countries which were making progress in promoting gender equity in science. The chapter provided information on women’s education worldwide as well as information about their participation in science. The chapter was a concise up to date look at the status of 132

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women in science that could be used by government officials interested in what the research had to say about their country’s international standing in terms of gender equity in science. It also provided scholars with a clear indication of the aspects of gender equity in science that needed additional research and identified for policy makers areas in which policy reform was needed.

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This chapter examines the status of women and minorities in science, especially within educational systems around the world. It is organised into the seven areas of (1) historical context, (2) the number of women and minorities in science, (3) the influence of schools, (4) the influence of the home, (5) sociocultural barriers, (6) the nature of science and (7) interventions. The roots of sexism and racism, cultural and parental values, the masculine and eurocentric face of science, disenfranchising pedagogy, assessment artefacts and exclusive instructional materials are explored as explanations of differences in achievement and participation. Finally, researchbased suggestions for promoting equity, access and success in science are presented. HISTORICAL CONTEXT OF THE PROBLEM

The problem of the low number of women and people of colour in science, excluding most Asians, is partly the result of when and where science first developed. The scientific revolution began in the 18th century in Europe and continued to flourish in these countries well into the 19th century. At that time, women were not in the labour market and their education reflected their traditional roles. Much of the rest of the world, which was under colonial rule or struggling for independence, was not provided with the opportunity of a scientific education (Barinaga 1994). Now that many former colonial nations are playing a greater role internationally and now that the role of women is changing in many countries, we can expect to see a wider range of participation in science. However, there still remain many barriers to overcome. This chapter examines those barriers as well as the strategies that have been successful in creating equal access to science for all. Women Educational opportunities for women to study science, until recently, were limited. In most European countries before the 1920s, women were barred from the academic high schools which provided the mathematics, Latin and Greek needed to enter the few universities that accepted women. To obtain the knowledge needed to qualify for university, the families of girls interested in science paid for a private tutor. Poland, Russia, Prussia and Austria barred women from their universities altogether until 1910. The more progressive French allowed women to audit classes at the University of Paris beginning in 1908 and later to attend as full-time students (McGrayne 1993). 134

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In the USA, public education did not go beyond the primary grades until the end of the 19th century (Cremin 1968). Private academies, especially for women, were established in the late 1800s to provide the necessary education for admission to universities. Few universities admitted women, although there were some institutions, such as Smith College, that were designed specifically for women (Behringer 1985). Female role models were missing from universities well into the mid-20th century. In the early part of the 20th century in the USA, women could be university teachers but were prohibited from doing scientific research. Their positions were in what were considered gender-appropriate departments such as home economics or physical education. In the 1930s, women often were unpaid research assistants working in their husband’s laboratories (McGrayne 1993). In Germany, arguably the centre of scientific learning prior to World War II, there were no female teachers until the 1920s. Also women worked without pay. Even today, women make up only 2.6 percent of the highest rank of the German professoriate, 7.3 percent at the middle and 24.2 percent at the entry level. In the UK, Joycelyn Bell Burnell, the discoverer of pulsars, is only the third woman to achieve the rank of physics professor and, across all academic disciplines, only 3 percent of English female faculty are professors (McGrayne 1993; Osborn 1994). The Netherlands and Ireland have similarly low levels of female professors. The percentage of female professors in Spain is 7.4 percent and in Portugal is 8.2 percent. The USA has 14.4 percent in the ranks of professor. On the other hand, Turkey has been more successful in getting women into the upper ranks of the professoriate in science (with 20 percent female). Minorities Until recently, science has been the exclusive province of European and North American upper class White men. Educational opportunities for minorities in the USA during the late 19th century and well into the 20th century were far more limited than those available to women. For example, teaching a Black slave to read was illegal in some states. After 1865, Black Americans attended segregated schools which provided an inferior education. Desegregation has improved the quality of education but problems still remain. Native Americans in both the USA and Canada also have suffered from an inferior educational system. The system is burdened by inadequate teachers and counsellors, irrelevant curricula and inappropriate learning materials (Cole & Griffin 1987; Matthews & Smith 1994). Similar parallels can be drawn in Australia and New Zealand regarding indigenous populations (McKinley, Waiti & Bell 1992; Ritchie & Butler 1990). In countries with large minority populations, inequities in education are the major stumbling block to minority participation in science. With so many barriers, it is not surprising that there are few minority role models in science. In the USA, fewer than 2 percent of Hispanics and Blacks hold doctorates in engineering, chemistry and biology combined (Culotta 1993a, 1993b). 135

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This poses an acute problem for the recruitment and retention of able minorities who, unable to see how they fit into science, choose other fields. NUMBER OF WOMEN AND MINORITIES IN SCIENCE

This section looks at the number of women and minorities in science in terms of school and university participation and opportunities, achievement and career choice. International comparisons, as well as within-country comparisons, are made where data are available. School Opportunities and Participation Rates The second IEA (International Association for Educational Achievement) study of science collected data in the 1980s (Postlethwaite & Wiley 1992) indicated that the percentage of students aged 17–19 years enrolled in secondary education varied among countries from 1 to 80 percent (Rosier & Keeves 1991). Although somewhat out of date, these data indicate that not all students have had access to science instruction and remind us that one of the goals of equity in science education is to  ensure that  all students have the opportunity to learn. More recent data presented  at GASAT (Gender and Science and Technology) conferences indicate that girls worldwide are less likely to study science than boys when it is no longer a compulsory school subject (Granstam & Frostfeldt 1990; Rennie, Parker & Hildebrand 1991). In English-speaking Canada, Israel, Japan, Thailand and Korea, equal numbers of male and female students take biology, chemistry and physics at the final year of secondary education (Keeves 1992). In the Middle East, more males than females remain in school to the end of secondary education because most females leave at the onset of puberty (Stromquist 1989). Poland is an exception with more females in all sciences (Keeves 1992). In countries with low secondary school enrolments, two factors are at work. The first is cultural values that do not promote equity and the second is limited resources that restrict opportunities. Of the two factors, values is the more important for school attendance. University and Career Participation Rates At the university level, in both the USA and Europe, 40–50 percent of students are female (Osborn 1994), but many more males than females are pursuing science. In non-Western countries, such as India, the number of women in science is small and the dropout rate is as high as 30 percent because of family pressures to marry (Sharma 1994). On the other hand, many more women than men choose science including physics in Turkey and Italy (Flam 1994; Kohn 1994). Encouraging as 136

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this might seem, these women represent a very small percentage of the potential student population. In Turkey, 1 percent of all females attend university and, in both Turkey and Italy, science is a low-paying, low-prestige occupation. Achievement by Gender International comparisons such as the IEA study (Postlewaite & Wiley 1992) and the 1988 International Assessment of Educational Progress in Mathematics and Science (IAEP) (Lapointe, Mead & Phillips 1989) reported gender differences in achievement, but these data are suspect because they often do not control for course-taking background and cultural values (Parker, Rennie & Harding 1995). For example, in Thailand where science is compulsory throughout secondary education, females outperformed males on every measure of chemistry achievement (Klainin & Fensham 1987) and performed as well as or better than males in physics (Klainin, Fensham & West 1989). Differences also disappear in countries such as Poland, Nigeria, Jamaica and Trinidad/Tobago where culture and values support women and where women always have played an important economic role (Parker, Rennie & Harding 1995). Furthermore, there are questions about bias, format, decontextualised problems and inconsistencies between the way in which we test and teach (Bateson & Parsons-Chatman 1989; Locke 1992; Rennie & Parker 1991, 1993b; Sudweeks & Tolman 1993). Achievement by Country and Ethnicity International assessments also can result in unfair comparisons because higher participation rates are associated with lower achievement scores. Consequently, ‘yield’, defined as a number that takes into account both the mean achievement and the number of students participating in the school system, might be examined. Yield answers the question ‘How many are brought how far?’ (Keeves & Soydhurum 1992, p. 246). Because the focus of this chapter is equity, it would be unjustified to conclude that a country is doing well in relation to science achievement if only a few students have access to science. For example, when the yield index is used for the second IEA data, Japan performed the best in terms of educating the largest number of students very well. Thailand was last because it was educating very few students and the mean achievement scores were lowest (Keeves & Soydhurum 1992). Another problem with large-scale international assessments is that, with the exception of Canada and the USA, there are few within-country comparisons for language or ethnic groups. Consequently, judgements cannot be made about the level of science achievement for these students. The absence of these data also precludes an examination of the combined effects of gender and ethnicity which, in some countries, increases barriers to science. 137

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Summary and Implications: Women and Minorities Who studies science? The answer is not very many. In some countries, only the elite go to school and study science. In other countries, opportunities for women and minorities are limited. The reasons for differential participation rates are varied and are examined in greater detail later in this chapter. However, the long-term implications for both developed and developing nations are obvious. If fewer people study science, then fewer people will be able to make informed decisions about critical scientific issues. INFLUENCE OF SCHOOLS

Because schools play a part in who succeeds in science through classroom interactions, pedagogy, curriculum, instructional materials, access to computers and laboratory experiences, this section explores how these factors can affect achievement to the disadvantage of females and minority students. Achievement and the Influence of Schools What do achievement differences tell us? Do they mean that males are more capable than females or that the students in some nations are inherently brighter than students in other nations? All that achievement differences tell us is that some students do not do as well as others on standardised tests. In part, the cause for this lies in how we teach science and to whom. This section explores those factors. Gender Whole-class teacher-centred instruction with discussion and individual seatwork, workbook exercises, a heavy reliance on textbooks and rote learning is the norm worldwide in science education (Tobin 1988; Tobin, Tippins & Gallard 1994). Knowledge is presented in isolation rather than within the context of real-life problem solving. These strategies benefit White males while placing female and minority students at a disadvantage, thus resulting in lower achievement and negative attitudes towards science (Oakes 1990). This form of instruction creates an environment in which males raise their hands and call out answers more often than do females, and in which teachers are more likely to call on males than females. Even when females raise their hands more often than males, the teacher is more likely to call on the males (Baker 1987; Becker 1981; Jones 1990). Often, teachers call on ‘target’ students (Tobin & Gallager 1987), who typically are a small subset of the males in the class, to provide the correct answers to questions. These students maintain their status year after year, which leads to a cumulative advantage (Marshall & Weinstein 1984). In self-paced science

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classrooms, Tobin (1988) found that females were less disadvantaged. However, Baker’s (1987) observations contradict Tobin’s findings in that teachers still favoured males by asking them about procedures for experiments. Teachers not only have more academic interactions with males, but they also can have more non-academic interactions (Jones 1990; Oakes 1990). Baker (1987) found that, when science teachers had more interactions with females than males, the interactions were often social. They were characterised by stereotypical remarks such as ‘My don’t you look pretty in that outfit’, suggesting that the teacher was more aware of the student’s appearance than academic ability. Jones and Wheatley (1989) also found that more remarks about appearance were directed to females than males in science classes. In Great Britain, gender imbalances in classroom interactions were found to be more pronounced in physics and biology and more often characteristic of female teachers (Crossman 1987). These interactions contribute to girls’ perceptions that science is a male domain and can be found in both developing and developed countries (Granstam & Frostfeldt 1990; Rennie, Parker & Hildebrand 1991). Ethnicity Teachers tend to favour high achievers. Because more minority than majority students are low achievers, minority students have fewer opportunities to learn. Typically, high achievers are asked more questions by the teacher, are offered more academic choices and receive more positive feedback than low achievers. Overall, high achievers have more positive interactions of every sort with the teacher than low achievers (Marshall & Weinstein 1984). A student’s ethnicity also affects teachers’ judgements of ability. In the USA, minority students often are placed in low-ability classrooms where often instruction is inferior, content is simpler, teachers use drill-and-practice, rote learning is emphasised and interactions do not promote achievement or positive attitudes (Cole & Griffin 1987; Oakes 1990). International Data Limiting the number of students who interact with the teacher, low-level questions, differential feedback and seatwork disadvantages low-achieving, female and minority students in Western societies. However, this might not be the case worldwide. In some hierarchical cultures, participatory pedagogy doesn’t always lead to achievement. For example, in Swaziland, listening to lectures and doing seatwork increased achievement and, in Botswana, questions and discussion reduced achievement. In the Philippines, participatory pedagogy also reduces achievement. Because there are a number of confounding variables in these studies, it is not clear whether the differences in achievement are due to an interaction between culture and 139

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pedagogy or to the lack of teaching materials and teacher background knowledge in science (Fuller & Clarke 1994). Nevertheless, we should be cautious about simply transplanting Western pedagogy without taking culture into consideration. Curriculum Most teachers around the world rely heavily on textbooks as their primary and sometimes only curriculum materials. Historically, these books have done a poor job of representing minorities. Powell and Garcia’s (1985) analysis of science textbooks in the USA showed that 75 percent of the adults and 65 percent of the children in the pictures and illustrations in these books were White. When members of under-represented groups were depicted, very few were engaged in science-related activities. Women and girls also are missing from many science textbooks and curriculum materials. When females do appear, they often are portrayed in sex-role stereotypes (Sadker, Sadker & Klein 1991). However, in the West, things are changing. For example, the gender bias in chemistry texts now is much less than in the 1970s. Of course, the degree of gender fairness varies according to publisher and no text has reached parity in terms of the amount of illustrations, pictures and content of interest to girls (Bazler & Simmons 1991). An examination of current life science textbooks is less encouraging (Potter & Rosser 1992). Although there is no sexist language, there are more illustrations and pictures of male scientists, and many females are in passive roles. The content reflects few female interests, such as women’s health, and career information is stereotypical. Women’s contributions and achievements in science are missing entirely. In traditional cultures, textbooks give socially and educationally sanctioned examples of women as submissive wives and mothers (Stromquist 1989). Science curriculum materials which adequately represent minorities in the USA also are limited. There is little that reflects the wide range of Native American cultures or has relevance to Native American students (Matthews & Smith 1994). Curriculum material aimed at Black students is controversial because many scientists feel that the material inaccurately depicts scientific developments (Travis 1993). Hispanic students are somewhat better represented but, as Barba and Reynolds point out in their chapter in this section, there is still a great deal to do. Educators in other countries which have indigenous peoples in a European educational system, such as Australia and New Zealand, also must deal with underrepresentation in the curriculum. Both Australian Aboriginal students and New Zealand Maori students experience a discontinuity between the science curriculum and textbooks as they presently exist and their language, culture and values (McKinley, Waiti & Bell 1992; Ritchie & Butler 1990). Although some attempts have been made to be culturally sensitive, the overall impact has been small. The invisibility of women, people of colour and indigenous people, together with the absence of their contributions and culture in science texts and curriculum 140

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materials, sends a powerful message about who can and should do science. The depiction of science as the product of White males working within a eurocentric culture has a deleterious effect on self-concept, motivation, achievement and interest in science. Opportunities to Learn In schools in the USA, Israel, Canada and Australia, access to computers and programming courses more often is available to males than females. This access is controlled by teachers who often believe that males are more interested in computers than females (Sutton 1991). Schools in countries which are poor, or schools in economically well-off countries in which there are large minority populations, face a different set of problems concerning computer access. First, these schools do not have computers always. For example, it is estimated that, in all of China’s primary and secondary schools, there are few (50,000–60,000) computers and little money for their maintenance (Qi 1988). But, even when schools have computers, their use is not well integrated into instruction. Many teachers have limited computer skills and the software used emphasises drill and practice (Cole & Griffin 1987; Qi 1988). Access to science laboratory classrooms also is not universal. Lazarowitz and Tamir (1994) conclude that few teachers in any country use practical work as part of their instruction. Furthermore, when practical work does occur, females in the USA, Australia and England often have less access to science equipment and handson activities because males appropriate the equipment and relegate females to an observing role (Kahle & Lakes 1983, Tobin 1988; Whyte 1984). The type of school to which students go also affects their opportunities to learn. In  developing countries in which single-sex schools are often the norm, boys’ schools have more and better resources and teachers. The location of these better schools in urban areas deprives students in rural areas of a good-quality education (Twoli & Power 1989). In the USA, students do better in suburban schools, which are better equipped and attract better teachers, than in urban and rural schools. In Malta, gender differences in achievement can be related directly to the academic orientation of schools. During the first two years of secondary education, there are no gender differences in science achievement. After this time, the data become inconsistent. Whereas some schools report that males do better than females in physics, others report that females do better than males. The differences lie in the type of school that the students attend and the rates of participation. In the junior lyceums, which are highly academic schools, females do better than males. In less academic institutions, females do less well. Furthermore, with the exception of those attending the lyceums, most females do not take physics because they lack the necessary mathematics background (Ventura 1992). In many countries, teacher expectations also affect opportunities to learn, and these expectations favour males and members of the dominant culture. Often, teachers do not provide females with career information that promotes nontraditional choices. 141

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For example, because teachers in the Philippines typically do not believe that females can be scientists, they transmit gender stereotypes about female intellectual inferiority in mathematics and science (Vitug 1994). This pattern can be seen most often in Africa, Asia and the Middle East (Stromquist 1989; Twoli & Power 1989). Summary and Implications: Influence of Schools A number of school factors work against the participation of women and minorities in science. Traditional instruction and inequitable classroom interactions establish barriers to learning. Teacher expectations and judgements influence how students are treated. Curriculum materials that are not inclusive send discouraging messages about who should do science. Finally, fewer opportunities to learn limit actual achievement. However, these factors do not answer completely the question of why so few women choose science. The sizes of gender differences in achievement are too small to account for the much larger gender differences in the number of men and women who chose science-related careers (Oakes 1990). To answer that question, we must look at how women make choices as a reflection of their affective and affiliative needs (Baker & Leary 1995). Furthermore, school influences might not be the whole answer to the low number of minorities in science. When income and parental education are controlled, many of the differences between Whites and minorities disappear. But, as in the case of females, the differences that do remain for minorities cannot be attributed to school experiences and prior achievement. Oakes (1990) concludes that the effects of historic and present-day discrimination on access to education could explain these remaining differences. INFLUENCE OF THE HOME

Parents are the first teachers and home is the first school. Consequently, both parental attitudes and socioeconomic circumstances influence what parents teach their children and what parents expect for them. Socioeconomic factors and parental attitudes are explored below as determinants of who does science. Socioeconomic Factors The second IEA study (Postlethwaite & Wiley 1992) found that the two most important factors in achievement were parents’ education and the amount of reading material in the home. The higher the educational level of parents and the more reading materials, the greater was a student’s science achievement. Family size was negatively correlated with achievement, presumably because smaller families allow parents more time with their children and present less economic pressures. In poorer countries, such as Ghana and Kenya, and in Israel among Arab and African 142

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populations, school and teacher effects are stronger than family effects because of the general low level of family literacy. However, when class and ethnicity are examined together, the effect of school diminishes. For example, in Indonesia, wealth and social class account for more than 50 percent of the variance in mathematics and science achievement (Fuller & Clarke 1994; Twoli & Power 1989; Zuzovsky & Tamir 1989). In developing countries, economics influences who goes to school and who stays there. Usually Kenyan males are chosen over females when money is an issue and low-achieving males can be allowed to stay in school while low-achieving females are not. The higher the family income, the greater is the likelihood that girls will be educated. But, in many Muslim countries, the education of girls is seen as not being  worth the cost and possibly spoiling them. In most traditional and Muslim countries, an educated daughter is also costly in terms of a dowry (Stromquist 1989). Parental Attitudes Keeves and Saha (1992) found that ‘the educative home tends to reproduce itself in the next generation despite the fact that the children of such homes are not necessarily those with the highest levels of aptitude and achievement’ (p. 183). This  is particularly true in terms of keeping children in school. In many Muslim and developing countries, parents believe that girls should marry early, bear children and provide domestic labour. These attitudes limit girls’ educational opportunities in all academic areas. For example, in Nigeria, almost twice as many Christian females attend the university than Muslim females, even though Christian families have lower incomes than Muslims and sending daughters to the university represents a bigger economic burden to Christian parents (Stromquist 1989). However, even within predominantly Muslim and traditional countries, such as Malaysia, Ethiopia and Zaire, there are differences in parental attitudes towards educational participation based on ethnicity (Fuller & Clarke 1994). The more traditional and rural a society is, the more likely it is that parents will hold lower educational aspirations for their daughters regardless of religious affiliation. In some parts of Papua New Guinea, education is seen as risky and unrelated to being a wife and mother. Educated girls are not expected to be good wives and mothers because they come to disrespect traditions, parents and husbands and will not work hard (Stromquist 1989). In some rural areas of the USA, a similar attitude is found. Many families and communities do not see the relevance of science and parents cannot see a connection between learning science and occupations for their children (Charron 1991). Summary and Implications: Influence of the Home Parental attitudes and the economic condition of the family could be the major determinant of whether a girl will receive an education. Changing parental attitudes 143

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towards the education of daughters in traditional cultures only will occur when the roles of women are expanded beyond the spheres of wife and mother. Even in some developed countries, stereotypical notions of the roles of women still hold. In some developing countries, it can be an advantage to be an ethnic or religious minority in terms of access to education but, in developed nations, ethnic minorities are affected adversely by their status. SOCIOCULTURAL BARRIERS

Two factors that cannot be ignored when examining equity are gender roles and culture. Gender roles are a barrier rooted in culture and culture can prevent participation in science because of dissonance that arises from being a minority student in a majority school. Despite the importance of culture, we know less about cultural dissonance and science than about gender roles and culture. Gender Roles Women’s place in science varies from country to country but few would argue that women are closer to the periphery than the centre. A few examples from around the world illustrate how wide-ranging the problem is. In Germany, generally there is social disapproval for women who work outside the home. Many schools have only morning hours and there is little day care, which is believed to hurt children. Consequently, many female scientists are childless and some have resorted to abortions. In addition, numerous female junior scientists are leaving science in Germany because there are few prospects for advancement within the ‘old boy’ network. Also, there are fewer women scientists in the system than before unification due to the restructuring of education. To counter this problem, the German government has allocated 700 million Deutsche Marks to be spent in the next decade to promote female participation. Unfortunately, some efforts are resulting in a male backlash and the success of these measures is yet to be determined (Aldous 1994). In the Philippines, cultural values can hinder women’s entrance and advancement in science. Many people believe that men are better at mathematics and science and that a woman’s role is to bear children. When women do science, typically they are given laboratory and desk work because field work is believed to be too hard. Women are concentrated in biology, chemistry and pharmacy because physics, industrial research and engineering have a masculine cultural label. They work in government and universities but are kept out of industry where higher-paying jobs are found (Vitug 1994). The role of women in science in India is an interesting case. The older generation does not believe that a problem exists while the younger generation does. The numbers seem to suggest that the younger women are right. Of the 698 fellows of the Indian Academy of Science, 15 are female. A similar pattern holds for the gender make-up of the fellows of the Indian Natural Science Academy. 144

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The perception that there is no problem for women in science among older female scientists can result from their class membership. Female scientists experience little overt discrimination because they are protected by their upper class status which is more important than their gender. However, lower class females in science (technicians and nurses) experience a great deal of harassment and discrimination. Despite the protection that class affords to a few upper class Indian female scientists, many cultural barriers exist that make entry into science difficult. For example, there are curfews for female doctoral students living in dorms but not for males. This limits access to laboratories and provides a disproportionate advantage for males. In addition, the cultural pressure to marry prevents many women from beginning scientific training and is the major reason why female students drop out (Sharma 1994). Nigerian women face cultural barriers too (Jegede & Okebukola 1992). Young women typically are expected to be passive and are discouraged from actively exploring the natural world. Girls often are encouraged to marry early and then, as married women, they are proscribed from participation in public arenas. This proscription means that there are few professional female role models with which girls can identify. These cultural factors have a negative effect on girls’ interest in science and effectively deny them access to employment even if they are trained in science. Italy and Turkey are two interesting exceptions to the pattern of cultural barriers. Italy has a long history of women intellectuals as far back as the Middle Ages. Women regularly attended the university during the Renaissance and there always have been role models in science and a strong tradition of women in physics. This tradition continues today. Most high school science teachers are female, even in physics, and the majority of university physics teachers are female. Professorships are awarded through a national competition conducted by a single panel of experts. This eliminates the influence of the old boy network considerably and places all applicants on an equal footing. Furthermore, moving up the academic ladder in Italy is more leisurely and there is time for starting a family. The extended family provides childcare for working scientist mothers. Families also often provide economic help during studies and afterwards to offset the poor salaries of professors. Despite these advantages, the route to science is open primarily to middle and upper class women (Flam 1994). Turkey contradicts the stereotypes of Muslim countries because it has a higher proportion of women in science and medicine than most Western countries, which can be traced to the policies of Kemal Attaturk who founded modern Turkey in 1923. He recognised the need for an educated population and opened the universities to women. Because science and mathematics are compulsory for all pre-college students, all females have the prerequisites for further science study at the university level. In addition, science does not have a masculine image. Family/work conflicts are reduced in Turkey because extended families provide childcare and household help is readily available. There is also a class bias in 145

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operation as in India. Many employers would rather have an upper class female scientist as an employee than a lower class male scientist. Despite the positive state of affairs in Turkey, there are still some problems. Academic salaries are low and, as elsewhere, there is a limit to advancement. Regardless of professional achievement, women still are expected to fulfil their traditional roles as homemakers. The dual roles are easy to balance early on in a woman’s career because of childcare and household help, but the increased demands that accompany advancement outpace the support systems available. Consequently, many Turkish women scientists remain unmarried and, in the top ranks, a women is five times more likely to be unmarried than a man (Kohn 1994). Cultural Dissonance Minority participation in science is extremely low for countries for which we have data. In the USA, minorities have high dropout rates and low achievement test scores, and they are mired in the culture of poverty that prevents them aspiring to careers in science (Haukoos & Chandayot 1988; Hill, Pettus & Heddin 1990; Mullis  & Jenkins 1988; National Science Foundation 1990; Oakes 1990). According to USA government statistics, Whites constitute the majority (73–89 percent) of PhDs in the workforce in science and engineering. Asians follow with 23.9 percent in engineering, 11.8 percent in chemistry and 88.7 percent in biology. Hispanics and Black Americans constitute less than 2 percent of PhD scientists in these fields. Native Americans are too few to be counted. Data at masters and baccalaureate levels reflect the same disparity in participation (National Science Foundation 1990). According to Olden (1993), Black American students who choose science must engage in a complete culture shift. Parents, peers and teachers know little about a scientist’s lifestyle and often do not respect the field because science is not perceived as a lucrative occupation. Black females seem to be less bounded by traditional roles and community and have a greater interest in becoming scientists (Hill, Pettus & Hedin 1990). Native Americans face unique barriers that keep them on the margins of science. Many are isolated in rural areas on reservations (Bureau of Indian Affairs, US Department of the Interior 1988). There are language barriers and cultural dissonance that affect success in majority culture schools (Atkinson 1985; Bureau of Indian Affairs, US Department of the Interior 1988). Those who make it to the university often drop out because of isolation and the loss of extended family and tribal support. Existing data on minority populations outside of the USA seem to indicate that these groups suffer from many of the same problems. In New Zealand, the majority of Maori students fail science in school and are thus shut out of the science pipeline early on in their education (McKinley, Waiti & Bell 1992). 146

