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Pygmalion in the Classroom and the Home: Expectation’s Role in the Pipeline to STEMM


by Se Woong Lee, Sookweon Min & Geoffrey P. Mamerow - 2015

Background/Context: Although students frequently begin forming ideas about potential college majors or career choices prior to entering college, research on Science, Technology, Engineering, Mathematics (STEM), and (M)edicine has almost exclusively focused on students’ experiences in postsecondary institutions. To better understand the full length of the STEMM pipeline—from high school through to postsecondary levels—it is essential to identify and explore factors that influence students’ choices in STEMM while they are in secondary schools, a setting that is arguably the first critical step of the pipeline.

Purpose/Objective: Among factors that influence students’ choices to pursue STEMM fields, this study examines the influence of students’ self-efficacy and expectation, as well as the expectation and encouragement they received from parents and high school teachers on their decisions to major in, complete a degree in, and pursue a career in STEMM. Given this focus on expectation specifically, the study employs a conceptual framework developed through the application of prior literature on teacher and parent expectations, as well as Social Cognitive Career Theory.

Research Design: Using the Longitudinal Study of American Youth (LSAY) 1987 data, the study investigated students’ decision making at three distinct time points along a typical STEMM education/career path and predicted their persistence in the STEMM pipeline by utilizing logistic regression analyses. To further examine whether such sets of expectations are moderated by gender, analysis also included interaction terms for gender and teacher expectation, as well as those of gender and parent expectation.

Findings/Results: The results of this study indicate that expectation plays a significant role in students’ choices in STEMM and teacher expectation is shown to be especially influential. Focusing on gender differences, males’ choices in STEMM were shown to be most affected by their teachers’ educational expectations and encouragement while females’ choices were most affected by those of their parents.

Conclusions/Recommendations: The decision to pursue education and a career in a STEMM is not a one-time decision, but a longitudinal process that begins during secondary education and carries on through into college. The findings of this study provide meaningful information about the importance of students’ self-efficacy and expectation within the STEMM pipeline, as well as the influence teacher expectations and encouragement can have on students’ pursuit of and persistence in STEMM.




INTRODUCTION


As modern life has become increasingly mediated by new and complex technologies, the study of Science, Technology, Engineering, and Mathematics (STEM) has become indispensable, if not paramount, to society’s future needs and wellbeing (Nicholls, Wolfe, Besterfield-Sacre, & Shuman, 2010; Ramaley, 2009). When grouped with (M)edicine, the inextricably linked STEM(M) fields form an array of disciplines that impact peoples’ lives in myriad, profound ways each day. Appropriately, a national focus on STEMM is now reflected in social and education policy at all levels, including the nation’s highest federal initiatives (Kuenzi, 2008; National Science Board, 2007), and substantial efforts and investment have been expended to bolster educational preparation to attract and educate sufficient numbers of students to maintain America’s global lead in technological fields (Carnevale, Smith, & Melton, 2011; Holdren & Landren, 2012; National Science Board, 2012).

Despite this growing attention, the number of students moving through the STEMM pipeline has been declining—even as 17 of the 20 fastest growing occupations in 2014 will be health care related (U.S. Department of Labor, 2007). Further, Gilbert and Jackson (2007) note, “While postsecondary enrollment has increased over the past decade, the proportion of students obtaining degrees in STEM fields has fallen” (p. 9). Recent research further underscores the misalignment, finding that 60 percent of new college students who expect to pursue a STEM major instead end up graduating with a non-STEM degree (Holdren & Landren, 2012). This expectation-reality gap is even more sharply revealed in the Medical fields where many STEM degree holders gravitate, and where there is a shortage of qualified professionals—most notably a 25,000 doctor shortage in primary care medicine (Green, Savin, & Lu, 2013; Petterson et al., 2012). These findings suggest a significant “leak” in the STEMM pipeline, with many promising students failing to meet their own expectations, and instead washing out, down the drain. Even more troubling are patterns of underrepresentation; female students and students from lower socioeconomic statuses are significantly underrepresented in many STEMM fields which, in turn, limits their participation in well-paying, high-growth STEM professions—including medicine (Malcom, Chubin, & Jesse, 2004; Stout, Dasgupta, Hunsinger, & McManus, 2011).

At the same time that policy makers have implemented initiatives to increase students’ participation and interest in STEMM fields, researchers have sought to better understand the characteristics of students who have successfully navigated the pipeline (e.g., Steffens, Jelenec, & Noack, 2010). Though many factors influence college-going, choice of major, and success in college, research has shown high school students’ self-efficacy and teacher and parental expectation to be significant factors that impact initial postsecondary enrollment (Engberg & Wolniak, 2010), persistence (Horn, Kojaku, & Carroll, 2001), and degree completion (Adelman, 2006). Much of the contemporary research into STEM specifically, however, has focused primarily on persistence and attainment among students already within STEM degree programs (e.g., Espinosa, 2011; Wyer, 2003), and has not factored in students’ high school experiences. Furthermore, research examining the pipeline from high school-to-career typically focuses solely on STEM career attainment; this ignores the reality that many students pursuing STEM degrees aspire to secure careers in (M)edicine (Dienstag, 2008; Muller & Kase, 2010; Yens & Stimmel, 1982). Taken together, few studies have placed the relationships between high school students’ self-efficacy in science and math—in conjunction with teacher and parent expectation and encouragement—at the center of inquiry into declaration of a STEMM major, completion of a STEMM degree, or eventual STEMM career attainment. Considering the fact that students often begin thinking and forming ideas about potential college majors or career choices well prior to entering college (National Research Council, 2009; Riegle-Crumb, Moore, & Ramos-Wada, 2011), it is crucial to think about the factors influencing students’ choices while in the secondary school setting, arguably the first critical steps within the STEMM pipeline.

With this context in mind, we utilized logistic regression to investigate these relationships longitudinally, tracing students’ decision-making pathways from high school through to successful degree attainment and career choice in STEMM. We further propose a conceptual framework that builds off of prior literature examining teachers’ expectations, parents’ expectations, and Social Cognitive Career Theory (SCCT). We hypothesize that a student’s STEMM efficacy and expectation to attend college, in conjunction with the college expectations held by teachers and parents, to be a good composite predictor of the likelihood a student enters and completes a STEMM degree program as well as attaining a STEMM career. Furthermore, reflecting research that has found significant gender differences with respect to pursuit and attainment within STEMM, as well as gender differences described in the literature on educational expectation, we hypothesize the impact of self-efficacy and expectation of parents and teachers to differ by gender. We used data from a nationally representative survey, the Longitudinal Study of American Youth (LSAY) 1987, to answer the following questions:

1.

What are the roles of self-efficacy in math and science and college expectations in students' likelihood to pursue a STEMM degree, attainment of that degree, and entry into a STEMM career?

2.

What are the roles of both teacher and parental educational expectations in students’ intentions to pursue a STEMM degree, attainment of that degree, and a STEMM career choice?

3.

If the relationships outlined in Questions 1 and 2 are significant, are they moderated substantially by a student’s gender?

LITERATURE REVIEW

INTRODUCTION

In the review that follows, we begin by describing relevant background on STEMM education, specifically highlighting findings that have informed our understanding of the dynamic characteristics within this critical context. We then move on to what research has revealed about students’ vocational and educational choices and what is known about specific factors associated with these choices in STEMM fields. Next, we detail the theoretical construct of SCCT and illustrate how it has been used to inform inquiry into similar contexts. Following this, we analyze the expectation literature and report findings from studies on both parental and teacher expectations and how they affect students. Finally, throughout this review, we draw attention to research that explores gender differences that have been revealed in each of the aforementioned domains of literature.

