Exploring Gender Differences across Elementary, Middle, and High School Students. Science and Math Attitudes and Interest. A Thesis Submitted by

1 Exploring Gender Differences across Elementary, Middle, and High School Students Science and Math Attitudes and Interest A Thesis Submitted by Julie LeGrand Doctoral Problem Results Submitted in Partial Fulfillment of The Doctorate of Education (Ed.D) in Educational Leadership in the College of Professional Studies from Northeastern University Boston, Massachusetts Dr. Chris Unger, Advisor February 2013

2 Abstract The issue of female underrespresentation in science, mathematics, engineering, and technology careers and courses has been well researched over the last several decades. However, as gender gaps in achievement close and representation becomes more equitable in certain academic domains, research has turned to social and cultural factors to explain why fewer women persist in STEM studies and careers than men. The purpose of this study was to examine gender differences in science and math attitudes and interests from elementary school, to middle school, to high school. To examine possible gender-specific shifts in students interest and attitudes in science and math, 136 students from a suburban, public school district were surveyed at the elementary school level (N=31), middle school level (N= 54), and high school level (N=51) and various constructs were used to assess the responses in accordance with expectancy-value theory. Utilizing a mixed-methods approach, a random sample of students from each grade level then participated in focus groups, and corollary themes were identified. Results from a logistical regression analysis and Mann-Whitney Test indicated that significant gender differences exist for interest, efficacy, expectancy, and value within science domains (p<.05), although these differences are not the same at each grade level or for each scientific discipline. Significant gender differences in mathematics are present only at the elementary school level. Key Words: math and science gender differences, math and science self-efficacy, expectancyvalue theory

3 Table of Contents Abstract... 2 Table of Contents... 3 List of Tables... 5 Chapter 1: Introduction... 6 Problem of Practice... 6 Significance of the Problem... 7 Practical and Intellectual Goals... 9 Research Questions... 10 Theoretical Framework... 10 Self-Efficacy Theory... 11 Expectancy-Value Theory... 15 Chapter II: Literature Review... 23 Cross-cultural Data on Student Interest, Attitudes, and Achievement in STEM... 24 Stereotypes... 31 Explicit and Implicit Stereotypes... 32 Stereotypes and Student Perceptions... 35 Stereotypic Beliefs Held by Teachers... 40 Teacher Behaviors Due to Stereotypes... 42 Implications for Further Research... 45 Chapter III: Methodology... 46 Research Questions... 46 Research Design... 47 Site and Participants... 48 Survey Data Collection... 50 Descriptive Analysis... 52 Statistical Analysis... 52 Focus Group Data Collection... 54 Qualitative Analysis... 56 Validity and Credibility... 57 Protection of Human Subjects... 60 Conclusion... 60 Chapter IV: Data Analysis... 61 Study Context... 62 Research Question #1... 63 Construct 1: Course Interest... 63 Construct 2: Career Interest... 69 Interest Summary... 72 Construct 3: Expectancy... 73

4 Expectancy Summary... 78 Construct 4: Efficacy... 79 Efficacy Summary... 85 Construct 5: Value... 86 Value Summary... 97 Research Question #2... 98 Teacher Influence... 98 Career Influences... 102 Stereotype Influence... 103 Other Influences... 105 Conclusion... 109 Chapter V: Discussion of Research Findings... 112 Summary of the Problem... 112 Summary of Major Findings... 113 Research Question 1... 114 Research Question 2... 115 Discussion of Findings in Relation to the Theoretical Framework... 116 Discussion of Findings in Relation to the Literature Review... 123 Significance of the Study... 130 Limitations... 131 Final Researcher Commentary... 132 Future Research... 134 References... 141 Appendices... 152 Appendix A... 159 Appendix B... 154 Appendix C... 164 Appendix D... 166 Appendix E... 168 Appendix F... 169 Appendix G... 170 Appendix H... 171 Appendix I... 172 Appendix J... 209 Appendix K... 217

