Full Terms & Conditions of access and use can be found athttps://www.tandfonline.com/action/journalInformation?journalCode=uste20

Journal of Science Teacher Education

ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/uste20

Preparation and Motivation of High SchoolStudents Who Want to Become Science orMathematics Teachers

Travis T. Fuchs, Gerhard Sonnert, Sandra A. Scott, Philip M. Sadler & ChenChen

To cite this article: Travis T. Fuchs, Gerhard Sonnert, Sandra A. Scott, Philip M. Sadler& Chen Chen (2021): Preparation and Motivation of High School Students Who Want toBecome Science or Mathematics Teachers, Journal of Science Teacher Education, DOI:10.1080/1046560X.2021.1908658

To link to this article: https://doi.org/10.1080/1046560X.2021.1908658

Published online: 28 May 2021.

Submit your article to this journal

View related articles

View Crossmark data

Preparation and Motivation of High School Students Who Want to Become Science or Mathematics TeachersTravis T. Fuchs a, Gerhard Sonnert b, Sandra A. Scott a, Philip M. Sadler c, and Chen Chen c

aDepartment of Curriculum and Pedagogy, The University of British Columbia, Vancouver, Canada; bHarvard College Observatory, Harvard University, Cambridge, Massachusetts, USA; cCenter for Astrophysics | Harvard & Smithsonian, Cambridge, Massachusetts, USA

ABSTRACTShortages of science and mathematics teachers are a serious concern in the United States. Despite the implementation of intervention strategies to address this problem at the high school level, little empirical research had been conducted to inform and strengthen these initiatives. This retrospective cohort study used multinomial logistic regression models to predict students’ science and mathe-matics teaching career intentions at the end of high school. These intentions were most strongly associated with having had an interest in science and mathematics teaching already at the beginning of high school. Other significant predictors included middle school interest in science, technology, engineering, and mathematics careers, high mathematics preparation, and a desire to work with, and help, others. Whereas students interested in science and mathematics teaching shared many motivational features with students interested in other teaching careers, their mathematics knowledge was significantly higher. This research counters blanket stereotypes about teacher ineptitude, while supporting a more positive view of teachers’ motiva-tion to teach. Future interventions targeting high school students should focus on mentorship opportunities and purposeful teaching experiences that mirror the roles science and mathematics teachers play in school life. Such interventions highlight both the potential positive impacts teaching has on students and provide further subject matter exploration. Examples of such programs can range from for-malized teaching assistant positions in science and mathematics class-rooms to full university partnerships in which colleges of education and science, as well as local school boards/districts, work together to spark interest in science and mathematics teaching among high school students.

KEYWORDS Teacher career choice; science teachers; mathematics teachers; career motivation; high school students

Introduction

In the United States (US) there has been a teacher shortage, especially in science and mathematics (s/m) (Garcia & Weiss, 2019; Gist et al., 2019; Sutcher et al., 2016). In the 2015–16 school year, for instance, over 40 states reported s/m teacher shortages for grades 6-12 (Cross, 2016). These shortages often force states to hire unqualified or unprepared

CONTACT Travis T. Fuchs tfuchs3@student.ubc.ca Department of Curriculum & Pedagogy, Faculty of Education, The University of British Columbia – Point Grey Campus, Neville Scarfe Building, 2125 Main Mall, Vancouver, BC, Canada V6T 1Z4.

JOURNAL OF SCIENCE TEACHER EDUCATION https://doi.org/10.1080/1046560X.2021.1908658

© 2021 Association for Science Teacher Education

teachers to fill vacancies, which, due to the high attrition rates of unprepared teachers, can be costly “for student achievement and for district pocketbooks” (Sutcher et al., 2016, p. 59).

To address teacher shortages, schools have taken it upon themselves to recruit students out of high school into the teaching profession (Alvarez, 2017; Simmons, 2018). The hope is that these “home-grown” teachers (Summerhill et al., 1998, p. 223) from Grow Your Own programs will return to their communities bolstering the teacher workforce (Gist et al., 2019). However, there is a lack of empirical evidence about specifically what motivates high schoolers to pursue s/m teaching careers (Fray & Gore, 2018; Heinz, 2015; Leech et al., 2019). More research is needed in this area to determine “ways potential teachers can enter the profession, experiences that may serve as indicators to identify potential teachers, and how to expand the recruitment process” (Luft et al., 2011, p. 464). The purpose of this research was to address the identified gaps in the literature by using multinomial logistic regression models to examine a range of variables that may predict students’ s/m teaching intentions at the end of high school.

Teaching as a career choice

The literature about teacher career choice is large and growing (Fray & Gore, 2018; Heinz, 2015; Watt et al., 2012). Primarily, this work seeks to respond to global concerns about the quality of teaching in schools (Organization for Economic Co-operation and Development [OECD], 2005, 2012, 2019), which often manifest themselves as suspicions that the “best and brightest” (Henry et al., 2012; Ingersoll & Mitchell, 2011) might not be entering the teaching profession (Borman & Dowling, 2008; Clifton, 2013; Feng & Sass, 2016; Lankford et al., 2014). This informal categorization usually refers to the motivational profiles (“best”) and academic standing (“brightest”) of pre-service, novice, and in-service teachers (Gore et al., 2016; Kelly & Northrop, 2015). Claims of low teacher quality are typically substantiated by investigating these categories (Auguste et al., 2010) and comparing teachers to other professions as a group (Vegas et al., 2001) or to specific professions, such as medicine, law, or engineering (Dastidar & Sikdar, 2015; Lai et al., 2005). Focusing specifically on academic standing, this comparison has resulted in a characterization of some teachers as mediocre in Australia (Gore et al., 2016), or even inept in the US (Goldstein, 2011). This, in turn, has been argued to deter “high quality” applicants from considering the profession (Dinham et al., 2008). Understanding teachers’ motivation to teach is important to elucidate factors that contribute to retention/attrition rates (Roness, 2011; Watt & Richardson, 2007; Watt et al., 2012), to the potential for burnout (Bruinsma & Jansen, 2010; Kieschke & Schaarschmidt, 2008), and to teachers’ professional learning commitments (Watt & Richardson, 2007). Findings about teachers’ motivation provide a basis for directing teacher education policy, improving teacher quality, and informing initiatives seeking to attract more individuals to teach (Nesje et al., 2018).

The variables most often studied to explain teacher career choice indeed relate to motivation (Heinz, 2015; Watt et al., 2017) and fall into three categories: intrinsic, altruistic, and extrinsic motivation. Intrinsic motivation relates to factors that speak to the enjoyment of teaching, such as job satisfaction, career development, talent use, and interest in the subject matter (Klassen et al., 2011). Altruistic motivation relates to individuals’ desire to enhance social equality, work with children, or contribute to society in a positive way (Reeves & Lowenhaupt, 2016). Extrinsic motivation relates to factors like the status of the

2 T. T. FUCHS ET AL.

teaching profession, time for self or family, levels of pay, as well as job security and transferability (Cheung & Yuen, 2016; Sinclair, 2008). The motivational profiles of exemp-lary teachers (e.g., a desire to help others learn or work with children) are increasingly recognized as being as important as teachers’ academic preparedness for student learning (Collinson et al., 1999; Gore et al., 2016).