STEM(M) EDUCATION

Coupled with medicine (M), the STEMM fields are the fastest growing occupational group in the United States (Carnevale et al., 2011; Solberg, Kimmel, & Miller, 2012). STEMM fields have fueled the United States’ global economic leadership and innovation for decades and have become a central research focus for many. While the bodies of literature on education and careers in STEM and (M)edicine have been traditionally segregated, there has recently been a growing realization they should be considered together (Miller & Solberg, 2012). This new understanding stems, in part, because pathways to medical disciplines and traditional STEM fields share many similarities —few colleges, for example, offer pre-med degrees, and many students pursuing traditionally designated STEM majors in college may –in fact—be pursuing STEMM majors. That is, a student majoring in biochemistry is more than likely pre-med (Fuchs & Miller, 2012; Miller & Kimmel, 2012; Pearson & Miller, 2012). Little research has taken this reality into account, and moreover, without adequate information about career outcomes beyond the college years, distinguishing between STEM and STEMM with respect to college degree programs can be a difficult. Still, acknowledging the historic division that is reflected in the bodies of literature describing STEM and Medicine research, we begin our review with the research exploring STEM that has helped to guide this study.

Notwithstanding a real or artificial partition between STEM and STEMM, industrial, educational, and political leaders have called for the recruitment and retention of large numbers of students from diverse backgrounds and with diverse interests in order to maintain and improve the nation’s competitiveness in STEM fields (Malcom et al., 2004). STEM fields, including (M)edicine have long experienced challenges in increasing the recruitment and retention of high competency students, instead experiencing what has been termed “brain drain” (Hossain & Robinson, 2012; Tyson, Lee, Borman, & Hanson, 2007). Despite a decade of high demand, the supply of STEM students has declined, resulting in shortages of skilled STEM workers (Carnevale et al., 2011; U.S. Department of Labor, 2007). In response, scholars have emphasized the development of a strong STEM pipeline from K–12 schools, since students build and develop their initial interests during their precollege years (Engberg & Wolniak, 2010; Leaper, Farkas, & Brown, 2012). Early introduction to math and science during primary and secondary school has been shown to help students gain and build motivation and aspiration toward STEM fields, making students’ K–12 experiences critical in their decisions to pursue STEM fields in college and beyond (Archer et al., 2013; Crisp, Nora, & Taggart, 2009). Research has shown, for example, how college students’ perceptions and conceptual understanding of physics were linked to their experiences in high school physics classes, including the encouragement they felt from their teachers (Enyedy, Goldberg, & Welsh, 2006; Hazari, Sonnert, Sadler, & Shanahan 2010).

Enlarging the context beyond students’ K–12 and precollege experiences, research has identified factors affecting students’ decisions to pursue STEM degrees in college, including state graduation requirements, high school course offerings, and choice of a technical focus (e.g., Federman, 2007; Maple & Stage, 1991; Trusty, 2002). More recently, Wang (2012) has categorized many of the factors that positively influence students’ choices to enter and complete a STEM degree. These include: early exposure and proficiency in math and science (Anderson & Kim, 2006; Hagedorn & DuBray, 2010); high school curricula (Elliott, Strenta, Adair, Matier, & Scott, 1996; Hazari et al., 2010); parental education (Grauca, Ethington, & Pascarella, 1988; Sonnert, 2009); amount of math and science completed during high school (e.g., Ethington & Woffle, 1988; Maple & Stage, 1991); and advanced courses in math and science (Robinson, 2003; Tyson et al., 2007). Other research has identified factors that militate against continuation in STEMM fields during college including opaque pedagogical approaches, lack of learner support, and an uninviting environment in key gateway courses (Barr, Matsui, Wanat, & Gonzalez, 2009).

Whether positive or negative, research has also shown that the factors associated with students’ decisions to pursue STEM degrees, as well as their STEM major retention, can vary substantially across students’ characteristics (Hanson, 2004; John, Hu, Simmons, Carter, & Weber, 2004; Trusty, 2002). Previous studies on inequities and disparities in STEM major choice and retention have focused, in particular, on gender differences (e.g., Espinosa, 2011; Wyer, 2003). Specifically, females have traditionally been marginalized in STEM fields with too few pursuing the fields during their postsecondary education (Blickenstaff, 2005; Kamerasde, 2007). More importantly, being less likely to complete a STEM degree, fewer women will secure a STEM career (National Science Foundation, 2009; Seymour & Hewitt, 1997). These gender disparities have contributed over the long term to women having fewer positive opportunities in STEM professions as compared to their male counterparts (Kamerasde, 2007). What is more, disparities of representation are evident in the pursuit of a premedicine track as well; traditionally underrepresented ethnic and racial minorities, as well as females, are less likely to continue through to the end and emerge from the STEMM pipeline (Barr et al., 2009).


Using stereotype threat theory, research has found that gender stereotypes are an important factor in the dropout rate of female students within math-intensive fields (Cheryan, Plaut, Davies, & Steele, 2009; Steffens et al., 2010). In addition, research has found such disparities to be more closely related to environmental factors than to an individual's personality traits or characteristics (Gunderson, Ramirez, Levine, & Beilock, 2012; Tiedemann, 2000). Specifically, Gunderson et al. (2012) argued that a female’s attitude toward math can be influenced by the expectations communicated by parents and teachers. Studies have shown that parents shape and influence female students’ perception of math and science subjects as well as their pursuit of careers in those fields (Jodl, Michael, Malanchuk, Eccles, & Sameroff, 2001; Tenenbaum & Leaper, 2003). Other studies have identified factors associated with a female’s attitude toward math and science, revealing that teachers’ levels of expectation are highly related to female students’ perception of the fields. Bodzin and Gehringer (2001) found that one reason why women too frequently exhibit low expectations and competence in these domains is because school curricula and teachers’ expectations often assume male competence and female disinterest in math and science. As a result of these stereotypes females were less likely to choose majors and careers in STEM (Davies, Spencer, Quinn, & Gerhardstein, 2002).

Despite a growing body of research describing the myriad factors that influence whether or not a student chooses a STEM or broader STEMM major, few studies have examined the lasting effect of those factors—an approach that necessarily requires longitudinal data and analysis of high school, college, and—in particular—career contexts. To inform an approach that incorporates factors influencing career (and therefore major) choice across a longitudinal context, we next turn to Social Cognitive Career Theory (SCCT).

SOCIAL COGNITIVE CAREER THEORY (SCCT)

Prior research has demonstrated that individuals’ self-efficacy and outcome expectations are critical influences on the effort they expend on a range of activities (Bandura, 1986; Pajares & Schunk, 2002). Building off of Bandura’s Social Cognitive Theory (1986), the tenets of Social Cognitive Career Theory (SCCT) (Lent, Brown, & Hackett, 1994) enlarge and enrich the applicable context by incorporating self-efficacy and outcome expectations into a coherent theory tailored to career-decision situations, including school choices prior to employment.

When applied to real-world contexts, SCCT suggests that individuals develop interest in those career activities in which they feel efficacious or perceive themselves as more competent, and for which they expect positive outcomes (Bandura, 1986; Lent et al., 1994). The first tenet of the theory, self-efficacy, affects the effort people expend on career-related activities such as education, perseverance when confronting obstacles, and resiliency when facing challenges (Bandura, 1977; Pajares & Schunk, 2002). Among scholars who study it, self-efficacy is most commonly defined as encompassing people’s beliefs in their own personal abilities to achieve goals or others desired outcomes (Diegelman & Subich, 2001). More specifically, self-efficacy is expressed in the degree of confidence people feel about executing specifically delineated tasks (Coopersmith, 1967). This definition stands in contrast to the more general self-esteem, which instead reflects a personal measure of self-worth or value that is un-tethered to specific actions one might perform (Zimmerman & Cleary, 2006). Prior research has also demonstrated strong relationships between self-efficacy and the effort people expend on career-related activities such as education, perseverance when confronting obstacles, and resiliency when facing challenges (Bandura, 1977; Pajares & Schunk, 2002).

Where self-efficacy focuses on perceptions of ability, outcome expectations, the second component of SCCT, refers to an individual’s outlook or perceptions concerning the likely results of his or her effort(s) toward a specific end (Bandura, 1986). Research has also shown that beliefs about one’s outcomes in specific activities influence academic and career choice behaviors (Bandura, 1986; Lent et al., 1994). Furthermore, according to SCCT, individuals develop interest in those career activities in which they feel efficacious, perceive themselves as more competent, and for which they expect positive outcomes (Bandura, 1986; Lent et al., 1994). In short, SCCT hypothesizes that both self-efficacy and outcome expectations influence educational and vocational choices, as well as success—both directly and indirectly (Lent et al., 1994).