5 List of Tables Table 1: Survey Participants by Gender and Grade Level... 52 Table 2: Positive Survey Responses for Science and Math Interest by Gender and Grade Level... 63 Table 3: Positive Survey Responses for Interest in Specific Math Disciplines... 68 Table 4: Survey Responses of Strong and Moderate for Career Interest... 69 Table 5: Survey Responses for Elementary School Students... 70 Table 6: Positive Survey Responses for Science and Math Expectancy... 73 Table 7: Positive Survey Responses for Science and Math Expectancy... 75 Table 8: Positive Survey Responses for Science and Math Efficacy... 79 Table 9: Positive Survey Responses for Science and Math Efficacy (MS and HS)... 81 Table 10: Positive Survey Responses for Science and Math Efficacy (ES)... 82 Table 11: Positive Survey Responses for Science Achievement Attribution... 84 Table 12: Positive Survey Responses for Mathematics Achievement Attribution... 84 Table 13: Positive Survey Responses for Science and Math Personal Value... 86 Table 14: Pearson Correlation Coefficient for High School Males... 91 Table 15: Pearson Correlation Coefficient for High School Fe... 92 Table 16: Pearson Correlation Coefficient for Middle School Fe... 93 Table 17: Pearson Correlation Coefficient for Middle School Males... 93 Table 18: Pearson Correlation Coefficient for Elementary School Males... 95 Table 19: Pearson Correlation Coefficient for Elementary School Fe... 95 Table 20: Positive Survey Responses for Science and Math General Value... 96 Table 21: Survey Results for Teacher Influence on Course Interest... 101 Table 22: Top Survey Responses for Career Influence... 102 Table 23: Positive Survey Responses for Questions Regarding Gender Stereotypes... 103 Table 24: Mann-Whitney Test Statistics for Middle School Students... 104 Table 25: Survey Responses for Exposure to Adults in STEM Careers... 106 Table 26: Positive Survey Responses for Parent and Teacher Encouragement... 106 Table 27: Survey Participants by Gender and Grade Level... 131

6 Chapter I: Introduction Problem of Practice Women have been historically underrepresented in science, technology, engineering, and mathematics (STEM) courses and careers. While the representation of US women and girls in STEM fields have increased in recent decades (NSF, 2006), the problem still persists. However, the issue of underrepresentation is not equal across all stem fields, and it has been found that enrollment in certain STEM areas are on the decline for as well. The number of degrees at all levels of mathematics and physics dropped significantly after the 1970s (Frazier-Kouassi, 1992). The number of people awarded physics degrees grew only 11% from 1983 to 2005, whereas the number of bachelor s degrees awarded in all fields increased by 47%, indicating that growth in this field is slow across both genders (National Science Board, 2008). Research shows that women are found more frequently in biological sciences and are even more prevalent than men many in the social sciences (Rosser & Taylor, 2008; VanLeuvan, 2004). Women actually outnumber men in college enrollment but their participation in the most in-demand and economically fruitful fields (engineering, computer science, and physical sciences) has declined in the last decade. Additionally, women that do enter the aforementioned careers leave the workforce at a rate greater than their male counterparts (Rosser & Taylor, 2008). The percentage of women graduating with biological sciences degrees has grown from 25% in 1960 to 62% in 2005, whereas only 21% of physical science degrees were awarded to women in 2005. Overall, women comprise 24.8% of computer and mathematical professionals, down from 27% in 2006 (Bureau of Labor Statistics, 2008, pp. 209-211). The lack of women participating in STEM, particularly computer science and physical science can be found at the high school level as well. These issues are evidenced at Smith

7 Academy, a college preparatory public school with an enrollment of approximately 200 students in grades 7 12 where the researcher is employed as a math teacher. Students are required to take three years of a science in order to meet graduation requirements. Currently, there is a lack of female students in technology, engineering and physical sciences. Smith Academy also had a junior engineering club, JETS. Female enrollment in physics and JETS has been consistently low or nonexistent. Additionally the technology coordinator takes on students for independent studies and also teaches a computer technology course. No female students have participated in either opportunity. Researchers have found that course-taking in high school has an influence on whether or not students will pursue a degree in science or math. In particular, the number of elective high school science and math courses is related to persistence in a science-related career for women, whereas taking high school physics had the greatest relation for men (Farmer, Wardrop, Anderson, and Risinger, 1995; Trusty, 2002). These findings highlight the necessity to encourage young women and men to become involved in the science and technology courses offered by their high school. This topic has been examined from societal, educational, biological, and personal perspectives. The research offers many possible reasons for the current underrepresentation of women in STEM but the problem still persists. Significance The lack of women in physical, mathematics, and computer sciences is a pertinent issue for many reasons. One large argument revolves around the issue of gender equity. Young girls lack role models in STEM industries and achievements. Differences between the two genders can develop at a young age as indicated by studies on young girls attitudes towards STEM.

8 Girls' attitudes toward mathematics and science and their interest in related careers seem to be independent of and do not necessarily parallel their high school achievements in mathematics and science (Catsambis, 1995). This is good cause to question what goes on at home and in the classroom that may influence girls attitudes about math and science careers. However, women s participation in scientific fields goes beyond civil rights and feminism. Achievement in math and science at the high school and college level are national social and political issues. The National Education Goals lists increasing high school science and math achievement as well as the numbers of students graduating with degrees in STEM a priority in Goal 5 (Trusty, 2002). Rosser and Taylor (2008) remind us that the United States is currently experiencing a decline in entry-level engineers and growing competition from foreign countries. The decline in U.S. leadership on the technological front can be attributed to the fact that less Americans are preparing for careers in STEM. The shortage of American scientists threatens our nation's ability to compete and innovate in the coming years, especially as the outsourcing of jobs to, and importing of science from, other nations continues to grow. By 2014, the U.S. will have added more than one million additional information technology jobs to the workforce. The loss of U.S. technical capabilities not only results in an economic loss but also in a threat to national security (Rosser & Taylor, 2008). Equitable representation would offer women equal access to well-paid, high-status STEM careers and add new perspectives to scientific and technical innovation. Research in this area may offer further insight into how STEM courses and careers are perceived by young women. This insight will provide schools administrators and teachers with a basis for developing programs or pedagogies that will further support young women in these areas