Academic preparedness and motivation to teach

The majority of the teacher career choice literature focuses on the experiences and percep-tions of teachers as a homogenous group (Akar, 2012; Heinz, 2015). Very few studies purposefully seek the perceptions of different types of teachers, whether that be primary/ secondary, special education, or subject specific (Watt et al., 2017). While grouping all teachers together does have utility for research methods and policy, it can nonetheless belie certain differences among teachers in terms of self-efficacy (Ross et al., 1996), subject matter knowledge (Ball & McDiarmid, 1990), and assessment priorities (Brown et al., 2011). This may be especially true regarding science and mathematics teachers, whose sub-cultures in schools have been well documented (Aikenhead, 2006; Seeger & Waschescio, 1998). The current research in the field also suffers from a narrow focus on the motives and prepared-ness of pre-service and in-service teachers (Demir et al., 2019). This captures the character-istics of those already set on becoming a teacher. However, the perspectives of those not yet in the profession (e.g., high school and undergraduate students) are also important to understand, especially if the aim is to encourage young people to teach (Coulthard & Kyriacou, 2002; Lai et al., 2005; Van Rooij et al., 2020). These perspectives reveal influences on teacher career choice of individuals not yet influenced by teacher training programs (Hutchison & Johnson, 1993/1994).

The intersection of literature pertaining to s/m teacher aspirations and literature about high school students’ motivations to teach is thin. As such, we chose to focus our review on the perspectives of high school students interested in the teaching profession (e.g., Christensen et al., 2019; Cross & Ndofirepi, 2015; Dastidar & Sikdar, 2015; Gore et al., 2016; Graham & Erwin, 2011; Han et al., 2018; Lai et al., 2005; Leech et al., 2019; Smith et al., 2004) as well as reports about efforts to recruit primarily undergraduate students from science, technology, engineering, and mathematics (STEM) majors into teaching (e.g., Demir et al., 2019; Luft et al., 2005; Tomanek, 1996; Van Rooij et al., 2020; Worsham et al., 2014). The literature mentioned above originates from diverse geographic regions and national contexts (e.g., Australia, Hong Kong, India, South Africa, Netherlands, US), with one study comparing perspectives of students from 56 countries (Han et al., 2018). While some similarities between backgrounds, motivations, and experiences are found, there are also vast differences in the teaching profession between these contexts in terms of types of teachers, societal perceptions of teaching, certification requirements, and salary. These factors, and others, influence an individual’s choice to pursue a teaching career (Fray & Gore, 2018). Nonetheless, these studies collectively sought to understand what drew students to teaching, with many focusing on the academic preparedness and motivational profiles of students interested in the teaching profession.

Concerning academic preparedness, the Lai et al. (2005) study comparing students from Hong Kong and the Han et al. (2018) study comparing students interested in teaching from 56 countries, reported that the majority of high school students interested in teaching had

JOURNAL OF SCIENCE TEACHER EDUCATION 3

lower academic achievement, on average, than did those interested in nonteaching profes-sions. These findings echo college and university level studies of a similar nature from the US (Bacolod, 2007; Gitomer et al., 1999). In contrast, the Gore et al. (2016) study of 821 Australian students in Years 3 to 12 found that 31% of those interested in teaching were from the top academic quartile on a nation-wide standardized test. The authors concluded that academically prepared Australian students were indeed interested in teaching.

Turning to high school students’ motivations, intrinsic factors (Dastidar & Sikdar, 2015), altruistic factors (Christensen et al., 2019; Cross & Ndofirepi, 2015; Han et al., 2018), or both (Gore et al., 2016; Lai et al., 2005; Smith et al., 2004) were the most cited reasons for intending a teaching career among students from the countries included in our review. Similar results can be found in several s/m teacher recruitment reports. In Van Rooij et al. (2020) survey of 905 STEM undergraduates from the Netherlands, the 26% of participants who indicated an interest in teaching were also distinct in their high rating of social factors (e.g., working with or helping people) as important characteristics of their future job. These participants also rated teaching self-efficacy higher than did any other career interest groups. In the Worsham et al. (2014) intervention study of 34 US college science majors, 28% joined the science teacher recruitment program due to their interest in working with and helping people. However, 41% joined because of their like of the subject. This finding dovetails with the Demir et al. (2019) US intervention study in which the majority of participants reported that the most attractive aspect of s/m teaching to them was continued engagement with mathematics and science. Consistent results have been reported in other undergraduate recruitment studies also from the US (e.g., Luft et al., 2005; Tomanek, 1996). In sum, high school students interested in teaching as well as STEM undergraduates interested in s/m teaching consistently comment that their like of teaching, of the subject matter, and of helping others contributed to their interest in the profession.

Other factors influencing teaching career aspirations

Empirical work shows that demographic factors such as race/ethnicity and gender are crucial predictors of interest in the teaching profession (Bergey & Ranellucci, 2021; Graham & Erwin, 2011; Leech et al., 2019; Smith et al., 2004). To understand these effects is important in light of demographic disparities between teachers and students: In the US, the teacher workforce is primarily White (82%) and female (76%), despite an increasingly diverse student population (Ingersoll et al., 2018; Putman et al., 2016). Using latent profile analysis, Bergey and Ranellucci (2021) found that race/ethnicity and gender formed dis-tinctive associative patterns across their four motivational profiles of US individuals inter-ested in teaching careers. Focusing on students of color, the Leech et al. (2019) US study found that participants were most interested in teaching for largely extrinsic reasons. On the other hand, specific research on high-achieving African American males has shown that this group of individuals considers teaching as a career to give back to their community (Smith et al., 2004) and to serve as role models (Graham & Erwin, 2011). Similar results have been reported among other people of color (Gordon, 2000).

Other factors such as past teaching and learning experiences, family influences, and perceptions of being good at teaching were also found to be important contributors to students’ career aspirations (Christensen et al., 2019; Gore et al., 2016; Worsham et al., 2014). These were reported in the national contexts of Australia, the Netherlands, India,

4 T. T. FUCHS ET AL.

South Africa and the US. In some cases, these factors drew individuals to the teaching profession, because they wanted to maintain the positive experiences they had in high school (Dastidar & Sikdar, 2015; Worsham et al., 2014), foster them for others (Tomanek, 1996), or carry on a family tradition (Cross & Ndofirepi, 2015). In others, participants cited the stressful experiences they had in their own K-12 education, which they did not wish to revisit (Graham & Erwin, 2011; Leech et al., 2019), or mentors pushing them to consider other careers (Christensen et al., 2019; Cross & Ndofirepi, 2015) as reasons for not pursuing teaching. Concerning teaching ability perceptions, research results have been varied. The Australia-based (Gore et al., 2016) and US-based (Christensen et al., 2019) studies found that those interested in teaching careers in high school rated self-assessed suitability for teaching high among motivations to teach. By contrast, the Leech et al. (2019) US study found that students tended to rate perceived teaching ability as relatively unimportant to future teaching intentions.