In addition to self-efficacy and outcome expectations, individual and contextual traits including gender (Lent & Brown, 1996), socioeconomic status (Howard et al., 2011; Trusty, Robinson, Plata, & Ng, 2000) and education level (Bandura, 1977, 1997) can each influence career interest, choice, and performance. Gender, especially, seems to affect the learning experiences and feedback to which a person is exposed, and SCCT provides a powerful lens through which to view the psychological and social effects of gender on education and career interests (Evans & Diekman, 2009; Lent & Brown, 1996, 2006; Schaub & Tokar, 2005). Research has also demonstrated ways both parent and teacher attitudes about gender roles can affect girls and boys differently, can directly influence girls' career-related self-efficacy, outcome expectations, and interest in “traditionally male” fields such as many in the STEM domain (Gunderson et al., 2012; Rosser, 2004; Shapiro & Williams, 2012) and Medicine (Bickel, 2001). SCCT suggests that gender’s influence on career interest, choice, and performance operates largely through self-efficacy and outcome expectations and the gendered learning experiences that shape students’ beliefs (Lent et al., 1994).

SCCT has been increasingly used as a theoretical lens to guide investigation of STEM field choice (Lent et al., 1994; Lent, Brown, & Hackett, 2000; Usher & Pajares, 2009; Wang, 2012, 2013; Zeldin & Pajares, 2000). Researchers have found a strong association between students’ efficacy and expectation and their interest and success in STEM fields (Byars-Winston & Fouad, 2008; Hackett & Betz, 1989; Pajares, 2005; Wang, 2012, 2013).

While SCCT provides a powerful lens in the aforementioned contexts, its explanatory power is derived from the way it successfully leverages expectation to explain behavior within a defined context. We now turn to what research has revealed about how expectation influence help to shape students’ decisions.

EXPECTATIONS OF TEACHERS AND PARENTS

A student’s educational and vocational choices are not only influenced by individual personality traits or characteristics but also by parents and teachers, both of whom are powerful influences on a child’s environment and education. Children spend a large amount of time both at home and in school, and their interaction with parents and teachers significantly mediates their educational experiences (Hill & Tyson, 2009) and helps form their academic attitudes, aspirations, and motivations (Chirkov & Ryan, 2001; Gunderson et al., 2012). It is no surprise, then, that numerous studies have pointed out the critical roles of parents and teachers play in students’ future planning including educational and vocational choices (Perry, Liu, & Pabian, 2010).

One influential study of expectation by Rosenthal and Jacobson (1968), “Pygmalion in the classroom,” examined how teachers’ expectations and behavior may accelerate or prevent a student from meeting his or her true potential revealed how teachers frequently behave differently towards different students, based on individual expectations (Brophy & Good, 1970). Teachers may create a warmer climate, build more intimate relationships, and give differential feedback to students for whom they hold higher expectations (Rosenthal, 1994). Ferguson (1998) further found that teachers typically develop these kinds of expectations based on students’ prior performance, race, ethnicity, gender, or social class. These expectations, positive or negative, can affect students’ academic behavior and performance in the classroom (Brophy & Good, 1970), including in math and science where early positive performance is critical to a student’s success within the STEMM disciplines (Crisp et al., 2009).

Studies since “Pygmalion” have continued to show how teacher expectations and encouragement in classroom situations can be linked both positive and negative student performance (Babad, 1993; Brophy, 1983; Cooper & Good, 1983; Good, 1987; Weinstein, 2002). Even after controlling for students’ prior performance, for example, research has shown teachers' expectations to predict gains in student achievement across disciplines (Frome, Lasater, & Cooney, 2005; Muller, Katz, & Dance, 1999). Furthermore, teacher expectations have been shown to influence students’ academic performance and long-term educational goals like college (Benner & Mistry, 2007; Mistry, White, Benner, & Huynh, 2009; Rubie-Davies, Hattie, & Hamilton, 2006), and Mistry et al. (2009) found teacher’s expectations to have a longer lasting effect on achievement than parent expectations. In particular, studies have shown that expectation and encouragement from math and science teachers each have a strong positive correlation with students’ academic success and pursuit of STEM degrees (Heaverlo, 2011). Research has further shown that math and science teachers can play a critical role in increasing the interest and motivation of underrepresented students in math and science —essential precursors to the pursuit of higher education and careers in STEM fields (e.g., Shumow & Schmidt, 2013).

While extensive research has explored the relationship between teacher expectations and outcomes within the elementary school setting (e.g., Babad, 1998; Good & Weinstein, 1986; Weinstein, 2002), relatively little research has examined the effects of secondary school teachers’ expectation and encouragement on college degree choice or completion. Without substantive evidence in the more general case, options are limited for conclusions that can be drawn within the specific STEMM context.

As with teachers, prior research on parent expectation has concluded that parental expectations can affect student outcomes—directly through parent–child interactions, and indirectly through parental beliefs and their own perceived efficacy in providing academic support to their children (Benner & Mistry, 2007; Catsambis, 2001; Wentzel, 1998). Ma (2001) found that parents’ expectations regarding college had more of an effect on students than either teacher or peer expectations. Even when controlling for factors such as socioeconomic status and academic achievement, Trusty, Plata, and Salazar (2003) found parental expectations had the greatest influence on enrollment in college while Brasier (2008) found parental expectation to be consistently important in students’ decisions to pursue post-secondary education. Furthermore, the influence of parental expectations appears to have a cumulative effect over time (Bleeker & Jacobs, 2004). Finally, aspirations in math and science-related areas do not appear to be shaped by coincidence or chance, but are instead influenced, in large part, by familial attitudes, especially those of parents (Archer et al., 2012). Parental expectation in math and science have also been shown to influences students’ academic and vocational choices as well as math and science efficacy; each is also highly correlated with students’ advancement in STEM(M) (Hou & Leung, 2011).

Recent literature has transitioned from using the “expectations” synonymously with “aspirations”(De Civita, Pagani, Vitaro, & Tremblay, 2004; Goldenberg, Gallimore, Reese, & Garnier, 2001). While parental expectations have been shown to have a positive relationship with educational aspirations (Benner & Mistry, 2007; Catsambis, 2001), it is important to distinguish between the two. In this context, aspirations consist of the most possible and desired options available; expectations are the most likely outcomes (Gottfredson, 1981; Markus & Nurius, 1986). The distinction relates to choice, and expectations are more realistic than aspirations. Expectations are formed through reflection over past performances, and therefore make better indicators of attainment than aspirations (Andres, Adamuti-Trache, Yoon, Pidgeon, & Thomsen, 2007). This contrast may be critical with respect to STEMM career aspirations and attainment and implicate expectation as a potentially robust predictor of success within the STEMM domain.

Yet despite prior understanding of linkages between teacher and parental expectations and student achievement and college aspirations, few studies have placed those relationships at the center of inquiry specifically into STEMM degree or career attainment. Fewer still have examined ways in which both parental and teacher educational expectations (Benner & Mistry, 2007), together with a student’s own educational expectation and sense of efficacy in math and science can affect students’ pursuit of degrees or careers in STEMM fields. This omission in the research record is surprising given the potential of expectation and efficacy to help illuminate the poorly understood dynamics of STEMM major choice, completion, and career obtainment. At the same time it presents an opportunity to develop better models for understanding an academic and economic domain of high demand that is marked, unfortunately, by equally high attrition. Efficacy and expectation are measures all students share, and they are formed far earlier than college matriculation. New understanding of their to the success of students within the STEMM fields can likely lead to policies that are more effective than what has yet been tried and address growing needs in this critical area.