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Culture and Interaction Patterns Minority students in the West often do poorly in school because they aren’t competent in the majority culture’s rules of classroom communication (Cazden & Legget 1981). For example, Greenbaum (1985) found that Choctaw Indian children have significantly different communication styles than Whites. They speak individually less often, make shorter utterances, interrupt the teacher and are more likely to gaze at their peers rather than the teacher when the teacher is talking. Cole and Griffin (1987) found silence and passivity in the classroom common among Navajos and Odawas. These students are not being deliberately rude; their behaviour is a reflection of the discourse patterns common to their cultures. Changing school communication patterns helps students from traditional cultures. Au and Jordan (1981) found that native Hawaiian students do better in school when the participation structure resembles the traditional forms for speaking found in the Hawaiian culture. This structure encourages more than one person to speak at the same time. Summary and Implications: Sociocultural Barriers Cultural factors affect women and minorities’ participation in science negatively through traditional views of appropriate roles and behaviours. Women and minorities also can be prevented from participation by cultural differences between school and home. Changing participation rates have implications for cultural norms and values and the balance of power within a society. Consequently, easy or quick changes are unlikely. NATURE OF SCIENCE

There is now strong evidence that science is a white European male domain in the way it is practised and described. These characteristics of science have not gone unnoticed by students whose attitudes towards science have been formed partly by them. However, there is another way to do science that is more characteristic of women and minorities that has led to a new language and way of thinking about natural phenomena. These two approaches to science are examined next. Science as a Male Domain Science is laden with imagery that makes the masculine dominant and the feminine subordinant, as the following two examples illustrate. Primatologists have described primate social organisation in terms of male dominance and hierarchy, and cellular biologist have described fertilisation as the sperm invading the passive egg. Both descriptions ignore evidence, often gathered by female scientists, that contradicts

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this androcentric conception of nature (i.e., female primates participate in and often control mate selection and the egg reaches out and encloses the cell with projections). Androcentric theories also have been used to oppress women and prevent them from obtaining an education. A once popular theory was that studying diverts blood from a women’s reproductive organs and interferes with her ability to bear children (Martin 1990; Sheperd 1993). Similar scientific theories of a racist nature have been used to keep non-Whites disenfranchised and to justify slavery and colonialism (Gould 1981). The masculine nature of science first was expressed in the 17th century. Henry Oldenburg of the Royal Society of London wrote that the first business of the society was to create a masculine philosophy. Being feminine was the ultimate epithet. Francis Bacon attacked Aristotle’s philosophy as being feminine and insulted French scientists by calling them effeminate (Sheperd 1993). In most Western countries, science still is a male domain. Kelly (1987a, 1987b) argues that this assertion is supported by participation rates, the way in which science  is presented in terms of examples, masculine interaction patterns in classrooms, and the inherent masculinity in scientific ways of thinking. When children in the USA, Australia, Ireland, Canada and Norway were asked to draw a scientist, they normally drew a man (Kahle & Meece 1994). This masculine image affects the kinds of decisions that girls and women make in relation to science. For example, Lewis (1983) found that high-achieving female physics majors did not want to do graduate work or go into research or academia. These activities were perceived as too male and impersonal in a field that already was overwhelmingly male. Physics in Norway also is perceived as masculine (Sjoberg & Imsen 1987). Science as a White European Domain Science also has a White eurocentric face. Much of the impetus for science arose out of the needs of Western expansion into Africa, Asia and the new world (Boorstin 1983) and today scientific questions continue to be framed to address problems of concern to the West. The conception of nature as expressed in schools and universities world-wide is a Western one. Models of nature are mechanistic. The conceptualisation of the laws of nature are embedded in a Judeo-Christian world view and man’s relationship to nature is one of steward, appointed by a Christian God (Harding 1993). Consequently, many Third World leaders in countries such as Indonesia and Malaysia believe that modern science is in crisis. They argue that science and technology will not become meaningful to the Third World until it evolves from the ethos and cultures of Third World societies. Presently Western science neither meets the cultural needs of the Third World nor reflects the creativity of those cultures (Third World Network 1988). Harding (1993) supports the need for the 148

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cultural context of science. She thinks that science might not be universal in that what works best for the West might not work best for the Third World. How Women do Science Girls often reject the masculine aspects of science, not science itself. Because bombs and war are seen as closely tied to chemistry and physics, these sciences and their products often are rejected. But, when girls come to understand science through affective relationships and when they can see how they can help, science is embraced. Many young women who choose science do so because they want to help people, animals and the earth (Baker & Leary 1995). When women practice science, relations are important. Women want to be connected to the objects of study and to members of their research teams (Sheperd 1993). The need to be connected and related to people and objects could be a function of women’s ways of knowing. Belenkey, Clinchy, Goldberger and Tarule (1986) found that women construct knowledge on the basis of both objective and subjective experiences that make connections with people, ideas, objects and the written word. In science, many women take a more holistic interactionist approach (Longino 1989) and reject characteristically masculine methodologies that decontextualise and  isolate discrete aspects of natural phenomena. Barbara McClintock often is cited as an example of the how women do science that is more connected. She talked about knowing her corn plants intimately and about feeling as if she were part of the plant and the plant was part of her (Keller 1983). Attitudes Towards Science The IEA data indicate that students in all countries have positive attitudes towards science, but there still are gender differences favouring males. This gender gap increases from age ten years to the final year of secondary education, with 14-yearold girls having the most negative attitudes of all age groups in all countries (Keeves 1992). However, Fleming and Malone’s (1983) meta-analysis found that males in primary and high school have more positive attitudes towards science than do females, but that females have more positive attitudes than do males at the junior high level. Baker also (1985) found that middle school girls had more positive attitudes towards science than boys. Consequently, the often assumed relationship between attitude and achievement is suspect. The existence of a link between attitude and achievement is highly dependent upon the particular component of science and attitude being measured and how they are measured (Keeves & Kotte 1992; Keeves & Morgenstern 1992; Steinkamp & Maehr 1983). In addition, negative attitudes found using paper-and-pencil measures do not necessarily mean that girls do not choose science. Baker and Leary (1995) found in 149

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interviews that girls in the USA did not like school science and were suspicious of some of the applications of science, but that they still liked the idea of real science and planned to become a scientist who would do good. Reports of within-country differences other than gender are scarce. In Canada, the International Assessment of Educational Progress data indicate that francophone students in the provinces of Quebec, Ontario and New Brunswick have more positive attitudes towards science than anglophones (Lapointe, Mead & Phillips 1989). Arab-born and African-born Israelis have poorer attitudes towards science than do Jewish  students whose parents were born in Israel (Tamir 1989). In the USA, Black students are more interested in and enjoy science more than their White counterparts, even though they typically have fewer science experiences and do less well on science assessments (Jones, Mullis, Razen, Weiss & Weston 1992). Summary and Implications: Nature of Science Science is a White European male domain and the face of science would be different if women and members of the Third World were asking more of the questions and doing more of the science. Despite the perception of science as a White European domain, women and people of colour around the world want to participate in science. The negative attitudes sometimes found could have more to do with not finding one’s place in science than in the act of doing science. INTERVENTIONS

Although the data presented here clearly indicate that equity and access to science have not been provided to everyone equally, things can change. There is a great deal that we can do in four critical areas: strategies for gender equity; strategies for  cultural equity; instructional materials; and policy and program guidelines. This last section explores these areas and provides suggestions about what to do to promote equity. Strategies for Gender Equity One effective way for educators to promote equity is to use teaching strategies that have the greatest likelihood of being successful with all students. Fortunately, the strategies that promote achievement and interest for girls are also successful in promoting achievement and interest for boys. Scantlebury and Kahle (1993) recommend using small group or cooperative learning group formats, closely monitoring and controlling discussions, and increasing wait-time (the duration of the pause between the teacher’s question and the students’ answers) for all students. Access to equipment also must be monitored closely and controlled by a sign-up sheet or by using a materials manager for group work. 150

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Group activities are likely to be successful only if students are taught to work in cooperative groups. Specific roles should be assigned and changed after a task is completed. Students also should be required to evaluate their performance when working as a group. Separate grades can be given for academic work and for the quality of the group interactions (Baker 1988). Baker (1988) also recommends controlling and monitoring classroom interactions by using a list of student names and checking the names after the student has been called on, or by calling on boys and girls alternately. Also, she suggests ignoring students who call out. If the teacher is consistent, students will understand that everyone will be required to frame an answer to a question and that everyone eventually will be called upon to respond. However, because teachers often are unaware of their engagement patterns, feedback mechanisms are needed as a stimulus to change the nature of classroom interactions (Baker 1988; Tobin 1988). Baker (1988) suggests videotaping a lesson that can be viewed and analysed at a later time. Alternatively, a colleague can observe a lesson and record the number and types of interactions. Corey, van Zee, Minstrell, Simpson and Stimpson (1993) found that the nature of discussions also can increase girls’ participation as well as that of silent males. Girls talk when teachers emphasise students’ ideas as a starting point for investigations and discussions, encourage student-to-student discussions and establish a nonjudgemental way of talking about ideas without the need for a right answer. Girls also talk more when their participation is valued. Other approaches to promote equity include frank discussions of bias in textbooks and curriculum materials. Theories, descriptions and explanations also should be examined critically so that students can see how science is a reflection of culture (Baker 1988; Hykle 1993; Martin 1990). Furthermore, teachers should make an effort to provide career information and use materials that are inclusive to temper the perception that science is a White male domain. The way in which science is presented is also important. It must be presented in real-world contexts so that students see that science cannot be separated from the knower and that science, technology and society are inextricably intertwined (Hykle 1993; Rennie & Parker 1991, 1993a). Instructional materials should be more inclusive and reflect females’ interests, such as the human body and the environment, and special efforts should be made to enhance interest in the physical sciences. Martinez (1992) found that boring physical science experiments can be made more interesting for female students with simple modifications. Females usually like physical science experiments that have social appeal. Experiments, such as observing a pendulum, can be made more interesting if the task requires cooperation and if students must work towards a common goal. Enhancing cognitive appeal through discrepant events and mastery through greater freedom to formulate and carry out plans appeals to males, but is less effective with females. 151

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Discovery learning is powerful for engaging students. There is much evidence that this has a positive effect on attitudes and achievement for males and females (Shymansky, Kyle & Alport 1983). Discovery learning with student-centred discussions (Kahle & Damnjanovic 1995) also can increase Black and White females’ enjoyment of physical science, reduce their stereotypes about science, and increase their estimates of their ability. In addition, we should look to the characteristics of successful schools. Results from the GIST (Girls into Science and Technology) Project in England indicate that more than pedagogy is important. The GIST intervention was successful in schools in which women were in positions of authority, science teachers were female and girl-friendly, girls were made aware of the problem of low numbers in science and technology, there was a commitment to change, and the community and school were willing to innovate (Whyte 1986). However, Harding and Sutoris (1987) concluded that interventions will not be successful unless they provide girls with opportunities for autonomy and ways of valuing themselves and unless changes make sense in terms of girls’ development. Harding (1996) further warns that gender constructs must be modified through changes within society before significant changes in science participation occur. Kelly (1987a, 1987b) believes that the promotion of equity requires more than changing teachers, schools and the image of science; science itself must change. However, to do so, we need more research on the relationship of capitalism, patriarchy and racism. Kelly argues that we need a better understanding of the vested interests supporting the status quo in order to create change. Strategies for Cultural Equity Many instructional strategies that work best for females are surprisingly similar to those recommended for minorities in many countries (Atwater 1994; Barba 1993). In Australia, Christie (1991) recommends the following principles taught by Aboriginal elders to facilitate the incorporation of indigenous science into Western science: placing everything in context; encouraging a multiplicity of perspectives; negotiating meaning among students and teacher; and seeking a balance that takes into account everyone’s points of view, even though not all points of view will be weighted equally. These principles are part of the Aboriginal cultural fabric and imply that teaching that emphasises group work, extensive discussion and placing problems in context will help aboriginal students to learn, whereas a hierarchical teacher-centred classroom will not. Ritchie and Kane (1990) and Ritchie and Butler (1990) recommend infusing Aboriginal topics into the science curriculum. They found that, when such topics were included in science instruction, Aboriginal students were more involved in activities and more interested in what they were learning. Aboriginal students came away from the experience with more positive impressions of the skills of traditional Aborigines. Using Aboriginal topics also promoted positive relations between 152

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Aboriginal students and their non-Aboriginal teachers and generally more positive attitudes towards science for all students. In New Zealand, there is a move to include the Maori language and culture in science education. A Maori vocabulary for science, that is a conceptual translation of ideas (McKinley, Waiti & Bell 1992), is being developed. Instruction and assessment emphasise cooperative group work and holistic approaches that do not separate science from other parts of the curriculum. These strategies are consistent with Maori culture which values cooperation and group work and effort. Myths and legends are used to bridge Maori science and Western science vis-a-vis local geological phenomena and formations. Indigenous technology in the context of water, seafood, forests and insects also is emphasised. In the USA, a program developed by Elizabeth Cohen and her colleagues to enhance students’ classroom status (Cohen & Lotan 1991) has been successful with primary and middle school minority students. The technique is based on working in groups on large-scale long-term projects that require the application of mathematics and science. Students are grouped so that their talents and skills complement one another, and tasks are structured so that they cannot be accomplished without everyone’s cooperation and participation. Central to the effectiveness of status enhancement is that teachers search for opportunities to praise students publicly for their contributions. Barba (1993) recommends that teachers use constructivist learning models with minority students rather than authoritarian models. Teachers should use inquiry, the learning cycle, peer tutoring, manipulatives, culturally familiar examples and the home language. Assessment should allow for multiple means for showing competence. Non-native or dialect speakers should be free to use written, oral and pictorial responses. Teachers should employ pictures, icons and real-world objects to communicate with students. Use of visuals and graphics is particularly important with Native American students (Haukoos & Satterfield 1986). The importance of instruction in the student’s native language, especially during the early years of schooling, cannot be underestimated. Ehindero (1980) found that Nigerian students taught in their native tongue were far superior to their counterparts taught in English in terms of their ability to understand science concepts requiring higher-order thinking skills. Rutherford and Nkopodi (1990) found differences in the recognition of science concepts among North Sotho speakers in South Africa when instructed in English. They recommend that science be taught using both English and the students’ first language, but with the amount of first language instruction decreasing by grade level. Materials Most curriculum materials reflect middle class White Western culture regardless of the country in which they are found. These materials often do not speak to all students who are required to use them. To make curriculum materials more culturally 153

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compatible, some educators are moving towards using indigenous resources. The theory behind this movement is that indigenous resources are grounded in students’ experiences and therefore are more motivating. Seeing aspects of one’s own culture prominently featured in the science curriculum leads to valuing oneself and one’s culture. Indigenous science and resources include oral traditions, language and street  science. Street science includes the traditional technologies of the past and present and folk beliefs about such things as nutrition and illness. Teachers in the Republic of Trinidad and Tobago who used indigenous science found that their students were more interested and eager to participate, grasped concepts better and showed enhanced problem-solving skills (George 1992). Policy and Program Guidelines The American Association of University Women (1992) published a set of guidelines for assessing equity that include gender and ethnicity. The guidelines suggest examining factors related to instruction (counselling practices, rates of participation in special programs, course-taking patterns, stereotyping) and policy (inservice programs, assessment practices, harassment and discrimination procedures, criteria used to award scholarships). Cole and Griffin (1987) created the following list of program development criteria gleaned from successful educational programs for improving science education for females and minorities: (1) a strong academic focus which enriches rather than remediates; (2) teachers who are subject matter specialists and believe that all students can learn; (3) an emphasis on applications and careers in science; (4) an integrated curriculum including technology; (5) active learning and handson instructional strategies; (6) multi-year student participation; (7) a strong director and stable committed staff; (8) stable, long-term funding from multiple sources; (9) university, business, school and industry collaboration; (10) active recruitment of  students; (11) out-of-school learning opportunities; (12) specific goals and strategies to eliminate the inequalities associated with ethnicity and gender; (13) teachers and staff who mirror the gender and ethnic composition of the students; (14) peer support systems; and (15) systematic evaluation and long-term follow-up based on well-planned and objective data collection. Furthermore, the program must be part of the established curriculum rather than an add-on, extra or special program. Without such integration, females and minorities still will be receiving messages that they are outsiders to science. The implementation of these recommendations, which are similar to those made by Malcolm (1984), are likely to begin to make a difference in the participation and success of girls and minorities in science. Strategies for the University Level Interventions at the university level to increase the participation of women in science not only should take into account instructional strategies and address the masculine 154

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perception of science, but also take additional steps that are unique to the university. Dresselhaus, Franz and Clark (1994) found that, in order for a female student to be successful in physics in the USA, 15 percent of the university staff in physics must be female and one must be in the senior ranks. The chair and key staff members must be committed to female students and there must be effective communication between students and staff. For university staff, reliable child care provisions appear to be the most important factor worldwide (Barinaga 1994; Osborn 1994). In Sweden, increased participation of women is attributed directly to subsidised child care and paid leave after childbirth, as well as to policies that do not penalise scientists for taking time out for bearing and raising children (Aldous 1994). Minority scientists are even more isolated in university settings than women and need support groups. In the USA, professional organisations have been formed to connect (possibly using technology) widely-dispersed individuals and to provide a forum for their concerns (Fox 1993). Summary and Implications: Intervention Getting more women and minorities into science and keeping them there requires a  multi-pronged effort that includes system-wide changes. For example, at the primary and secondary school levels, we need changes in the way in which we teach so that competition is reduced and all students are encouraged to succeed. Curriculum materials should include the contributions of women and minorities and present science as a career for everyone. Assessment methods and materials should be examined for bias. Policies on the language of instruction, the way in which money is spent on programs for encouraging participation in science, and university staff-hiring practices all should be scrutinised in light of equity. We need a university climate that supports and encourages, rather than isolates and discourages, women and minorities from participating through mentor programs and role models for students and a review of hiring, retention and promotion practices for staff. Finally, governments must take the lead when custom or culture establishes barriers to equity  through policies, programs and laws which insure that all citizens have access to and opportunities in science. Without a system-wide multilevel approach, even the best thought-out changes will have a limited impact. CONCLUSION

This chapter has presented a broad picture of equity issues in science education, including the roots of the problem of the relatively small number of women and minorities in science. Consideration was given to the influence of the complex tangle of school, home and cultural influences, as well as the nature of science itself, on equity in science and science education. Such factors include traditional instruction, inequitable classroom interactions, teacher expectations, curriculum materials, 155

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parental attitudes and cultural differences between school and home. Interventions which can alleviate inequity were discussed. The other chapters in the equity section of the Handbook expand on many points in this chapter by providing greater detail and placing them in the context of specific lives, cultures and nations. Lesley Parker and Léonie Rennie’s chapter entitled ‘Equitable Assessment Strategies’ address the problems in providing equitable assessment in science education. What works is addressed by Jan Harding in her international review of grass-roots equity projects. Culture, minority status and instruction are examined in Robertta Barba and Karen Reynold’s chapter dealing with  Hispanics  in the USA. Alejandro Gallard, Elizabeth Viggiano, Stephen Graham, Gail Stewart and Michael Vigliano explore similar issues in the context of the distinction between and consequences of being a voluntary and an involuntary minority. Rose Agholor and Peter Okebukola provide an account of the JETS program in Nigeria. Finally, a personal face is put on the struggle for equity in science by Sharon Nichols, Penny Gilmer, Anthony Thompson and Nancy Davis who present biographical vignettes that explore the marginalisation of women in their own words. NOTE 1

Originally published as Baker, D. (1998). Equity issues in science education. In B. Fraser & K. Tobin (Eds.), International Handbook of Research in Science Education (pp. 869–896). Amsterdam: Kluwer.

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EQUITY ISSUES IN SCIENCE EDUCATION Klainin, S., Fensham, P. & West, L.: 1989, ‘Successful Achievement by Girls in Physics Learning’, International Journal of Science Education 11, 101–112. Kohn, P.: 1994, ‘A Prominent Role on a Stage Set by History’, Science 263, 1487–1490. Lapointe, A., Mead, N. & Phillips, G.: 1989, A World of Differences: An International Assessment of Mathematics and Science, Educational Testing Service, Princeton, NJ. Lazarowitz, R. & Tamir, P.: 1994, ‘Research on Using Laboratory Instruction in Science’, in D. Gabel (ed.), Handbook of Research on Science Teaching and Learning, Macmillian, New York, 94–128. Lewis, I.: 1983, ‘Some Issues Arising From an Examination of Women’s Experience of University Physics’, International Journal of Science Education 5, 185–193. Locke, R.: 1992, ‘Gender and Practical Skill Performance in Science’, Journal of Research in Science Teaching 24, 227–241. Longino, H.: 1989, ‘Can There be a Feminist Science?’, in N. Tuana (ed.), Feminism and Science, Indiana University Press, Bloomington, IN, 45–57. Malcolm, S.: 1984, Equity and Excellence: Compatible Goals, American Association for the Advancement of Science, Washington, DC. Marshall, H. & Weinstein, R.: 1984, ‘Classroom Factors Affecting Students’ Self-Evaluations: An Interactional Model’, Review of Educational Research 54, 301–325. Martin, J.: 1990, ‘What Should Science Education do about the Gender Bias in Science?’, in M. Matthews (ed.), History and Philosophy of Science Teaching, Falmer Press, Bristol, PA, 151–165. Martinez, M.: 1992, ‘Interest Enhancement to Science Experiments: Interactions with Student Gender’, Journal of Research in Science Teaching 29, 167–177. Matthews, C. & Smith, W.: 1994, ‘Native American Related Materials in Elementary Science Instruction’, Journal of Research in Science Teaching 31, 363–380. McGrayne, S.: 1993, Nobel Prize Women in Science: Their Lives, Struggles and Momentous Discoveries, Carol, Secaucus, NJ. McKinley, E., Waiti, P. & Bell, B.: 1992, ‘Language, Culture and Science Education’, International Journal of Science Education 14, 579–594. Mullis, I. & Jenkins, L.: 1988, The Science Report Card: Elements of Risk and Recovery, Educational Testing Service, Princeton, NJ. National Science Foundation: 1990, Women and Minorities in Science and Engineering, Author, Washington, DC. Oakes, J.: 1990, ‘Opportunities, Achievement and Choice: Women and Minority Students in Science and Mathematics’, in C. Cazden (ed.), Review of Research in Education #16, American Educational Research Association, Washington, DC, 153–222. Olden, K.: 1993, ‘Bringing Science Back to the Neighborhood’, Science 263, 1116. Osborn, M.: 1994, ‘Status and Prospects of Women in Science in Europe’, Science 263, 389–131. Parker, L., Rennie, L. & Harding, J.: 1995, ‘Gender Equity’, in B. Fraser & H. Walberg (eds.), Improving Science Education, National Society for the Study of Education, Chicago, IL, 186–210. Postlethwaite, T. & Wiley, D.: 1992, The IEA Study of Science II: Science Achievement in Twenty-Three Countries, Pergamon, Elmsford, NY. Potter, E. & Rosser, S.: 1992, ‘Factors in Life Science Textbooks That May Deter Girls’ Interest in Science’, Journal of Research in Science Teaching 29, 669–686. Powell, R. & Garcia, J.: 1985, ‘The Portrayal of Minorities and Women in Selected Elementary Series’, Journal of Research in Science Teaching 22, 519–534. Qi, C.: 1988, ‘Computer Education in Secondary Schools in the People’s Republic of China’, Journal of Research in Science Teaching 25, 493–500. Rennie, L. & Parker, L.: 1991, ‘Assessment of Learning in Science: The Need to Look Closely at Item Characteristics’, The Australian Science Teachers Journal 37(4), 56–59. Rennie, L. & Parker, L.: 1993a, ‘Curriculum Reform and Choice of Science: Consequences for Balanced and Equitable Participation’, Journal of Research in Science Teaching 30, 1017–1028. Rennie, L. & Parker, L.: 1993b, ‘Assessment in Physics: Further Exploration of the Implications of Item Context’, The Australian Science Teachers Journal 39(4), 28–32. Rennie, L., Parker, L. & Hildebrand, G. (eds.): 1991, Action for Equity: The Second Decade, National Key Center for School Science and Mathematics, Curtin University of Technology, Perth, Australia.