9 Practical and Intellectual Goals The issue at hand is complex, with no single explanation or solution. The influences of societal norms and the stereotypical view of women s roles on developing are deeply entrenched and contribute greatly to women s attitudes and confidence in the mathematical and scientific abilities. These norms are changing but it is a slow process. It is unrealistic to think that this research will solve the problem on a nationwide scale, but it is reasonable to focus on improving the situation within my own school district. According to Joseph A. Maxwell (2005) practical goals are focused on accomplishing something- meeting some need, changing some situation, or achieving some objective (pg. 21). The practical research goal in this study is to understand whether female students at Smith Academy perceive their ability in science and math across grades differently than and the basis of those differences so that, as a faculty, Smith Academy faculty can consider possible practices to address any significant differences that may be of concern to the faculty. Ultimately I would like to find a way for our school to support and encourage female students in their scientific and mathematical development so that future enrollment and the pursuit of STEM education and careers might increase for the young women in the Smith Academy community. Intellectual goals focus on understanding what is happening and why (Maxwell, 2005). The intellectual goals for my research are then as follows: Intellectual Goal #1: To understand how elementary, middle, and high school and at Smith Academy state their interest, abilities, and experience in science and math as different. Intellectual Goal #2: To examine how their stated experience in science and math classes may have influenced these perspectives.

10 Research Questions The following two research questions guided the design of this study, including the data collected and how it was analyzed: 1. Is there a difference between boys and girls at Hatfield Public Schools in their attitudes, interests, and self-efficacy towards math and science courses and careers, and do these differences change from elementary, to middle, to high school? 2. As reported by students, is there a difference between girls and boys in how personal and academic experiences have influenced their attitudes toward, interests in, and pursuit of STEM education and careers? Theoretical Framework Two main theories provide a theoretical framework in which to examine the gender disparities in STEM attitudes and interest. Self-efficacy theory is a major construct within the larger social cognitive theory and attempts to explain the decisions and behaviors of individuals based on their perception of potential success at a task. Expectancy-value theory integrates aspects of self-efficacy theory to explain why individuals assign value and persist in certain tasks. These and other achievement motivation theories have been used in recent research on the attitudes and interests of students towards their academic courses and potential career decisions. Eccles and Wigfield (2002) have noted the connection and overlap of their expectancyvalue theory to those theories that focus on expectancy, theories focused on reasons for engagement, theories that integrate expectancy and value constructs, and theories that integrate motivation and cognition. Each category of theories focuses on motivation in terms of beliefs, values, and goals with action. While there is much from each of these theories that can be applied to research on the participation of in STEM, this paper focuses mainly on self-

11 efficacy theory as developed by Bandura, and the expectancy-value theory developed by Eccles, Adler, Fuerman, Goff, Kaczala, Meece, & Midgley (1983). Self-efficacy theory. Albert Bandura s (1977) social-cognitive theory focuses on learning as a social activity and is often viewed as a bridge between cognitive and behavioral learning theories. A basic premise of this theory is that people learn not only through their own experiences, but also by observing the actions of others and the results of those actions. Social cognitive theory provides a reminder how a person s life trajectory is the culmination of many factors, some of which will have a greater impact than others at different times. Bandura (1977) referred to the interactions of such factors as reciprocal determinism, which finds a link between cognition, biology, environment and behavior. Each sphere influences the other and plays a role in determining life paths. Biological conditions in addition to familial and educational systems can influence the path of a young girl as she is determining her goals. As stated by Bandura (1989), social and technological changes alter, often considerably, the kinds of life events that become customary in the society (p. 5). The images girls are presented with, the norms of society and the views of influential people in the girl s life are all contributors to life decisions. Through social learning theory the path of an individual s life can be examined. Who influenced their attitudes towards math and science careers? How have employees in these industries been portrayed? What images does the media project? Have they been encouraged by teachers and family members? These and many other questions fall under the scope of social cognitive theory. Social cognitive theory has been used to analyze educational and occupational preferences and decisions. Key factors that influence these decisions include genetic factors, environmental conditions and events, learning experiences, and task approach skills. The