To guide investigation of our research questions, we propose a conceptual framework that builds on teacher and parent educational expectation theory as well as SCCT theory. Although SCCT incorporates three broad theoretical strands—self-efficacy, outcome expectations, and personal goals—this study focuses on the combination of self-efficacy and outcome expectations, the pairing of which is critical to understanding student’s educational and vocational choices in STEMM. Further, current research also suggests that student expectation and efficacy interact consistently with, and change, based on teacher and parent encouragement and expectation (Benner & Mistry, 2007; Mistry et al., 2009; Wang, 2012, 2013). This study hypothesizes that experiencing higher levels of expectation from parents and teachers to pursue college, in conjunction with a students’ self-efficacy in math and science to be good predictors of increased entrance into STEMM degree programs, completion of those degrees, and attainment of STEMM careers.

Still, considering the persistent inequities and disparities with respect to gender representation in STEM(M) fields (Barr et al., 2009; Espinosa, 2011), it is unrealistic to assume that both parents and teachers demonstrate the same levels of expectation to pursue STEM(M) fields irrespective of student gender. To wit, previous research has explored gender’s differences in the pursuit and completion of STEM(M) education. While such research, guided by stereotype threat theory (Steffens et al., 2010), has explored how parental expectation, as well as teacher expectation often differ by gender (Bodzin & Gehringer, 2001; Tenenbaum & Leaper, 2003) little research has considered their impacts in tandem. Thus, we hypothesize that male and female students each experience different levels of expectation to pursue college from their parents and teachers. These expectations, as a result, influence students’ choices to enter into STEMM degree programs, complete those degrees, and attain STEMM careers.

METHODS


DATA

The study’s data are from the Longitudinal Study of American Youth (LSAY) designed by the Institute for Social Research at the University of Michigan, Ann Arbor. The LSAY is a national 6-year panel study of mathematics and science education in public middle and high schools. Data collection began in fall of 1987 by surveying seventh and 10th grade students (younger cohort and elder cohort, respectively) as well as their parents, teachers, and school administrators at public schools across the United States. The original 7-year data collection period ended in 1994 when the elder cohort was 4 years out of high school and the younger cohort was one year out of high school. The LSAY’s 5,945 respondents consist of 3,116 seventh graders (younger cohort) and 2,829 10th graders (elder cohort).

Descriptively, LSAY participants include 3,026 males (50.9%) and 2,919 females (49.1%), as well as 4,122 White students (74.2%) and 1,432 non-White students (25.8%). More than a half of the participant’s parents (55.1%) had obtained a maximal level of education of high school or less, 14.6% had received some college education, and 30.3% earned a BA degree or higher. Annual data collection resumed in 2007 with a proportional sample of the original study cohorts and participants’ educational and occupational activities from high school through to their early 30s were captured. Of the original LSAY participants, 3,574 completed the follow-up survey in 2007, or approximately 70% of the original sample (Miller & Kimmel, 2012).

SAMPLE

The study utilizes data from the elder LSAY cohort1 (n = 2,829) and focuses on the 1,776 students who entered college and completed the follow up career survey in 2007. The combination of LSAY’s two survey tranches allowed us to examine relationships between students’ high school experiences and, in turn, their college and career choices. Analysis began with LSAY participants who matriculated into college, was refined to include only those who declared STEMM majors, was refined again to further focus on STEMM graduates, and converging finally on those students who has secured a STEMM career.

Though this approach naturally caused our sample size to decline at each stage of analysis, it also placed us directly (though metaphorically) inside the pipeline along with those who had persisted, allowing us to focus directly on the students whose experiences could address the questions posed by our study. This procedure identified those students who “survived” the pipeline, enabling us to analyze how the effects of students’ self-efficacy, expectation, as well as expectation from teachers and parents during the high school years lasted throughout the STEMM pipeline. Simply put, we identified and focused on the “persisters” rather than others who had leaked out along the way.

The presence of missing data from individual cases, as with even the highest quality data sets, was unavoidable and had a requisite effect on the sample sizes available for our analysis time points. During the first year of college, for example, 415 of the original 1,776 students had selected STEMM majors while 1,324 pursued non-STEMM options. Data were completely unavailable for 37 students. With respect to the bachelor’s degree completion, 239 of the 1,776 students received a STEMM degree, while 1,513 students completed non-STEMM options. Data were unavailable at this time point for 24 students. Finally, 231 of the original 1,776 students selected and secured a STEMM-related career, while 1,544 students either selected a non-STEMM career or were not represented in the workforce. The data for only 1 student were unavailable.

We examined the selected cases for missing values of which varying degrees were found on a number of independent variables. Analysis revealed, however, that with the exception of the student ethnicity variable where 7.2% of students’ information was missing, no other variables exceeded more than 5% missing values. In order to begin addressing the challenges presented by missing cases, we conducted Little’s “Missing Completely at Random” (MCAR) test (1988), which returned a “significant” outcome. This result suggests that the missing values in the data set are not at random.

To then compensate for these missing values when estimating the logistic regression models in our analysis, we employed a list wise deletion method. In other words, only whole, intact cases with no missing variables were included during the final analysis. Although list wise deletion reduces the final number of cases available for inclusion in modeling, as evaluated against other accepted practices for dealing with missing data it is both powerful and tolerant in analyses with nonrandom missing cases. Furthermore, its application is particularly appropriate with respect to missing values on independent variables within logistic regression estimation (Allison, 2002). Still, any procedure used to help mitigate the ill effects of missing data can bias estimates, reduce statistical power, and should be considered holistically when interpreting the results of analyses such as those presented in this study.

After examining all of our sample’s cases for the presence of missing values, we calculated that 242 (12.63%) were missing individual observations at the first year of college time point, 242 (13.64%) at college completion, and 246 missing (13.87%) at the career choice time point.

Table 1. Student Demographic Information (N = 1,776)

 

N

Percentage

GENDER

  Female

905

50.9

  Male

871

49.1

  Total

1,776

100

   

RACE

  Hispanic

124

7.6

  Black

157

9.6

  White

1,287

72.4

  Other

79

4.8

  Total

1,648

92.7

  Missing

128

7.3

   

PARENT EDUCATION LEVEL

  High School or less

937

52.8

  Some college

240

13.6

  BA or higher

599

33.7

  Total

1,776

100


MEASURES

This section summarizes the information of the variables included in this study. Table 2 lists the names, descriptions, and LSAY labels of all variables that were used. Although LSAY’s postsecondary education variables such as quality of program or the number of math and science courses taken in college may provide meaningful information about a student’s retention in STEMM, we focused exclusively on secondary education variables to examine the influence of self-efficacy and expectation, as well as the expectation and encouragement received from high school teachers and parents, on students’ decisions to major in, complete a degree, and pursue a career in STEMM.

Outcome Variables

In order to capture the relationships between these various factors and students’ choices to persist at critical time points throughout the STEMM pipeline, this study utilizes three dependent variables as follow: declared STEMM major during first year of college, STEMM degree attainment, and employment in a STEMM field.

Student Demographic Background Measures

Gender and race variables were included in our analysis. We created dummy variables for racial groups including variables for Black and Hispanic. A student’s socioeconomic status was determined using his or her parents’ education level as a proxy.

College Encouragement and Expectation Variables

The study includes two independent “college expectation” variables that measure the levels of expectation students experienced from both their’ parents and high school teachers. The “teacher’s college expectation” variable measures how much students perceived their high school science and math teachers as supporting or expecting them to go to college. The “parents’ college expectation” variable measures the degree to which parents reported encouraging or pushing their children toward college. In both cases, high values indicate high level of expectation or encouragement, while low values indicate low levels of expectation. The study also includes a “student educational expectations” variable that captures the level of education students expected themselves to achieve. The mean value of each was used to center the variables and account for the differing scales used to measure expectation of parents, teachers, and students.

Math and Science Efficacy Variables

The study includes the independent variables “math efficacy and “science efficacy.” Each measures students’ perceptions of how confident they are about their performance in math and science. The study focuses on math and science efficacy, in particular, because those subjects have been shown to have a greater impact on (or be directly linked to) students’ educational and vocational choices with respect to STEM(M)-related fields (Webb, Lubinski, & Benbow, 2002). More specifically, LSAY items measuring students’ perceptions of “being good at math” and of “understanding math well” were combined to create a composite math efficacy variable. Students’ perceptions of “being good at science” and of “understanding science well” were combined to create a corresponding science efficacy variable.