Students in India mentioned that the working conditions of their own teachers were prime deterrents from considering teaching (Dastidar & Sikdar, 2015). Seeing the realities of a school day (e.g., teaching upwards of 40 students, five times a day, and the classroom management required to do that well) gave pause to many undergraduate students in the US (Tomanek, 1996; Worsham et al., 2014). Other US undergraduates commented on the pay of teachers as a reason for not pursuing the profession (Worsham et al., 2014). Being overworked and underpaid was found to be a major contributor to overall teacher attrition in the US (Kelly & Northrop, 2015).

Combining similarities across national contexts, the studies reviewed above highlight that undergraduate STEM students and high school students tend to report interest in teaching for generally intrinsic and altruistic reasons and that their academic standing is of some concern. The review also shows that the academic preparation and motivational profiles of teachers are still contested in the current teaching career choice literature. To address the dearth of studies investigating high school students’ interest in s/m teaching careers and to offer further perspectives on the preparedness and motivational profiles of prospective s/m teachers, this study is framed to answer two questions: (1) what factors predict students’ interest in s/m teaching careers at the end of high school? (2) how do the profiles of students interested in s/m teaching compare with those of students interested in other teaching, STEM, and non-STEM careers?

Theoretical framework

We recognize that factors beyond the typical intrinsic, altruistic, and extrinsic motivations also influence students’ teaching career intentions. Thus, variables outside of the typical motivation tradition need to be considered when studying high school students’ career choice. To guide the inclusion of explanatory variables in our study, we turned to one of the most prominent empirical teacher career choice perspectives: Factors Influencing Teaching Choice (FIT-Choice) scales (Watt & Richardson, 2007, 2008). These instruments have been used internationally and encompass many of the aforementioned motivational factors (Heinz, 2015) as well as several other factors previously identified as contributing to teacher career choice.

The FIT-scales were developed based on expectancy-value theory (Atkinson, 1957; Battle, 1965; Eccles, 2005). In the expectancy-value theory framework, competency and

JOURNAL OF SCIENCE TEACHER EDUCATION 5

value beliefs determine the motivation to engage in particular practices (Eccles (Parsons) et al., 1983; Wigfield et al., 2017). In essence, individuals’ motivation to do a task (such as pursuing a teaching career) derives from perceptions of “how well they do the task and the extent to which they value that task” (Eccles, 2005, p. 7). Using this theory as a lens enabled Watt and Richardson (2007) to provide a theoretically grounded perspective on teacher motivation and a more concise means of interpretation. Because of its international use and inclusion of a wide range of variables, we chose to emulate the teaching career choice framework of Watt and Richardson (2007, 2008). We will discuss in the following, as well as in the variable section, how this theoretical framework was implemented for our study’s focus on s/m teachers and high school students.

In the FIT-Choice model, common motivational variables, now based on expectancy- value theory, were reconfigured and called intrinsic value, social utility value, personal utility value, and task return. Intrinsic value is very similar to intrinsic motivation (Brookhart & Freeman, 1992), and social utility value is very similar to altruistic motivation (Watt et al., 2012). Previous studies labeled quality of life issues (such as time for family) as extrinsic motivation (Kyriacou & Coulthard, 2000); Watt and Richardson (2007) labeled them as personal utility value. This separates portions of extrinsic motivation (such as time for family) from other portions (such as salary). In the FIT-Choice model, the latter portions were labeled task return.

In addition, the FIT-Choice survey included prior teaching and learning experiences and teaching ability-related beliefs. In expectancy-value theory, these relate to prior socialization influences and expectancy self-perceptions, respectively (Eccles, 2005). Prior teaching and learning experiences were included in the FIT-Choice model because the positive experi-ences individuals have in schooling can make them “loath to leave” (Lortie, 1975/2002, p. 29). Conversely, our literature review identified negative experiences in US schooling as factors that make students loath to return (Graham & Erwin, 2011; Leech et al., 2019). Teaching ability-related beliefs were included in the FIT-Choice model because they tend to be among the highest rated reasons why individuals in teacher education programs chose teaching as a career (Heinz, 2013; Younger et al., 2004). Interestingly, these beliefs often centered on subject knowledge, where pre-service teachers in England and the US com-mented that their preparedness and strength in the subject matter influenced their teaching ability-related beliefs (Thomson et al., 2012; Younger et al., 2004). These findings, when coupled with expectancy-value theory, imply that individuals with strong science and mathematics subject knowledge would more likely be interested in becoming a s/m teacher. That said, policymakers still fear that individuals talented in mathematics may be leaking out of the s/m teacher pipeline (Han et al., 2018). Subject knowledge is an important characteristic of exemplary teachers in mathematics and science fields (Kind, 2014; Nixon et al., 2016) and teachers’ mathematics knowledge is increasingly seen as a crucial compo-nent in supporting national STEM workforces (Han et al., 2018).

Materials and methods

We used a retrospective cohort study that compared past experiences of four different groups of college students—those who planned to pursue a s/m teaching career, another teaching career, a STEM career, or a non-STEM career at the end of high school—in a nationally representative sample of college entrants in the US. The retrospective cohort

6 T. T. FUCHS ET AL.

study design is a standard method in epidemiological and medical research (MacMahon, 1965; Mantel & Haenszel, 1959). In such studies, members of cohorts who currently fall into different categories recall their earlier experiences. Through statistical methods, these types of studies are then able to simultaneously test the strength of many different hypotheses. We asked students at the beginning of college to report retrospectively about their earlier experiences and career interests. We deliberately homed in on these students’ prior career intentions for methodological reasons. This design allowed us to gather information on high school events from a “clean” perspective (i.e., no potential influences and changes that may have occurred after the end of high school). Participants reported career interests at three points in their schooling as well as on a variety of experiences and background variables.

Accurate recall by participants is essential and attainable. The accuracy of self-report depends primarily on context, relevance, and survey clarity (Bradburn, 2000; Niemi & Smith, 2003; Pace et al., 1985). In particular, self-reports of course taking, grades earned, and standardized test scores made by college students can be even more accurate than transcript records (Anaya, 1999; Baird, 1976). A review of research on self-report by Kuncel et al. (2005) concluded that self-report may be characterized as particularly accurate in samples where the surveys address issues relevant to the respondents. As such, our surveys are conducted in fall semester classes of mostly freshmen students where reflection on students’ prior experience is commonplace. In addition, students’ own instructors adminis-tered the surveys. We have conducted test–retest reliability studies of students taking our surveys twice, two weeks apart, where their responses were compared and found to be highly reproducible (Tai et al., 2005).

Sample and instrument

This research is based on a large-scale study (Persistence Research in Science and Engineering [PRiSE]) that sampled students from 34 US two- and four-year colleges, selected through a stratified random sample that accounted for institution size and type. The data was collected during the fall semester of 2007 in mandatory introductory college English courses, which generated a general sampling of college students with the widest range of study and career intentions. The full dataset of usable surveys for this study after listwise deletion included 6,265 students. 56.4% (3,533) attended four-year, and 43.6% (2,732) attended two-year institutions. The survey was constructed to gather information on the full range of student experiences in high school that might impact a student’s choice to pursue a STEM or STEM- related career. To establish validity of the survey, a pilot survey was taken by 49 undergraduate students to adjust scales and wording. Additionally, a test–retest reliability study of 96 students who took the survey and then completed it again after a two-week interval was conducted. Combining tests of both dichotomous and continuous variables (correlation coefficient and Cohen’s Kappa), the reliability of the survey was 0.70. For detailed information on PRiSE sampling procedures see Sadler et al. (2012) and Fuchs et al. (2015).