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CHAPTER 6 Ritchie, S. & Butler, J.: 1990, ‘Aboriginal Studies and the Science Curriculum: Affective Outcomes from a Curriculum Intervention’, Research in Science Education 20, 349–354. Ritchie, S. & Kane, J.: 1990, ‘Implementing Aboriginal Content in the Science Program: A Case Study’, The Australian Science Teachers Journal 36(4), 88–91. Rosier, M. & Keeves, J.: 1991, The IEA Study of Science I: Science Education and Curricula in TwentyThree Countries, Pergamon, Elmsford, NY. Rutherford, M. & Nkopodi, N.: 1990, ‘A Comparison of Some Science Concepts Definitions in English and North Sotho for Second Language English Speakers’, International Journal of Science Education 12, 443–456. Sadker, M., Sadker, D. & Klein, S.: 1991, ‘The Issue of Gender in Elementary and Secondary Schools’, in G. Grant (ed.), Review of Research in Education #17, American Educational Research Association, Washington, DC, 263–334. Scantlebury, K. & Kahle, J.: 1993, ‘The Implementation of Equitable Teaching Strategies by High School Biology Teachers’, Journal of Research in Science Teaching 30, 537–545. Sharma, K.: 1994, ‘Is Overcoming Diffidence the Root to Success?’, Science 263, 1495–1496. Shepherd, L.: 1993, Lifting the Veil, Shambhala Press, Boston, MA. Shymansky, J., Kyle, W. & Alport, A.: 1983, ‘A Meta-Analysis of Discovery and Traditional Instruction on Attitude Toward Science’, Journal of Research in Science Teaching 20, 387–404. Sjoberg, S. & Imsen, G.: 1987, Gender and Science Education (Report No. 3.), Centre for Science Education, University of Oslo, Oslo, Norway. Steinkamp, M. & Maehr, M.: 1983, ‘Affect, Ability, and Science Achievement: A Quantitative Synthesis of Correlational Research’, Review of Educational Research 53, 369–396. Stromquist, N.: 1989, ‘Determinants of Educational Participation and Achievement of Women in the Third World: A Review of the Evidence and Theoretical Critique’, Review of Educational Research 59, 143–183. Sudweeks, R. & Tolman, R.: 1993, ‘Empirical Versus Subjective Procedures for Identifying Gender Differences in Test Items’, Journal of Research in Science Teaching 30, 3–19. Sutton, R.: 1991, ‘Equity and Computers in the Schools: A Decade of Research’, Review of Educational Research 61, 475–504. Tamir, P.: 1989, ‘Home and School Effects on Science Achievement of High School Students in Israel’, Journal of Educational Research 83, 30–39. Third World Network: 1988, Modern Science in Crisis: A Third World Response, Author, Panang, Malaysia. Tobin, K.: 1988, ‘Differential Engagement of Males and Females in High School Science’, International Journal of Science Education 10, 239–252. Tobin, K. & Gallager, J.: 1987, ‘The Role of Target Students in the Science Classroom’, Journal of Research in Science Teaching 24, 61–75. Tobin, K., Tippins, D. & Gallard, A.: 1994, ‘Research on Instructional Strategies for Teaching Science’, in D. Gabel (ed.), Handbook of Research on Science Teaching and Learning, Macmillan, New York, 45–93. Travis, J.: 1993, ‘Schools Stumble on an Afrocentric Science Essay’, Science 262, 1121–1122. Twoli, N. & Power, C.: 1989, ‘Major Influences on Science Achievement in a Developing Country: Kenya’, International Journal of Science Education 11, 203–211. Ventura, F.: 1992, ‘Gender, Science Choice and Achievement: A Maltese Perspective’, International Journal of Science Education 14, 445–461. Vitug, M.: 1994, ‘Fighting the Patriarchy in Growing Numbers’, Science 263, 1491–1494. Whyte, J.: 1984, ‘Observing Sex Stereotypes and Interactions in the School Lab and Workshop’, Educational Review 36, 75–86. Whyte, J.: 1986, Girls into Science and Technology: The Story of a Project, Routledge & Kegan Paul, London. Zuzovsky, R. & Tamir, P.: 1989, ‘Home and School Contributions to Science Achievement in Elementary Schools in Israel’, Journal of Research in Science Teaching 26, 703–714.

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AN INTERVENTION TO ADDRESS GENDER ISSUES IN A COURSE ON DESIGN, ENGINEERING, AND TECHNOLOGY FOR SCIENCE EDUCATORS

WHY I CONDUCTED THE STUDY

My interest in engineering education and engineering in the K-12 system began when I  started to collaborate with engineers in a center housed in the College of Engineering on my campus. We were writing grants and thinking about ways to get engineering practices into the K-12 classroom. These efforts occurred before the publication of A Framework for K-12 Science Education: Practices. Crosscutting Concepts, and Core Ideas (National Research Council, 2012) so infusing engineering into the K-12 curriculum was an uphill battle. An earlier study in engineering education, Development of a Survey to Assess K-12 Teachers’ Perceptions of Engineers and Familiarity with Teaching Design, Engineering and Technology (Yasar, Baker, Robinson-Kurpius, Krause, & Roberts, 2006) came out of center work and indicated that teachers knew very little about design engineering technology. They lacked the confidence to teach it and held stereotypical views of the skills and knowledge needed to be an engineer. There were even unexpected gender differences with female teachers rating design engineering technology as more important than male teachers. Given the rather serious lack of knowledge and confidence, strong stereotypes, and perceived barriers to infusing design engineering technology into the curriculum, my co-authors and I decided to do something about it. We had some ideas based on research in science education that we thought would work well in engineering and that we could develop into an intervention based on this research. We didn’t want to go through the involved process of looking for a funding source, writing a grant proposal, and waiting the six months to see if we had been funded to conduct a large scale project. We wanted to start immediately and to try out some of our ideas with a small group of teachers. We decided that we could create a course with the goals of increasing science teachers’ knowledge of the engineering design process and their confidence to teach design engineering technology. We also wanted to help science teachers see the societal relevance of design engineering technology in the hope that relevance would increase the likelihood that they would make design engineering technology part of their curriculum. One of us (Chell Roberts) had a manufacturing teaching lab that was not being used late afternoons. Chell graciously donate the space, tools, and materials so that the course was a genuine experience in design engineering technology. 161

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The four of us (Dale Baker, Chell Roberts, Steve Krause, Sharon RobinsonKurpius) then put our heads together and designed the course. After all the pieces were in place I recruited my science education graduate students who were also practicing teachers to be our first cohort of students. Not satisfied with just teaching the course, we wanted to know if it had an impact and so we decided that we needed to collect data to determine if we had met our goals of increasing confidence and knowledge to teach design engineering technology. METHODOLOGICAL DECISIONS

We wanted to place our assessments in a strong theoretical framework and so decided to use Bandura’ theory of self-efficacy (2003) rather than the more general concept of confidence. Since so much of design engineering technology requires both tinkering and technical knowledge we focused on this specific aspect of selfefficacy. We asked the self-efficacy questions pre and post course and administered a delayed post assessment in order to measure any increase in self-efficacy we could attribute to the course and to see if there were any long term effects of the course. The delayed post was given two weeks after the course during the scheduled exam period. This was not ideal for a delayed posttest and we would have liked to have had a longer period of time between the posttest and delayed posttest. However, we knew that some students would be leaving for the summer and we were not sure that we would be able to find them in order to administer a delayed posttest at a later time. We also used reflection assignments to see if there was change over time in reaction to assigned readings. Since I, along with Steve Krause and Chell Roberts were teaching the course, we turned to our colleague Sharon Robinson-Kurpius to conduct data gathering using focus groups. This allowed the students to speak freely and Sharon was able to provide the focus group information for our analysis while not revealing which student had made specific comments. Each instructor (Dale Baker, Chell Roberts and Steve Krause) was responsible for teaching part of the time during each class meeting allowing the others to make observations and take photos of team interactions as the students engaged in building robots and other artifacts. After class, we three instructors met and talked about the observations and made notes for later analysis. SCIENCE EDUCATION AT THE TIME OF THE STUDY

In general the research in science education at this time focused on the same content areas as in the past with biology, physics, general science, and chemistry as foci and with some work in the area of geology. Engineering was beginning to emerge as an area of research interest but on a very limited scale. High school, elementary, and junior high/middle school continue to be contexts of interest and there was an increased interest in studying under graduate education. Studies set in the community college continued to be scarce. Students were most frequently the subjects of studies 162

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followed by teachers. Despite an increase in studies situated at the undergraduate level, faculty were rarely studied. Studies of pre-service teachers were equally divided between those preparing to teach at the elementary and secondary level. Studies of practicing teachers focused on (1) learning to teach, (2) teaching in a reformed way, (3) improving teaching through professional development, (4) teacher impact on students, and (5) the use of specific pedagogical techniques such as questioning. Equity issues were less about gender and more about individuals of color, and linguistic and cultural diversity. Studies that used computers investigated the impact of simulations. Few studies addressed informal science, or test development, or inquiry. There was also an emphasis on assessment as indicated by the special issue of Journal of Research in Science Teaching on this topic in 2006. By and large, engineering in any form was not part of the educational research foci in science education. In the International Journal of Science Education in the years 2006 and 2007 there was not a single article that addressed design engineering technology in the K-12 classroom or the preparation of teachers to teach design engineering technology. Science Education published a few studies addressing gender in science in 2007 (Hazari, Tai, & Sadler, 2007; Johnson, 2007) and one addressing technology as a tool to enhance inquiry without an engineering context (Kim, Hannafin,  & Bryant, 2007). Some scholars who published in the Journal of Research in Science Teaching were beginning to be concerned about infusing engineering concepts into the K-12 curriculum to prepare the 21st century workforce (Bybee & Fuchs, 2006) and as a way to use design to bridge science and technology (Lewis, 2006) or to use technology to support inquiry without an engineering context (Waight & Abd-El_Khalick, 2007). Others were bringing engineering activities into the classroom for children building and racing mouse trap cars, and using the design build test cycle for soda straw constructions (Svarovsky & Shaffer, 2007; Klar, Triona, & Williams, 2007). The gender biases and issues resulting from these initiatives can be seen in the 10 males and 2 females recruited by administrators and teachers to participate in the soda straw construction and the lower tinkering selfconfidence among girls building the mouse trap cars. Concerns about equity for females and individuals of color were prominent in the Journal of Research in Science Teaching in 2006 and 2007. Calabrese-Barton and Lee (2006) wrote an editorial on behalf of the NARST Equity and Ethics Committee calling for more diversity among the NARST membership in order to increase research that would help all learners succeed. This call for action did address gender but there was a greater emphasis on race/ethnicity and linguistic diversity. The lesser emphasis on women may be attributable to the large female membership of NARST with women taking leadership roles. This change in focus was represented by a special issue of the Journal of Research in Science Teaching on the topic of culture in science education. Other scholars addressed integrating feminist pedagogy into teaching (Capobianco, 2007), the impact of the NSF funded GK-12 program on female participants (Buck, Leslie-Pelecky, Lu, Clark, & Creswell, 2006), understanding the 163

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experiences of successful women of color in science (Carlone & Johnson, 2007), the impact of the gender ratio of high school physics teachers on students’, science identities (Gilmartin Denson, Li, Bryant, & Aschbacher, 2007), and a re-evaluation of the Women in Science Scale (Owen et al., 2007). Analytical approaches were not skewed in favor of the qualitative or quantitative. In fact many studies used both quantitative and qualitative analysis. This reflected the focus on conceptual change and conceptual development where students are interviewed or written responses are analyzed along with numerically based assessments. This more nuanced approach replaced older views of achievement. Case studies, in-depth interviews, and classroom observations as well as sophisticated statistical techniques replaced simpler data analytical techniques. RESEARCH IN THE WIDER FIELD OF EDUCATION

Scholars publishing in the American Educational Research Journal paid scant attention to science education with two articles appearing in 2006–2007 (Windschitl & Thompson, 2006; O’Reilly & McNamarra, 2007) and no research addressed engineering and or design. Two peripherally related articles appeared in the Review of Educational Research. One with a lead author who is an engineering educator synthesized the research about women in computer-related majors (Singh, Allen, Scheckler, & Darlington, 2007) and another examined professional development to support the integration of technology into teaching (Lawless & Pelligrino, 2007). Even the engineering education community, despite ongoing efforts by engineering professional societies to get engineering in K-12 classrooms, published very little research. In 2006–2007 there were two studies of interventions with K-12 students to introduce them to engineering but nothing about the preparation of teachers to infuse design engineering technology into the classroom besides the study discussed in this chapter and our previous study (Yasar, Baker, Robinson-Kurpius, Krause, & Roberts, 2006). On the other hand, research into the experience of women undergraduates in engineering and women in the engineering in the workplace were topics of broad interest to a range of scholars in engineering, engineering education, technology, higher education, and the social sciences. For example, Murphy, Stelle, and Gross (2007) examined how situational cues affected women undergraduate engineers while Sonnert Fox, and Adkins (2007) studied how the percent of female faculty in an engineering department affected the number of female students in the department. More specifically, Kilgore, Atman, Yasuhara, Barker, and Morozov (2007) examined the effect of gender on engineering design tasks’ and Lowe and Gonzalez-Brambila (2007) examined the impact of gender on entrepreneurship and research productivity. Other scholars have applied various theoretical constructs such as feminist and social cognitive theories to understand women and girls’ success and participation in engineering (Phipps, 2007; Vogt, Hocevar, & Hagedorn, 2007). 164

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There were four interesting and influential books published in 2007 related to science and engineering education. The first was the Handbook on Science Education edited by Sandra Abell and Norman Lederman. This handbook contained a section Part II – Culture, Gender, Society, and Science Learning. One of the chapters in this section was written by Katheryn Scantlebury and myself (Gender Issues in Science Education Research: Remembering Where the Difference Lies) that started with a history of science education for girls. The chapter also addressed rates of participation; race, ethnicity, and socioeconomic status; the masculine nature of science; science majors and careers; attitudes; classroom environment; high stakes testing; single-sex classes; and heteronormative science education. In addition to the data from Anglophone countries, we looked at gender issues in Latin American, Asia, Europe, and the Middle East. The psychologists Stephen Ceci and Wendy Williams (2007) edited a series of essays titled Why Aren’t More Women in Science. The essays were written by psychologists, medical doctors, neuroscientists, linguists, geneticists, women’s studies professors, endocrinologists, and psychiatrists. Ceci and Williams noted that the same data examined through different lenses led authors to different conclusions. They themselves concluded that sex differences appear to be neither as unambiguous as earlier researchers suggested nor as insubstantial as current critics claim. (Ceci & Williams, 2007, p. 223) Their answer to the question of whether sex differences are large enough to matter was …it depends upon what we are trying to predict. (Ceci & Williams, 2007, p. 225) Engineering was examined by Karen Tonso (2007) in On the Outskirts of Engineering with the subtitle Learning Identity, Gender and Power Via Engineering Practice. This large scale ethnography looked at the process of becoming an engineer from the first to fourth year in engineering schools. Tonso documented the development of an engineering identity for women by studying team interactions, the culture of public engineering schools, curriculum, and design versus traditional classes. She concluded that there was much need for reform in order to retain many talented women as well as men. The last influential book of 2007 was How Students Learn (Donovan & Bransford, 2007). The book provided in-depth examples of learning in mathematics, history and science. The chapters applied the framework of learner-centered, knowledgecentered, assessment-centered, and community-centered research found in How People Learn (Bransford, Brown, & Cocking, 1999). Despite this useful framework, the chapters did not address the research on the effects of sex differences or gender differences within the framework. 165

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THE CULTURE OF THE TIMES

Women were more visible in politics in 2007. Pratibha Patil was sworn in as India’s first woman president and Cristina Fernandez de Kirchner became the first elected female president of Argentina. Former Prime Minister of Pakistan, Benazir Bhutto returned to her homeland after eight years in exile to resume her political career. Tragically, on the night of her return, her convoy was attacked by suicide bombers killing more than 100 of the cheering people that turned out to greet her. Later in the year, she was assassinated by a suicide bomber. In the United States, Nancy Pelosi was elected the first female Speaker of the House of Representatives in U.S. history (Historical Events for Year 2007, 2015). In addition, for the first time, three women of color in the U.S House of Representatives chaired congressional committees. Representative Stephanie Tubs Jones chaired the Committee on Ethics, Representative Juanita Millender-McDonald, chaired the Committee on House Administration, and Representative Nydia Velazquez, chaired the Committee on Small Business. Colleen Hanabusa also made history as the first women of color to hold a top leadership position in a state legislature by becoming the President of the Hawaii Senate (Rutgers Center for American Women in Politics, 2015). Two thousand and seven was a deadly year world-wide. Terrorist attacks took place  in a Baghdad market where a bomb killed 135 people and two car bombs exploded at the Constitutional Court building in Algiers and the United Nations office killing forty-five people. An explosion also occurred at the House of Representatives of the Philippines killing four people. A mall in the Philippines was also the site of a bomb explosion killing eleven people and injuring an additional 100. In Scotland, A car crashed into Glasgow International Airport. Although no one was killed authorities believed the crash was a terrorist attack (Historical Events for Year, 2007, 2015). Not all terrorist attacks were successful. Attempts were foiled in both Great Britain and Germany. Two unexploded car bombs were found at London’s Piccadilly Circus and three terrorists believed to be members of Al-Qaeda were arrested in Germany. The individuals arrested in Germany were suspected of planning attacks on the Frankfurt International Airport and several United States military installations (Historical Events for Year, 2007, 2015). Jeninne Lee St. John reported on the top ten stories of 2007 in Time magazine. Among those she selected for the United States were the bursting of the housing bubble and the mortgage crisis (Lee-St. John, 2007a), and the Virginia Tech tragedy in which Cho Seung-Hui went on a rampage killing thirty-three people (27 students, 5 professors and himself) (Lee-St. John, 2007b). Lee St. John also selected protests in Myanmar (Burma) where monks protested against the rising cost of food staples such as cooking oil and rice. In retaliation, the government of Myanmar used tear gas and beat protestors with batons, as well as censoring media and raiding Buddhist monasteries (Lee-St. John, 2007c).

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All of the world-wide violence has a greater impact on the mental health of women than men. After violent incidents such as the attack on the United States on September 11th women experienced increased distress, mental health issues and alcohol abuse (Richman, Cloniger, & Rospeda, 2008). Terrorism also has a detrimental effects on the mental health and adjustment of children. The effect can be caused by witnessing the terrorism, traumatic events and life stressors such as losing a home, losing a loved one to terrorism, or simply viewing reports on television (American Psychological Association, 2015). Learning is disrupted under these circumstances and the effect of violence becomes an additional factor to take into consideration when evaluating the impact of educational interventions. IMPACT OF MY WORK

This work was ahead of the science education community in terms of exploring the preparation of teachers to infuse engineering concepts into their classroom practice. It preceded the Next Generation Science and Engineering Framework (National Research Council, 2012). It was one of the first studies to examine how gender considerations must be part of preparing teachers to teach about design engineering technology. It spoke directly to the concerns of engineering educators who wanted to work with teachers but were unaware of how to go about this work. It provided guidance for what professional development in engineering education should look like and what to look for in terms of issues of gender. It also situated one of the authors (Şenay Yaşar) as an emerging leader in engineering education with a K-12 outreach focus.

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DALE BAKER, STEPHEN KRAUSE, ŞENAY YAŞAR, CHELL ROBERTS AND SHARON ROBINSON-KURPIUS

AN INTERVENTION TO ADDRESS GENDER ISSUES IN A COURSE ON DESIGN, ENGINEERING, AND TECHNOLOGY FOR SCIENCE EDUCATORS1

ABSTRACT

A course on design, engineering, and technology based on Bandura’s theory of self-efficacy was taught to nine science education graduate students who were also practicing teachers. The interpretive analysis method was used to code and analyze qualitative data from focus groups, weekly reflections on classes and readings, and pre-, post-, and delayed-post course questions. The improvement in tinkering and technical self-efficacies for five males was limited because of initially higher selfefficacies while that for four females was moderate to high, especially when working in same-sex teams in a non-competitive environment. All students also increased their understanding of the societal relevance of engineering and their ability to transfer engineering concepts to precollege classrooms. Implementing the principles employed in this intervention in pre-college science and university engineering classrooms could help recruit students into engineering as well as help retain both male and female undergraduate engineering students. Keywords: societal relevance of engineering, technical self-efficacy, tinkering selfefficacy I. INTRODUCTION

With a workforce that is only 11 percent female, engineering is among the least gender-equitable professions in the United States. In contrast, female lawyers and doctors are approaching a level of 50 percent representation in their professions overall, although there are some differences by specialization [1]. Even mathematics, which was seen in the past as a male domain, is now perceived by students to be a female or gender-neutral domain [2]. This pattern is repeated in Muslim countries, the European Union, and Asia [3]. For example, women constitute between 17 and 20 percent of engineers in China, South Africa, Sweden, and Portugal. For Switzerland, Germany, and Japan, women constitute 2 percent or less of engineers [4, 5]. In the United States, there are several psychosocial factors related to the low percentage of women in engineering. This research focuses on three of the most important. 168

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The first of the three factors is societal relevance of engineering, which refers to the positive relationship between engineering products and services and how they can improve individual lives and benefit society and the environment. Examples would include devices such as engineered prosthetic devices that allow amputees to enjoy skiing or energy efficient appliances that conserve resources. Most women do not consider engineering as a viable career choice because they are unaware of such connections and, as a result, believe that it is incompatible with their interests and values of helping individuals and improving society [6]. The second factor is tinkering self-efficacy, which refers to women engineers’ experience, competence, and comfort with manual activities [7]. Specifically, it is the confidence and belief individuals have about their competence to engage in manual activities such as manipulating, assembling, disassembling, constructing, modifying, and breaking and repairing components and devices. Examples of such activities would include assembling a bicycle or taking apart a computer. Women’s lack of experience in using tools and machinery and taking things apart and putting them back together results in low tinkering self-efficacy. The third factor is technical self-efficacy, which refers to individuals’ confidence and belief in their competence to learn, regulate, master, and apply technical academic subject matter. Women engineering majors’ self-assessment as problem solvers and as future engineers is lower than men’s [8], which leads to lower technical selfefficacy. According to Bandura [9], “… self-efficacy refers to beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments” (p. 3). According to Bandura’s [9] Social Learning Theory, a person’s self-efficacy will influence what courses of action will be pursued, how long that person will persevere in the face of obstacles, whether his or her thoughts are self-hindering or self-aiding, and how much effort will be expended to reach that attainment or outcome [10–12]. Self-efficacy is context specific. That is to say, it refers to individuals’ beliefs in their causative capacity to control a given attainment, such as learning geometry, writing an English paper, or running a marathon. This selfbelief becomes an innate motivator of behaviors [9]. While the term self-efficacy is well-recognized in the psychology literature, non-psychologists may think of self-efficacy as self-confidence that is narrowly related to a specific context. For example, individuals may be generally self-confident but lack self-efficacy related to specific areas of skill, knowledge, or ability. Thus, two important factors that contribute to women’s lack of confidence in their ability to succeed in engineering are tinkering self-efficacy and technical self-efficacy. A comparison of male and female engineering students who dropped out of engineering indicated that the males who dropped out were more efficacious of their technical ability, even more so than were the females who graduated as engineers [12]. In contrast, international female engineering students studying in the United States have high self-efficacy which they attribute to early tracking into science and mathematics [13]. In addition, self-efficacy is related to performance, even when 169

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ability is controlled, making self-efficacy an important factor for success in engineering [9]. Furthermore, women question the societal relevance of science, technology, engineering, and mathematics (STEM) careers and leave these academic  majors, despite high grades and other indicators of academic success, for majors that will lead to more personally fulfilling careers [14]. Thus, it may be asserted that a primary underlying cause for women’s low levels of participation in engineering education and careers is not due to their cognitive ability or academic performance [15], but rather to psychosocial factors that draw them toward non-engineering careers. The negative influences on women’s participation in engineering can be examined through a psychological and socio-cultural lens to expose barriers to participation. Since science educators in kindergarten through grade 12 (K-12) can influence their students’ attitudes and perceptions, there are strategies that can be used to enhance students’ tinkering self-efficacy, technical self-efficacy, and perceived societal relevance of engineering. One strategy might be a course to increase teachers’ awareness of the societal relevance of engineering, which affects career choice [16]. Another strategy would be to cultivate tinkering and technical self-efficacies through addressing the four sources of self-efficacy (i.e., mastery experiences, vicarious experiences, verbal persuasion, and physiological and affective states) [9]. These strategies could address the fact that teachers usually do not have the tinkering self-efficacy and technical self-efficacy to use engineering activities in their classrooms. As a result, implementing these strategies could help teachers to infuse design, engineering, and technology (DET) into their curricula and to explore the societal relevance of engineering as a way to increase their students’ interest in engineering careers. These strategies could also help teachers show their students the connections between science and technology (engineering) and the associated impact on their students’ daily lives. In order to address the issues discussed above, a course on design, engineering, and technology was developed, taught, and evaluated for the Science Education graduate program at Arizona State University (ASU). The goals of the course were: to teach the engineering design process; to use the design process to create realworld artifacts; to utilize design process concepts to create prototype lesson plans for possible K-12 classroom activities; and to identify and address gender issues in science and engineering classrooms. The objectives of the research reported here were to examine, analyze, and understand the effects of a DET course on both men and women with respect to: (1) their understanding of the societal relevance of science and engineering; (2) their technical self-efficacy; and (3) their tinkering self-efficacy. II. REVIEW OF THE LITERATURE

A. Gender-Based Interests and Attitudes in K-12 Science Classrooms Recent enrollment figures for high school mathematics (geometry, algebra II, trigonometry, pre-calculus, calculus) and science (biology, chemistry, physics, 170

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engineering) courses indicate that the gap between the number of males and females taking these courses is minimal. Nevertheless, there are still differences in choice of university major. Despite taking more high school courses in science and mathematics, the percentage of women interested in majoring in science and engineering has risen only slightly since 1977 [17]. Women with an interest in science are more likely to enter fields such as psychology and the biological and agricultural sciences [18]. This is not surprising since an interest in physical science on the part of boys and an interest in the biological and social sciences on the part of girls has been found in children as early as first grade [19]. Girls in the K-12 system report wanting to help people, the earth, and animals, all domains of which are perceived to be linked to the biological but not the physical sciences [16]. There may be several explanations for only a slight overall rise in interest in science and engineering. One explanation is that the differences between males’ and females’ interest may be linked to differences in perceived competency. Girls in grades as early as K-3 report that they are less competent in physical science than boys report [20]. A second explanation is that some female students are more likely to see science and mathematics classes as a means to an end than male students—a pathway to insure college entrance. These female students are more interested in maintaining a good student identity than in the science itself [21]. A third explanation may be that gender stereotypes still exist. Science and mathematics careers are still perceived as jobs for men among students as young as age five [20]. A fourth explanation may be that differences in interest are linked to negative experiences in science classrooms. For example, Guzzetti and Williams found that both girls and boys were aware of the gender inequities in physics and this made girls fearful of participating in large and small group discussions and activities [22]. It is highly likely that no one explanation can provide the complete answer for the  differences in rates of participation in STEM education and careers between men and women. Most likely, there are combinations of causes that, taken together, account for the current lower rate of participation by women. The underlying psychosocial factors are discussed in greater detail below. B. Psychosocial Factors in Gender Issues in Science and Engineering Classrooms The research literature reports a variety of psychosocial issues that affect female attitudes, interests, motivation, and persistence in science and engineering classrooms. We are presenting results reported on three of the most important factors—perceived societal relevance of engineering, tinkering self-efficacy, and technical self-efficacy. 1) Societal relevance of engineering—contributing to the greater good: Women often do not see the societal relevance of an engineering career. This perceived lack of societal relevance is supported by the work of O’Hara, who concluded that women  would be attracted to engineering if they thought that their goals of contributing to the improvement of society could be better met by studying 171