Interaction Terms

In order to explore whether relationships between efficacy, expectation, and participants’ persistence in STEMM varies across gender groups, we utilized several interaction variables. We analyzed the interaction effects of gender against variables measuring parent and teacher expectation that students go to college.


Table 2. List of Variables in the Study

Variable Name

Description

 

LSAY Label

Dependent Variables

 

 

 

  College Major

Respondent's Choice in STEMM in the 2nd semester at college. 1=yes, 0=no

 

RMAJFIRST2

  College Completion

Respondent's College completion in STEMM. 1=yes, 0=no

 

RMAJLAST2

  Career Field

Respondent's Entrance into STEMM career. 1=yes, 0=no

 

RSTEMMB

 

 

 

 

Demographic Variables

 

 

 

  Male

1=Male, 0=Female

 

Recoded from GENDER

  Black

1=Black, 0=Non-Black

 

Recoded from RACE

  Hispanic

1=Hispanic, 0=Non-Hispanic

 

Recoded from RACE

  Parents’ education level

Parents' Highest Level of Education

 

PEDUC

 

Items based on five-point scales with 1 indicating "LT HS diploma", 2 indicating "HS diploma", 3 indicating "Some college", 4 indicating "4 year college degree" and 5 indicating "Advanced degree"

 

 

 

 

 

 

Main Independent Variables

 

 

 

  Student expectation

Student Educational Expectation during High School (mean-centered)

 

SEDEXHS

 

Items based on six-point scales with 1 indicating "High School only", 2 indicating "Vocational training", 3 indicating "Some college", 4 indicating "Baccalaureate", 5 indicating "Masters", 6 indicating "Doctorate or Professional Degree"

 

 

  Parental expectation

Parent expectation to go to College during High school (mean-centered)

 

PCOPHHS

 

Items based on four-point scales with 1 indicating "do not push to go to college" and 5 indicating "strongly push to go to college"

 

 

  Teacher expectation

High School Teachers' (math and science) College Expectation (mean-centered)

 

TCOEXHS

 

Items based on 2-point scales with 1 indicating "Doesn't expect college" and 2 indicating "Expects college"

 

 

  Math efficacy

- I am Good at Math

 

GA32B, IA37B, KA46B

 

- Understand Math Well

 

GA32C, IA37C, KA46C

 

Items based on five-point scales with 1 indicating "strongly agree" and 5 indicating "strongly disagree"

 

 

  Science efficacy

- I am Good at Science

 

GA33B, IA38B, KA47B

 

- Understand Science Well

 

GA33C, IA38C, KA47C

 

Items based on five-point scales with 1 indicating "strongly agree" and 5 indicating "strongly disagree"

 

 

 

 

 

 

Interaction Terms

 

 

 

  Gender X Teacher expectation

Male X Teacher expectation

 

GENDER, TCOEXHS

  Gender X Parental expectation

Male X Parental expectation

 

GENDER, PCOPHHS



DATA ANALYSIS

As this study employs dichotomous dependent variables to indicate whether or not students persisted through each time point to a STEMM major or career, we employed binary logistic regression to test our models. We investigated students’ decision making at three separate, but distinct, times along a typical STEMM education/career path by conducting six logistic regression analyses to predict students’ persistence in the STEMM pipeline based on the independent variables included in the study. The structure of the LSAY survey data necessitated the use of analytic weights to better represent the original population (Miller & Kimmel, 2012). The weights adjusted each respondent’s differential probability of being selected into the sample, and all estimates were calculated from statistical weights using the “WEIGHTR” variable provided with the LSAY data set.

The analysis proceeded in several steps, beginning with descriptive statistics for all variables. The statistics for selected participants of LSAY’s elder cohort are presented in Table 3.


Table 3. Descriptive Data for Participants (N = 1,776)

Variables

Range

Mean

SD

 Gender

   

   Male

0~1

0.51

0.5

    

 Race/Ethnicity

   

   Hispanic

0~1

0.07

0.26

   Black

0~1

0.1

0.3

    

 Family Background

   

   Parents’ Education Level

1~5

2.91

1.22

    

 Student Academic Efficacy

   

   Math Efficacy

1~5

3.51

0.89

   Science Efficacy

1~5

3.44

0.88

    

 Educational Expectation

   

   Student Expectation

-3~2

0.26

1.39

   Parental Expectation

-1.98~2.02

0.13

1.05

   Teacher Expectation

-1.31~4.69

0.38

1.89


We then conducted correlation tests to check for multicollinearity between independent variables (see Table 4). Results indicate that student educational expectation, parent expectation, and teacher expectation are positively associated with one another as well as with student math and science efficacy. Moreover, student math and science efficacy are positively associated with each other as well. As all variables exhibited low to moderate correlations (from .07 to .51), we were able to eliminate problems related to multicollinearity (Kline, 2005).


Table 4. Correlations between the Independent Variables

 

Student expectation

Parental expectation

Teacher expectation

Math efficacy

Science efficacy

Student expectation

1

    
     

Parental expectation

.51**

1

   
     

Teacher expectation

.45**

.34**

1

  
     

Math efficacy

.13**

.07**

.24**

1

 
     

Science efficacy

.27**

.16**

.28**

.23**

1

 

 

 

 

 

**. Correlation is significant at the 0.01 level (two-tailed).


Finally, we tested the six models to answer each of the research questions. The aim of this study was to first examine the relationship between a student’s self-efficacy and expectation, as well as teacher and parent expectation, and his or her decision to choose a STEMM major by the second semester of college (Model 1), complete a STEMM degree in college (Model 2), and attain a STEMM career (Model 3).

To explore our additional research questions concerning whether expectations from teachers and parents are moderated by gender, we examined interplay between these factors by including the interaction terms for gender and teacher expectation, as well as those of gender and parent expectation in the analyses. By including these interaction terms we could examine how personal efficacy and expectation, as well as teacher and parent expectation differ across gender and consequently affect students’ decisions to choose a STEMM major by the second semester of college (Model 4), completion of a STEMM degree (Model 5), and attainment of a STEMM career (Model 6).



Table 5. Logistic Regression Coefficients (SE) and ORs for Students’ STEMM Major/Career Choice (N = 1,776)

 

Model 1

 

Model 2

 

Model 3

 

Model 4

 

Model 5

 

Model 6

 B

Exp(B)

 

 B

Exp(B)

 

 B

Exp(B)

 

 B

Exp(B)

 

 B

Exp(B)

 

B

Exp(B)

Constant

2.899***

(0.42)

0.055

 

-5.298*** (0.61)

0.000

 

-6.64*** (0.82)

0.001

 

-2.84*** (0.42)

0.058

 

-5.26*** (0.62)

0.005

 

6.55*** (0.83)

0.001

Parents’ education level

-0.037

(0.056)

0.964

 

0.13

(0.07)

1.141

 

0.012

(0.09)

1.012

 

-0.04

(0.06)

0.960

 

0.133

(0.07)

1.142

 

0.013

(0.09)

1.013

Male

0.19

(0.13)

1.205

 

-0.03

(0.18)

.968

 

0.297

(0.23)

1.346

 

0.09

(0.14)

1.090

 

-0.29 (0.24)

0.752

 

0.101 (0.337)

1.106

Student expectation

0.27***

(0.06)

1.310

 

0.39*** (0.096)

1.483

 

0.23

(0.12)

1.254

 

0.28*** (0.06)

1.317

 

0.41*** (0.09)

1.499

 

0.229 (0.123)

1.257

Parental expectation

0.04

(0.07)

1.042

 

0.21*

(0.10)

1.233

 

0.40**

(0.14)

1.497

 

0.16

(0.10)

1.179

 

0.476** (0.147)

1.609

 

0.699** (0.212)