Variables

The dependent variable for this study was career interest at the end of high school (EHS). Whereas most participants were freshmen, a few others were in higher years, which would

JOURNAL OF SCIENCE TEACHER EDUCATION 7

have made “current” career interest problematic as a dependent variable. Interest in s/m teaching (n = 111) served as the reference category for our multinomial logistic regression models. The other categories of the dependent variable were other teaching (n = 524), STEM (n = 2,994), and non-STEM (n = 2,636) career interest at EHS. The career intention categories were collected based on a survey question asking participants about their career aspirations. S/m teacher and other teacher were options in the survey. The STEM group contained careers in STEM fields (e.g., biologist, earth/environmental scientist, astronomer, chemist, physicist, engineer, computer scientist, and mathematician) as well as STEM- related fields (e.g., medical professional and health professional) while the non-STEM group contained careers outside of those already mentioned (e.g., lawyer, businessperson, English/language arts specialist, other non-science career). Social scientists were included in the non-STEM group. Participants who checked multiple career interests inside the same category (e.g., biologist and engineer) were coded as that category. Participants who checked multiple career interests inside different categories were excluded from the analyses.

The independent variables were selected based on the FIT-Choice model (Watt & Richardson, 2007) and STEM career literature, because s/m teaching is often described as a STEM career (Chen, 2009; Sadler et al., 2012). These included background demographic variables, motivation variables, academic preparedness variables, and variables relating to past teaching and learning experiences. Background variables included gender (male = 1, female = 0), parental education (less than high school = 0, high school diploma = 1, some college = 2, bachelor’s degree = 3, master’s degree or higher = 4), a parent having a science- related career, and middle school (MS) and beginning of high school (BHS) interest in a s/m teaching career. Interest in a s/m teaching career was exhibited by 2.9% of our sample of 6,265 students when they were in MS (65.5% female and 35.5% male). The interest level decreased to 2.5% at BHS (59.5% female and 40.5% male) and to 1.8% at EHS (67.6% female and 32.4% male). The percentages of the other three career categories can be found in Table 1.

Furthermore, race/ethnicity variables were collected using the U.S. Census Bureau’s (Humes et al., 2010) classification system. Individuals were asked if they were of Hispanic origin and with which race they identified. In our study, participants who identified as having Hispanic origin were labeled Hispanic, and participants who did not identify as having Hispanic origins were grouped into the following (Non-Hispanic) categories: White, Black, Asian, and Other (including American Indian, Alaskan Native, Pacific Islander, Other, and any individuals who identified as two or more races). Of the students interested in s/m teaching at EHS, 67.6% identified as White, 13.5% as Hispanic, 7.2% as Black, 7.5% as Asian, 4.5% as Other. In the sample overall, 67.3% identified as White, 13.8% as Hispanic, 7.9% as Black, 5.1% as Asian, 5.9% as Other. White will serve as the baseline in the regressions while the other racial/ethnic categories will serve as controls. This is because,

Table 1. Percentages of sample indicating four career intentions at three levels of schooling.Career Intention

Level of Schooling S/m teacher Other teacher STEM Non-STEM

Middle School 2.87% 8.72% 47.30% 41.11%Beginning of HS 2.54% 7.76% 49.38% 40.32%End of HS 1.77% 8.36% 47.79% 42.08%

8 T. T. FUCHS ET AL.

as mentioned above, demographic factors such as race/ethnicity can be important predic-tors of individuals interest in a teaching profession.

Motivation variables were collected through a survey question that asked partici-pants to “Rate the following [fifteen] factors in terms of their importance for your future career satisfaction” on a scale from 0 (not at all important) to 5 (very impor-tant). We assume that the strength of the motivation variables reported by participants already existed at EHS as they can be considered fairly stable and not to have changed in the few months between EHS and beginning of college. A factor analysis of the 15 items was performed to reduce the overall number of variables, yielding four distinct factors, which were consistent with Watt and Richardson (2007) framework and were named accordingly. They included Intrinsic Motivation, Social Utility, Personal Utility, and Task Return (Table 2). For each factor, individual motivation variables were standardized, summed, and the sums were standardized again to produce easily inter-pretable final variable composites.

We constructed two subject knowledge composite variables to represent academic preparedness. Science Knowledge contained the number of science courses taken in high school and the average grade in those courses. Mathematics Knowledge con-tained the number of mathematics courses taken in high school, participants highest mathematics course grade, and SAT/ACT mathematics score. Mathematics education during the school years forms a hierarchical sequence in which courses build on one another. Hence, it appears reasonable to consider the number of mathematics courses taken and the grade in the highest mathematics course as suitable indicators of a student’s mathematics preparation. The situation is different in the sciences where a student’s typical selection of courses spans several disciplines (e.g., biology, chemistry, physics, earth sciences). In this case, we used the number of science courses taken and the average grade in those courses as the indicator of a student’s science preparation. Like in Watt and Richardson (2007), past teaching and learning experience data were collected through a survey question that asked participants to rate the quality of their last science teachers. Each component of the subject knowl-edge composites and the teaching and learning experience composites were standar-dized, added, and the sums were standardized again to produce easily interpretable final variable composites.

For a rough evaluation of the statistical power of our analysis, we determined the size of effects that could be found significant between our two smallest groups: s/m teachers (n = 111) and other teachers (n = 524). At the conventional power level of 0.8 and alpha level of 0.05, our sample allows us to detect effects as small as 0.29 standard deviations between these two groups. According to the common definition of effect

Table 2. Distinct factors from factor analysis of the survey question “Rate the following factors in terms of their importance for your future career satisfaction.” Variables were named based on Watt and Richardson (2007) framework.

Variable Intrinsic Motivation Social Utility Personal Utility Task Return

Individual Components

Use of talent, development of skills, exciting job, making own decisions, inventing

Working with people, helping others

Time for myself, time for family

Leading, fame, easy job, job opportunities and money

JOURNAL OF SCIENCE TEACHER EDUCATION 9

sizes (Cohen, 1992), this soundly beats a medium effect (0.5) and comes close to a small effect (0.2).

Results

To address our research questions, we first constructed logistic regression models that simultaneously tested multiple independent variables for significance. We then compared the significant predictors from the main effects model by each EHS career interest group.

Predicting science and mathematics teaching interest

Factors that predict high school students’ interest in s/m teaching careers can be found in Table 3. The models were constructed by iteratively adding explanatory variables detailed in the sections above and removing those variables which were found to be non-significant. Model 1 presents the significant predictors from this process while keeping race/ethnicity variables as controls. It presents interest in a s/m teaching career at EHS as the reference category compared with EHS interest in another teaching career (1A), a STEM career (1B), and a non-STEM career (1C). We also tested all possible interactions and presented the significant ones in Model 2.