12 interaction of these factors over time is interdependent and can produce different decisions at each junction. Genetic factors include race, sex, physical appearance, and special abilities in music, art, intelligence or muscular coordination. Environmental conditions and events encompasses a number of social, economic, and political factors, just as job availability, training opportunities and requirements, technological developments, family experience and resources, educational organizations, and natural disasters. Each one of these conditions can create constraints or facilitators on the opportunities of the individual. For example, a girl born in the United States in 1980 by middle class parents would be expected to graduate high school, whereas a girl born in rural China may have fewer educational opportunities and expectations (Krumboltz, Mitchell, & Jones, 1976). The learning experiences one is exposed to are complex. No one theory has adequately accounted for the infinite variations in patterns of stimuli and reinforcement that influence educational and career preferences. In general, the consequences of the learning experience influence the probability of having a similar experience in the future. Success and positive feedback may lead to an affinity for that particular task, while negative feedback and poor performance may have the opposite effect. Learning experiences produce preferences for various activities and develops cognitive and performance skill that the individual brings to each new task. These task approach skills affect the outcome of the task and these results will modify future approaches (Krumboltz, Mitchell, & Jones, 1976). Central to many theories of academic and career motivation and decision-making, Bandura developed the key concept of self-efficacy within his cognitive learning theory, and provided guidelines for measuring self-efficacy beliefs across different domains. Bandura

13 (1977) formally defined perceived self-efficacy as personal judgments of one s capabilities to organize and execute courses of action to attain designated goals, and he sought to assess its level, generality, and strength across activities and contexts. He hypothesized that self-efficacy influences how much effort will be expended and sustained on a given task or goal in the face of adversity. Bandura (1977) distinguishes between outcome expectancy and efficacy expectancy: An outcome expectancy is defined as a person's estimate that a given behavior will lead to certain outcomes. An efficacy expectation is the conviction that one can successfully execute the behavior required to produce the outcomes. Outcome and efficacy expectations are differentiated, because individuals can believe that a particular course of action will produce certain outcomes, but if they entertain serious doubts about whether they can perform the necessary activities such information does not influence their behavior. (193) Self-efficacy refers to expectations and perceived abilities related to a specific task or domain, unlike the closely related construct of self-esteem, which is an overall assessment and feeling of one s self-worth (Betz, 2001). Individuals who have high confidence in their abilities are likely to approach difficult tasks as challenges they can master instead of threats that need to be avoided. Those who doubt their own abilities are likely to attribute setbacks to personal deficiencies instead of a lack of effort or knowledge (Bandura, 1994). Vocational researchers have used the self-efficacy construct to explain an individual s willingness to choose a given career option and persist throughout the necessary educational programs required to participate in the chosen vocation. According to Bandura, there are four sources of information that develop and modify self-efficacy expectation: past performance on the specific task, vicarious learning experiences provided by social models, encouragement or

14 discouragement from others, and emotional arousal in connection with the behavior. Through positive applications of these four sources self-efficacy can be altered (Betz, 2001; Bandura,1994). Because self-efficacy can be influenced by others, implications of self-efficacy theory on teaching and career guidance practices are particularly relevant for the topic of women in STEM. Modeling appropriate behaviors, providing students with a variety of models, helping students set realistic academic goals for themselves and instilling a belief in the student that they are capable of accomplishing school tasks are all things that parents and teachers can do to encourage all students and bolster their self-efficacy within STEM domains. According to Bandura (1994), the best way to create a strong sense of efficacy is through mastery experiences within the domain. This does not mean that the individual should be provided experiences that are not challenging and do not present the possibility of failure. Some setback and difficulties are required to teach the individual that success generally requires perseverance (Bandura, 1994) Self-efficacy plays a role in four major psychosocial processes: cognitive, motivational, affective, and selection. Cognitive processes, such as personal goal setting, are regulated by an appraisal of personal capabilities. Those who experience ample self-doubt will find it more difficult to perform and manage critical thinking in a demanding environment. The second process, motivation, is cognitively generated. Cognitive motivators come in the form of causal attributions, outcome expectancies, and cognized goals, all influenced by self-efficacy beliefs. Affective processes, such as anxiety and depression, arise from people s perceptions of their coping capabilities in stressful situations. Those who cannot manage threats or control disturbing thoughts feel stress and may not be able to function at optimal levels. Again, socialcognitive theory recommends mastery experiences to strengthen coping efficacy. Selection