2.013

Teacher expectation

0.12**

(0.04)

1.128

 

0.18*** (0.05)

1.199

 

0.23*** (0.06)

1.263

 

-0.005

(0.05)

0.995

 

-0.023 (0.07)

0.977

 

0.035 (0.094)

1.035

Black

0.07

(0.21)

1.076

 

-0.63

(0.35)

0.533

 

-0.48

(0.43)

0.619

 

0.09

(0.21)

1.094

 

-0.614 (0.35)

0.541

 

-0.494 (0.43)

0.610

Hispanic

-0.001

(0.25)

1.001

 

0.078

(0.37)

1.081

 

0.054

(0.48)

1.055

 

-0.036

(0.26)

0.964

 

0.008

(0.375)

1.008

 

-0.018 (0.48)

0.982

Math efficacy

0.12

(0.08)

1.123

 

0.15

(0.11)

1.157

 

0.27

(0.16)

1.316

 

0.12

(0.08)

1.121

 

0.145 (0.114)

1.157

 

0.277

0.16)

1.319

Science efficacy

0.34***

(0.08)

1.400

 

0.51*** (0.12)

1.663

 

0.60***

(0.17)

1.822

 

0.34*** (0.08)

1.398

 

0.51***

(0.123)

1.667

 

0.59*** (0.17)

1.805

Gender X Teacher expectation

         

0.24** (0.07)

1.264

 

0.39***

(0.09)

1.479

 

0.323** (0.116)

1.382

Gender X parental expectation

         

-0.25

(0.14)

0.782

 

-0.54** (0.199)

0.585

 

-0.485

(0.265)

0.616


RESULT

Table 5 presents results of logistic regression analyses for the elder cohort at the time of high school graduation. Analysis reveals that, from among the potential factors, measures of students’ science efficacy and teacher expectation turn out to have lasting effect on whether a student will declare a STEMM major during the first year of college, complete a STEMM degree, and choose a STEMM career after college. A student’s self-expectation is revealed to predict his or her likelihood to pursue STEMM during the first year of college, as well as complete a STEMM degree. In addition, parent expectation, unlike teacher expectation, is a significant predictor of a student’s likelihood to pursue STEMM at only two time points: completion of a STEMM degree and choice of a STEMM field career.

MODELS 1–3

To be specific, while controlling for their background characteristics (parents’ education level, ethnicity, and gender), students who reported higher levels of teacher expectation are 1.13 times more likely to choose a STEMM major during the first year of college (p < .01), 1.20 times more likely to complete a STEMM degree (p < .001), and 1.26 times more likely to enter a STEMM profession (p < .001) than students who reported lower levels of educational expectation from their teachers.

In addition, students with higher degrees of self-expectation are 1.31 times more likely to choose a STEMM major in the first year of college (p < .001) and 1.48 times more likely to complete a degree in STEMM (p < .001) than students with lower self-expectation. Furthermore, students with higher science efficacy are 1.40 times more likely to choose a STEMM major during the first year of college (p < .001), 1.66 times more likely to complete a college degree in STEMM (p < .001), and 1.82 times more likely to enter a STEMM profession (p < .001) than students with lower science efficacy. While parental expectation was not revealed to have a significant predictive relationship with students’ choices of STEMM majors during the first year of college, students with high parental expectation are 1.23 times more likely to earn a STEMM degree (p < .05) and 1.50 times more likely to choose a STEMM career (p < .01).

MODELS 4–6

Inclusion of interaction variables in the analysis reveals that when controlling for average parental expectation, male students are overall more likely to be affected by their teacher’s expectations than female students with respect to pursuing STEMM at each point in time. Conversely, when controlling for average teacher expectation, male students are less likely to be affected by their parent’s expectation than female students when considering degree completion in STEMM.

More specifically, when controlling for average parental expectation, male students are 1.26 times more likely to choose a STEMM major during the first year of college (p < .01), 1.48 times more likely to have earned a STEMM degree at graduation (p < .001), and 1.38 times more likely to enter a STEMM profession (p < .01) than female students who reported similar levels of teacher expectation. Further, when controlling for average teacher expectation, the parental expectation reported by male students’ amounted to about 54% the magnitude of that reported by females. In comparison, when male students are used as the reference group to conduct the same analysis, female students are revealed to be 1.7 times more likely to earn a degree in STEMM (p < .01) than male students.

DISCUSSION


Analysis of our models addresses several important aspects of how teacher and parent expectation affect students’ pursuit of STEMM education and careers. In addition, the models also reveal interesting differences in the way a students’ gender interacts with expectation as students explore a future in STEMM. Discussion of these results follows.

IMPORTANCE OF TEACHER AND PARENT EXPECTATION

Expectation clearly matters for students pursuing STEMM. Consistent with prior research showing the substantial influence teachers have on students’ long-term educational goals and vocational choices (de Boer, Bosker, & van der Werf, 2010; Rubie-Davies, 2010), our findings show that teacher expectation does, in fact, affect students’ choice of major and degree completion in STEMM. Building upon this, our findings uniquely show that in addition to influencing their key academic decisions with respect to STEMM, teacher expectation also has a lasting effect on students’ vocational choices within STEMM fields. Where prior studies have illustrated this relationship generically, our findings illustrate the power of teacher expectation specifically within the STEMM domain.

Taking all the results into account, by implication, this suggests that teachers can boost students’ interest in pursuing STEMM, specifically, by both having and communicating high expectations for their students. In light of this finding, efforts to boost positive teacher expectation during secondary education represent a promising approach to promoting greater student interest, completion, and pursuit of careers in STEMM fields. What is more, of the limited levers available to policy makers, those contained within the classroom setting may prove the most tractable and implementable.

Parental expectation has also long been shown to be influential in students’ educational and general vocational choices (Hou & Leung, 2011; Sawitri, Creed, & Zimmer-Gembeck, 2013). In alignment with this research, we found parental expectation to play a significant role in shaping students’ degree completion and career attainment in STEMM. This study confirms and builds upon that understanding by focusing specifically on students with interest in STEMM domains, finding that those experiencing higher levels of parental expectation in high school are not only more likely to complete STEMM degrees but that the effects of expectation are lasting and influential through to students’ attainment of STEMM careers.

These results illustrate how teacher and parental expectation can have a positive and lasting effect on students’ decisions to enter into STEMM fields including the choice of a STEMM related career post-college, in advancing our understanding of how these critical influences impact students, in relation to one another. More specifically, while previous research has shown that teachers and parents can each play a prominent role in framing student academic and career choices, scant research has compared their relative impacts with respect to STEMM specifically. By utilizing a longitudinal approach, this study has shown that even though both sources of expectation play a vital role in students’ decisions, teachers’ expectations have a longer lasting impact on students. This finding is in keeping with prior literature suggesting that educational expectations are one of the best predictors of subsequent degree completions (e.g., Pascarella, Pierson, Wolniak, & Terenzini, 2004).2

IMPORTANCE OF SELF-EXPECTATION AND EFFICACY

In alignment with prior research drawing on SCCT, this study has shown student self-expectation and efficacy to be important factors in a student’s choice of major and degree completion (Lent et al., 1994). The findings of this study confirm and extend this understanding by showing that students with higher levels of self-efficacy and expectation in math and the sciences, specifically, were more likely to pursue and secure education and a career in STEMM. Using SCCT as a theoretical lens to guide this study has offered additional empirical evidence of how students’ self-efficacy and expectation affect their likelihood to enter into and persist within postsecondary education, and further expand that understanding to relevance within the STEMM–specific domain.

While prior scholarship has emphasized the usefulness of math efficacy as an important variable in predicting success in STEMM (e.g., Marra, Rodgers, Shen, & Bogue, 2009; Williams & Williams, 2010), this study has uniquely found science efficacy to be a more powerful predictor. This insight may prove meaningful at a time when education reform more frequently emphasizes math performance than science or other measures (Dee & Jacob, 2011). To increase the likelihood that students pursue educational opportunities and careers in STEMM, it is crucial to provide them with adequate exposure to science in classes that build their sense of self-efficacy around science. Results from this study show how students’ attitudes toward science can predict whether they choose to pursue or complete a degree in STEMM, thus highlighting the importance of cultivating students’ positive attitudes toward science from early on.