As indicated in Model 1, the odds of reporting a s/m teaching career interest at EHS relative to other teaching, STEM and non-STEM careers are between 6 (p < .01) and 11 (p < .001) times higher, respectively, for students who reported an interest in s/m teaching at BHS than for students who did not report such an interest at BHS. The odds of reporting another teaching, STEM, or non-STEM career interest at EHS relative to a s/m teaching career are about 8.5 (p < .001), 4.5 (p < .001), and 6 (p < .01) times higher, respectively, for students who reported the same career interest at BHS than for students who did not report

Table 3. Summary of multinomial regression models. Model 1 contains only main effects; Model 2 includes significant interactions. Reference category is interest in s/m teaching career at EHS.

Model 1A Interest in Other Teacher at EHS

Model 1B Interest in

STEM at EHS

Model 1 C Interest in non-

STEM at EHS

Model 2A Interest in Other Teacher at EHS

Model 2B Interest in

STEM at EHS

Model 2 C Interest in non-

STEM at EHS

N 6265 6265 6265 6265 6265 6265Pseudo R2 0.22 0.22 0.22 0.23 0.23 0.23

Odds RatiosConstant 3.60 (0.49) ** 16.44 (0.45) *** 15.33 (0.45) *** 3.22 (0.50) * 14.44 (0.45) *** 13.33 (0.45) ***Interest s/m teacher BHS 0.17 (0.57) ** 0.09 (0.48) *** 0.09 (0.49) *** 0.14 (0.62) ** 0.06 (0.55) *** 0.06 (0.56) ***

s/m teacher BHS, Math. Knowledge interaction

1.39 (0.33) 2.36 (0.30) ** 2.46 (0.31) **

Interest other teacher BHS 8.58 (0.58) *** 0.66 (0.55) 1.25 (0.55) 8.85 (0.61) *** 0.69 (0.58) 1.30 (0.58)Interest STEM BHS 1.80 (0.53) 4.48 (0.48) ** 1.97 (0.48) 2.25 (0.54) 5.58 (0.49) *** 2.51 (0.49)Interest non-STEM BHS 2.14 (0.53) 1.51 (0.48) 5.81 (0.48) *** 3.22 (0.57) * 2.34 (0.52) 9.21 (0.53) ***

non-STEM BHS, Social Utility interaction

0.37 (0.40) * 0.33 (0.39) ** 0.31 (0.39) **

Interest STEM MS 0.57 (0.26) * 0.72 (0.24) 0.56 (0.24) * 0.58 (0.26) * 0.73 (0.24) 0.57 (0.24) *Gender (M = 1) 1.01 (0.25) 1.72 (0.23) * 1.55 (0.23) * 1.04 (0.30) 1.79 (0.28) ** 1.62 (0.28) *Task Return 0.99 (0.12) 1.52 (0.12) *** 1.51 (0.12) *** 0.94 (0.15) 1.43 (0.14) ** 1.42 (0.14) *Social Utility 0.99 (0.14) 0.75 (0.12) * 0.69 (0.13) ** 1.43 (0.19) 1.08 (0.18) 1.09 (0.18)Math. Knowledge 0.59 (0.12) *** 0.98 (0.12) 0.65 (0.12) *** 0.49 (0.16) *** 0.79 (0.15) 0.52 (0.14) ***Asian 0.67 (0.62) 1.75 (0.52) 1.17 (0.53) 0.68 (0.61) 1.75 (0.51) 1.19 (0.52)Black 0.56 (0.46) 1.03 (0.41) 0.79 (0.41) 0.57 (0.45) 1.03 (0.41) 0.79 (0.41)Hispanic 0.89 (0.34) 0.91 (0.31) 0.79 (0.41) 0.89 (0.34) 0.91 (0.32) 0.78 (0.32)Other 0.48 (0.47) 0.73 (0.40) 0.64 (0.41) 0.52 (0.47) 0.79 (0.41) 0.70 (0.42)

*** p < .001, ** p < .01, *p < .05. Parentheses indicate standard error.

10 T. T. FUCHS ET AL.

such an interest. Overall, students have very stable career intentions across all groups from BHS to EHS.

The odds of reporting a s/m teaching career interest at EHS relative to other teaching and non-STEM careers are about 2 times higher for students who reported a STEM career interest at MS than for students who did not report such an interest at MS (p < .05). The odds of reporting EHS career interest in STEM or non-STEM careers relative to s/m teaching careers are about 1.6 times higher for males relative to females (p < .05). For every standard deviation increase in the Social Utility score, the odds of reporting a career interest at EHS in s/m teaching relative to STEM (p < .05) or non-STEM careers (p < .01) increased by around 1.4 times. Conversely, for every standard deviation increase in the Task Return score, the odds of reporting a career interest at EHS in s/m teaching relative to STEM or non-STEM careers decreased by around 1.5 times (p < .001). For every standard deviation increase in the Mathematics Knowledge score, the odds of reporting a career interest at EHS in s/m teaching relative to another teaching career or non-STEM career increased by around 1.6 times (p < .001).

All interactions between the independent variables were tested for significance. We therefore used the more stringent significance threshold of 0.01 to curb type I error. Two interactions were found and are presented in Model 2. Because of our focus on s/m teaching interest, we concentrate on the interaction between BHS interest in s/m teaching careers and Mathematics Knowledge score (Figure 1). As noted above, performance in mathematics is a frequent teacher quality measure (Han et al., 2018) and is especially important for s/m teachers (Kind, 2014; Nixon et al., 2016).

The interaction is plotted in Figure 1 to show the difference in probability of preferring the four career categories at EHS between a prototypical (average) student who had BHS interest in s/m teaching careers (Figure 1, top) and a prototypical student who did not have such an interest at BHS (Figure 1, bottom), as Mathematics Knowledge score changes. We formed student prototypes by holding the relevant coefficients in Model 2 at the means of the respective variables in the sample. Each EHS career category is marked by a different colored line.

Figure 1 shows that the probability of an average student indicating interest in a STEM career at EHS increases as Mathematics Knowledge increases, irrespective of BHS interest in s/m teaching careers. For an average student interested in a s/m teaching career at BHS, as Mathematics Knowledge score increases, the probability of indicating interest in a non- STEM career at EHS stays relatively the same, decreasing slightly between −2 and +2 Mathematics Knowledge standard deviations. For an average student not interested in an s/m teaching career at BHS, as Mathematics Knowledge score increases, the probability of indicating interest in a non-STEM career at EHS decreases. For an average student interested in s/m teaching careers at BHS, as the Mathematics Knowledge score increases the probability of indicating interest in another teaching career at EHS decreases. This relationship is also true for an average student not interested in s/m teaching careers at BHS; however, the decrease in probability is less. Finally, for an average student interested in a s/m teaching career at BHS, as the Mathematics Knowledge score increases, the probability of preferring that same career at EHS decreases. This is in contrast to an average student not interested in s/m teaching careers at BHS. For them, as the Mathematics Knowledge score increases, the probability of preferring a s/m teaching career at EHS increases. Here, it is important to note that small changes in probability

JOURNAL OF SCIENCE TEACHER EDUCATION 11

at the lower end of the y-axes of Figure 1 represent substantial changes in odds ratios. As an example, a change in probability from 0.4% to 2.1% represents an odds ratio of around 5.3, whereas a change in probability from 40% to 80% represents a similar odds ratio of around 6.