15 processes are shaped by the social influences and experiences that have promoted certain competencies, values, and interests. Career choice is one example of how self-efficacy beliefs can affect the course of life paths. Strong self-efficacy beliefs generate a wider range of occupations for consideration and increase the likelihood of persistence and success along that chosen path (Bandura, 1994). Expectancy-value theory. In addition to career selection, the self efficacy construct has been applied across a number of disciplines over the years. Recently, there has been an increase in academic motivational studies that attend to self efficacy beliefs (Pajares, 1996). Bandura s ideas have led to the generation of a number of other pertinent theoretical models that include the self-efficacy construct. Of these models, the expectancy-value theory, as developed by Eccles and colleagues, is of particular interest since it has been widely applied to female achievement and persistence in mathematics and science. Expectancy-value theories fall under the broader category of achievement motivation theories, which attempt to explain people s choice of achievement tasks and persistence and performance on those tasks (Wigfield & Eccles, 2000). The constructs of expectancy and value were initially defined by Lewin and Tollman in the 1930 s, but it was Atkinson (1957) who developed the first formal expectancy-value model to describe achievement-related behaviors (Wigfield, Tonks, & Klauda, 2009). Modern expectancy-value theories are based on Atkinson s work but have important distinctions. The model developed by Eccles et al. (1983) is linked to a broader array of psychological, social, and cultural determinants that have been show to affect young people s motivational behavior and is based on empirical evidence. Expectancies and values are connected to and influenced by cultural gender stereotypes in certain subject area and careers, as

16 well as the broader cultural and social environment the individual experiences (Wigfield, Tonks, & Eccles, 2004; Wigfield, Tonks, & Klauda, 2009; Bøe, Henrikson, Lyons, & Schreiner, 2011). According to Eccles and colleagues (1983), individuals choice, persistence, and performance can be explained by their beliefs about how well they will do on the activity and the extent to which they value the activity. Eccles et al. (1983) initially used their expectancy-value model in the mathematics achievement domain. Figure 1 depicts the most recent statement of the model (Wigfield & Eccles, 2000). According to the model, expectancies and values are influenced by task-specific self efficacy, perceived difficulty of the task, and individuals goals and affective memories. These social cognitive variables are mediated by the individuals experiences and socialization influences. Expectancies and values then, in turn, influence performance, effort, and persistence

17 (Wigfield & Eccles, 2000). Task-specific efficacy is related ability beliefs, defined as the individual s perception of his or her current competence at a given activity. Ability beliefs focus on perceived present abilities while expectancies focus on the future (Eccels et al., 1983). The model connects the following components of achievement values: importance or attainment value, intrinsic value, utility value of the task, and cost. Attainment value is defined by Eccles et al. (1983) as the importance of doing well on a given task. Intrinsic value and utility value are two types of subjective task value. Intrinsic value is the enjoyment one gets from doing the task and drives spontaneous behaviors. Performing tasks that are intrinsically valued has positive psychological consequences. The utility value of a task is a measure of how that task fits into an individual s future plans. The personal reward for completing the task may not be immediate in this case. Cost refers to the degree engaging in the task limits access to other activities and is especially important in choice making (Wigfield & Eccles, 2000; Wigfield, Tonks, & Eccles, 2009). Bandura (1994) included expectancies in his discussion of self-efficacy and distinguishes between efficacy expectancy, an individual s belief that they can accomplish a task, and outcome expectancy, the belief that a certain action will lead to a certain outcome. Bandura argued that while expectancy-value theorists have historically focused on outcome expectations in their models, it is actually efficacy expectations that are more predictive of performance choice. Eccles and her colleagues, however, have measured individuals own expectations for success, rather than their outcome expectations. Therefore, their expectancy construct is more similar to Bandura s efficacy expectation construct than it is to the outcome expectancy construct (Wigfield & Eccles, 2000).

18 Expectancy-related beliefs and subjective values are influenced by a number of factors throughout an individual s lifetime. Mastery experiences during infancy and preschool develop a sense of competency in these areas. Parental feedback is also a major influence. Appropriate encouragement and feedback leads to a sense of competence and control, whereas overly critical parents can degrade the child s expectations for their future. When children enter the school system they are evaluated more systematically by adults that are not their parents or caregivers, and they begin to engage in social comparisons with their peer in order to judge their own abilities. These social comparisons can alter the sense of competence the child developed from their own mastery experiences. A child who once felt they were good a particular task may reassess her own competencies when she sees a peer performing at a higher level on the same task (Wigfield, Tonks, & Klauda, 2009). According to Schunk and Pajares (2009), these information sources are likely one of the reasons why children s ability and expectancy beliefs are aligned more strongly with performance as they age. In terms of value and interest, children s own experiences with activities, parental and teacher feedback, cultural norms, and the interests of peers can influence the value a child assigns to an activity. For example, if engineering is defined as a male-dominated field in a culture, then are less likely to value it (Wigfield, Tonks, & Klauda, 2009). The concept of task value is intricately connected with interest. For young children, task value likely appears in the form of interests in different toys and activities and is related to enjoyment. Early interests can be short-lived but can develop into lasting interests over time as the individual forms a more elaborate understanding of usefulness (Wigfield, Tonks, & Klauda, 2009). Enjoyment and interest are important constructs within participation models. These constructs are separate but complementary to exploring new objects, situations, and acquiring