GENDER DIFFERENCES IN PATHWAYS TO STEMM

Previous studies exploring expectation and STEM have generally emphasized the lack of encouragement and support both teachers and parents have “traditionally” shown female students in math and sciences. As a result, they conclude that disparities in STEM participation exhibited by female vis-à-vis their male counterparts can be explained –at least in part—by the less frequent and less robust attention and encouragement received from the most influential actors in their lives: parents and teachers. Although this study confirms that males are more likely to pursue STEMM fields, it also departs from that body of literature by finding that even when males and females experience equivalent levels of expectation from teachers, females still exhibit lower levels of participation in STEMM disciplines. In addition, the findings also provide a nuanced perspective by showing that females are more likely to attain STEMM degrees when they perceive higher levels of expectation from their parents.

That female students’ STEMM participation is still lower with similar levels of teacher expectations and encouragement, suggests that—though clearly still an important and powerful predictor—expectation and encouragement may not be the most influential factor females students weigh in their decisions. In other words, with respect to choosing a major, completing a degree, or selecting a career in STEMM, females seem to be swayed by forces beyond the scope of expectation and encouragement experience in the school and home. One potential explanation is the insidious effects of negative acculturation and the societal dictates of gender norms in various vocational fields. It is no surprise, then, that numerous scholars have emphasized the importance of both generating and enhancing female students’ interest in STEMM majors by introducing them to role models that demonstrate that STEMM careers are not only for males (Lockwood & Kunda, 1997).

Another possible explanation is self-stereotyping about personal abilities. Though studies have found that many female students exhibit comparably high efficacy measures in math and science as well as the requisite achievement scores required to pursue these demanding fields, they are still more likely to select majors such as education, nursing, and childcare—again, vocations that have been traditionally dominated by women. And yet, energizing teachers to show additional encouragement and positive expectations to young women who show interest or aptitude in STEMM-related fields appears insufficient to overcome this gender differential. On the other hand, the findings show that similar efforts undertaken by parents can have a more influential (though still insufficient) effect. It is likely that a broader societal effort is necessary to slowly re-align these norms. Recent research supports this thesis, and suggests that many women do not pursue some STEMM fields (for example, engineering), not because they do not believe they would excel in the workforce, but because they do not believe they would enjoy its study (Zafar, 2013).

This study reveals new nuance and informs our understanding of how female students navigate the complex factors that influence pursuit of STEMM education and careers. It reinforces the fact that female students seem to pursue STEMM fields “against all odds,” whereas males navigate the same pipeline in a "business as usual” fashion. While simultaneously underscoring the power of expectation as a predictor of STEMM attainment, it further illustrates that female students’ choices and attainment can be undermined at multiple junctures in the pipeline and that while strengthening expectation and encouragement for females is necessary, it is also insufficient to overcome the gender gap in STEMM. Still, while teachers must redouble their supportive efforts in the classroom, it is crucial that parents of female students provide renewed support to their children in order to encourage their pursuit in STEMM.

The STEMM pipeline is complex and includes a number of influential components. This study indicates that expectation from both teachers and parents during high school years are is a powerful predictor of students’ general pursuit and persistence within the STEMM disciplines. Meanwhile, it also implies that much more can be done to support students—especially females in pursuit of gender parity. It suggests that if teacher and parents are to improve their students’ chances of success in pursuing STEMM fields, they need to understand their critical roles in facilitating students’ interest and confidence. In other words, both parties need to re-examining their practices with respect to projecting expectations as well as providing encouragement in both the classroom and the home.

LIMITATIONS


Despite its contributions to our understanding of the STEMM pipeline, this study has several limitations. First, while this study used the most recent LSAY survey from 2007, the data were originally collected more than a decade ago and may not capture dynamic trends in education and the economy of recent years. Nonetheless, the fact that the data were collected more than a decade ago enables us to identify students who persisted through and entered STEMM careers, allowing us to analyze the full length of the STEMM pipeline.

Second, this study exclusively uses variables from secondary school and does not include any variables reflecting students’ experiences during postsecondary education. This inhibits our ability to explore aspects of postsecondary education including institutional or departmental effects and their impacts on students pursuing STEM, which can be critical to understanding student’s retention. However, by concentrating on secondary school variables only, we maintained our focus on measuring the impact of expectation on students—regardless of institutional differences.

Third, despite the great value that using SCCT brought to our analysis of students’ experiences, only the self-efficacy and expectation components of SCCT were included in our models. Other elements addressed in SCCT such as personal goal setting, for example, may also prove to have a relationship with student success measures within STEMM and should be explored in data sets that support inclusion of this component. Incorporating additional elements to refine the analysis may lead to a better understanding of this dynamic context, and therefore future research should to strive to include them.

Lastly, as with any survey, data from self-report questionnaires have their own limitations. Self-reported data are often criticized as being less objective and being susceptible to favorability bias. Well-designed surveys, however, including the LSAY, work to reduce the tendency to misrepresent information by assuring respondents of anonymity and privacy (Miller & Kimmel, 2012). Further, by asking students about their perceptions of teacher expectations rather than asking teachers what they expect of their students, we are positioned to capture a genuine effect of teacher expectation from an authentic student perspective.

CONCLUSION


The decision to pursue education and a career in a STEMM is not a onetime decision, but a longitudinal process that begins during secondary education and carries through into college. As a result it is important to consider a variety of important points in the pipeline to better understand which experiences have the most lasting effect for students. By approaching the problem from a perspective that spans grades 9–16 as well as the career context beyond college, we have identified factors that influence students’ decisions to pursue overall STEMM decisions—prior to their arrival at college, arguably the first critical step in the pipeline. Based on the results of this study, it is clear that expectation plays a significant role in STEMM, and teacher expectation, especially, is shown to be influential. By focusing on expectation, this study addresses a significant gap in the literature and indicates important policy implications for parents, teachers, and policy makers at the state and national level who are interested in better supporting students pursuing STEMM education and careers.

Research over the past decade has begun exploring the impact of secondary school educational expectations in a postsecondary context, but there is still much work to be done. This study contributes to that effort and illuminates the importance of cultivating positive expectations for students in STEMM.

Notes

1. This study analyzed both the younger and elder cohorts and found most coefficients to be similar and in magnitude and statistical significance. Although we have attached results for the younger cohort in Appendix A, we focus on the elder cohort throughout the paper mainly because participants from the elder cohort had a lengthier opportunity to obtain STEMM related career, a primary focus of the study.

2. To examine if teacher and parent expectation influence white and non-white students differently, this study conducted additional analyses that disaggregated sample by race (see Appendix B and C). Consistent with overall sample findings, teacher expectation was shown to have lasting influence on both White and non-White students’ choices of major and degree attainment in STEMM. Parent expectation, on the other hand—though a significant predictor of students’ STEMM major and career choice in the overall sample—was not a consistent predictor of non-White students’ major and career choice in STEMM. To be sure, dividing the sample into groups of White and non-White students resulted in increasingly small sample sizes at each stage of analysis. As a result, interpretation of these results should necessarily be approached with caution.