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engineering [23]. Similarly, Sax found that university women did not persist toward STEM careers because they did not see the societal good that could result from this career choice [24]. Baker and Leary also found that both elementary and high school girls made STEM career choices based on their perceptions of the societal contributions of those careers [16]. Even among women already in engineering, there is a greater emphasis on working with people and making a contribution to society than among men in engineering [25]. According to Dominico Grasso, the founding Dean of Smith College’s engineering program, “Women see law and medicine as offering an opportunity to make a difference in society. They don’t see that opportunity in engineering. We have treated engineering as an end in itself, not a vehicle to make society better” [26]. Consequently, there have been recent efforts to demonstrate that engineering is socially relevant. We cite two of the many programs that are addressing this problem  of relevance [27]. One is the interdisciplinary Bachelor of Arts and Engineering program at Lafayette College that marries skills that women see as their strengths (writing and interacting with people) to engineering. Another is the Engineers Without Borders program, which provides engineering students with opportunities to work on projects with social relevance, such as providing fresh water to villages. Both programs have shown a positive impact of the socially relevant curricular activities on interesting and retaining women as engineering majors [27]. 2) Technical self-efficacy: Baumert, Evans, and Geiser found that gender influenced technical self-efficacy, which in turn affected technical problemsolving ability in females as young as age ten [28]. The students in their study had lower self-estimates of competence and technical problem solving scores and attributed failure to lack of ability rather than to lack of persistence. This is in sharp contrast to females’  perceptions of their problem-solving abilities and persistence in mathematics, a foundational skill for success in engineering. In the case of mathematics, females believed they were better and more persistent problem-solvers than males [2]. However, even women in engineering who intended to go on to graduate school, or who were already in graduate school, expressed less efficacy in their technical abilities than did their male counterparts [25, 29]. The research of Grandee [25] and Wood [29] indicates that we cannot expect standard engineering curricula to do much to improve women’s technical selfefficacy. Only long-term programs that are specifically designed to address perceived competence have resulted in positive outcomes [30], such as increasing self-efficacy in women who enroll in engineering programs [31]. 3) Tinkering self-efficacy: Crismond found that even academically well-prepared females at a technical high school were fearful of using simple mechanical devices and  were tentative in handling them when engaged in design activities [32]. In contrast, males were confident and explored the devices to the fullest. Margolis and Fisher also found that female computer science majors did not take the computers apart and then reassemble them when playing with them [33]. In contrast to men, tinkering was not something women chose to do in their free time while growing up, 172

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and they felt unprepared to conduct manual tasks in engineering as a consequence. This lack of motivation and perceived preparation can lead to lowered tinkering selfefficacy since self-efficacy is built through a variety of pathways, interventions and bridging programs, they must go beyond just providing authentic mastery experiences through engaging activities [34, 35]. Interventions and programs must target other sources of self-efficacy. Women must have many vicarious experiences through watching others like themselves succeed. They must receive verbal persuasion from other significant individuals in their lives (teachers, family members, and friends) who tell them that they can succeed. They must also experience positive physiological and affective states (e.g. fun) while engaged in engineering activities [36]. C. Teachers and Design, Engineering and Technology The National Academy of Engineering is cognizant of the important role that teachers play in developing students’ interest in engineering careers. In the book, Educating the Engineer of 2020: Adapting Engineering Education to the New Century, one of the “guiding strategies” recommends engaging and supporting K-12 teachers in classroom practices that support the development of future engineers [37]. The importance of teachers was also supported by a study by Munro and Elsom  who  examined  the  influences on STEM-related career decisions of 155 high school students [38]. Science teachers had the major influence on students’ motivation to do science and to be employed in science. This resulted from the students’ experiences in the science classroom and through extra-curricular activities. Unfortunately, many teachers lack the self-efficacy and knowledge to teach about DET. Additionally, not all teachers feel it is an important topic to be added to an already packed curriculum [39]. This is in spite of the fact that the issues of technology  and  society are addressed in the National Science Education Standards [40]. Lack of knowledge in this area also results in teachers having a limited view of technology that is principally focused on computers, which they pass on to their students [41]. Furthermore, teachers’ knowledge and perceptions of science and, by extension, of engineering, are related to teachers’ self-efficacy and associated reticence for teaching and conducting activities related to technology or engineering [42]. III. RESEARCH QUESTIONS

We believe that infusing engineering activities into the curriculum of science teachers, especially women, can do a great deal to encourage their students to select engineering careers. This is difficult, however, if teachers have low tinkering and technical self-efficacies and if they do not understand the broader impact of engineering on our society. Therefore, we developed a graduate course on design, engineering, and technology (DET) for practicing science teachers who were also ASU graduate students. One of the major goals of the course was for it to act as 173

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an intervention to increase students’ tinkering and technical self-efficacies and the understanding of the societal relevance of engineering. To assess the impact of this course on students’ attitudes, we examined the following research questions for a group of nine science education graduate students: 1. What is the effect of a course on DET on students’ tinkering self-efficacy? 2. What is the effect of a course on DET on students’ technical self-efficacy? 3. What is the effect of a course on DET on students’ understanding of the societal relevance of engineering? 4. Are there gender differences in the effect of a course on DET on students’ tinkering self-efficacy, technical self-efficacy, and understanding of the societal relevance of engineering? IV. PROCEDURE

A. The Bridging Engineering and Education Course An interdisciplinary team that consisted of two faculty from the Mary Lou Fulton College of Education, a counseling psychologist and a science educator, and two engineering faculty from the Ira A. Fulton School of Engineering developed, taught, and evaluated a 15-week Science Education graduate course on design, engineering and technology (DET). As part of an NSF grant in the Bridges for Engineering Education (BEE) program, the course fulfilled one of the grant’s objectives—to infuse engineering design concepts into the teacher education curriculum at the graduate level in the College of Education. This course was unique because few BEE grantees focused on this objective and science education graduate programs do not include engineering principles in stand-alone courses or within their traditional offerings. The course met one evening a week for three hours in an integrated engineering manufacturing laboratory. The course design was based on Bandura’s theory which  asserts that self-efficacy can be enhanced by direct mastery experiences [10]. For the course, we sought to achieve this through experiences with tinkering activities and through the application of technical skills. We also believed that linking engineering activities to real-world contexts and society’s needs would enhance students’ understanding of the societal relevance of engineering. The course design was based on three resources. One was the research literature. A second was the feedback from a statewide survey for Arizona of K-12 teachers that queried them about their perceptions and familiarity with DET, as well as their preparation to teach DET. An important survey finding was that K-12 teachers wanted to teach DET content, but lacked the knowledge or training to do so. A third resource was the content of two of the National Science Education Standards that aligned with the course outcomes: E) Science and Technology; and F) Science in Personal and Social Perspectives [40]. 174

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In keeping with these standards, course activities took place primarily in three formats. The first format included readings, discussions, and written reflections supplemented by lectures and demonstrations. There were classroom discussions related to readings on three aspects of the course. One aspect was about the research on DET instruction and activities in K-12 classrooms. Another was about the engineering design process. The third aspect was on gender issues in technology and engineering instruction [32, 43, 44]. There were short, 10-15 minute lectures about the functions of sensors, actuators, and microprocessors and about the design process. These were supplemented with demonstrations and team-based activities. Students were also required to write reflection papers on the articles that had been read and discussed. The second instructional format was one in which students worked in threeperson  teams on various activities. Students began by exploring how sensors, actuators, and microprocessors worked (characterization labs). They then wrote computer code for the microprocessors so that input from the sensors could be used to control the actuators. The interaction of the sensors, actuators, and microprocessors emphasized the engineering design process in implementing the operation and control of simple robotic devices. For each of these activities the students wrote corresponding prototype lesson plans that integrated the specific DET content into the grade levels in K-12 curricula they had taught. The results of each team’s activities were regularly presented in class so classmates and instructors could provide feedback for revisions. The activities and presentations were iterative and used the learning cycle format. For this, students were engaged by being presented with a problem (what do the sensors do?), they explored the sensors characteristics, they explained what they did, they extended their knowledge by using them in devices, and they evaluated the success of using their knowledge in the operation of the devices [45]. The first half of the semester (seven weeks) was spent on these activities. The second half of the semester (eight weeks) emphasized the design and fabrication of real-world artifacts, as described below. The third instructional format was a final capstone design project in which student  teams used the iterative engineering design process to create functioning real-world artifacts. During this activity, the education professors conducted discussions and focus groups and worked with students to develop their lesson plans. The engineering professors, with the help of the laboratory technicians, provided help  with design, fabrication, and testing of artifacts. The process consisted of identifying a problem, specifying functional requirements and constraints, proposing alternate solutions, selecting the most appropriate solution, and developing a prototype of the solution, which was articulated with a detailed picture of the device. After that, materials were ordered and the device was built, tested, and refined. The first artifact was a cyberwand for blind children that detected distance and could be used instead of a cane. The second artifact was a training golf-club putter with a pressure sensitive head that provided feedback to the user about where the putter 175

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head struck the ball. The third artifact was an electronic license-plate tracker that used a global positioning system (GPS) to determine the location of a car. None of the activities were structured or evaluated as competitions or challenges in which one team’s product or outcome was compared to or competed with the product or outcome of the other teams. B. Participants Since the graduate level DET course was being offered for the first time, a limited number of students (five females, four males) were enrolled and served as participants in this study. Five students were Euro-American, one was African American, one was Latina, and two were international students (Korean and Turkish). All were Science Education masters or doctoral students with strong content backgrounds in science and mathematics. In addition, one male and one female student also had engineering backgrounds. All participants had experience teaching science in the K-12 system and/or at the university as teaching assistants. All were taking the course to learn more about DET with the intent of infusing engineering design into their science curricula. One additional student participated in the class and also served as a member of the research team. Because of her dual role, we did not collect data from her but used her insights as a participant to understand the data. At this time, one of the participants had completed her master’s degree and returned to elementary teaching, one had dropped out of the doctoral program to pursue a career in business, and one had completed his master’s degree and lost contact with us. All of the other six students are currently enrolled in studies in the doctoral program in Science Education and are in various stages of completing their degrees. Since the changes in self-efficacy and understanding the societal relevance of engineering occurred in the context of a course that bridged the fields of education and engineering, and because we emphasized connecting the ideas and activities encountered in the course to the students’ classroom practice, we have chosen to emphasize the fact that the students were more than graduate students. They were also K-12 teachers who were implementing what they had learned in the course in their own classroom teaching. Consequently, we will present the results both in terms of what happened to the graduate students as learners, but also how they changed as teachers. C. Data Sources and Analysis Data sources consisted of 78 reflection papers, a self-efficacy assessment, informal unstructured classroom observations, and three focus group transcripts. Reflection papers were written in response to assigned readings. These included: research studies  about infusing DET into K-12 classrooms; articles about the relationship between science and technology and the associated societal relevance of engineering; 176

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and chapters from various engineering design textbooks about the process and the steps in the engineering design process. Students were also asked to respond to a self-efficacy assessment three times as a pre-, post-, and delayed post-assessment. It had two open-ended statements: (a) honestly describe your tinkering self-efficacy; and (b) honestly describe your technical self-efficacy. Students discussed self-efficacy in an engineering context and were given definitions of self-efficacy before taking the assessments and were instructed to respond in terms of the DET course. The assessments were posted on-line, and students responded on-line outside of class within a week of the posting. The  pre-assessment occurred during the first week of the semester. The post-assessment occurred during the last week of the semester. The delayed postassessment was completed two weeks after the course ended during the university exam period. Two weeks for a delayed post test is far from ideal and does not provide evidence for the truly long term effects of the course. However, we chose the two week period for the delayed post-test because we were concerned that we would lose contact with some of the students after the course because of graduation and or leaving the graduate program. Classroom observations of team-member interactions were made as teams created lesson plans and engaged in the design process to develop and build their robotic devices and their real-world artifacts. After each class meeting, the instructors discussed what they had observed and notes were made about the nature of the team-member interactions. Observations of team-member interactions were also captured in photographs, which were used to help understand and explain aspects of the written text that referred to their team experiences. Focus groups took place during the fourth week of the semester, the eleventh week of the semester, and during the final exam week. Mixed-gender focus groups were conducted by the faculty team member who is a counseling psychologist and an expert on focus groups. She was responsible for leading the discussion and synthesizing the data. A mixed-gender format was used because of scheduling and time constraints of the students and the psychologist. The first focus group question queried about what was going well and what the students particularly liked. From this discussion, the issue of gender relations and gender differences arose, which the psychologist probed in-depth. The group was then asked what they would like to see changed. The focus group discussions lasted approximately 45 minutes, and the same three questions were asked at the two subsequent focus group meetings. An interpretational analysis approach was used to analyze data, which involves a systematic set of procedures to code and classify data [46]. The qualitative data in the reflection papers, self-efficacy assessments, and focus-group transcripts were analyzed by using a rubric that was closely linked to our research questions and the goals of the course. The rubric had four categories: (1) societal relevance of engineering; (2) technical self-efficacy; (3) tinkering self-efficacy; and (4) gender and diversity. These constituted the relevant themes for the data analysis. Within these themes we identified specific types of low inference statements (categories) 177

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we expected to find in the text that would help us understand the impact of the course (Table 1). The next step in the data analysis was to sort the text within these themes into data  units and then into categories (e.g., metacognitive statements, changes in practice). In order to examine how well the rubric captured the data, we selected statements from two students who wrote the longest responses on the preliminary evaluation. One of the selected students was a male and the other was a female. We analyzed their data using the scoring rubric to insure that we did not need a more nuanced category system. Changes in the categories were made until we determined that our rubric was sufficient to code all of the text related to societal relevance, self-efficacy, and gender and diversity; and to capture changes over time. Text about the course or activities not related to these themes and categories was not coded. Next, the data were coded and analyzed in the following way. First, the written responses of each student were coded using the rubric. Second, statements were compared across time for changes in self-efficacies and understanding of societal relevance. Third, differences in statements made by males and females were identified, and exemplar text was chosen. Exemplar text contained the most specific statements or examples of the coding category and required the least amount of interpretation or inference. Inter-rater reliability for the coding of text and selection of exemplars was established by having two of the authors do the data coding independently [47]. They then reconciled differences through discussions to A.  GENDER AND DIVERSITY 1.  Shifts in understanding 2. Stereotypes or gender roles of self/others/students 3.  Changes in behavior 4.  Connections to readings and research 5.  Metacognitive processes B. SOCIETAL RELEVANCE OF ENGINEERING 1.  Metacognitive processes 2.  Shifts in understanding 3.  Connections to science 4. Recognition of engineering in daily life 5. Awareness of misconceptions (self/ others/students) 6.  Applications (present/future) Table 1. Scoring rubric.

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C.  TECHNICAL SELF-EFFICACY 1.  Metacognitive processes 2.  Shifts in understanding 3. Stereotypes or gender roles of self/others/students 4.  Changes in behavior 5.  Shift in negative to positive self-talk 6.  Comments on competency D.  TINKERING SELF-EFFICACY 1.  Metacognitive processes 2.  Shifts in understanding 3. Stereotypes or gender roles of self/ others/students 4.  Changes in behavior 5.  Shift in negative to positive self-talk 6.  Comments on competency

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obtain complete agreement on coding. We took this somewhat structured approach because  we were constructing cases and, as such, we had to limit and clarify the phenomena of interest carefully [48]. Each student was given a pseudonym before data analysis, and this pseudonym is used in the section on Results and Discussion. Although focus groups had been conducted three times across the semester, these data did not include student names in order to encourage frank discussion and because the focus-group feedback was used to evaluate the instructors and to guide changes in the course. Consequently, we could not track changes in individuals in the focus groups over time. However, it was possible to use the focus-group data for reporting the perceptions of the class as a whole or for gender-specific statements when the gender of the speaker was noted. V. RESULTS AND DISCUSSION

Although the students in this study were science education graduate students, what we learned can be transferred to engineering students, especially women, for several reasons. The activities we used were based on those found in introductory design courses in engineering, the course took place in an engineering lab, and the issues of tinkering and technical self-efficacy and understanding the societal relevance of engineering have been identified as barriers to keeping women in engineering. A. Tinkering Self-efficacy Tinkering self-efficacy is defined as the confidence and belief individuals have about  their competence to engage in manual activities such as manipulating, assembling, disassembling, constructing, modifying, and breaking and repairing components and devices. Using this definition, we have evidence that the course had a positive impact on tinkering self-efficacy for the female students. Two of the females started the course with high tinkering self-efficacy and their experiences did not have a negative impact on their self-efficacy, which remained high throughout the course. Amy said, “I hate directions, I’ll play with something until I can’t do it anymore!” Isabel said “I am pretty confident about attempting to try and fix broken items although I am not always successful.…I will make an attempt without getting frustrated. As a child I helped my Dad tinker with my old beat-up car.” These  two women exhibited several important aspects of tinkering self-efficacy. These statements show females viewing tinkering as playing, a willingness to try, a lack of frustration, and tinkering at an early age. These comments also highlight the importance of prior mastery experiences and the support of significant others, such as family members [9]. These are both important sources of self-efficacy reported by successful women in STEM careers [37]. Two other females started with low tinkering self-efficacy and, by the end of the course, their self-efficacy had increased. For example, Carol started the course by saying, “If there is a good mentor to show me an example I can do it. But with 179

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manual [tasks] I am a slow learner.” By the post-assessment, she said, “… my fear of tinkering (mechanical or technological) decreased positively in daily life.” Helen began the course by saying, “My tinkering self-efficacy is very good if I know the area … but I don’t know the area of engineering.” By mid-course she said, “I did it [tinkering] without having an engineering background. I learned what tinkering should be.” In these statements we see the importance of mastery experiences, which are one of the most important sources of self-efficacy [9]. We also see the contextspecific nature of self-efficacy that, according to Bandura, is not general or global but is related to specific tasks and abilities [49]. Beth, the fifth female, who had an engineering background, also wrote about the context-specific nature of self-efficacy. She stated that her level of self-efficacy was related to the object of the tinkering. She said: “My tinkering self-efficacy all depends upon the value of the object that I tinker with. If it is expensive or a hard to find object, I am much more cautious with tinkering. But, if it’s cheap and easily replaced, I am very confident with tinkering.” At the end of the course, Beth described herself as “confident with tinkering” but  her self-efficacy was still influenced by context. She wrote, “I notice my confidence level increases when there is less at stake.” This last statement seems to indicate that, for Beth, risk is a component of self-efficacy and that for her to feel efficacious when there is high risk, she will need many more mastery experiences in high-risk contexts. The stress and anxiety that is associated with low self-efficacy when engaging in high-risk tinkering activities is clearly evident. Beth’s statements of tinkering self-efficacy were in sharp contrast to the unconditional statements of tinkering self-efficacy expressed by Eric, a male who had a similar background in engineering. Each time he was asked he referred to his self-efficacy as “very high.” Although Beth and Eric are not employed as engineers, but as educators, the contrast between their tinkering self-efficacy is similar to that commonly found among female and male engineering students. Tinkering was not something the females did spontaneously for pleasure or without purpose, even for a student like Isabel who started the course with high selfefficacy. For example, Isabel said, “I don’t necessarily enjoy pulling things apart just to see what is inside or how I could make it better.” Helen acknowledges that she had the ability to tinker but could not think of anything to do with this ability. She said, “[at the] beginning of this course I cannot imagine creating any kind of devices using my tinkering ability.” This comes as no surprise, since even most female engineering majors have little to no prior tinkering experience and women have greater self-efficacy when tasks require working with people rather than working with things [50]. A somewhat different pattern emerged for the four male students. When asked to describe his tinkering self-efficacy at the beginning of the course, Frank, a master’s degree student in physics education, provided a one word answer, “low.” The class experiences did not alter his perception of his tinkering ability. In response to the same post-assessment question, he said, “I am not very confident in my tinkering 180

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skills.” In contrast, George started class with moderate tinkering self-efficacy. He said “I like to tinker with tools, materials (wood, metal, erector sets, and science lab materials). OK self efficacy.” By the end of the course he said, “I have a great deal of tinkering efficacy. I will accept the risks and I don’t mind embarrassing myself. I’ve made a career out of that it seems.” As with Beth, risk seems to be related to George’s tinkering self-efficacy. However, unlike Beth, the course experiences seem to have had a stronger impact on George’s tinkering self-efficacy so that he is willing to accept the risks involved while she is not. The differential effect of the course may be related to the timing of the course as an  intervention. According to Knight, Carlson, and Sullivan, it is difficult at the college level to close the gender gap in which males express more confidence than females [51]. They recommended longer-term interventions as well as earlier interventions. They found, as we did, that interventions can close some, but not all, of the gender gap and that the effects of interventions were greater for females than males. Eric had an engineering background and had very high tinkering self-efficacy at the beginning of the class. Unlike most of the females, tinkering is not just a means to an end for him. For Eric, the purpose of tinkering is to tinker and the pleasure is intrinsic. It satisfies his curiosity. He started the course by saying, “Very confident. I’ve taken apart many things just to see how they work.” He ended the course by repeating his earlier sentiments—“Very high, I like nothing better than to pull something apart or put things together to see how they work.” Clearly Eric’s prior experience as a successful engineer was responsible for his high self-efficacy and reflects the view of engineering as an end in itself as described by Dean Grasso [26]. The fourth male, David, also started with high tinkering self-efficacy saying: “I love ripping apart stuff. I give myself a 10 of 10 on this. I give myself an 8 of 10 on putting it back together. The amount of parts left over is directly proportional to how badly it works after I get hold of it.” His response on the post-assessment about his tinkering self-efficacy was “the same” referring to his earlier responses. Given the strength of David’s comments, it may not be possible to have higher tinkering self-efficacy. His comments, like those of George, also seem to indicate that David is willing to risk not getting it right. And, like Eric, there is a strong element of pleasure in tinkering not found among the responses of the women. David could be the exemplar for high tinkering selfefficacy. According to Pajares, willing to risk “not getting it right” is characteristic of individuals with high self-efficacy [52]. They rebound quickly after failure and attribute failure to lack of knowledge or effort rather than some inherent personal deficiency. They also have an intrinsic interest in challenging activities which provide them with pleasure. In contrast to the women, Eric, George, and David liked to tinker for the intrinsic pleasure of tinkering. Eric and David liked to take things apart with their hands, while George also tinkers in his head. He said, “I like to think about solving design problems and visualizing what component parts I could use, how I could assemble 181

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them, and how they might work to solve a problem.” Unlike the women, context and risk-taking do not seem to be factors in the self-efficacy of these men. The impact of the course on the tinkering self-efficacy of men was less than it was for the women because we saw fewer changes in their statements over time. For Frank, the course had no effect on his low tinkering self-efficacy. His statements reflected low tinkering self-efficacy at the beginning and end of the course. For Eric and David, the course had no effect because their initial tinkering self-efficacy was so high. George, who first reported his self-efficacy as “ok”, reported high tinkering self-efficacy at the end of the course. B. Technical Self-efficacy Technical self-efficacy is defined as an individual’s confidence and belief in their competence to learn, regulate, master, and apply technical academic subject matter. Using this definition, we have evidence that all of the female students expressed less technical self-efficacy than they did tinkering self-efficacy. For Beth, opportunities to engage in hands-on activities were related to technical self-efficacy, and she was aware of the difference between her perceived self-efficacy and her actual ability. At the beginning of the course she said, “My technical self-efficacy is probably much lower than my actual capability. I usually gain more confidence with additional experience.” Her statement at the end of the course indicated that context remained important to her technical self-efficacy. She stated that, “I am moderately confident technically, but I notice that my confidence level increases in a non-competitive environment.” Beth’s comments highlight the importance of the effect of competition on women. Not only does competition negatively impact women’s self-efficacy, but it is also among the top reasons women consistently give for experiencing difficulty or for opting out of science, engineering, or technology education [53]. Like Beth, Isabel also felt that context, especially competition, affected her technical self-efficacy. In addition, there appears to be a negative emotional tenor associated with competition that may explain how competition affected her technical self-efficacy. Isabel stated that: “I think I am more tinkering confident than technically savvy [sic]. I don’t get a charge out of trying to make robots go faster or farther when we are competing to find the right answer, nor do I feel confident in trying to reprogram a code I am not familiar with by just manipulating the numbers. I’d rather just have our own ramp and our own unique set of robotic variables that don’t relate to what the other robots are doing.” Like Beth, Carol’s technical self-efficacy was affected by opportunities and experiences. Her technical self-efficacy started out low but slowly improved as she engaged in design activities over the duration of the course. At the beginning of the course she said, “Actually, I like a simple description to operate something. So if there are lots of directions, I am always lost.” Mid-course she said, “With many trials I think I can do it better.” By the end of the course she expressed much 182