19 new knowledge. According to Ainley and Ainley (2010), interest consists of a relation between person and object defined by a combination of emotion and value (p. 6). Enjoyment involves experiencing pleasure in an activity. For instance, students who experience joy and interest while working on a science topic are likely to express a desire to continue their engagement with the topic (Ainley & Ainley, 2010). This kind of enjoyment and interest pertains to an immediate situation, but current research distinguishes between measurement of these constructs at the immediate and general level. Situational interest can be transient, whereas general interest is more enduring. Over time, a maintained situational interest can accrue knowledge and value components that result in a more extended commitment to the domain and seek to extend experiences within that domain (Hidi & Renninger, 2006). Interest and value are heavily influenced by social factors that are present from birth. Parents, other adults may respond differently to boys and girls and encourage them to interact with the world and with people in different ways. Expectations for boys and girls may also be different and are reflected in the activities and toys they provide for them and in their reactions to them. Boys and girls quickly learn to engage in different hobbies and pastimes. Consequently, girls and boys develop different ways of viewing and responding to the world, which influences how and what they learn as well as what they find interesting or valuable (Murphy & Elwood, 1998). Task value components develop and change during middle childhood and adolescence. The individual is able to assign value based on how the task fits in with future plans. For instance, a student may value achieving a high grade in a math course, even if they do not find it particularly interesting, because it is necessary for entrance into medical school. Additionally, a

20 student who consistently performs well in math class may see math as an important part of their academic identity (Wigfield, Tonks, & Klauda, 2009). Higgins (2007) identifies five sources for assigning a value to different tasks. The first is the basic need satisfaction, like eating or drinking when we are hungry and thirsty. The second source is shared beliefs, which stems from cultural and social contexts. Although the individual person ultimately determines their own beliefs, the shared norms and values of the culture play a strong role in what one finds desirable. The third source of value comes from the relation of one s current self to desired or undesired end states. Social comparison is an important source of information for this value and parental views on what the child s end state should be can be exceptionally influential. Parents and teachers are likely to communicate their beliefs about the value of schooling and particular school subjects, as well as what it takes to become a productive member of society. Tasks and activities that promote congruence with the actual self and ideal self are more valuable to the individual. Fourthly, value from evaluative inference, involves how individuals make inferences about themselves and attempt to evaluate their own actions. Activities will be valued to the extent that the aid the individual in accurately judging themselves. The fifth and last source of value is experience. Higgins (2007) has tied experience to the distinction between belief and action. Beliefs and cognitions alone are not enough to generate action, and therefore cannot be the sole source of value. A major aspect of experience as a source of value comes from the extent the activity or task causes pleasure or pain. Children s experiences with their performance and school, as well as their experiences with specific teachers and classes can influence whether or not the child values school. In general, people will avoid pain-inducing activities and seek out

21 pleasure-inducing activities. However, moral and ethical experiences differ from the above because they involve consideration of other s feelings and approval and disapproval. Ability and expectancy beliefs are critical constructs in the expectancy-value theory of motivation. According to Weiner s attribution theory (1985), ability is viewed as somewhat stable characteristic over which the individual has little control. Assessing oneself as having ability or a lack of ability has important motivational consequences. An individual that attributes their achievement to their ability is positively motivated, whereas an individual that attributes a lack of achievement to a deficit in their ability experiences negative motivation. Eccles and colleagues have tested their expectancy-value model across a number of longitudinal studies. The studies address how children s expectancies for success, ability beliefs, and subjective values change across the school years and how children s ability-related beliefs and subjective task values predict performance and choice. These ideas, particularly the former, provide a specific lens for addressing the research questions in terms of how expectancies and values shape the attitudes and interests of children and adolescents towards STEM courses and careers (Wigfield & Eccles, 2000). In three such studies students from were assessed on their achievement beliefs and values in mathematics, English, music and sports. Results indicated that ability beliefs and expectancies for success were not empirically distinguishable even though they are theoretically distinct. These constructs were found to be domain specific even for children as young as the 1 st grade. Even during the very early elementary grades children appear to have distinct beliefs about what they are good at and what they value in different achievement domains (Wigfield & Eccles, 2000).