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APPENDIX A


Logistic Regression Coefficients (SE) and ORs of 7th grade (younger) cohort students’ STEMM major/career choice (N = 2,435)

 

Model 1

 

Model 2

 

Model 3

 

Model 4

 

Model 5

 

Model 6

 

B

Exp(B)

 

B

Exp(B)

 

B

Exp(B)

 

B

Exp(B)

 

B

Exp(B)

 

B

Exp(B)

Constant

3.99*** (0.42)

0.018

 

7.80*** (0.71)

0.000

 

8.37***

(0.84)

0.000

 

3.97***

(0.42)

0.019

 

7.85***

(0.72)

0.000

 

8.36***

(0.84)

0.000

Parents’ education level

-0.014 (0.053)

0.986

 

0.34***

(0.08)

1.410

 

0.42***

(0.09)

1.528

 

-0.01

(0.05)

0.987

 

0.34***

(0.08)

1.407

 

0.42***

(0.09)

1.527

Male

0.197

(0.12)

1.217

 

0.08

(0.18)

1.084

 

0.16

(0.22)

1.175

 

0.15

(0.13)

1.163

 

0.105

(0.24)

1.110

 

0.12

(0.26)

1.122

Student expectation

0.15*

(0.06)

1.157

 

0.26**

(0.099)

1.296

 

0.23*

(0.12)

1.263

 

0.14*

(0.06)

1.155

 

0.27**

(0.10)

1.308

 

0.24*

(0.12)

1.273

Parental expectation

0.04

(0.07)

1.043

 

0.17

(0.12)

1.188

 

0.05

(0.13)

1.051

 

0.03

(0.10)

1.029

 

0.30

(0.17)

1.351

 

0.18

(0.20)

1.191

Teacher expectation

0.13*** (0.04)

1.133

 

0.18***

(0.05)

1.201

 

0.15**

(0.05)

1.167

 

0.09

(0.05)

1.091

 

0.14*

(0.07)

1.144

 

0.08

(0.08)

1.082

Black

0.14

(0.21)

1.148

 

-0.53

(0.38)

0.587

 

-0.55

(0.46)

0.575

 

0.15

(0.21)

1.160

 

-0.51

(0.38)

0.602

 

-0.52

(0.46)

0.597

Hispanic

-0.10

(0.23)

0.903

 

-1.54**

(0.59)

0.214

 

-1.54*

(0.73)

0.215

 

-0.10

(0.23)

0.902

 

-1.54**

(0.59)

0.214

 

-1.55*

(0.73)

0.212

Math

Efficacy

0.23** (0.08)

1.260

 

0.50***

(0.14)

1.649

 

0.46**

(0.16)

1.584

 

0.23**

(0.08)

1.263

 

0.51***

(0.14)

1.667

 

0.47**

(0.16)

1.601

Science

Efficacy

0.55*** (0.09)

1.739

 

0.64***

(0.15)

1.895

 

0.65***

(0.17)

1.913

 

0.55***

(0.09)

1.734

 

0.64 ***

(0.15)

1.888

 

0.64***

(0.17)

1.895

Gender X Teacher expectation

         

0.07

(0.06)

 1.074

 

 0.08

 (0.09)

 1.085

 

0.12

(0.10)

 1.131

Gender X Parental expectation

         

0.23

(0.14)

 1.023

 

 -0.23

 (0.22)

 0.795

 

-0.21

(0.26)

 0.813

Note. OR = odds ratio; *p < .05. **p < .01. ***p < .001. The analysis includes 2,435 students (78.1%) from the LSAY 7th grade cohort (n = 3,116)


APPENDIX B


Logistic Regression Coefficients (SE) and ORs of 10th grade (elder) cohort white students’ STEMM major/career choice (N = 1,334)


 

 

Model 1

 

Model 2

 

Model 3

 

 

B

 

Exp(B)

 

B

 

Exp(B)

 

B

 

Exp(B)

Constant

 

-2.64*** (0.51)

 

0.072

 

-4.85*** (0.73)

 

0.008

 

-5.82*** (0.99)

 

0.003

Parents’ education level

 

-0.15* (0.07)

 

0.865

 

0.07

(0.08)

 

1.070

 

-0.07

(0.11)

 

0.936

Male

 

-0.37* (0.15)

 

0.692

 

-0.25

(0.20)

 

0.778

 

-0.69* (0.28)

 

0.504

Student expectation

 

0.26*** (0.07)

 

1.302

 

0.42*** (0.11)

 

1.527

 

0.21

(0.15)

 

1.234

Parental expectation

 

0.03

(0.09)

 

1.027

 

0.10

(0.12)

 

1.106

 

0.35*

(0.16)

 

1.412

Teacher expectation

 

0.11*

(0.04)

 

1.111

 

0.17** (0.05)

 

1.183

 

0.28*** (0.07)

 

1.316

Math efficacy

 

0.10

(0.09)

 

1.101

 

0.02

(0.13)

 

1.022

 

0.23

(0.19)

 

1.257

Science efficacy

 

0.44*** (0.01)

 

1.545

 

0.58*** (0.14)

 

1.787

 

0.57** (0.20)

 

1.762

Note. OR = odds ratio; *p < .05. **p < .01. ***p < .001. This analysis includes 1,334 students (47.2%) from the LSAY 10th grade cohort (n=2,829) who entered college and completed the follow up career survey in 2007. Model 1 is a student’s decision to choose a STEMM major by the second semester of college, Model 2 is completing a STEMM degree in college, and Model 3 is attaining a STEMM career.



APPENDIX C


Logistic Regression Coefficients (SE) and ORs of 10th grade (elder) cohort non-white students’ STEMM major/career choice (N = 315)


 

 

Model 1

 

Model 2

 

Model 3

 

 

B

 

Exp(B)

 

B

 

Exp(B)

 

B

 

Exp(B)

Constant

 

-2.97** (0.92)

 

0.051

 

-8.84*** (0.81)

 

0.000

 

-9.29*** (2.08)

 

0.000

Parents’ education level

 

0.31** (0.11)

 

1.366

 

0.44** (0.17)

 

1.554

 

0.33

(0.19)

 

1.388

Male

 

0.42

(0.28)

 

1.522

 

1.11*

(0.44)

 

3.03

 

0.73

(0.48)

 

2.078

Student expectation

 

0.33** (0.13)

 

1.389

 

0.36

(0.20)

 

1.434

 

0.34

(0.22)

 

1.398

Parental expectation

 

0.04

(0.14)

 

1.036

 

0.47*

(0.23)

 

1.599

 

0.42

(0.25)

 

1.523

Teacher expectation

 

0.16*

(0.08)

 

1.176

 

0.26*

(0.11)

 

1.300

 

0.16

(0.12)

 

1.170

Math efficacy

 

0.12

(0.17)

 

1.125

 

0.66*

(0.29)

 

1.929

 

0.55

(0.31)

 

1.730

Science efficacy

 

0.08

(0.17)

 

1.082

 

0.45

(0.25)

 

1.573

 

0.78*

(0.31)

 

2.190

Note. OR = odds ratio; *p < .05. **p < .01. ***p < .001.This analysis includes 315 students (11.1%) from the LSAY 10th grade cohort (n = 2,829) who entered college and completed the follow up career survey in 2007. Model 1 is a student’s decision to choose a STEMM major by the second semester of college, Model 2 is completing a STEMM degree in college, and Model 3 is attaining a STEMM career.





Cite This Article as: Teachers College Record Volume 117 Number 9, 2015, p. 1-40
https://www.tcrecord.org ID Number: 18051, Date Accessed: 1/25/2022 6:25:54 PM

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About the Author
  • Se Woong Lee
    University of Wisconsin-Madison
    E-mail Author
    SE WOONG LEE is a doctoral candidate in University of Wisconsin-Madison’s School of Education, Educational Leadership and Policy Analysis program. His interests include research to inform teacher hiring and teacher evaluation policy. Specifically, he focuses on the influences of teachers’ qualifications, effectiveness, and their impacts on students’ post-secondary outcome. He is currently investigating the long-term effects of secondary teacher quality on students’ college success.
  • Sookweon Min
    University of Wisconsin-Madison
    E-mail Author
    SOOKWEON MIN is a doctoral candidate at the University of Wisconsin-Madison, Educational Leadership and Policy Analysis. Her research interests include the long-term effects of K–12 experience on student outcomes in postsecondary institutions, the demographic shifts of K–16 schools, and schools’ organizational capacities for student diversity.
  • Geoffrey Mamerow
    University in Tiffin
    E-mail Author
    GEOFFREY P. MAMEROW is the Director of Institutional and Market Research at Heidelberg University. His interests include quality in first-year programs and initiatives, college readiness, undergraduate teaching and learning, and general education and the purpose of college.
 
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