The other interaction between BHS interest in non-STEM careers and Social Utility score shows that the probability of an average student indicating interest in a STEM career at EHS stays relatively the same as Social Utility score increases, irrespective of BHS interest in non- STEM careers. It also shows that the probability of an average student indicating interest in non-STEM careers at EHS decreases as Social Utility score increases for students both

Figure 1. Graphs depicting interaction of beginning of high school career interest with mathematics knowledge when predicting end of high school interest in four career categories (Model 2). The top panel shows those with a beginning of high school interest in science/mathematics teaching careers; the bottom panel shows those with no beginning of high school interest in s/m teaching careers. Shaded areas are ±1 SE.

12 T. T. FUCHS ET AL.

interested and not interested in non-STEM careers at BHS. Finally, the interaction shows that the probability of a student indicating interest in a teaching career in general at EHS increases as Social Utility score increases, irrespective of BHS interest in non-STEM careers.

Influential outlier diagnosis is not straightforward with multinomial logistic regression. A solution is to simplify the model to binary logistic regression models. Therefore, we specified a binary logistic regression (s/m teacher = 1, others = 0) with the same predictors as the multinomial logistic regression. We found only two entries whose Cook’s D values were substantially larger than the rest. Removing these two entries yielded nearly the same model result as our original model. For this reason, we kept the complete dataset and accepted our original model as the final model.

Comparing significant predictors

Addressing our second research question, we compared the average Mathematics Knowledge, Social Utility, and Task Return scores of individuals interested in s/m teaching, other teaching, STEM, and non-STEM careers at EHS and presented these results graphi-cally (Figure 2). Overall, students interested in other teaching careers had the lowest Mathematics Knowledge scores, while s/m teaching interested students had the highest, being significantly different from all other groups. STEM and non-STEM interested stu-dents were between these two groups, also significantly different from each other. Social Utility scores were highest among students interested in teaching in general, differing from non-STEM and STEM interested students. Finally, Task Return scores were lowest among students interested in teaching in general, again differing from non-STEM and STEM interested students.

Figure 2. Standardized social utility and task return means plotted against mathematics knowledge means for four end of high school career intentions. Career intentions include science/math teacher, other teacher, STEM, and non-STEM. Area of circles are proportional to the sample size of each career intention. Error bars are ±1 SE.

JOURNAL OF SCIENCE TEACHER EDUCATION 13

Discussion

This research generated new knowledge about what motivates US high school students’ interest in a s/m teaching career. Unsurprisingly, our models show that BHS interest in s/m teaching careers predicts EHS interest more strongly than any other tested variable. It is likewise true for the other career categories. BHS interest is the strongest predictor of EHS interest in the same career. The early formation of career intentions has been reported in other career aspiration studies (Fuchs et al., 2015; Gore et al., 2017) and other teaching career studies (Gore et al., 2016; Leech et al., 2019; Marso & Pigge, 1997). They highlight how important early strategies (e.g., career education classes, Grow Your Own programs) may be if one wants to foster students’ interest in teaching. Interestingly, MS interest in STEM careers also predicts EHS interest in s/m teaching careers when compared to other teaching and non-STEM careers. This finding points to MS STEM interested students as a potential target group for early interventions.

In our sample, students who identified as female were more likely to prefer a s/m teaching career at EHS than STEM or non-STEM careers. Females’ preference for teaching is commonplace in studies reporting on high school and undergraduate STEM students’ interest in teaching careers (Gore et al., 2016; Lai et al., 2005; Van Rooij et al., 2020). It also reflects data describing the current US teacher workforce (Ingersoll et al., 2018). Females are far more likely to prefer a teaching career than are their male counterparts, and the Ingersoll et al. (2018) report notes that, should this trend continue, the vast majority of teachers in the US will soon be female. This will certainly spark policy concerns, as US students will be increasingly likely to go through their entire K-12 education without having a male teacher. That said, there is limited evidence that teacher’s gender impacts student academic perfor-mance (Holmlund & Sund, 2008), although female teachers’ attitudes toward mathematics were found to have an impact on female students’ mathematics achievement (Beilock et al., 2010). Controlling for all other variables, our findings indicate that females are as likely as males to be interested in s/m teaching careers vis-à-vis other teaching careers. Although females are increasingly dominating the teaching profession, our results indicate this may be in similar proportions in s/m and non-s/m teaching fields in the US. Other demographic variables such as race/ethnicity were found to be non-significant in our main effects or interaction models. This is likely due to the low overall numbers of racial/ethnic categories among students interested in s/m teaching at EHS. Due to space constraints, non-significant variables in our models (e.g., race/ethnicity, past teaching and learning experiences) will not be discussed.

We constructed the composite Mathematics Knowledge to represent academic prepared-ness. Academic preparedness by subject is often a central component of pre-service teachers’ teaching ability-related beliefs. However, the importance of teaching ability- related beliefs to high school students’ interest in teaching careers is currently debated in the literature (Christensen et al., 2019; Gore et al., 2016; Leech et al., 2019). More research is needed to gauge the importance of perceived teaching ability, and its connection to subject matter knowledge, in high school students’ motivation to teach. That said, the significance of the Mathematics Knowledge composite does indicate that subject matter knowledge plays a role in s/m teaching career intentions for US high school students when compared to other teaching and non-STEM interested groups. This is congruent with expectancy-value theory (Eccles, 2005) and contrasts fears that mathematically talented individuals may not

14 T. T. FUCHS ET AL.

be interested in s/m teaching careers (Han et al., 2018). However, this finding must be viewed in light of the significant interaction between Mathematics Knowledge and BHS interest in s/m teaching careers (Figure 1).

Figure 1 shows a hypothetical student, not initially interested in s/m teaching, being slightly drawn to the profession as their mathematics knowledge increases, although that rise is small in comparison to the rise in preference for a STEM career. This result echoes research among Australian and Hong Kong high school students and US STEM under-graduates that indicates that subject affinity is an important determinant of teaching career interest (Gore et al., 2016; Lai et al., 2005; Worsham et al., 2014). In other words, as a student does better at a given subject, they may be drawn to consider teaching that subject as a career. As described above, this result is also found in some pre-service teacher career choice studies (Heinz, 2015; Thomson et al., 2012). For another hypothetical student in whom BHS interest in s/m teaching was already present, increasing mathematics knowledge diminishes, rather than promotes, their interest in a s/m teaching career. This result aligns with research among US high school students in which high achievers in general are drawn away from the teaching profession (Christensen et al., 2019). Reasons are multifaceted and diverse and reflect different geographical and national contexts including community encouragement (Christensen et al., 2019), perceived fulfillment of potential (Goldhaber et al., 2011), other factors related to teacher working conditions (Dastidar & Sikdar, 2015), and salary (Han et al., 2018).