22 The study also revealed that ability-related beliefs decreased across elementary school and then the most dramatic decrease occurs at the transition to junior-high school. Declines continue into high school. Subjective value declines varied across domains. Math, reading, and music declined in value across elementary school but sports value increased. Beliefs about the usefulness and importance of math, reading, instrumental music, and sports decreased over time but interest in math and sports did not. In math, students importance ratings continued to decline across 7th grade, whereas their importance ratings of English increased somewhat during 7th grade. During high school, value increases but does not reach the levels found in elementary school for math. English, on the other hand, is valued more by the older students (Wigfield & Eccles, 2000). In short, children s ability-related beliefs and values tend to decline for most domains until early adolescence. This drop may occur because of the increase of social comparison with peers, leading to more realistic self-assessments, or from a change in the school environment that enhances competition and make evaluation more relevant. Lastly, the studies found interesting results regarding performance and choice. Even when previous performance and achievement is controlled, belief s about personal ability and expectancies for success were the strongest predictors for subsequent performance in math. Subjective task values were the strongest predictors of children s decisions to keep taking math (Wigfield & Eccles, 2000). Initially, Eccles et al. (1983) developed their model to explain gender difference in mathematical performance and choice. Their model acknowledges the powerful role that cultural and social factors play in developing one s expectancy beliefs and values and also links these beliefs and values to choice, persistence, and performance. It is clear that the application

23 of this model is not limited to the field of mathematics, and can be applied across other STEM domains. Pajares (1996) cautions that instruments assessing self efficacy must be task specific but Eccles and colleagues suggest a domain-specific approach. This difference in approaches is crucial when selecting the proper assessment instrument. The researcher agrees that items that generalize mathematics or science ignore the differences that exist within the sub-domains of these disciplines but does not feel it necessary to focus solely on specific tasks. Chapter II: Literature Review As modern society advances it is apparent that limiting individuals to gender-specific roles is counterproductive. Although issues surrounding gender equity have improved greatly in the last few decades women continue to shy away from careers and courses in science, technology, engineering, and mathematics (STEM). This underrepresentation has been a long standing problem and has been well researched from a variety of perspectives over the last several decades. The following literature review began with a general search of women in STEM, which resulted in an overwhelming number of results. Past research has focused on a variety of social and biological factors, such as spatial abilities, self-concept, attitude, sex-role perceptions, parent and teacher influences, and even the possibility of a math gene. These studies have encompassed a variety of disciplines including sociology, biology, psychology, neuroscience, education, and economics. It was necessary to narrow down the results to those that fit within the proposed theoretical framework and research questions. The literature has been divided into the following two categories: cross-cultural data on student interest, attitudes, and achievement in STEM and social stereotypes regarding women in STEM. Research in the past few decades has identified differences in male and female performance on standardized testing, but recent data indicates that gender differences in both

24 math and English achievement are diminishing, although girls tend to outperform boys on school grades and boys outperform girls on standardized achievement tests (Marsh & Yeung, 1998; McGraw, Lubienski, Strutchens, 2006). Women who drop out of STEM majors generally do not have lower entrance scores or grade point averages, making the need to investigate other social identity factors pressing (Felder, Felder, Mauney, & Dietz as quoted in Pronin, et al., 2004). If the achievement gap is closing between the genders other factors must be at work, as women are still outnumbered by men in many STEM careers and advanced courses. Women make up over 50% of college graduates, yet, as of 2006 they only make up 21% of physics degrees and 20% of engineering degrees (National Science Foundation as cited in Rudasill & Callahan, 2010). Cross-cultural data has revealed a number of interesting details regarding general gender patterns in STEM participation, attitudes, and interest across the world. Interest includes potential career interest, course-taking, and the desire to learn more about math and science. Attitudes refers to student s perceptions of themselves within the specific domain and general feelings of like, dislike, or apathy towards the subject. It is closely linked to the self efficacy construct. The literature review then focuses on the interests and attitudes of students in United States. Cross-Cultural Data on Student Interest, Attitudes, and Achievement in STEM Early cross-national surveys by the International Association for the Evaluation for Educational Achievement (IEA) indicated that although boys outperformed girls in science and had more positive attitudes towards science, the gap shrank over time for several countries, indicating that societal, rather than biological factors were prevalent. The international surveys found mixed results when it came to achievement in mathematics. Typically, girls were better at

25 computation and algebra, while boys tended to perform better in geometry, measurement, and proportional thinking (Murphy & Elwood, 1998). The ROSE project was developed to gather and analyze information from 15 year old students on their attitudes and interest in learning science and technology. The survey instrument includes items regarding out-of-school experiences, interest in learning different science and technology topics, future hopes and aspirations, and feelings of empowerment. Although the survey was administered to 40 different countries, data has been received and analyzed for 34 African, European, and Asian countries, thusfar. Similarities between countries are mainly dependent upon geographical proximity and level of development. Initial results indicate that the majority of students express a positive view of the importance of science and technology within society. However, the students convey less positive results regarding whether or not they themselves would like to become a scientist or get a job in technology. Developed countries reported lower means all-around in addition to larger discrepencies between male and female responses with expressing less interest (Sjøberg & Schreiner, 2005). The researchers hypothesize that young people s values and ways of understanding themselves is a function of the culture in which they are growing up (Sjøberg & Schreiner, 2005). Since the numbers of students choosing to study biology, medicine, and environmental studies is not falling in developed countries it is possible that young people view health and environmental issues to be more important societal challenges (Kessels, Rau, & Hannover, 2006; Bøe, Henrikson, Lyons, & Schreiner, 2011). Most students have never met a research scientist in any field, while the majority are exposed to physicians through personal experiences and through portrayals of medical practices on popular television shows. Sjøberg s (2002) previous work