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more technical self-efficacy saying, “After this course, I realized that if something went wrong ‘THINK’ what are the problems before you panic.” However, she still qualified her answer by saying, “Still, I feel that if I am to understand or get confidence, I need more courses or skills.” The comments of these women again emphasize the importance of mastery experiences for developing high technical self-efficacy, as well as the time needed with a variety of successful accomplishments for this self-efficacy to develop [9]. Helen’s technical self-efficacy also seemed to increase with experience, with some  qualifiers, as the course progressed. In addition, she realized that her technical self-efficacy was something over which she had control. At the beginning of the semester she said, “Actually, I am not good at fixing in my house. I think I don’t have the ability to build or fix anything.” By mid-semester she became more confident saying “I am not bad, with my friend and a little help I can do easy mechanical stuff. I have enough technical self-efficacy.” After additional experiences that may have been more challenging than earlier ones, she expressed somewhat lower self-efficacy but did not feel helpless saying, “Before this class, I have not fixed any kind of electronic devices. This is my first time to use wires, sensors, etc. My technical self-efficacy is weak, but I realized that I could improve it.” Helen’s comments reflect several characteristics of individuals with high selfefficacy. She challenges herself and is committed to improving her technical selfefficacy. Furthermore, she sees technical activities as something to master rather than avoid [51]. Oddly, Amy, the female with the most tinkering self-efficacy, had the lowest initial technical self-efficacy, which was not affected by the course. She described her technical self-efficacy as “Mediocre at best” and repeated this self-evaluation at the middle and at the end of the course. She stated that she must “look for resources to help me” leading us to conclude that, despite what appears to be a strong science background, Amy felt that she needed to feel more technically selfefficacious. When data on technical self-efficacy for men were examined, Frank stood out because the course did not seem to improve his self-efficacy. He said his technical self-efficacy was “low” at both the beginning and end of the course. George initially felt that his technical self-efficacy was “OK, self confidence on a 1-5 scale, maybe a 3.” His later comments, like those of some of the females, reflected a lack of experience with the definite possibility of improvement (e.g. Helen, Carol, Isabel). He said: “My technical self-efficacy is not as high as my tinkering self-efficacy. I think I can follow a diagram or follow instructions for programming and key it in to a computer and transmit it to a processor. My experience in these activities is low (just this class) and that is why my confidence is lower for technical selfefficacy. Also, my knowledge about how electronic components work (what they do, how I  decide which ones are correct according to voltage or current) is not strong. Perhaps if I were to acquire more electrical engineering knowledge and more experiential skill, my technical self-efficacy would rise.” 183

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Although he reports low technical self-efficacy, George is on his way to developing higher self-efficacy. He believes in his ability to acquire the knowledge and learn the skills needed to accomplish technical tasks, and he is engaging in self-reflective judgments needed to execute a successful course of action [52]. Both Eric and David started the course with very high levels of technical selfefficacy that remained high. Their self-efficacy was clearly related to their greater experience. Eric said, “Very confident. I’ve taken apart many things just to see how they work,” and “very high, I’m an engineer as well as a space scientist, so I am very comfortable working with technology and its products.” David commented, “Very good. I’ve actually have a lot of time logged in on electron microscopes, AFM, etc. I have a pretty good understanding of circuits as well.” The comments of all but two of the students (Amy and Frank) seem to indicate that technical self-efficacy, like tinkering self-efficacy, is strongly related to multiple opportunities to gain mastery experiences with technical tasks and that, for some women, these experiences should not be competitive. C. Societal Relevance of Engineering In her article, “Engineering a Warmer Welcome for Female Students,” Farrell states that the challenge for undergraduate engineering education is to show that mathematics and science have societal value and relevance [53]. This lack of connection to relevant experiences in students’ lives is also a criticism of the way science is taught in the K-12 system [54]. However, if the curriculum integrates humanistic and socially relevant content, students will acquire better attitudes toward science and STEM careers [55] and learning is enhanced by linking the abstract ideas of science to real-world applications [56]. Thus, there are good reasons for engineering educators to be concerned about students’ understanding of the societal relevance of science and engineering and the relationship of science to technology. In our study, all of the men had a good understanding of the relationship between science and technology (engineering). As the course progressed, their understanding expanded to include a greater number of commonalities between science and technology, the societal impact of science and technology (engineering), and the reciprocal relationship of science and technology (engineering). The women’s initial understanding varied from incorrect or simple to quite good. The one female student (Amy) with an incorrect understanding did not have her views changed by her experiences. She answered the question about the relationship of science and technology by saying “It is merging designing an item and making it work.” She gave the same answer at the end of the course. She did not grasp the reciprocal nature of science and technology nor any commonalities. Instead, she focused on design and did not elaborate her ideas. At the end of the course, the other three women’s understanding became more complex with a greater emphasis on the societal impact (e.g., help make the world better), the reciprocal nature of science and technology, 184

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and the commonalities between the two fields. There was a consensus among the females that they looked at the built-world differently. One female in the focus group  said “my attitudes toward mechanical things in everyday life changed positively.” A recurring theme in all students’ responses was an understanding that technology was more than just computers. Our previous work with a large-scale survey indicated that teachers have a narrow view of technology and that they see technology as limited to computers [39]. Thus, one of the accomplishments of the course was to introduce teachers to the broader nature of technology as a whole and to open their eyes to the technology in the world around them. There were some gender differences evidenced in the reflection responses. All of the women noted that there were both positive and negative societal effects of technology. For example, Isabel wrote about a positive impact of technology stating that “Auto flush toilets and water faucets benefit people with disabilities, convenience and ease of use.” On the other hand, Helen wrote about possible negative effects of technology stating that “My concern is that increasingly powerful technologies of the future will almost without a doubt create extremely dangerous impacts on society unless these technologies are controlled.” Carol saw both the positive and negative impacts of technology in the spaceship Challenger disaster. She wrote that “Engineers carefully studied the separation of a piece of insulation … engineers sought to analyze what would happen if the debris had caused the loss of tile.…Working to rule out a possible cause of the disaster.” Baker and Leary also identified this theme of negative and positive impacts of science and technology in their interviews with female students in grades K-12 [16]. For example, the female students were concerned about building bombs and the destruction associated with bombs. Amy saw the societal relevance of science and engineering somewhat differently. She called technology the “Knowledge of society,” and thought of technology as a tool to understand society, She wrote that “Technology could be used to go into depth and really study a society, what was going on at the time, in order to create what they did.” She took a historical/sociological perspective and linked an understanding of technology to a characteristically female interest in people. This was not unexpected. Viewing science and technology in terms of its impact upon people, animals, plants or the earth and rejecting physical science and the products of physical science (e.g. bombs) has been found previously in Baker and Leary’s work among girls in grades K-12 [16]. Men focused on the relationship of science and technology instrumentally, with science as the more important partner. Frank and Eric thought that scientific knowledge was necessary to understand technology. Frank, as a physics instructor, was concerned that students did not know what makes most technology work and thus could not make decisions about how technology affects society. He wrote: “Little attention is paid on the subject of helping students to understand the science behind technology… one cannot fully understand an item of technology unless the 185

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logic behind it is understood. Modernity consists of a complex web of technology, knowledge and issues. The citizen of a free society must be prepared in several ways to make informed decisions. The first thing students must be knowledgeable of is how things work in science. For example, a citizen trying to understand the recent difficulties with California and its power shortage would need to understand the method of power transmission as well as its limitations.” George saw science and technology as very similar. He posited that: “…both involve testing, analyzing, evaluating, and making judgments. Science is more about understanding how natural phenomena happen. Technology involves  the applications of what we have learned in science to make tools or products to better our lives. Science is a body of knowledge (understanding of natural phenomena) and a process. Technology is a body of knowledge, a process (DET), and the creation  of  products. The two are intertwined because the technology we acquire allows scientists to gain further understanding. The further our scientific understanding of phenomena is, new functional requirements and constraints come about and the need for new technological solutions emerges. This is a reciprocal series of driving forces.” The males in our study focused on using technology as a vehicle to justify the need for understanding science and also for the effect of science on society. This is understandable since most science teaching in the recent past did not emphasize the connection between the science and technology and was, for the most part, focused on decontextualized facts and concepts [58]. All but one of the students gained a more sophisticated view of the relationships among society, science, and technology (engineering). The women became aware that technology (engineering) was all around them and had both negative and positive impacts on society. The men emphasized understanding the science behind technology (engineering) as a way to moderate negative societal impacts and to promote positive societal impacts. They also thought of technology as means to an end. Technology was a vehicle to justify the need for understanding science and the effect of science on society rather than viewing technology as fulfilling the needs of society or a drive of science. Although we were quite successful with this aspect of the course, it is unclear why we failed to help Amy make the connections among society, science, and technology (engineering). Perhaps the activities and readings were not closely connected to personal concerns or to relevant experiences in this student’s life. D. Team-member Interactions In addition to the nature of the course that was designed to improve self-efficacy, team-member interactions were examined as a possible explanatory mechanism for the differential impact of the course on tinkering and technical self-efficacies. Teams were self-selected and the faculty was not involved in the process. Before the focus group there were two teams with one female and two males and one team with two 186

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females and one male. After the first focus group, where gender issues had been discussed, the teams themselves decided to disband and then reform. One new team had three males, another team had two males and one female and a third team was composed of three of the women who decided not to work with the males in the class. This was because they found the men to be too competitive and did not let the women use the equipment. For the team with two males and one female, the males realized that an all-male outlook would impede progress and that they needed a female member to be successful. The fact that the teams, as initially composed, did not function well, supports research that indicates that teamwork is not always a positive experience and does not always result in productive work [59]. In particular, Tobin [60] found gender inequities in access to laboratory equipment while working in groups, while Guzzetti and Williams [22] found that females preferred working in all female groups. In particular, an emphasis on competition among students within teams, between teams, or among STEM practitioners has often been pointed to as a barrier to female participation in science by science educators and scientists alike [61, 62]. These multiple-team configurations provided the opportunity to observe allfemale, all-male, and mixed-gender teams. The first focus-group discussion helped the men become aware of the reasons for some of the women leaving the mixedgender teams to form an all-female team. Subsequently, we observed that the men tried to be more sensitive to the needs of the woman who remained in their team by insuring that she had opportunities to use tools and test the devices. Everyone indicated, in a subsequent focus-group discussion, that they were very pleased about how the feedback about teams was handled regarding the roles that had been taken by the men and the women in the class. The men were much more aware of who undertook which role in a team and the importance of making sure everyone had input into projects, especially input from the women. The women also became more aware of the passive traditional roles they had taken by allowing the males to do the tinkering and technical tasks that made them uncomfortable. One woman in a focus group said that “when we changed the gender, we had to focus on our own hesitations.” Classroom observations indicated that the all-female team and the mixed-gender teams worked more slowly and cooperatively than an all-male team. The all-female team made sure that all members had an opportunity to use the equipment and tools.  The members of the all-male team worked less cooperatively, and not all members of the mixed-gender team had equal access to building their project and thus had fewer tinkering experiences. Some of the problems the teams initially encountered could have been our fault as instructors. We had assumed that we did not need to provide instruction on effective ways to work in teams. This was because all of the students had been exposed to team-based activities and were familiar with the literature on teamwork through other graduate courses. Additionally, most of the students had 187

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used team activities in their own teaching. Our assumption proved to be wrong and provides evidence that transfer of knowledge is extremely difficult and that what is learned in previous courses is not always retained. We should have provided more guidance on effective teaming since research indicates that instructing students in cooperative behaviors in teams has a positive impact on the learning experience of everyone in the team [63, 64]. In addition, the guidance would have served as reinforcement of cooperative behaviors leading to greater retention and better teaming. Eric’s reflections show he was aware of inequities in team interactions when he wrote: “The paper (class reading assignment) was a vivid demonstration of the gender inequity present in our schools and in our society. In two of the three groups described [in the reading assignment], the girls tended to accept the boys as the tool leaders and were content to serve as recorders of data and readers of directions. I have, incidentally, noticed this behavior in my own working group in this class!” David’s reflections support these conclusions for the all-male team interactions. He wrote: “A good example of the male team struggling for ownership over the design process is when Eric, Frank, and I all clunked heads trying to manipulate our robot. I know learning hurt mentally, but I now add physically to the list. Meanwhile, the all female team had a plan laid out and were already testing hypotheses of the problems. We were just pulling something off and seeing if that made a difference (without applying much thought to it). The males were acting more than thinking, while (it seems) the females were planning more than acting.” The increased awareness on the part of students in this course on the effects of gender on team-member interactions suggests that readings, discussions, and selfreflection can change behavior. Both female and male students became aware of how their roles in teams contributed to or detracted from the team’s success. Given the increased emphasis on teamwork in industry and teamwork in engineering education, it would be interesting to research what the impact of tinkering and technical self-efficacy might be on male/female team-member interactions in teambased activities. The results of such research could reinforce the importance of the ABET requirement for teamwork as a way to maximize benefits to students. It could also provide useful information for curricular reform and efforts to retain women in engineering. VI. CONCLUSIONS AND IMPLICATIONS

A. Conclusions This study makes two important contributions to the literature. First, it demonstrates that tinkering self-efficacy and technical self-efficacy are malleable and can be improved in women who are provided with the appropriate educational experiences. Second, it documents the kinds of educational experiences that are most likely 188

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to bring about changes in these self-efficacies and also an understanding of the societal relevance of engineering. Taken together, the contributions of this study add to the literature on factors that could positively impact the participation of women in STEM careers. For engineering specifically, this work has addressed two significant areas omitted in previously NSF-funded projects designed to increase female participation and have been identified as neglected in previous research on interventions. The course was different in that it also emphasized affective aspects of engineering while still using the design process but with participants who were university students and who were also practicing teachers [65]. The course was also part of the regular curriculum rather than a weekend or summer program. The results of this study indicate that a course designed to increase tinkering and technical self-efficacies can make a difference on attitudes, especially among women if they can work on design projects that require them to build prototype real-world artifacts and refine their designs in a non-competitive environment. The course had a positive impact on the two women who started with low tinkering self-efficacy as well as the four women who reported low technical self-efficacy or confidence lower than their ability. By the time these women responded to the post-test and delayed post-test, all but one reported an increase in technical selfefficacy. The women attributed the increases in their tinkering and technical selfefficacies to  opportunities to tinker in a non-competitive environment. We agree and also attribute the increases to the development of students’ awareness of their roles in teams and of gender with respect to the nature of team-member interactions. Although the sample size was limited and we exercised caution with the generality of conclusions, we believe that this research provides insights for the design of interventions or courses for teachers who have as their goal infusing engineering into the K-12 curriculum. This is especially so, since we know that teachers’ selfefficacy beliefs in relation to technology (engineering) influence whether they will use technology (engineering) content and activities in their teaching [66]. It has also been shown that self-efficacy beliefs also influence other classroom practices, including how teachers plan and make instructional decisions [67]. Variously, men started the course with low, moderate, and high tinkering and technical self-efficacies. The course was not an effective intervention for the one man (Frank) who reported low tinkering self-efficacy. However, it did improve George’s moderate tinkering self-efficacy. There was no reported change in the men’s technical self-efficacy at the time of the delayed post-test. For two of the men (David and Eric), there was clearly a ceiling effect. It was probably not possible to increase the self-efficacies of these two extremely confident men. The lack of impact on George for tinkering self-efficacy and on Frank and George for technical selfefficacy could be the result of the team-member interactions or some other factor. Nevertheless, despite a lower amount of change reported among the males than among females, everyone noted that the primary strength of the class was “playing with the sensors and stuff.” Such activities are characteristic of the instructional design that closely follow the learning cycle format, as previously described. This 189

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instructional model has been found to have a positive impact on attitudes toward science and may be responsible, in part, for the positive responses to the course [45]. Our pre-course data support the research literature that reports that women, even women with strong science or engineering backgrounds, lack technical selfefficacy and tinkering self-efficacy [7, 33]. This also holds true for some of the men in this study whose expertise was in physics and biology but who lacked experience in an engineering context. Our data also support the positive value of tinkering and technical experiences in a low risk, non-competitive atmosphere that contributes to the effectiveness of the intervention to increase self-efficacy. The course was equally effective in improving both the men’s and women’s understanding of the relationship of science and technology (engineering), although in different ways. With this particular group of participants, women’s lesser understanding of the relationship of science and technology (engineering) can be directly related to having fewer experiences in this realm. After the course, however, women more clearly saw the societal relevance and impact of engineering than did men. Women wrote about this relationship in terms of positive and negative impacts on society, which reflected their interest in and concern for the societal relevance of science and technology (engineering). Men had a more instrumental view and were more concerned about the structural and reciprocal relationship of science and technology (engineering) and interpreted relevance in terms of how one was needed to understand the other. These differences in viewing the societal relevance and impact of engineering, as well as the relationship between science and technology, are rooted in differences in the way men and women deal with knowledge and learning. Women tend to be “connected knowers” who understand through context and relationships among people [68]. Men, on the other hand, base their knowledge on logical systems [69]. Until these differences are accommodated in science and engineering curricula in the K-12 and university education systems, females in STEM education and careers will continue to be underrepresented. In addition to the limited sample size, other limitations of this study are also acknowledged. The students involved in the DET course had good-to-excellent backgrounds in science. They were predisposed to like the class because they were taking the course as an elective. All students, except Frank, had prior experience with one of the instructors and knew that they would be in a low-threat environment where they could fail or admit to limited tinkering and/or technical self-efficacy in their reflections. Whether the same kinds of results can be obtained with larger samples, with students with more limited science backgrounds, or in situations where the perceived risk level is higher, needs additional research. If this intervention was replicated with different types of activities or in a different learning environment, the results could differ. This is because learning environments that are interactive, cooperative, experiential, and learner-focused create comfortable STEM learning environments for both women and men and are  more likely to result in positive outcomes compared to learning environments that lack these characteristics [70]. 190

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B. Implications for Practice and Research Currently, technology (engineering) and related DET topics, despite being in the National Science Education Standards, are rarely part of elementary, middle, or high school science curricula. However, we believe they should receive greater attention. Although we are not recommending that engineering faculty take a course such as the one we developed, we are recommending that they think about incorporating some of the characteristics of our course into their own classes. Our work demonstrates that using a DET framework has the potential of improving affective factors that could increase the participation of women in DET university courses and DET-related careers. The DET approach addresses psychosocial barriers by providing opportunities to improve self-efficacy and by making science and technology (engineering) relevant and connected to societal issues, which is important to women. The DET approach creates more context and opportunities for tool use than do the science curricula currently found in schools. The DET approach also shares many of the characteristics of feminist and liberative pedagogies [71-73] such as the creation of community, reflection, independent inquiry, and connection [74]. As noted by one of our students, the steps in the design process are nearly identical to the description of inquiry in the national standards, with the difference being an engineering context. Furthermore, the DET approach and focus require a deep understanding of the foundational science and mathematics underpinning engineering, which reinforces the relevance and importance of learning science and mathematics. Even though we are not advocating stand-alone DET courses, infusing DET content and activities into current curricula has many barriers. First, schools must take a more balanced approach to the National Science Education Standards and devote time to teaching Standards E and F, which address issues of technology and society. This will foster an infusion of DET content and activities into the curriculum. Second, assessment of students must reflect this shift, since it drives the curriculum—teachers teach what will be tested. Third, teacher preparation should include DET content and activities. Here we recommend identifying commonlyused activities and labs that have a strong technology component and bringing that component to the foreground by helping teachers see the links between science and technology and society. Fourth, educators and policy makers need to be informed of the value of infusing DET into the science curriculum. This is not only for increasing technical and tinkering self-efficacies, but also for the development of a literate citizenry who can make informed decisions about the societal relevance and impact of engineering innovations on their lives. This study provides valuable insights on a limited sample size and would have greater validity if it were replicated with a larger and more diverse sample. It would then be possible to determine if the relationships we observed could be generalized to other groups of students, especially underrepresented populations, who are less positively inclined toward science and engineering careers. Furthermore, the types 191

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of DET activities that will bring about the greatest change in promoting equity in the technical workforce needs to be investigated. Students in this course explored sensors, activators, and microprocessors, but there are other tools and technologies that could be employed. We worked primarily within a physical science context and  did not explore links to the biology curriculum or use a context of a more gender-friendly field of engineering, such as bioengineering. Perhaps a biological linkage to engineering would have had an even greater impact on women. We also express concern about men who were unchanged by their experience in this course. This study also provides some insights for university engineering programs concerned about supporting and retaining women in their engineering programs, as well as reforming their curricula. The structure and philosophy of a DET course for teachers could be replicated at the undergraduate level and appropriate activities could be developed that address the sources of self-efficacy in an engineering context. An emphasis could be placed on selecting activities that promote mastery and  positive affective states (e.g. reducing competition), as well as developing strategies that could be used by instructors that were effective forms of verbal persuasion. We undertook this research because we believe that increasing the number of women, as well as men, interested in engineering careers is everyone’s job, whether it is an individual who teaches in the K-12 system or a faculty member at a university. Neither teachers in the K-12 system nor faculty at a university can do this job alone. Consequently, we recommend greater school/university partnerships and collaborations that are grounded in sound research in the educational, sociological and psychological literature and that can address the root problems of recruitment, retention, and achievement. We also recommend more in-depth and systematic studies of the effects of interventions and other activities in order to enhance and build the research base on the effectiveness of different approaches to creating interventions. Others who wish to develop a course similar to the one described in this study can build on the foundation we have laid with additional information available on a Web site that presents papers and information about the course organization, activities, and readings [75]. The original program solicitation for the original NSF Bridges for Engineering Education now has a Web site [76]. The course design should generally be applicable to pre-college teachers across the country since it was based upon teachers’ needs determined by the survey of practicing K-12 teachers who were located across the state of Arizona [39]. The course design also drew upon the content about society and technology in the National Science Education Standards [40], which are widely accepted and are strongly supported by the research literature and provide information on the types of activities that build tinkering and technical self-efficacies. The primary barriers to offering such a course are determining where it best fits in a science education program as well as the associated barrier of how to convince science education faculty of the value of such a course in their curriculum unless, of course, if it is offered as an elective. In general, creating such a 192

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new course is not necessarily difficult, but modifying a curriculum is. Clearly, there is much work to be done in effectively communicating the importance of the type of course presented here and the associated factors discussed in this article to faculty and administrators both in education and in engineering. ACKNOWLEDGMENT

The research was supported with NSF funding from the Bridges for Engineering Education Program (EEC0230726). NOTE 1

Originally published as Baker, D., Krause, S., Yasar, S., Roberts, C. & Robinson-Kurpius, S. (2007). An intervention to address gender issues in a course on design, engineering and technology for science educators. Journal of Engineering Education, 96, 213–226. Reprinted here with permission.

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CHAPTER 7 [16] Baker, D., and R. Leary, “Letting Girls Speak Out About Science,” Journal of Research in Science Teaching, Vol. 32, 1995, pp. 3–27. [17] National Science Board, Science and Engineering Indicators 2004, Vol. 2, Arlington, VA: National Science Foundation Publications, 2004. [18] National Research Council, To Recruit and Advance, Washington, DC: National Academy Press, 2006. [19] Adamson, B., M. Foster, M. Roark, and D. Reed, “Doing a Science Project: Gender Differences in Childhood,” Journal of Research in Science Teaching, Vol. 35, 1998, pp. 845–857. [20] Andre, T., M. Whigham, A. Hendrickson, and S. Chambers, “Competency Beliefs, Positive Affect and Gender Stereotypes of Elementary Students and Their Parents about Science versus Other School Subjects,” Journal of Research in Science Teaching, Vol. 36, 1999, pp. 791–747. [21] Carlone, H., “The Cultural Reproduction of Science in Reform-based Physics: Girls Access, Participation and Resistance,” Journal of Research in Science Teaching, Vol. 41, 2004, pp. 392–414. [22] Guzzetti, B., and W. Williams, “Gender, Text, and Discussion: Examining Intellectual Safety in the Science Classroom,” Journal of Research in Science Teaching, Vol. 33, 1995, pp. 5–20. [23] O’Hara, S., “Freshman Women in Engineering: Comparison of Their Backgrounds, Abilities, Values, and Goals with Science and Humanities Majors,” Journal of Women and Minorities in Science and Engineering, Vol. 2, 1995, pp. 33–47. [24] Sax, L. “Retaining Tomorrow’s Scientists: Exploring the Factors that Keep Female College Students Interested in Science Careers,” Journal of Women and Minorities in Science and Engineering, Vol. 1, 1994, pp. 45–61. [25] Grandee, J., “Gender Differences in the Experiences, Achievements, and Expectations of Science and Engineering Majors,” Journal of Women and Minorities in Science and Engineering, Vol. 3, 1997, pp. 119–143. [26] Sanoff, A., “Competing Forces,” Prism, Vol. 15, No. 2, 2005, pp. 24–29. [27] Loftus, M., “Circle Support,” Prism, Vol. 15, No. 2, 2005, pp. 40–43. [28] Baumert, J., R. Evans, and H. Geiser, “Technical Problem Solving Among 10-year-old Students as Related to Science Achievement, Out-of-School Experiences, Domain-specific Control Beliefs, and Attribution Patterns,” Journal of Research in Science Teaching, Vol. 35, 1998, pp. 987–1013. [29] Wood, S., “Perspectives on Best Practices for Learning Gender-inclusive Science: Influences of Extracurricular Science for Gifted Girls and Electrical Engineering Women,” Journal of Women and Minorities in Science and Engineering, Vol. 8, 2002, pp. 25–40. [30] Haussler, P., and L. Hoffman, “An Intervention Study to Enhance Girls’ Interest, Self-concept, and  Achievement in Physics Class,” Journal of Research in Science Teaching, Vol. 39, 2002, pp. 870–888. [31] Marra, R., M. Schurman, C. Moore, and B. Bogue, “Women Engineering Students’ Self-efficacy Beliefs—The Longitudinal Picture,” (CD) Proceedings, 2005 American Society for Engineering Education Annual Conference and Exhibition, 6 pages. [32] Crismond, D. “Learning and Using Science Ideas When Doing Investigate-and-Redesign Tasks: A  Study of Naive, Novice, and Expert Designers Doing Constrained and Scaffolded Design Work,” Journal of Research in Science Teaching, Vol. 38, 2001, pp. 791–820. [33] Margolis, J., and A. Fisher, Unlocking the Clubhouse: Women in Computing. Cambridge, MA: The MIT Press, 2002. [34] Carlson, L., and J. Sullivan, “Exploiting Design to Inspire Interest in Engineering across the K-12  Engineering Curriculum,” (CD) Proceedings, 2003 Frontiers in Education Conference, Institute of Electrical and Electronic Engineers (6 pages). [35] Sullivan, J., D. Reamon, and B. Louie, “Girls Embrace Technology: A Summer Internship for High School Girls,” (CD) Proceedings, 2003 Frontiers in Education Conference, Institute of Electrical and Electronic Engineers (6 pages). [36] Zeldin, A., and F. Pajares, “Against the Odds: Self-efficacy Beliefs among Women in Mathematical, Scientific, and Technological Careers,” American Educational Research Journal, Vol. 73, 2003, pp. 215–246. [37] National Academy of Engineering. Educating the Engineer of 2020: Adapting Engineering Education to the New Century. Washington DC: The National Academies Press, 2005.