What makes the interaction interesting is that the time of forming s/m teaching career aspirations may influence both of these strands of research. Given policy concerns that too many high-achieving mathematics students are not interested in s/m teaching (Han et al., 2018), reasons why increased mathematics knowledge might deter those already interested in s/m teaching careers from maintaining that interest should be explored. In general, an interesting avenue of research concerns the interacting roles of subject achievement and time of career aspirations in the dynamic formation of career intentions among high school students.

In addition to predicting s/m teaching career intentions at EHS, the Mathematics Knowledge variable provides a snapshot of academic achievement among participants. Teaching-interested high school students in general did not have significantly different mathematics scores than their non-STEM interested counterparts, though they were sig-nificantly lower than students interested in STEM careers. Breaking the teaching category into other teaching and s/m teaching groups, our results indicate that high schoolers interested in other teaching careers have relatively low mathematics achievement. However, we found that students interested in s/m teaching careers had higher Mathematics Knowledge scores than did their counterparts interested in other teaching, STEM, and non-STEM careers (Figure 2). Our Mathematics Knowledge variable represents a very limited piece of overall academic achievement. Nevertheless, the fact that US students interested in s/m teaching had overall higher Mathematics Knowledge scores than all of their counterparts, and that students interested in teaching in general had Mathematics Knowledge scores no different than their non-STEM interested counterparts, represents an encouraging finding that works to dispel policy fears that mathematically prepared indivi-duals are not interested in teaching careers (Han et al., 2018).

Turning to motivation variables, students who rated task return less and social utility more important to their future career satisfaction were more likely to prefer s/m teaching

JOURNAL OF SCIENCE TEACHER EDUCATION 15

careers at EHS relative to non-STEM and STEM careers. Students interested in teaching careers overall had higher Social Utility and lower Task Return scores than their STEM and non-STEM counterparts. This is congruent with nationally diverse studies of high school student teaching career interest in which students rated their love of working with children or helping others as more important than money or other rewards to their motivation to teach (Christensen et al., 2019; Dastidar & Sikdar, 2015; Gore et al., 2016). This was also found in Van Rooij et al. (2020) STEM undergraduate teacher recruitment report. Han et al. (2018) found that teacher salary was a positive predictor of high school student teaching career interest, but not for individuals with high mathematics achievement. Our results support this finding and provide evidence that individuals interested in s/m teaching want to work with, and help, others. This could manifest itself in diverse ways, many of which are currently advocated for future cohorts of s/m teachers, such as addressing various dimen-sions of underrepresentation in STEM fields (Tsui, 2007), tackling broader social issues through s/m education (Fazio, 2020; Hodson, 2011; Zeidler, 2016), and ensuring students of all sexual orientations and gender identities feel comfortable in s/m class (Fuchs, 2016).

Overall, students interested in s/m teaching in our study appear to have the academic and motivational profiles often sought in exemplary teachers (Heinz, 2015). In this regard, they appear to share many of the positive stereotypes of teachers’ motivation to teach, while countering arguments of teacher ineptitude, at least when these are applied to teachers in general. Thus, a major contribution of this study is evidence against the common portrayal of would-be-teachers as unsuitable for their job in terms of academics and motivation. Agreeing with Gore et al. (2016), we conclude that our results support an “alternative representation of who wants to teach” and thus “suggests a more hopeful future of teaching being in good hands” (p. 546).

Limitations and future research

It is practically impossible to account for all variables associated with EHS interest in a s/m teaching career. Clearly, other variables were at play that we did not measure, such as experiences students had within their high school courses outside of science and mathe-matics, interactions with parents, friends, or other community members, and the many intersectionality variables related to race, ethnicity, gender, and socio-economic level. Additionally, our study is associative; it cannot prove causal connections between variables and outcomes. Nonetheless, we were able to simultaneously test the strength of associations for many hypotheses for which controlled experiments would be difficult to perform. In this light, the relationships identified by this kind of study are worthy of controlled, experi-mental studies (to the extent possible) that may establish, with increasing certainty, the suggested causal connections. Our study relies on self-report data, which comes with limitations based on the honesty, bias, memory, and introspective ability of participants. Nonetheless, our survey was designed and administered in accordance with literature detailing ways to achieve accuracy with self-report data. Finally, this study examines career intentions at EHS. Some of those who wish to become s/m teachers will end up in different careers and some of those with other career plans will work as s/m teachers.

Concerning future research, an example of a recent experimental design can be found in Giersch’s (2021) US study, where questions regarding teaching career interest were paired with different motivational values. The goal was to understand the effects and potential of

16 T. T. FUCHS ET AL.

promotional materials with explicit intrinsic, extrinsic, and altruistic leanings on varied groups of potential teachers. Case studies and longitudinal studies should also be consid-ered. Several Grow Your Own programs are already underway in the US, and transferable principles of best practices should be studied in context and shared widely. This is especially important when considering the various intersectionality variables already mentioned above. Long-term studies may elucidate the impacts of high or even middle school level interventions on future career intentions.

Conclusion and implications

This work is timely and needed given the current trends regarding the s/m teaching workforce in the US. Programs need to begin targeting potential candidates in more effective ways and this study provides valuable insights regarding this process, highlighting implications for future research, policy, and for K-12 educators and administrators. Our results support the claim that some of the “best” (motivations) and “brightest” (academics) US high schoolers are interested in s/m teaching careers. This provides a potential target group for interventions. Students with high mathematics knowledge and a desire to work with and help others should be enthusiastically encouraged to consider s/m teaching as a career. Those designing and running Grow Your Own programs should also take interest in early high school, or even middle school students, and specifically those middle school students interested in STEM careers, as potential target groups. Career aspirations form early and interventions at this stage should be explored for their impact.

Focusing on what these interventions might look like, in the context of science and mathematics classrooms, as well as before/after school STEM programs, special attention should be given to s/m teaching as a potential career, in addition to the more commonly discussed careers in STEM and STEM-related fields. Importantly, explicit encouragement to teach is a relatively easy, highly influential, and yet underutilized strategy all educators can employ in their classrooms and programs (Christensen et al., 2019). For Grow Your Own programs, our results highlight the relative importance of working with and helping others and unimportance of money and other rewards to high school students interested in s/m teaching. These programs should thus provide less emphasis on the extrinsic rewards of teaching and more on the positive impact it has on students and communities. Embedded in these programs should also be continual subject matter exploration because our results highlight the importance of mathematics knowledge to s/m teaching career aspirations. Interventions that combine these recommendations may be accomplished through mentor-ship opportunities (Lawrence et al., 2019) as well as purposeful teaching experiences that mirror the roles s/m teachers play in school life (Luft et al., 2011). The latter experiences highlight both the impact teaching has on students and provide further subject matter exploration. These experiences may also contribute to students’ teaching ability-related beliefs, an area that deserves further focus in research on high school students’ interest in teaching careers. Some of these field experiences have shown promise with undergraduate STEM majors (Scott et al., 2006) as well as a small numbers of high school students (Demir et al., 2019), even though they sometimes deter potential individuals from the profession (e. g., Tomanek, 1996; Worsham et al., 2014). Focusing specifically on high school students, examples of such programs can range from formalized teaching assistant positions in science and mathematics classrooms, cross-grade teaching opportunities where students

JOURNAL OF SCIENCE TEACHER EDUCATION 17

of higher grades plan and teach those at lower grades, to full university partnerships in which colleges of education and science, as well as local school boards/districts, work together to spark interest in teaching and STEM fields among high school students. The Peaking an Interest in Mathematics and Science (PIMAS) program (Luft et al., 2011) is one example of these types of partnerships.