26 indicated that developing countries have somewhat heroic images of scientists and engineers. They see scientific achievements that stemmed from physics and technology as a way to a more comfortable life. Murphy and Elwood (1998) agree that perspective, interest, and values are the result of social and psychological factors that are experienced from birth. They evaluated a number of studies to determine the origin of gender differences and how they influence learning experience both inside and outside of the school. Expectations are generally different for boys and girls. They are offered different activities and receive different reactions from the adults in their lives. By providing boys and girls with different socialization patterns, their interests become increasingly divergent and they develop different ways of interpreting their world. As is the case in the United States, education officials in England have noticed that the number of students participating in higher level high school physics courses has been on the decline for the past decade while biology participation has remained the same. The decline has been more dramatic for female students then for. Murphy and Whitelegg (2006) postulate that prior achievement and perceptions of the difficulty of physics are factors in students decisions about whether to study physics. Cultural influences on gendered activities outside of school may heighten these factors for girls. As Murphy and Whitelegg (2006) put it: The contents, contexts and ways of approaching problems and investigations in physics more closely reflect what boys, more than girls, engage with outside school, and those activities associated with what culture defines as masculine rather than feminine attributes. These exert a negative influence on girls engagement with physics, their sense of self-efficacy in relation to it, and their perception of its personal relevance (p. 281).

27 A number of cross cultural surveys have noted that more boys than girls wanted to study physical science topics, whereas girls interests lay more in biological and environmental sciences. In a survey of 362 students in England, Warrington and Younger (2000) that overall more boys enjoy science than girls. 63% of girls reported enjoying biology, followed by 37% for chemistry and 22% for physics. When asked if science was their favorite subject, only 6% of girls agreed compared with 37% of boys. The International Assessment of Mathematics and Science (IAEP) conducted in 1988 and 1990 found that boys outperformed girls in science in the majority of the countries, particularly in physical and earth and space sciences. The Assessment of Performance Unit (APU) conducted surveys of adolescents in the United Kingdom. The results again indicated that boys were stronger at applying physics concepts. This gap increased with age (Murphy & Elwood, 1998). Cross-cultural data support gender differences in perceived ability despite similar actual performances in STEM courses. Else-Quest, Linn, & Hyde (2010) analyzed the 2003 Trends in International Mathematics and Science Study (TIMSS) and the Programme for International Student Assessment (PISA) and reported similar mean achievements between the two genders from 69 countries, although there was considerable variability in the gender-gap among countries. However, reported higher levels of confidence, self-concept, and more positive math attitudes and affects. In support of the gender stratification hypothesis, specific domains of gender inequity lead to greater achievement gaps, although this was lessened when composite indicators were used. Interestingly, the data showed that had lower mathematical self concepts and motivation and more anxiety in countries with greater gender equity.

28 Similar results appear in studies that focus on students in the United States. Researchers have consistently found that male students express a higher self perception and more positive attitudes towards mathematics courses and their own performance within these courses, despite similarities in actual achievement (McGraw, Lubienski, & Strutchens, 2006; Trusty, 2002; Fan, 2011). In an application of the expectancy-value model, Fan (2011) found that female students actually had greater confidence in their English abilities, higher educational expectation, academic engagement, and utility and intrinsic value when compared to. Additionally, female students were more likely to feel like they had teacher support and friends that value academics. Math utility value was similar, but reported lower math ability beliefs. Utility value was the strongest predictor of academic engagement for both genders. Teachers were found have an influence on intrinsic subject interest for math and English, while peers contributed to the students subject and task value (Fan, 2011). Other cross-cultural studies have examined the link between student interest in science and personal value. Analysis of the PISA (Program for International Student Assessment) reveals that there is a strong relationship between personal value of science, enjoyment of science, and interest in learning more about specific topics within the scientific field. Personal meaning and relevance were particularly important factors in gaining student interest and engagement across the 57 countries that participated in the PISA (Ainley & Ainley, 2011). In a study by Lindahl (2007) Swedish students reported that interest was the most important factor in choosing to follow science in school. As social cognitive theory would predict, scientific exposure and encouragement from parents and teachers are predictors of interest in science. Therefore, the classroom environment should be correlated with science career interest. Students in Australia, Sweden, and England