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AN INTERVENTION TO ADDRESS GENDER ISSUES [38] Munro, M. and D. Elsom, Choosing Science at 16: The Influence of Science Teachers and Career Advisors on Students’ Decisions About Science Subjects and Science and Technology Careers, National Institute for Careers Education and Counseling, Cambridge, England: Sheraton House, 2000. [39] Yasar, S., D. Baker, S. Robinson-Kurpius, S. Krause, and C. Roberts, “Development of a Survey to Assess K-12 Teachers’ Perceptions of Engineers and Familiarity with Design, Engineering and Technology,” Journal of Engineering Education, Vol. 95, No. 3, 2006, pp. 205–216. [40] National Research Council, National Science Education Standards, Washington, DC: National Academy Press, 1996. [41] American Association for the Advancement of Science, Science for all Americans, New York, NY: Oxford Press, 1989. [42] Ramey-Gassert, L., M.G. Shroyer, and J. Staver, “A Qualitative Study of Factors Influencing Science Teacher Self-efficacy of Elementary Level Teachers,” Science Teacher Education, Vol. 80, No. 3, 1996, pp. 283–315. [43] Dominick, P., J. Demel, W. Lawbaugh, R. Freuler, G. Kinzel, and E. Fromm, Tools and Tactics of Design, New York, NY: John Wiley and Sons, 2001. [44] Penner, D., N. Giles, R. Lehrer, and L. Schauble, “Building Functional Models: Designing an Elbow,” Journal of Research in Science Teaching, Vol. 42, 1997, pp. 125–143. [45] Cavallo, A., and A. Laubach, “Students’ Science Perception and Enrollment Decisions in Differing  Learning Cycle Classrooms,” Journal of Research in Science Teaching, Vol. 38, 2001, pp. 1029–1062. [46] Tesch, R., Qualitative Research: Analysis Types and Software Tools, New York, NY: Falmer, 1990. [47] LeCompte, M., and J. Preissle, Ethnography and Qualitative Design in Educational Research, San Diego, CA: Academic, 1993. [48] Gall, J., M. Gall, and W. Borg, Applying Educational Research, Boston, MA: Allyn and Bacon, 2005. [49] Bandura, A., “Self-efficacy: Toward a Unifying Theory of Behavioral Change,” Psychological Review, Vol. 84, 1977, pp. 191–215. [50] Whiston, S., “Self-efficacy of Women in Traditional and Non-traditional Occupations: Differences in Working with People and Things. Journal of Career Development, Vol. 49, 1993, pp. 175–185. [51] Knight, D., L. Carlson, and J. Sullivan, “Gender Differences in Skills Development in Hands-on Learning Environments,” (CD) Proceedings, 2003 Frontiers in Education Conference, Institute of Electrical and Electronic Engineers (6 pages). [52] Pajares, F. “Self-efficacy Beliefs in Academic Contexts: An Outline. http://www.emory.edu/ education/mfp/efftalk.html (accessed 11/16/06). [53] Farrell, E., “Engineering a Warmer Welcome for Female Students,” Chronicle of Higher Education, Vol. 48, 2002, A31. [54] Cronin, C., and A. Roger, “Theorizing Progress: Women in Science, Engineering and Technology,” Journal of Research in Science Teaching, Vol. 36, 1999, pp. 637–661. [55] Roychoudhury, A., D. Tippins, and S. Nichols, “Gender Inclusive Science Teaching: A Feminist Constructivist Approach,” Journal of Research in Science Teaching, Vol. 32, 1995, pp. 897–924. [56] Mason C., and J. Kahle, “Student Attitudes Toward Science and Science Related Careers: A Program  Designed to Promote a Stimulating Gender-free Learning Environment,” Journal of Research in Science Teaching, Vol. 21, 1988, pp. 25–39. [57] Musil, C., Gender, Science and the Undergraduate Curriculum: Building Two-Way Streets, Washington, DC: Association of American Colleges and Universities, 2001. [58] National Research Council, National Science Education Standards, Washington, DC: National Academy Press, 1996. [59] Felder, R., G. Felder, M. Mauney, C. Hamrin, and J. Dietz, “A Longitudinal Study of Engineering Student Performance and Retention III: Gender Differences in Student Performance and Attitudes,” Journal of Engineering Education, Vol. 84, 1995, pp. 152–163. [60] Tobin, K., “Differential Engagement of Males and Females in High School Science,” International Journal of Science Education, Vol. 10, 1988, pp. 239–252.

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CHAPTER 7 [61] Howes, E. Connecting Girls and Science, New York, NY: Teachers College Press, 2002. [62] Rosser, S., Re-engineering Female Friendly Science, New York, NY: Teachers College Press, 1997. [63] Cohen, E., “Restructuring the Classroom: Conditions for Productive Small Groups,” Review of Educational Research, Vol. 64, 1994, pp. 1–35. [64] Lew, M., D. Mesch, D. Johnson, and R. Johnson, “Positive Interdependence, Academic and Collaborative Skills, Group Contingencies, and Isolated Students,” American Educational Research Journal, Vol. 23, 1986, pp. 476–488. [65] American Association of University Women, Under the Microscope, Washington, DC: American Association of University Women Education Foundation, 2004. [66] Albion, P., “Self-Efficacy Beliefs as an Indicator of Teachers’ Preparedness for Teaching with Technology,” http://www.usq.edu.au/users/albion/papers/site99/1345.html, (accessed Nov 22, 2006). [67] Pajares, F., “Teachers’ Beliefs and Educational Research: Cleaning Up a Messy Construct,” Review of Educational Research, Vol. 62, 1992, pp. 307–332. [68] Belenky, M., Women’s Ways of Knowing: The Development of Self. Voice and Mind, New York, NY: Basic Books, 1986. [69] Anderson, V., “How Engineering Education Shortchanges Women,” Journal of Women and Minorities in Science and Engineering, Vol. 1, 1994, pp. 99–121. [70] Seymour, E., “The Loss of Women from Science, Mathematics, and Engineering Undergraduate Majors: an Explanatory Account,” Science Education, Vol. 79, 1995, pp. 437–473. [71]  Rosser, S., Re-Engineering Female Friendly Science. New York, NY: Teachers College Press, 1997. [72] Rosser, S. and B. Kelly, “From Hostile Exclusion to Friendly Inclusion: USC System Model Project for the Transformation of Science and Math Teaching to Reach Women in Varied Campus Settings,” Journal of Women and Minorities in Science and Engineering, Vol. 1, 1994, pp. 29–44. [73] Tobias, S., They’re Not Dumb, They’re Different: Stalking the Second Tier, Tucson, AZ: Research Group, 1990. [74] Eschenbach, E.A., E.M. Cashman, A.A. Waller, and S.M. Lord, “Incorporating Feminist Pedagogy into the Engineering Learning Experience,” (CD) Proceedings, 2005 Frontiers in Education Conference, Institute of Electrical and Electronic Engineers (6 pages). [75] Bridging Engineering and Education: Design, Engineering, Technology Expansion for K-12 Educators, http://www.eas.asu.edu/~cme/ASUBEE/documents.htm (accessed Feb. 26, 2007). [76] National Science Foundation, Bridges for Engineering Education program, http://www.nsf.gov/ publications/pub_summ.jsp?ods_key=nsf03561, (accessed Feb 26, 2007).

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WHAT WORKS Using Curriculum and Pedagogy to Increase Girls’ Interest and Participation in Science and Engineering

WHY I CONDUCTED THE STUDY

I was contacted by Drs. Okhee Lee and Cory Buxton, the editors for a proposed issue of the journal Theory into Practice. Each issue of Theory into Practice addresses a current topic in education developed by a guest editor or editors. The topic selected by Drs. Lee and Buxton was equity and diversity in science education. Specific attention was given to questions about who the students in our classrooms are and how we should teach them so that science is both relevant and accessible. Since the focus of the issue was equity and diversity, authors wrote about identity, race, gender, culture, language, special needs, ethnicity, religion, social class, family, and community. My specialty is gender, so I addressed the research about the aspects of organizational structure, curriculum, and the pedagogy that was most effective in fostering girls’ participation and achievement in science. METHODOLOGICAL DECISIONS

Methodological decisions were straightforward. Since this was a synthesis of research about what works, I did a search of the research literature using some of my earlier work as a starting point. I searched in science, computer, early childhood, and engineering education, education in general, and psychology. I selected studies to include in the review only if they had hard data about impact. From these studies, I chose the ones that had clear robust outcomes. I endeavored to address some of the myths about effective organizational structures, curriculum, and pedagogy. For example, I laid out the research, given the constraints of length, for the lack of impact of single-sex schools and classrooms and the negative impact of using fictional literature in teaching science. Finding research related to young children and science was difficult. Not much had been written and science plays a small role in early childhood education programs and early childhood teacher education. I was mindful of my audience and the goals of the journal as I wrote. The journal strives to be both scholarly and practical. To meet these goals, I wanted to present as clear a picture as possible of what the research said without the usual tentativeness of most academic reports of research. Thus, the studies I selected had to have broad generalizability. Organizational structures, curriculum, or pedagogy that worked in 197

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only very limited contexts did not make it into the review. I also made sure to describe organizational structures, curriculum, or pedagogy in such a way as to be useful to practitioners and to make specific recommendations for their implementation. For example, when I wrote about role models I indicated who the role models should be, how they should be used, and their impact on girls. SCIENCE EDUCATION AT THE TIME OF THE STUDY

There were several special issues in science education journals that were representative of the research of the time. One special issue in 2013 in the Journal of Research in Science Teaching focused on discipline centered postsecondary undergraduate science education research and a second special issue focused on culture. Two special issues in 2012 focused on large scale interventions and on assessment. As a consequence, a large number of publications appeared on these topics. Other areas that received substantial attention were teacher knowledge, including pedagogical content knowledge; argumentation, writing in science, learning progressions, conceptual understanding and conceptual change, computerbased instruction, especially simulations; and self-efficacy. Despite the special issue on undergraduate education, no study focused on the university instructor or the community college. Most studies, regardless of content area or grade level, examined the student. Studies situated in high school and elementary schools were more common than those in middle/junior high school. Biology and physics/physical science were the topics studied the most followed by chemistry. Studies of Earth science and astronomy topics increased from earlier years but still lagged far behind other content areas. Gender was superseded by concerns about culture, language, and ethnicity as reflected in the special issue on culture. Even the special issue of Theory into Practice (i.e. Diversity and Equity in Science Education) in which this article appeared included only one article about girls. Other topics appearing in the equity issue of Theory into Practice were race and ethnicity, rural issues, students with special needs, urban education, and religion. Because fewer studies were about gender in science education, there were fewer studies examining pedagogies that improved female participation and achievement in science and more studies about improving learning for underachieving students in general. For example, Sadler, Romine, Stuart, and Merle-Johnson, (2013) studied the impact of game-based curricula and found that it improved achievement for students of all academic levels; Duaer, Momsen, Speth, Makohon-Moore, and Long, (2013) studied the effect of model-based instruction to close the achievement gap between low and high achieving GPA students; and Dega, Kriek, and Mogese, (2013) compared cognitive conflict and cognitive perturbation to improve achievement. A study by Gonsalves, Rahm, and Carvalho, (2013) did look at girls interested in science topics in an out of school science club with implications for classroom instruction. 198

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One study does stand out for its in-depth look at career choices of males and females in fifty countries (Sikora & Pokroprk, 2012). Sadly, the authors found that gender segregation still exists with females preferring biology, agriculture, and health careers and males preferring careers in computing, engineering, and mathematics. The authors also found that these preference differences were stronger in industrialized countries and that males in all countries had more self-confidence in their science abilities than females. Methodological approaches used in studies showed no biases toward qualitative or quantitative approaches. Furthermore approximately 40% used both qualitative and quantitative approaches. Studies applied a wide range of techniques from observations, interviews, and case studies to HLM, path analysis, and factor analysis. The sophistication and complexity of the analytical techniques reflects the maturity of the field of science education. RESEARCH IN THE WIDER FIELD OF EDUCATION

The Review of Educational Research in 2013 contained one study that examined the impact of a garden-based curriculum with a focus on science achievement by grade level and ethnicity of school populations but not gender (Williams & Dixon, 2013). In addition, Galman and Mallozzi wrote about the disappearing focus on gender in research on the elementary school teacher (2012). The American Educational Research Journal published several studies about girls and science with attention on girls of color. Eagan, Hurtado, Change, and Garcia (2013); Wang (2013); Woolley, Rose, Orthner, Akos, and Jones-Sanpei, (2013); and Barton, Kang, Tan, O’Neill, Bautista-Guerra, and Brecklin, (2013) all looked at factors impacting enrollment in STEM majors or programs at the graduate level, high school, and middle school level. Two articles addressed pedagogy. One for promoting African American students’ interest in science (Xu & Coats, 2012) and another for improving physics instruction and attitudes (van der Veen, 2012). Although a bit late to the game, science journals were becoming concerned about girls and women in science. The March 2013 issue of the well-respected journal Nature dedicated an entire issue to women in science. The features addressed the gender gap in pay and research funding, the number of women in research groups and industry, what it is like to be a women scientist, women running labs, and having children and a science career. As with journal articles, book authors were interested in the intersectionality of gender and race. Gutierez y Muhs, Nieman, Gonzalez, and Harris (2012) in Presumed Incompetent examined the intersection of race and class for women in academia but did not specifically address women’s experiences in science departments. Bluhm, Jacobson, and Maibom, (2012) took up, once again, the issue of the biological basis of gender differences in Neurofeminsim: Issues at the Intersection of Feminist Theory and Cognitive Science. Chapters in this book addressed what neuroscience 199

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can tell us about gender differences, gender stereotypes, and the relationship between neuroscience and feminist theory. THE CULTURE OF THE TIMES

The years 2012 and 2013 were important ones for reform in science education. The National Academy Press published the National Research Council’s A Framework for K-12 Science Education: Practices, Crosscutting Concepts and Core Ideas (2012) and Next Generation Science Standards: For States by States (2013). These documents replaced Project 2061: Science for All Americans (American Association for the Advancement of Science, 1989) and the National Science Education Standards by the National Research Council that drove the previous wave of standards reform (1996). Two thousand thirteen was a good year for women in politics in the United States with the largest number of seats in congress. There were twenty women in the Senate (20%) and 79 in the House of Representatives (18.2%). It was also a good year for women of color. Mazie Hirono (D-H), an Asian Pacific Islander, served in the Senate. In the United States House of Representatives, there were 12 African American women, 6 Asian Pacific Islander women and 9 Latinas. All but 2 Latinas were Democrats. Two U.S. house delegates were also women of color (i.e. African American, Caribbean American). Party identification in 2013 revealed a gender gap. Forty percent of Democrats were women while 26% of Republicans were women (Center for American Women in Politics, Rutgers University, 2015). The National Women’s history Project (2013) chose STEM for the 2013 women’s history month theme. The theme was titled Women Inspiring Innovation Through Imagination: Celebrating Women in Science, Technology, Engineering and Mathematics (National Women’s history Project, 2013). The sixteen honorees for 2013 represented trailblazers in their respective fields from Elizabeth Blackwell, the first fully accredited woman doctor, to contemporary scientists like Helen Greiner, an engineer and roboticist, and Flossie Wong-Staal, a virologist and molecular biologist. Two thousand and thirteen was also a good year for the U.S. economy. Twitter issued an IPO and its stocks increase 133%. It was also the year that online signup for the Affordable Care Act began providing medical coverage to thousands of Americans who could not obtain medical insurance through traditional avenues. Social change was evident in 2013. One indicator of social change was that the United States Supreme Court struck down parts of the Defense of Marriage Act deciding that United States government must provide equal treatment to same-sex spouses. Two thousand and thirteen also saw the election of a new Pope with a different style. As the first Jesuit and the first Pope from Latin America, his message to the faithful emphasized social issues of poverty and a more inclusive church. On the international front, the United States and Iran began talks about Iran’s nuclear program (Wall Street Journal, Jan 14, 2014). 200

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There were also less positive events and some quite horrible events that marked 2013. Perhaps the most politically important event was Edward Snowden’s release of a secret court order that forced Verizon to turn over phone records to the National Security Agency. Snowden also released information that the National Security Agency collected account information for Google and Yahoo (Wall Street Journal, Jan 14, 2014). Some Americans saw Snowden as a patriot for these revelations and others felt he was traitor to his country. It should come as no surprise that Chancellor of Germany, Angela Merkel, was outraged at the allegations that the National Security Agency also tapped her cell phone (Rayman, 2013). Terrorist attacks and unrest continued around the world. In the United States, Tamerlane and Dzhokar Tsarnaeve, ethnic Chechens, but naturalized United States citizens, carried out a bombing of the Boston Marathon killing and maiming spectators and runners. In Egypt, democratically elected President Morisi was ousted by the military backed Abduhl Fatah el Sisi, and charged with treason, espionage, and sponsoring terrorism. El Sisi subsequently cracked down on dissidents and the Muslim Brotherhood that was led by President Morisi (Rayman, 2013, 2014). In Kenya, Islamic terrorists attacked an upscale shopping mall and killed approximately 69 people in a four day gun battle with police (Wall Street Journal, 2014). Islamic terrorists also took hostages in an Algerian oil field, Boko Haram conducted attacks in Nigeria, and attacks by Islamic rebels in the Central African Republic was labeled genocide. The Syrian civil war also saw atrocities. Evidence suggests that Syrian President Bashar Assad launched a Sarin gas attack on civilians while denying the usage of the gas (Rayman, 2013). The rise of Islamic fundamentalism has serious consequences for girls. Boko Haram means Western education is forbidden. This position has led to attacks on schools, especially girls’ schools and the kidnapping of girls from their schools (National Counter Terrorism Center, 2015). This is an extreme position vis a vis education but a look at Islamic societies in general finds that there is a patriarchal gender system that encourages early marriage resulting in a gap in educational attainment when comparing males to females (Offenhauer, 2005). The war in Syria and elsewhere has also had negative consequences for the education of all children. Conflict in the Middle East and Africa has left 50 million school age children without access to schooling. Children have been attacked to prevent them from going to school, and schools have been bombed. In Syria about 4,000 schools have been destroyed or damaged or are being used for non educational purposes. In the Central African Republic more than half of the schools have been closed by Islamic rebels. However, even when schools are open parents fear sending their children to schools (Tran, 2013). War, terrorism, and Islamic extremism in many part of the world have made increasing girls’ participation in science and engineering through effective pedagogy moot. Girls and boys in many parts of the world are being denied even the most basic education. The condition of women was in the spotlight in 2012 with protests over gang rapes in India that brought attention to women’s rights and safety in that country. 201

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In Bangladesh, the collapse of the Rana Plaza building that killed more than 1,100 individuals most of whom were women, brought attention to poor working conditions in the garment industry. In response, retailers such as Walmart, Gap, and H&M agreed to stricter standards of working conditions for overseas garment workers who are among the lowest paid in the world (Rayman, 2013). IMPACT OF MY WORK

This work speaks to teacher educators, and classroom teachers. It provided an explicit list of curriculum and pedagogy that works for girls that is evidence-based. It also provided recommendations for systemic reform in the areas of pre-service science teacher education, professional development, and policy reform. The work also emphasized that we do know how to improve girls’ participation in science. It reminded readers that there is still work to be done to increase girls’ achievement in science as well as their interest in science careers and that this work can not be accomplished unless we make a concerted effort to change what and how we teach.

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WHAT WORKS: USING CURRICULUM AND PEDAGOGY TO INCREASE GIRLS’ INTEREST AND PARTICIPATION IN SCIENCE1

This article identifies instructional strategies, curricula, and organizational structures in the research literature that have been successful in encouraging girls’ participation and achievement in science: science instruction in prekindergarten and kindergarten, relevant curricula that address girls’ interests and provide opportunities for genuine inquiry and tinkering experiences, greater emphasis on physical science and the use of computers, integration of reading and writing in science, attention to how groups are formed in classrooms, activities that build self-efficacy, appropriate role models, messages that science is for everyone, and student-centered teaching. Special attention is given to the needs of children in preschool and kindergarten. In addition, research on the impact of single-sex classrooms and grouping is reviewed, along with the use of children’s fictional literature to teach science. Implications derived from research literature include changes in what is taught, how it is taught, how teachers are prepared, and how these changes are paid for. There are many research-based instructional strategies that teachers can use to enhance achievement, self-efficacy, and participation of girls in science. Some of these strategies seem self-evident, but others go against common assumptions about what works. But first, let’s take a look at why educators are concerned about girls and science. Women now earn about half of the overall number of bachelor’s degrees in science. In contrast, they are conspicuously absent in certain subfields, including engineering, computer science, and physics. In fact, women’s share of bachelor degrees in these areas has even declined in recent years (National Science Board, 2010). Given these statistics, this literature review focuses on science in general, with an in-depth look at physics, which is a gate-keeping subject for engineering and  computer science.  An  in-depth look at these areas is warranted because engineering and computer science are growth areas with increased opportunities for employment.

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STRATEGIES TO IMPROVE ACHIEVEMENT

According to our national report card (National Assessment of Educational Progress 2010), boys outperform girls in science at grades four, eight, and twelve. This lower performance in science has the potential to put females at an economic and employment disadvantage because the rate of job growth in science, technology, engineering, and mathematics (STEM) fields is 26% greater than non-STEM fields and salaries in STEM fields are three times greater than salaries in non-STEM fields (US Department of Commerce, 2011). Thus, it is important that we encourage and prepare girls for STEM careers, especially in computer science and engineering where women continue to be underrepresented. Improving achievement requires changes in the curriculum. Standards-based thematic units, framed around a few primary concepts that address real-world experiences of interest to girls, such as investigating bicycle safety helmets to study forces and motion or investigating the relationship of physics to the human body, can make a real difference in achievement when these activities are writing intensive, involve hands-on work, and require genuine inquiry (National Science Foundation, 2003). Improving achievement also requires changes in how teachers teach. Instructional strategies that focus on the student, rather than the teacher, have been successful in narrowing the achievement gap between boys and girls, especially in the physical sciences in high school. These strategies include real-world experiences of interest to girls, student presentations to classmates, student participation in the development of rubrics to assess their own learning, and classroom interactions that value the students’ points of view (Haussler & Hoffman, 2002). In particular, increasing hands-on laboratory experiences and active involvement enhance girls’ achievement, especially in physical science (Kahle & Meece, 1994). Design-based learning, such as building an electrical alarm system to learn about electricity, also enhances achievement, especially for low-achieving middle school African American girls (Mehalik, Doppelt, & Schunn, 2008). However, for these strategies to be successful, the teacher must provide sufficient materials so that everyone can participate. Having enough materials to go around prevents some girls from being passive observers and prevents some boys from dominating the use of materials. Furthermore, the teacher must allocate enough time to complete hands-on inquiry activities, including time for revision and discussion. Providing girls with out-of-school academic activities and homework also contributes to higher achievement (Chambers & Schreiber, 2004). In addition, metacognitive self-management when reading science texts (how much of the text is understood and what to do when the text is not understood) can help girls (Spence, Yore, & Williams, 1999) if they are instructed in how to use reading strategies that focus attention on the structure and organization of the text, accessing prior knowledge of the text topic, identifying the main ideas, using context to define words, and summarizing. 204

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Different grouping strategies can be effective when the goal of grouping is to foster purposeful discussion and understanding. All-girl groups are sometimes more  effective than mixed-sex groups (Bennett, Hogarth, Lubben, Campbell, & Robinson, 2010) but, contrary to what many think, single-sex classrooms do not have an impact on science achievement (Baker, 2002). STRATEGIES TO ENHANCE CONFIDENCE, COMPETENCE, AND SELF-EFFICACY

Single-sex classrooms do have an impact on increasing girls’ confidence about their work (Rennie & Parker, 1997) and the single-sex environment creates a more comfortable atmosphere for asking questions (Streitmatter, 1999; Wollman, 1990). Design-based activities, such as building a mousetrap car that goes the farthest, also increases elementary and middle school girls’ confidence in their technological competence. Self-efficacy is a more specific form of competence/confidence. Rather than a general belief about one’s ability to be successful, self-efficacy is the belief that one can be successful in well defined areas such as science. Increasing self-efficacy in science is particularly important for girls, especially gifted girls, because they often have less belief in their competence on tasks they think of as masculine (Usher & Pajares, 2008). For example, girls often do not take courses like chemistry in high school because they fear failing and do “not wish to move out of their comfort  zone” (Cousins, 2007, p. 724) and because they have received messages from their peers that chemistry is difficult and a male subject. As a consequence, instructional strategies and curriculum that builds self-efficacy is a must. Self-efficacy can be increased for girls by providing them with opportunities to increase their mastery experiences through the successful completion of sciencerelated tasks. Girls also need to receive positive messages about their competence in science from those who count most, such as their teacher and school principal. Vicarious experiences, such as observing others like themselves (peer models) succeed at science-related tasks, is another way that teachers can increase girls’ selfefficacy in biology, physical science, and especially Earth science (Britner, 2008). Increasing girls’ self-efficacy results in the perseverance and resiliency to pursue careers in science-related fields (Zeldin & Pajares, 2000). All of these strategies work best in a threat-free environment where risk taking is encouraged. STRATEGIES TO INCREASE PARTICIPATION

Increasing participation is, in part, dependent upon developing a science identity and interest in science careers. Bringing role models into the classroom can increase girls’ science identity and interest in science careers. The most effective role models for girls are women near their own age, such as female undergraduate science majors or graduate students who can talk about their experiences in nontraditional 205

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fields (Evans & Whigham, 1995; National Science Foundation, 2003). However, before bringing role models into the classroom, the teacher should provide some coaching to insure equitable interactions with male and female students in terms of asking questions and providing feedback to questions (She & Barrow, 1997). Female teachers can also serve as role models, especially if they are seen as experts. Having a female science teacher can increase female students’ self-efficacy, identification with a domain of science, and increased commitment to science careers (Stout, Dasgupta, Hunsinger, & McManus, 2011). A curriculum that has a strong conceptual framework and is contextualized with real-world problems, rather than abstract problems, also contributes to girls’ science identity, especially in physics (Hazari, Sonnert, Sadler, & Shanahan, 2010). A  curriculum that has a strong affective component and relevant topics that addresses  girls’ concerns, such as saving the Earth and helping animals and people,  is  another way to enhance girls’ interest in science (Baker & Leary, 1995) as does a curriculum that emphasizes the aesthetics of science, technology, and engineering (Tobin, 1996). Using the learning cycle (sometimes called the five E cycle) where there is an expectation that students will find the answers to investigations and develop concepts for themselves, while working in groups, also results in girls liking science more and increases their intentions to take more science courses in the future (Cavallo & Laubach, 2001). However, to create a girls’ science identity, more than the curriculum must change. The messages sent during instruction must also build a science identity. Teachers who talk about the innate science talent of both women and men, refer to scientists as smart women and men, use gender neutral language and examples of women in science, and do not create classroom hierarchies that place boys at the top of the class send messages that science is for everyone (Carlone, 2004; National Science Foundation, 2003). Again, contrary to what many think, single-sex science classrooms do not have an impact on girls’ intentions to take more science courses in the future (Forgasz & Leder, 1996), but single sex-classrooms have increased the number of girls in some technology courses that emphasized computer programming with girl-friendly design and drawing activities (American Society of Engineering Education, 2011). In addition, single-sex classrooms provide more opportunities for girls to participate in science activities and have more interactions with the teacher than coeducational classrooms (Parker & Rennie, 2002). The conclusions one can draw from a large body of research on single-sex education is that, although it does no harm, the evidence for benefits is equivocal and context specific (Mael, Alonzo, Gibson, Rogers, & Smith, 2005). Grouping arrangements within classrooms also have an effect on increasing girls’ participation in science. Placing middle school girls in mixed-sex groups in classrooms where inquiry is the norm results in increased interest in science and intentions to take more science courses in the future (Lee & Burkham, 1996; Mathews, 2004). 206

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Future participation can also be increased by providing girls with a variety of science experiences in the early years of school (K–5). Many women scientists report that their experiences in the early grades, such as science projects and science investigations, were important in developing a life-long interest in science and choosing their science careers (Maltese & Tai, 2010). STRATEGIES FOR EFFECTIVE USE OF COMPUTERS

Volman and van Eck (2001), in their large review of the research on gender and computers, found that compared to boys, girls did less well with problem solving using computers and had less general computer literacy. They also noted that girls had  less confidence in their computer competency. In particular, girls from poor families had less interest in computers. In addition, computer-based work did not enhance girls’ achievement in physical or life science (Burkham, Lee, & Smerdon, 1997). As a consequence, Burkham et al. recommended more time and experience with computers for girls, such as exploring a Lego/logo computer building environment to increase girls’ technological confidence (Beisser, 2005). In addition, attention must be paid to the type of computer-based science work. More girls than boys perceived computers as useful tools for conducting science investigations, graphing and organizing data, and understanding concepts in greater depth (Lawrenz,  Gravely, & Ooms, 2006). These studies suggest that how much, and in what ways, the computer is used is critical in supporting girls in science. SPECIAL CONSIDERATION FOR YOUNG SCIENCE LEARNERS

Although all of the curricular changes and instructional strategies recommended in this article work equally well for elementary and secondary school students, albeit with some modifications for the younger students, there are some instructional strategies and curricular issues that are unique to preschool and elementary students. The earlier girls participate in science, the greater their interest in science is likely to be. Even girls in kindergarten engage in gender stereotyped behavior, and pre-K girls are unlikely to choose science for their free choice activities. Thus, a focus on what works for the young students is important (Patrick, Mantzicopoulous, & Samarapungavan, 2009). A popular instructional strategy with young children is to use literature, both nonfiction and fiction, as part of science instruction. However, care must be taken to choose the right combination of literature and instructional strategies. When nonfiction literature is integrated with inquiry, kindergarten girls understand science better and perceive themselves to be competent science learners. Traditional science instruction using thematic units with fictional literature leads to opposite results—less understanding, motivation, and perceived competence (Patrick et  al., 2009). Listening to books being read aloud that explore nonstereotypical male and female roles, in concert with role playing and visits from role models, 207

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can reduce even preschool children’s perception of male and female stereotypical occupational roles (Tropanier-Street & Romatowski, 1999). However, when using children’s literature, the teacher must be careful to examine the materials carefully because many contain  science content errors and misconceptions, fantasy, gender stereotyping, and anthropomorphisms (Sackes, Trundle, & Flevares, 2009). Elementary school girls like messy science and should have as many experiences as possible to engage in tinkering (National Science Foundation, 2003) and to develop competence in using tools (Kinnear, Treagust, & Rennie, 1991). Perceived science competence and liking of science is also enhanced for girls in kindergarten when they are given the opportunity to engage in inquiry activities in biology, such as  the life cycle of the butterfly (Samarapungavan, Mantizicopoulos, & Patrick, 2008). The science curriculum should also include more physical science topics because, as early as kindergarten, girls engage in more science activities in biology at home and are more interested in biology than physical science in school (Appleton, 2007). Older elementary girls also benefit from complex problems, longer wait-time, authentic assessment, and class discussions about gender-related issues (Beisser, 2005). A SUMMARY OF CURRICULUM AND PEDAGOGY THAT WORKS FOR GIRLS

We can summarize what works for girls in science as follows: 1. Early science instruction beginning in pre-kindergarten, 2. Relevant curriculum that addresses girls’ interests and provides many opportunities for genuine inquiry and tinkering experiences, 3. Greater emphasis on physical science and the use of computers, 4. Integration of reading and writing in science, 5. Careful attention to how groups are formed, 6. Activities that build self-efficacy, 7. Appropriate role models, 8. Voiced and unvoiced messages that science is for everyone, and 9. Student-centered teaching. IMPLICATIONS FOR EDUCATIONAL POLICIES

Proposed revisions of the Elementary and Secondary Education Act (No Child Left  Behind; US Department of Education, n.d.) include a greater emphasis on STEM and a focus on innovation, initial teacher preparation, and professional development. These policy changes and the research on increasing girls’ achievement and participation in science have significant policy implications. The most important implication is the need for a focus on systemic change that addresses how teachers are prepared, their roles in schools, the science curricula taught, certification and recertification requirements, and professional development. 208

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District policies regarding the number of teachers hired, the positions for which they are hired, and the nature of their responsibilities would also have to change to allow for a greater degree of specialization at the elementary and secondary levels. In addition, school funding policies would have to be rethought to support science specialists and to purchase increased amounts of materials and expendables, as well as computers, to support curricula that emphasize genuine inquiry, building and tinkering opportunities, and increased time and access to computers. Revisions to science curricula would have to include more girl-friendly topics and inquiry activities that involve real-world experiences, while still addressing standards. Finally, teachers would need professional development to learn how to teach the revised curriculum using appropriate pedagogies. However, rather than waiting for the entire system to change, teachers can jumpstart the process to increase girls’ achievement and participation in science. They (a) can increase opportunities to learn science content, particularly the physical sciences through mentoring and encouragement, (b) engage girls in genuine science inquiry, and (c) develop their pedagogical content knowledge through professional development. More specifically, early childhood and elementary teachers can become science specialists through professional development and secondary teachers can use professional development to acquire the expertise to integrate science with other content areas. High-quality professional development to increase girls’ participation and achievement in science can be obtained through opportunities provided by the National Science Teachers Association (NSTA) at regional and national conferences. In addition, the NSTA Web site includes resources for do-it-yourself learning and on-line seminars and classes. Systematic change is not easy, but teachers play a major role in bringing it about. Every journey begins with a single step and although this is a big step, it can be done. NOTE 1

Originally published as Baker, D. (2013). What works: Using curriculum and pedagogy to increase girls’ interest and participation in science. Special Issue Theory into Practice, 52, 14–20. Reprinted here with permission.

REFERENCES American Society of Engineering Education. (2011). First bell custom briefings. Retrieved January 6, 2011, from http://www.asee.org. Appleton, K. (2007). Elementary science teaching. In S. Abell & N. Lederman (Eds.), Handbook on research in science education (pp. 499–536). Mahwah, NJ: Lawrence Erlbaum Associates. Baker, D. (2002). Good intentions: An experiment in middle school single-sex science and mathematics classrooms with high minority enrollment. Journal of Women and Minorities in Science and Engineering, 8, 1–23. Baker, D., & Leary, R. (1995). Letting girls speak out about science. Journal of Research in Science Teaching, 32, 3–27.

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CHAPTER 8 Beisser, S. (2005). An examination of gender difference in elementary constructionist classrooms using Lego/Logo instruction. Computers in Schools, 22, 7–19. Bennett, J., Hogarth, S., Lubben, F., Campbell, B., & Robinson, A. (2010). Talking science: The research  evidence on the use of small group discussion in science teaching. International Journal of Science Education, 32, 69–95. Britner, S. (2008). Motivation in high school science students: A comparison of gender difference in life, physical, and earth science classes. Journal of Research in Science Teaching, 45, 955–970. Burkham, D., Lee, V., & Smerdon, B. (1997). Gender and science learning early in high school: Subject matter and laboratory experiences. American Educational Research Journal, 34, 297–331. Carlone, H. (2004). The cultural production of science in reform-based physics: Girls’ access, participation and resistance. Journal of Research in Science Teaching, 41, 392–414. Cavallo, A., & Laubach, T. (2001). Students’ science perceptions and enrollment decisions in differing learning cycle classrooms. Journal of Research in Science Teaching, 38, 1029–1062. Chambers, E., & Schreiber, J. (2004). Girls’ academic achievement: Varying associations of extra curricular activities. Gender and Education, 16, 327–346. Cousins, A. (2007). Gender inclusivity in secondary chemistry: A study of male and female participation in secondary school chemistry. International Journal of Science Education, 29, 711–730. Evans, M., & Whigham, M. (1995). The effect of a role model project upon attitudes of ninth grade students. Journal of Research in Science Teaching, 32, 195–204. Forgasz, H., & Leder, G. (1996). Mathematics classrooms, gender and affect. Mathematics Education Research Journal, 8, 153–173. Haussler, P., & Hoffman, L. (2002). An intervention study to enhance girls’ interest, self-concept, and achievement in physics. Journal of Research in Science Teaching, 39, 870–888. Hazari, Z., Sonnert, G., Sadler, P., & Shanahan, M. (2010). Connecting high school physics experiences, outcome expectations, physics identity, and physics career choice: A gender study. Journal of Research in Science Teaching, 47, 978–1003. Kahle, J., & Meece, J. (1994). Research on gender issues in the classroom. In D. L. Gabel (Ed.), Handbook on research in science teaching and learning (pp. 542–557). New York, NY: McMillan. Kinnear, A., Treagust, D., & Rennie, L. (1991). Gender inclusive technology materials for the primary school: A case study in curriculum development. Research in Science Education, 21, 224–233. Lawrenz, F., Gravely, A., & Ooms, A. (2006). Perceived helpfulness and use of technology in science and mathematics classrooms at different grade levels. School Science and Mathematics, 106, 133–149. Lee, V., & Burkham, D. (1996). Gender difference in middle grade science achievement: Subject domain, ability level, and course emphasis. Science Education, 80, 613–650. Maltese, A., & Tai, R. (2010). Eyeballs in the fridge: Sources of early interest in science. International Journal of Science Education, 32, 669–685. Mathews, B. (2004). Promoting emotional literacy, equity and interest in science lessons for 11–14 year olds: The Improving Science and Emotional Development project. International Journal of Science Education, 26, 281–308. Mael, A., Alonso, A., Gibson, D., Rogers, K., & Smith. M. (2005). Single-sex versus coeducational schooling: A systematic review. Washington, DC: US Department of Education. Mehalik, M., Doppelt, Y., & Schunn, C. (2008). Middle-school science through design-based learning versus scripted inquiry: Better overall science concept learning and equity gap reduction. Journal of Engineering Education, 97, 71–82. National Assessment of Educational Progress. (2010). The Nation’s report card: Science 2005. Retrieved October 27, 2010, http://nationsreportcard.gov/science_2005/s0110.asp. National Science Board. (2010). Science and engineering indicators (National Science Foundation Report, 10-01). Retrieved October 25, 2010, http://www.nsf.gov/statistics/seind10/. National Science Foundation. (2003). New formulas for America’s workforce: Girls in science and engineering. Arlington, VA: National Science Foundation. Parker, L., & Rennie, L. (2002). Teachers’ implementation of gender-inclusive instructional strategies in single-sex and mixed-sex science classrooms. International Journal of Science Education, 24, 881–897.

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WHAT WORKS Patrick, H., Mantzicopoulos, P., & Samarapungavan, A. (2009). Motivation for learning science in kindergarten: Is there a gender gap and does integrated inquiry and literacy instruction make a difference? Journal of Research in Science Teaching, 46, 166–191. Rennie, L., & Parker, L. (1997). Students’ and teachers’ perception of single-sex and mixed-sex mathematics classes. Mathematics Educational Research Journal, 9, 257–273. Samarapungavan, A., Mantizicopoulos, P. & Patrick, H. (2008). Learning science through inquiry in kindergarten. Science Education, 92, 868–908. Sackes, M., Trundle, K., & Flevares, L. (2009). Using children’s literature to teach standards-based science concepts in early years. Early Childhood Education Journal, 36, 415–422. She, H.-C., & Barrow, L. (1997). Gifted elementary students’ interactions with female and male scientists in a biochemistry enrichment program. Journal of Elementary Science Education, 11, 15–30. Spence, D., Yore, L., & Williams, R. (1999). The effects of explicit instruction on grade 7 students’ metacognition and comprehension of specific science text. Journal of Elementary Science Education, 9, 45–66. Streitmatter, J. (1999). For girls only: Making a case for single-sex schooling. Albany, NY: Albany State University Press. Stout, J., Dasgupta, N., Hunsinger, M., & McManus, M. (2011). STEMing the tide: Using ingroup experts to inoculate women’s self-concept in science, technology, engineering, and mathematics (STEM). Journal of Personality and Social Psychology, 100, 255–270. Tobin, K. (1996). Gender equity and the enacted curriculum. In L. Parker, L. Rennie, & B. Fraser (Eds.), Gender, science and mathematics (pp. 119–127). Boston, MA: Kluwer. Tropanier-Street, M., & Romatowski, J. (1999). The influence of children’s literature on gender role perceptions: A reexamination. Early Childhood Education Journal, 26, 155–159. US Department of Commerce. (2011). NOAA: Cultivating the next generation of STEM workers, one student at a time. Retrieved July 27, 2011, http//www.comerce.gov/. US Department of Education. (n.d.). ESEA reauthorization act blueprint for reform. Retrieved April 27, 2011, http://www2.ed.gov/nclb/landing.jhtml. Usher, E., & Pajares, F. (2008). Sources of self-efficacy in school: Critical review of the literature and future directions. Review of Educational Research, 78, 751–796. Volman, M., & van Eck, E. (2001). Review of Educational Research, 71, 613–634. Wollman, J. (1990). The advantage of same sex programs. Gifted Child Today, 13, 22–24. Zeldin, A., & Pajares, F. (2000). Against the odds: Self-efficacy beliefs of women in mathematical, scientific, and technological careers. American Educational Research Journal, 37, 215–246.

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GIRLS’ SUMMER LAB An Intervention

SYNOPSIS

Since I was not given permission by the publisher of the Journal of Women and Minorities in Science and Engineering (i.e. Begell House) to reprint Girls’ Summer Lab: An Intervention (Baker, Lindsey, & Blair, 1999) I will instead provide a synopsis of the study. Girls’ Summer Lab is an evaluation study of the impact of an intervention to support middle school minority girls’ in science. Forty-two urban  African American girls living in Texas were selected from among 200 applicants by evaluating the written portion of an application in which girls wrote about their interest in science, a desire to help others, a desire for new experiences and some indication that they anticipated learning new things. The intervention was funded by a grant from the Department of Energy and conducted and carried out by the Fort Worth Museum of Science and Technology. As a consequence, the activities planned had an energy theme and a museum theme. The activities and field trips focused on energy, electricity, fossils, and fossil fuels. The girls were also given the opportunity to work as docents in the museum. As the title indicates, the activities took place during the summer over a ten week period meeting three days a week. Demographic data was collected as well as role-specific self-concept and attitude.  Instead of choices or a Likert scale, the attitude and self-concept survey consisted of sentences to complete in which girls provided answers about their attitudes to specific aspects of the summer lab, and their feelings about themselves engaged in specific science activities. Five girls with the most positive and negative attitudes before Girls’ Summer Lab began were chosen to be interviewed at the end of Girls’ Summer Lab. A few of the activities planned by the museum staff backfired because they were based on what the staff thought the girls would like and topics that reflected the energy focus of the funding agency. One activity in particular stands out. That was a field trip to a dinosaur dig that included experience unearthing fossils. Rather than exciting the girls, the girls found the experience hot, dirty, and dull as well as very bad for their manicures! Midsummer in Texas was not a good time to introduce urban minority girls to paleontology in the field. Working with female scientists did not have a big impact and was the least successful aspects of Girls’

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Summer Lab and overall perceptions of scientists, especially negative perceptions of male scientists were not altered. On the other hand, being paid to be a museum docent was positive and empowering. As docents, the girls were able to take leadership positions, and show their mastery of content to their family, friends, and other museum visitors. They felt empowered, appreciated, and mature. They were also paid for their work as docents making the activity more authentic than some of the other activities engaged in by the girls. The girls also liked the all-girl setting and hands-on activities. The experience had a positive impact on self-concept and attitude and the girls felt that they were in charge of their own learning. WHY I CONDUCTED THE STUDY

I was contacted to study the summer lab program because one of the museum staff, Colleen Blair, had been reading my work. She called me to ask me to evaluate a program for urban, middle school minority girls that was to take place in the summer. She was greatly relieved to find out that I was a woman, and not a man, as my name might suggest. Although we had never spoken before, the conversation about the mistake of my sex, based on my name, created a bond between us and I agreed to do the study. I had long been concerned that interventions to increase girls’ interest and participation in science often lacked substantive evaluation of the impact of the efforts. Many interventions relied on quick and short surveys that did not provide a deep understanding of the impact of the experience on girls. Other interventions did not conduct any evaluations being satisfied that the intervention was provided and that girls attended. This invitation from the museum provided me with an opportunity to do more. METHODOLOGICAL DECISIONS

Colleen told me that there was money in the grant for the evaluation but no money for me to travel to the museum to do an onsite evaluation. Through a series of phone conversations, Robert Lindsey, Colleen Blair and I clarified the goals of the summer lab so that I could begin to think about the best way to conduct the evaluation. I decided that surveys and interviews with the participants after their summer experience would give us the information we wanted. I used the data from the application to understand the girls initial interests, attitudes, and self-concept as a learner of science. I wrote a series of interview questions to determine if the summer lab goals had been met, the impact on the girls’ interests in science and sense of empowerment, and also to determine which activities the girls liked, which activities the girls disliked and the reasons for their likes and dislikes. Robert Lindsey and Colleen Blair helped me refine the questions to meet their grant reporting needs and to check that the questions aligned with the goals of the summer lab. They also provided me with an 214

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in-depth description of the structure of the summer lab and the individual activities and experiences so that I could develop the self-concept and attitude questions. Since I could not travel to the museum to administer surveys and conduct the interviews myself, museum staff who were not instructors for the summer lab, administered the surveys and conducted the interviews. The museum transcribed the interviews and sent them to me along with copies of the surveys. I then did an analysis of interview question by interview question, using them as a priori categories. I also looked within the responses to the interview questions for additional themes. The sentence stems for the attitude and self-concept data provided the initial framework for themes from this data and new themes were identified as they emerged. I did not do a member check with the girls since I had no way to contact them for a follow-up. Nor, did I do a member check with the museum staff since my role was to be an outside evaluator. Despite the absence of member checks, I strove to let the girls speak for themselves and did not impose my own perspective. Thus, I believe the results accurately reflect the impact of the summer lab on minority girls who participated in it. SCIENCE EDUCATION AT THE TIME OF THE STUDY

At the time that Girls’ Summer Lab was published, science educators were conducting studies mainly in the areas of (1) conceptual understanding and conceptual change; (2) topics related to gender and ethnicity such as self-efficacy, perceptions of competence, and persistence, with a greater emphasis on ethnicity; (3) science education in developing countries; (4) writing and graphical representations; (5)  teacher development, skills and beliefs; and (6) reasoning. These topics were found in all science education journals as well as in three themed issues of the Journal of Research in Science Teaching in 1999 (i.e. 36,3; 36,6; 36,9) addressing the topics of science education in developing countries; science, science education and life histories; and inscriptions. Biology, physical science and physics, and general science were the commonest contexts followed by chemistry. Engineering contexts were limited but being examined for the first time. As usual, astronomy and Earth science contexts were scarce. Students in high school and elementary school were the primary focus of most studies and little attention was paid to teaching at the postsecondary level. When the instructor was the focus of the study, they were teachers in a pre-service program or practicing teachers in the K-12 system. The dominant theoretical frameworks were constructivist and socio-cultural with a few studies using a Vygotskyian framework. Two books published around this time reflected the dominant theoretical frameworks. They were Michael Mathews’ Constructivism in Science Education: A Philosophical Examination (1998) and William Coburn’s Socio-Cultural Perspectives on Science Education: An International Dialogue (1998). In terms of analytical methodologies, there were twice as many qualitative studies as quantitative studies. A little less than a quarter of the studies examined employed both qualitative and quantitative analytical methodologies. 215

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RESEARCH IN THE WIDER FIELD OF EDUCATION

Research in the wider field of education reflected a greater concern for cultural and linguistic minorities. Only one article was published in the American Educational Research Journal (Lee, 1999a) and only two in the Review of Educational Research (Lee, 1999b; Springer, Stanne, & Donovan, 1999) addressed science. In one of these articles, Lee (1999a) explored gender, socioeconomic status, and ethnic differences in children’s world views and knowledge of hurricanes and in another she critiqued reform documents for equity (Lee, 1999b). She critiqued the National Science Education Standards, Project 2061, and large scale assessments such as TIMMS, and NAEP. She concluded that these documents presented a narrow view of equity, an assimilationist perspective, and Western views of science with no consideration of alternative ways of knowing. Overall, Lee’s critiques were more concerned about culture than gender. Springer, Stanne and Donovan’s (1999) meta-analysis provided evidence that small group learning leads to better outcomes in terms of attitude, achievement, and persistence for undergraduate students in STEM majors. There were several notable publications concerning girls by the American Association of University Women. The 1999 publication Gaining a Foothold: Women’s Transitions Through Work and College (American Association of University Women, 1999) addressed how and why girls and women made educational decisions after high school. As also noted in an earlier chapter, The American Association of University Women also published Voices of a Generation: Teenage Girls on Sex, School, and Self that explored the societal forces on girls and societal expectations for girls as well as identity formation and sexual activity with an emphasis on the role schools play (Haag, 1999). Separated by Sex: A Critical Look at Single-Sex Education for Girls was a concise synopsis of the research on the impact of single-sex education. The conclusion derived from the research on single-sex education was that it was not a solution for equity (American Association of University Women, 1998). In addition, the National Council of Research on Women (1998) published an important document called The Girls Report: What We Need to Know About Growing up Female. This report examined identity, health, sexuality, violence, and schooling. THE CULTURE OF THE TIMES

Several major events marked 1999. In Kosovo, NATO intervened to stop another round of ethnic cleansing. This intervention was necessary despite the 1998 United Nations’ security resolution number 1199 demanding a ceasefire, as well as negotiations and withdrawal of Serbian forces from Kosovo. The Serbians rejected the United Nations resolution. This rejection lead to additional forces being sent to Kosovo as well as a UN peacekeeping mission (Whitaker, 2014). Elsewhere in Europe the Euro became the currency in 11 countries; the former communist countries of Poland, Hungary, and the Czech Republic joined NATO; the United 216

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Kingdom ended 25 years of direct rule of Northern Ireland by transferring power to the new Irish assembly; Russia funded attacks on rebels in Chechnya; and a 7.4 earthquake in Turkey killed over 15,000 people. Less important but also grabbing the headlines was the decision of a judge that Princess Diana’s death was caused by an intoxicated driver (CBS News.com staff, 1999). The war in Kosovo had a devastating impact on education of school age and university age youth (European Commission/ World Bank, n.d.). According to the UNICEF report (1999) Bringing Children Back to School in Kosovo, the schools were heavily damaged and lacked supplies and adequate sanitary facilities. Under these circumstances the work of UNICEF and the work of the World Bank to reconstruct the country constitutes a major educational intervention. The niceties of special museum programs take a backseat to addressing more fundamental educational needs. In the United States, the nation was horrified by the Columbine High School shooting in Littleton, Colorado. This event was of particular interest to young people  because of the setting and the youth of the shooter. The nation was also saddened by the death of John F. Kennedy Jr., his wife, and sister-in-law in an airplane accident. And depending upon political persuasion, the impeachment trial of President Clinton was an event that sparked anger or feelings that it was more than justified (Pew Research Center US Politics and Policy, 1999). A strong US economy also had an impact on the mood of the country. Distrust of government and elected officials was down, tempered by public hostility toward Republicans for the impeachment of President Clinton, giving Democrats the better public image. Republican Party affiliation was also down and a moderate wing of the Republican Party emerged. Overall, the general public was very satisfied with state of the nation and their personal financial security. These feelings of satisfaction and security may have been responsible for more tolerance for outsiders and a reported willingness to help the poor (Kohut, 1999). Women’s issues received a boost in President Clinton’s proclamation for women’s history month in 1999. It called for support of the Affordable Care Act,  women’s  rights, equal access to education for women, and equal pay for women.  His women’s history month proclamation in 2000 added political issues important to women (Sauerwein, 2009). Congress also acknowledged the work of civil rights activist Rosa Parks by awarding her the highest civilian honor, the Congressional Medal of Honor (National Women’s History Museum, 2015a). Nineteen-ninety-nine also saw the passing of another women whose contributions improved the lives of many around the world. Gertrude Belle Elion, the holder of 45 patents and Nobel Prize winner in medicine died in 1999. Both Elion and Parks faced discrimination but persisted in the face of opposition to make the world a better place (National Women’s History Museum, 2015b). Women of color were making their mark in 1999 in the United State congress too. There were 12 women serving in the Senate (Infoplease, 2015a) and 53 in the House of Representatives (Infoplease, 2015b). Of these women, 19 were women of 217

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color serving in the House of Representatives but none were serving in the Senate. Thirteen of the representatives were African Americans, 1 was Asian American/ Pacific Islander, and 5 were Latina women. All but one Latina were Democrats (Rutgers Center for American Women and Politics, 1999). IMPACT OF MY WORK

This research pointed out the importance of asking girls what they want in science programs, camps, and other events designed to interest girls in science. By taking an adult perspective, the summer lab was less successful than it could have been. Strictures by the funding agency about the topics to be explored and activities that the museum staff thought would be interesting and important did not always resonate with the girls participating in the summer lab. In fact, some of the activities selected made the girls dislike a particular aspect of science. However, not all of the activities were unsuccessful. In fact, the docent activity was extraordinarily and unexpectedly successful. As such, this work points out the importance of providing authentic activities that empower girls. Not only were the girls in charge of explaining exhibits as docents, they were in charge of their own learning. Mastery experiences, such as the docent activity, are one of the most powerful ways to build self-efficacy. Self-efficacy in a content area such as science is a strong predictor of future science engagement. Although self-efficacy is a necessary but not sufficient condition for pursuing a career in science, (Bandura, 2003) without mastery experiences that build self-efficacy, interventions to interest girls in science will be unsuccessful. To paraphrase a student I interviewed in another study, ask them what they like, it’s them that has to do it (Piburn & Baker, 1993). Science that is relevant to the concerns, interests, and world in which young people live in provides motivation. Science that is relevant to the concerns, interests, and world of adults may not interest young people. There is no way to know without talking with young people. This was a difficult message for the museum staff to hear and they were discouraged by some of the outcomes of the intervention. On the other hand, the information provided by the evaluation, and especially the girls’ own words as well as the overall positive impact on the girls were taken to heart by the museum. They used the evaluation of Girls’ Summer Lab to inform future funded projects to interest urban minority girl in science.

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GOOD INTENTIONS An Experiment in Middle School Single-Sex Science and Mathematics Classrooms with High Minority Enrollment

STUDY SYNOPSIS

This study, Good Intentions: An Experiment in Middle School Single-Sex Science and Mathematics Classrooms with High Minority Enrollment (Baker, 2002) also appeared in the Journal of Women and Minorities in Science and Engineering and as a consequence cannot be reprinted. I will give a short synopsis and encourage those of you who are interested to seek out the article in the Journal of Women and Minorities in Science and Engineering. Good Intentions took place in a middle school in a high minority district in a large urban area in the Southwest United States. The majority of the students were Latinos with a small number of African American students. Only one or two European American students were in the classes studied. Most of the students were recent immigrants or had parents who were immigrants. Many of the Latino student were not native speakers of English. Some spoke only Spanish and relied on classmates to translate and the rudimentary Spanish of their teachers. Some of the students, mostly male, were gang members. The school was true to the middle school model and consisted of only 7th and 8th  grade. Students were organized in units of 120 and taught by a team of collaborating teachers. The classes were on a block schedule. The teachers were experienced with 29 and 14 years of teaching. Both teachers had extensive content background and were able to relate to the cultural, socioeconomic, ethnic, and racial issues their students faced. The study consisted of classroom observations of single-sex science and mathematics classes within a co-educational school. To determine what was happening in the single-sex classrooms, I made observations twice a week. One day of the week I observed the science class of girls while the mathematics teacher taught the boys and then observed the mathematics class of girls while the science teacher taught the boys. Another day of the week I observed the boys’ science class and mathematics class. I took notes of the teachers’ direct instruction, and rotated among groups as students worked on tasks together in the classroom and outside on the school grounds, and I interviewed the teachers and students. I also collected data on the number of elementary schools the students had attended, homework completion, and the grades the students achieved in the single-sex classes and in their co-educational classes. The data was analyzed qualitatively looking for themes. 219

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I began my study during the third year of the single-sex experiment. The previous year the teachers had scheduled an all-boys’ day and then an all girls’ day. The teachers found teaching the boys all day exhausting. One teacher said she dressed for combat on the all-boys’ day. Consequently, the teachers change the schedule alternating an all boy class with an all-girl class on the same day. Despite this schedule change, the boys took a toll on the teachers and at the end of the study year, the teachers decided that in the fall they would return to co-educational science and mathematics classes. The results of the study were not encouraging. Although the girls had higher grades in both science and mathematics than the boys, it was not clear that single-sex classrooms were responsible for this finding. Girls also had higher grades than the boys in their other co-educational classes. The all-girl classes did make the girls feel empowered and they liked the all-girl classes but the all-boy classes had a negative effect on the boys and they did not like the all-boys classes. The boys wanted to be with the girls because the girls helped them with group tasks whereas, the boys had difficulty working together. The teachers also noted that the girls moderated the boys’ behavior and this became another reason why the teachers returned to the coeducational structure. The boys were more obvious discipline problems and received more reprimands.  The girls were also off task a considerable amount of time but they were less obvious and reprimanded less. For example, girls quietly applying nail polish under their desks were not disciplined for this action. I attributed the boys’ disciplinary problems to the nature of the curriculum despite reflecting what science and mathematics educators would consider the right way to teach. The science teacher planned many hands-on inquiry activity in class and on the school grounds with a variety of scientific tools. The mathematics teacher engaged students in real world mathematics activities such as planning a model town to scale. Unfortunately, the activities were more suited to girls’ interests and maturity level. The boys had difficulty working together and needed more structure than the girls but the teachers did not make modifications to accommodate the boys’ structure needs or topic interests. They used the same curriculum and pedagogy for both girls and boys. The students in this school presented challenges. Many did not speak English, most were on free or reduced lunch, some of the girls and boys were in gangs, and some had been in as many as seven elementary schools before coming to the middle school. Simply separating students by sex was not sufficient to improve their educational outcomes and and it had negative unintended consequences for the teachers and boys. WHY I CONDUCTED THE STUDY

I was asked to give a talk to teachers about gender equity issues by a school district administrator. Attending that talk were two female middle school teachers. One teacher taught science and the other taught mathematics. After the talk, the teachers 220

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approached me and introduced themselves. They had been lively participants in the question and answer period and as we continued our conversation after the talk they revealed that they were strong advocates for gender equity in mathematics and science. They also told me that, on their own, they convinced their principal to allow them to create single-sex science and mathematics classrooms that they would teach within their co-educational middle school. They explained that they had heard about the importance of single-sex environments in science and mathematics for increasing girls’ interest and achievement and that they wanted to create this supportive environment for the largely minority population that attended the school. They were in the third year of their experiment and they were proud of taking the initiative. As a consequence of our conversations, they invited me to study what they were doing. Of course I jumped at the chance, despite the fact that the school was quite a distance from my home and the university. This worked to my advantage since the school had not been inundated with requests to do research. The principal was very accommodating and once I received district permission and IRB permission to conduct the study, I was free to work through the teachers and come and go on my own schedule. I had always wanted to study single-sex instructional environments because advocates were so sure about their positive impact. On the other hand, existing studies had problems that raised questions about the impact of single-sex environments for girls. Many studies were old or conducted outside the United States in educational systems very different from ours. In addition, there were few qualitative studies of public schools that took into consideration cultural, linguistic, and class differences. Since I had never been able to identify a place where singlesex instruction in mathematics and science was being implemented in a public school, this invitation was something I could not decline. Furthermore, the school had the added advantage of a high minority population. If a single-sex environment could have a positive impact with these students, it would be a major contribution to the research literature. METHODOLOGICAL DECISIONS

I had no preconceived notions of what I would see. I decided that the best course of action was to spend approximately an entire year in the school. I observed from mid-August through the end of March concluding when state wide testing began in order to see how the single-sex experiment progressed over time. I made 38 classroom observations (20 in science, 18 in mathematics), taking notes, scripting as much as I could with attention to the types of instructional activities, pedagogy, and classroom management and discipline strategies the teachers used. Each observation lasted 100 minutes due to the block schedule of the school. The total number of hours of observation was 63.3. After school, I added to the notes details I was not able to include as I observed. I  also collected artifacts (curriculum materials, assessments, samples of student 221

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work and lab sheets) as well as grades at three points during the study. I used what I saw as the starting point of my interviews with the teachers, classroom interns, and students. I did not code the data until all of the observations were completed although after a few weeks of observation, it was clear that the teachers had routines and a consistent pattern of interactions with students. I used the routines and patterns to code the data as well as allowing new themes to emerge from the data. The interview questions structured the data analysis of the interviews of the teachers and students and helped me understand the observations. Grades were used to group students so that I could look at how the larger themes played out for students receiving As and Bs, Cs, and Ds and Fs. SCIENCE EDUCATION AT THE TIME OF THE STUDY

In 2002 I was co-editor along with Michael Piburn of the Journal of Research in Science Teaching. In volume 39, 8, I published an editorial titled Where is Gender Equity in Science Education (Baker, 2002). This was an analysis of publications addressing gender in the Journal of Research in Science Teaching from the 1970s to 2002. My analysis indicated that in the 1970s gender was not a priority and there  was  little research on the topic in 1970s and 1980s. In the early 1980s the limited number of studies used deficit theories but by the late 1980s there was an increase in studies of gender using less mechanistic and more sophisticated analytical  frameworks. By the 1990s there was official recognition, with special journal issues, that gender was a topic for exploration in science education. These later studies employed feminist theories to understand girls and women in science. Assessment was an area of interest represented in publications such as Classroom Assessments and the National Science Education Standards (Atkin, Black, & Coffey, 2001) and Formative Assessment and Science Education (Bell & Cowies, 2001). Other popular topics were conceptual change and conceptual understanding; the effect of pedagogy of various types as well as other teacher factors such as communication; factors affecting achievement such as attitude and personality; reform; equity in relation to race, urban schools, culture, and language; and the nature of science. Theoretical frameworks most often used were sociocultural, Vygotskyian, phenomenology, constructivism and theories of conceptual change. More than twice as many studies were qualitative than quantitative with about half of the studies using both qualitative and quantitative analytical approaches in the same study. Studies were just as likely to be situated at the high school, junior high school/ middle school or elementary level. Studies at the university level were less frequent and examined aspects of pre-service teacher preparation. Students were the object of study in the majority of research comprising a bit less than two thirds of the studies. Biology or general science such as that taught at the elementary level were the most frequent content areas studied followed by chemistry, physics/physical science and 222

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Earth science. Gender was addressed occasionally when looking at differences in male female performance. Only one study stands out that tackled gender issues head on (Bianchini, Hilton-Brown, & Breton, 2002) by using an explicit feminist theoretical orientation to help university faculty understand issues of equity and diversity in science. Several interesting books were published at about the same time as my study. Elaine Howes (2002) addressed gender and many of the concerns of the time with Connecting Girls and Science: Constructivism, Feminism and Science Education Reform as did Sue Rosser in Women, Science, and Society: The Crucial Union (Rosser, 2000). Biagioli (1999) edited The Science Studies Reader. Although this was not a science education book per se, it presented studies of science as a social/historical enterprise that had implications for the nature of science research in science  education. A popular science education book by Campbell and Fulton (2003) that was written for teachers was Science Notebooks: Writing about Inquiry. This book capitalized on the research in the 1990s on writing to learn in science. RESEARCH IN THE WIDER FIELD OF EDUCATION

Two-thousand and one saw multiple publications addressing girls’ and women’s issues by the American Association of University Women. Hostile Hallways addressed sexual harassment in schools and found that girls are more likely than boys to experience sexual harassment (American Association of University Women, 2001a). Beyond the Gender Wars (American Association of University Women, 2001b) was the result of a symposium in which scholars from a variety of disciplines came together to present and discuss research about the development of healthy identity and equitable and effective education for both boys and girls. The Third Shift: Learning Online (American Association of University Women, 2001c) changed the focus from girls to women and explored how technology can help women achieve their educational goals. The publication was titled The Third Shift because the researchers found that women used technology to further their education after they had completed their work and family obligations. Finally, Si, Se Puede! Yes We Can: Latinas in School (American Association of University Women, 2001d) explored the challenges of Latina girls in school and what the research says about how to help them succeed academically. An encyclopedic and historical look at girls was Girlhood in American: An Encyclopedia (Forman-Brunell, 2001). Entries were written by leading scholars in a variety of fields (e.g. sports, education) that synthesized research on topics from A through Z. Research found in leading journals such as the Review of Educational Research and the American Educational Research Journal in 2002 addressed democracy in education, race, poverty, urban schools, language issues for English Language Learners, reform, and standards. Theses educational concerns were reflected in books such as Race in the Schoolyard (Lewis, 2003). 223

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THE CULTURE OF THE TIMES

National Women’s History month honored Mary Louise Defender Wilson, an educator of the Dakotah people; Gerda Lerner, a women’s studies scholar; Alice Coachman, the first black women to win an Olympic gold medal for the high jump;  Dolores Huerta, a civil rights activist; Dorothy Height, a women’s and civil rights activist, especially for African American women; and congresswoman Patsy Mink. What is notable about this list is that the honorees include a Native American, Latina, African Americans, and a Japanese American (National Women’s History Project). Despite recognition of women, especially women of color by the National Women’s History month, representation in congress did not reflect the race, ethnicity and percentage of women in the United States. In the Senate there were 13 women (8 Democrats, 5 Republicans) and none were women of color (Rutgers Center for American Women in Politics, 2002a). In the House of Representatives there were 59 women (42 Democrats, 10 Republicans). Thirteen of these female representatives were African American and six were Latinas (Rutgers Center for American Women in Politics, 2002). Federal law, in the beginning of the 2000s, allowed single-sex schooling for physical education and sex education as well as remedial programs. Despite this limitation, there were attempts to implement single sex educational arrangements for other academic areas and to skirt the requirements of Title IX. Schools in California were experimenting with single-sex schooling as early as 1997 but researchers concluded in 2001 that the experiment was a failure. There were some advantages for poor and minority boys and girls but the data was far from conclusive because of unrelated factors such as smaller class sizes or more resources. Furthermore, the single-sex arrangement reinforced gender stereotypes where boys were seen as bad and girls as good (Viadero, 2001, 2002). The United States Department of Education’s responses, at this time, to single-sex experiments that clearly violated Title IX was disturbing to many scholars concerned with gender equity. A stunning example of the Department of Education’s disturbing response was their reaction to the Principal of Thurgood Marshall elementary school to divide students into male and female classes because of discipline problems and low academic performance of the boys. Despite being in violation of Title IX he was not sanctioned. Instead, the Department’s response was to state that it should be easier to educate boys and girl separately and that the Department of Education planned to ease the rigid requirements of the law. This decision was a direct result of the No Child Left Behind Legislation of 2001 (Viadero, 2002). Multiple incidents of violence marked 2002. India experienced some of the worst Hindu-Muslim violence in a decade. A Muslim mob fire-bombed a train, killing Hindu activists and in response, Hindus retaliated. The violence resulted in the death of over 1,000 people. In Russia, Chechen rebels took 763 people hostages in a Moscow theater. In an attempt to free the hostages, Russian authorities release a gas into the theater, killing 116 of the hostages. The remaining 647 hostages were 224

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freed. In Bali Indonesia, a terrorist bomb in a popular vacation area of the island killed hundreds of people, mostly Western tourists. Israel also experienced 14 suicide bomber attacks killing several dozens and wounding hundreds of people. In response Israeli tanks and warplanes attack the West Bank towns of Nablus, Jenin, and Bethlehem. In the United States, a sniper in the District of Columbia suburbs killed ten individuals. Police subsequently arrested John Allen Muhammad and John Lee Malvo who were suspected of carrying out the killings (Infoplease, 200–2015). In the political arena in the United States, George W. Bush campaigned and won the 2002 election as a compassionate conservative promising in his campaign that he would be a uniter not a partisan divider in his political decisions on domestic issues. Despite this promise a CNN/USA Today/Gallup poll found that 40% of Americans thought that politics played too large a role in the president’s decisions and 50% felt that politics did not. Not surprisingly, the majority of Democrats felt that politics played too big a part (Carlson, 2002). Another Gallup poll of 1,003 adults found that the public felt that Republicans did a better job in relation to terrorism, military and defense, and the economy. However, those polled believed that Democrats did a better job addressing problems such as education, social security, and prescription drugs for the elderly. Some of President Bush’s foreign policy actions in 2002 were to announce that the United States would not recognize an independent Palestinian state until Yasser Arafat was replaced; calling for a regime change in Iraq in his address to the United Nations; abandoning the antiballistic missile treaty; and in his first State of the Union address, vowing to fight terrorism calling Iran, Iraq and North Korea an axis of evil (Infoplease, 200–2015). This strong position was, in part, a response to the September 11, 2001 terrorist attack on the World Trade center as was Bush’s creation of a cabinet-level department of Homeland Security. Worldwide, the Pew Research Center (2014a) found that only 6 of 33 countries whose citizens were surveyed in 2002 believed that their country’s economic condition was good. When asked about future economic conditions, citizens in 25 out of the 33 countries expected their future economic situation to improve (2014b). IMPACT OF MY WORK

This single-sex study was the first to look at the impact of single-sex classes on minority students in a co-educational school. The findings of this study were a good example of what seems intuitively like a good idea, but does not work as expected. This work convinced me and others that the solution for increasing girls’ participation and achievement in science and mathematics, especially minority girls, is more complex than whether there are boys in the classroom. It revealed the need for systemic change. The work also raised the question of whether it is sufficient to have an impact on affective factors without an impact on achievement. Furthermore, a single-sex environment for girls that results in negative impacts on boys is not a good educational outcome. The interest in single-sex science and mathematics 225

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classes has dwindled, in part, because of this research and the work of others. We have concluded that what, at first, seemed like a good idea, has not proven to be the solution to increasing girls’ participation and achievement in science. Furthermore, the constraints of title IX, the difficulty of the work, and the culture of accountability make a true test of single-sex environments an idea whose time has passed.

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SUMMARY What Does It All Mean?

What does it all mean? The short answer is that we have a better understanding of the barriers and affordances that affect whether girls and women will participate in science. My own contributions have included intensive examination of the role of cognitive variables (mathematics, spatial and verbal ability, Piagetian tasks) psychological variables (role-specific self-concept, sex-role identity, personality, interviewer effects), classroom variables and interventions (teacher/student interactions, informal programs, single-sex classrooms) affective variables (attitude), and summaries of research that examined gender equity in science internationally, as well as effective instructional strategies to promote achievement and interest in science. My work documents that there is not just one place where problems related to gender exist. Girls and women encounter barriers to achievement and participation in science from K-12 through undergraduate and graduate levels in the United States and elsewhere in the world. But my work is hopeful, because it documents that there are also ways to overcome barriers. Determining how to support girls and women in science has been the goal of my research. By taking a hard look at classroom interactions, interventions, and research based instructional strategies I have been able to identify how teachers, informal science providers, and university faculty can promote and support achievement and interest in science for girls as well as documenting what not to do. This last is an important point. Identifying what seem like good ideas (e.g. single-sex classrooms) that do not work, is as important as identifying what does work. My research has been by and large, situated in messy contexts. But these contexts have been critical to understanding girls. Their voices, what they like and dislike, and how they feel about science and themselves has been one of my major contributions. The same can be said about my research on teachers. How teachers interact with girls, how they perceive their role in the classroom, and the instructional strategies and activities they choose all have a bearing on girls’ achievement and interest in science. I’ve also raised questions about what we are actually measuring when we ask girls to take assessments. In Sex Differences in Formal Ability: Task and Interviewer Effects I and my co-author showed that what was thought to be a deficit in reasoning was an artifact of the sex of the interviewer and interviewee. I hope that this study and others like it have contributed to laying to rest the notion that girls and women lack the cognitive abilities needed to succeed in science and that the work highlighted the sociocultural and psychological barriers. 227

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Some of my work was exploratory but much was driven by the theories of the time. Some of the theories have been superseded (e.g. Piagetian theory) but at the time were quite fruitful in helping me understand the phenomena I was studying. Feminist theories, in particular, were critical to my scholarship. Each of the various theories I employed contributed to the bigger picture of understanding the relationship of girls and women to science. The same can be said of the methodological decisions I made. Quantitative and qualitative approaches both helped me answer the questions I was seeking answers to. Consequently, I did not privilege one over the other in order to keep the possibility of asking all kinds of questions open. Connections and collaborations across campus resulted in a broadening of my interests to include engineering and my work was among the earliest to be concerned about engineering in the K-12 classroom. It anticipated the current interest in infusing engineering design into classrooms and the Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas (National Academy of Sciences, 2013). It also anticipated the need for examining how best to prepare teachers to understand engineering design concepts and to infuse engineering design into the curriculum. An Intervention to Address Gender Issues in a Course on Design, Engineering, and Technology for Science Education demonstrated how engineers and science educators could collaborate in developing curriculum and teaching a course that could serve as a model for science teacher education. To summarize my work, I found strong evidence that socialization was more important in who chose science than cognitive ability, personality or attitude. In particular, I found that role specific self-concept and self-perceptions of whether science was appropriate for females was a factor in choosing science. Although scholars often found sex differences favoring males on reasoning tasks as measured by Piagetian interviews and tests, my co-author and I found that bias played a significant role in the evaluation of performance on clinical interview assessments to the detriment of females. Also, contrary to most attitude research of the time that used paper and pencil assessments, my interviews revealed that girls do like science but were critical of school science. School factors, such as who the teacher calls on and the type of feedback a student receives revealed another source of bias. Classroom interactions that favor males send a message of invisibility in science classrooms. On other hand, my chapter What Works: Using Curriculum and Pedagogy to Increase Girls’ Interest and Participation in Science very clearly identified that opportunities for inquiry and hands-on exploration of objects, an emphasis on physical science, use of computers, integrating reading and writing in science, effective grouping strategies, role models, positive messages, and student centered teaching can have a positive impact on all students, not just girls. My research using classroom observations also concluded that single-sex classrooms alone, without significant changes in the curriculum are not the solution. Helping to debunk, a popular idea was and is an important contribution. My work also clearly identified the strengths and weakness of other interventions finding that it is importance to talk to girls when designing an intervention. Working from the perspective of an adult when 228

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deciding what kinds of activities and experiences to provide girls does not always work. However, starting from a strong theoretical basis such as the sources of selfefficacy in a content area does. Looking at girls and women in science education internationally and historically put gender equity research in a broader context. It allowed me and my readers to see how much progress has been made and how much work needs to be done. A review of the research in science education and in education in general indicated that a small number of studies in relation to the size of the problem have focused on gender equity. In science education, the work has been conducted by a handful of scholars. One organization, the American Association of University Women, has allocated many resources resulting in publications concerning girls and women, but with a broader focus than just science. Psychologists and women scientists have notably written about and studied girls and women in science and issues of gender equity in science. However, the interest in gender in science in the fields of science education and education in general has waned and has been replaced by a broader interest in equity concerning linguistic and cultural minorities, poverty, and urban youth. To me this change of focus is shortsighted given the data that women continue to be underrepresented in physical science classes and careers (Hazari et al., 2013) and males earned the majority of degrees in physics, engineering, and computer  science at both the undergraduate and graduate level (National Science Foundation, 2014). The culture of the times in which I conducted my research has had periods of movement forward in terms of women’s rights, especially reproductive rights, as well as times when conservative elements in the culture have pushed back. Most notably, the Equal Rights Amendment to the United States Constitution failed. Nevertheless, over time, women became more visible in politics and leadership positions world-wide. This increased visibility of women may be the reason for fewer studies of women and more research on other groups where equity is an issue. Yet, the American Association for the Advancement of Science stated in the opening sentence of an EurekaAlert (American Association for the Advancement of Science, 2015) that gender equality had not been achieved in science, medicine, and engineering. Clearly, more work needs to be done not only in the United States but in other countries as well, especially those where traditional values are strong and the status of women is low. Even though my career has spanned more than 35 years with a strong focus on gender equity, the evidence is clear that the job is not done. Although Ceci and Ceci (Bernstein, 2015a) found that female applicants for university positions are more likely to be chosen than male applicants, their study has been criticized. Their study used hypothetical applicants in a review process that did not mirror the real world process. Critics also noted that barriers to advancement can occur after the hiring process has been completed. Critics felt that this study will leave people with the false belief that gender is no longer an issue in STEM fields. 229

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A recent peer review supports the view that gender is still an issue in science. The review generated a great deal of comment about gender equity because a reviewer suggested that a manuscript submitted by a female evolutionary geneticist examining the transition from PhD student to post-doc should have male co-authors to strengthen the manuscript. The reviewer wrote It would…be beneficial to find one or two male biologists to work with or at least obtain internal peer review from, but better yet as active co-authors. (Bernstein, 2015b, p. 1) This was to prevent the manuscript from drifting too far away from empirical evidence into ideologically biased assumptions. (Bernstein, 2015b, p. 1) The author of the manuscript posted the reviews which brought an apology from PLOS online and numerous outraged responses as well as indications that this biased response did not come as a surprise. Another indication that gender equity has not been achieved is that even in countries with generous parental leave policies, women become less competitive because they have taken time off. Women who do return to work early find that there is a lack of good quality all day care for young children often forcing a choice between family and career (Munk & Ruckert, 2015). Overall, science is still seen as a male domain even in industrialized countries where one would expect gender equity as in the Netherlands (Bernstein, 2015c). Thus, I can say with confidence that there is still a need for continued research and interventions to insure that every girl who dreams of being a scientist can fulfill her dream. It is with great sadness that I end writing this book in the same month that Sandra Bem, one of the pioneers in gender studies, took her own life. Her work had a major impact on my early thinking. When Sandy found out she had Alzheimer’s, she planned her own death and took it May 20, 2014. We will all miss her and her insightful scholarship. We know so much about sex-role stereotyping because of her (Hening, 2015).

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