Finally, this work supports future research that differentiates teaching interest by subject. Grouping all potential teachers into one category belies the internal differentiation on certain academic achievement measures that, if taken globally, may be potentially harmful to public perception of teaching (Gore et al., 2016), and work to dissuade high-achieving individuals from the profession. In contrast, presenting the profession as attracting acade-mically excellent students, which s/m teaching careers seem to do, works to highlight teaching as a professionally challenging career that also makes substantial societal contribu-tions. This view of teaching is argued to attract more academically talented individuals to the profession (Han et al., 2018).

Acknowledgments

The authors would like to thank the PRiSE team for their dedicated work and all the participating English professors and their students for making this study possible.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Funding

This work was supported by the National Science Foundation [#0624444]; Social Sciences and Humanities Research Council of Canada [Canadian Graduate Scholarship Doctoral].

ORCID

Travis T. Fuchs http://orcid.org/0000-0002-6713-1828Gerhard Sonnert http://orcid.org/0000-0003-4138-2044Sandra A. Scott http://orcid.org/0000-0002-5363-3253Philip M. Sadler http://orcid.org/0000-0001-7578-4047Chen Chen http://orcid.org/0000-0002-6065-8889

References

Aikenhead, G. S. (2006). Science education for everyday life: Evidence-based practice. Teachers College Press.

Akar, E. O. (2012). Motivations of Turkish pre-service teachers to choose teaching as a career. Australian Journal of Teacher Education, 37(10), 67–84. https://doi.org/10.14221/ajte.2012v37n10.7

Alvarez, B. (2017, May 24). A growing recruitment strategy for a diverse teacher workforce. National Education Association Today. http://neatoday.org/2017/05/24/grow-your-own-teacher-diversity/

Anaya, G. (1999). Accuracy of self-reported test scores. College and University, 75(2), 13–19.Atkinson, J. W. (1957). Motivational determinants of risk taking behavior. Psychological Review, 64(6,

Pt.1), 359–372. https://doi.org/10.1037/h0043445

18 T. T. FUCHS ET AL.

Auguste, B., Kihn, P., & Miller, M. (2010). Closing the talent gap: Attracting and retaining top-third graduates to careers in teaching. McKinsey.

Bacolod, M. P. (2007). Do alternative opportunities matter? The role of female labor markets in the decline of teacher quality. The Review of Economics and Statistics, 89(4), 737–751. https://doi.org/ 10.1162/rest.89.4.737

Baird, L. (1976). Using self–reports to predict student performance (Research Monograph No. 7). College Entrance Examination Board. https://files.eric.ed.gov/fulltext/ED126116.pdf

Ball, D. L., & McDiarmid, G. W. (1990). The subject matter preparation of teachers. In W. R. Houston (Ed.), Handbook of research on teacher education (pp. 437–449). Macmillan.

Battle, E. S. (1965). Motivational determinants of academic task persistence. Journal of Personality and Social Psychology, 2(2), 209–218. https://doi.org/10.1037/h0022442

Beilock, S. L., Gunderson, E. A., Ramirez, G., & Levine, S. C. (2010). Female teachers’ math anxiety affects girls’ math achievement. Proceedings of the National Academy of Sciences, 107(5), 1860– 1863. https://doi.org/10.1073/pnas.0910967107

Bergey, B. W., & Ranellucci, J. (2021). Motivation Profiles of Urban Preservice Teachers: Relations to Socialization, Initial Career Perceptions, and Demographics. Contemporary Educational Psychology, 64(2021), 101936. https://doi.org/10.1016/j.cedpsych.2020.101936

Borman, G. D., & Dowling, N. M. (2008). Teacher attrition and retention: A meta-analytic and narrative review of the research. Review of Educational Research, 78(3), 367–409. https://doi.org/ 10.3102/0034654308321455

Bradburn, N. (2000). Temporal representation and event dating. In A. A. Stone, J. S. Turkan, C. A. Bachrach, J. B. Jobe, H. S. Kurtzman, & V. S. Cain (Eds.), The science of self-report (pp. 49–61). Lawrence Erlbaum Associates.

Brookhart, S. M., & Freeman, D. J. (1992). Characteristics of entering teacher candidates. Review of Educational Research, 62(1), 37–60. https://doi.org/10.3102/00346543062001037

Brown, G. T., Lake, R., & Matters, G. (2011). Queensland teachers’ conceptions of assessment: The impact of policy priorities on teacher attitudes. Teaching and Teacher Education, 27(1), 210–220. https://doi.org/10.1016/j.tate.2010.08.003

Bruinsma, M., & Jansen, E. P. (2010). Is the motivation to become a teacher related to pre-service teachers’ intentions to remain in the profession? European Journal of Teacher Education, 33(2), 185–200. https://doi.org/10.1080/02619760903512927

Chen, X. (2009). Students who study science, technology, engineering, and mathematics (STEM) in postsecondary education. National Center for Educational Statistics. https://nces.ed.gov/pubs2009/ 2009161.pdf

Cheung, A. C., & Yuen, T. W. (2016). Examining the motives and the future career intentions of mainland Chinese pre-service teachers in Hong Kong. Higher Education, 71(2), 209–229. https:// doi.org/10.1007/s10734-015-9897-3

Christensen, S. S., Davies, R. S., Harris, S. P., Hanks, J., & Bowles, B. (2019). Teacher recruitment: Factors that predict high school students’ willingness to become teachers. Education Sciences, 9(4), 282. https://doi.org/10.3390/educsci9040282

Clifton, R. A. (2013). Obtaining better teachers for Canadian public schools: A review of the teacher effectiveness research literature. Barbara Mitchell Centre for Improvement in Education. https://www. fraserinstitute.org/sites/default/files/obtaining-better-teachers-for-canadian-public-schools.pdf

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/ 0033-2909.112.1.155

Collinson, V., Killeavy, M., & Stephenson, H. J. (1999). Exemplary teachers: Practicing an ethic of care in England, Ireland, and the United States. Journal for a Just and Caring Education, 5(4), 349–366.

Coulthard, M., & Kyriacou, C. (2002). Does teaching as a career offer what students are looking for? In I. Menter, M. Hutchings, & A. Ross (Eds.), The crisis in teacher supply: Research and strategies for retention(pp. 29–57). Trentham.

Cross, F. (2016). Teacher shortage areas nationwide listings 1990–1991 through 2016–17. U.S. Department of Education Office of Postsecondary Education. https://www2.ed.gov/about/offices/ list/ope/pol/tsa.pdf

JOURNAL OF SCIENCE TEACHER EDUCATION 19

Cross, M., & Ndofirepi, E. (2015). On becoming and remaining a teacher: Rethinking strategies for developing teacher professional identity in South Africa. Research Papers in Education, 30(1), 95– 113. https://doi.org/10.1080/02671522.2013.851729

Dastidar, A. G., & Sikdar, S. (2015). Occupation choices of high school and college students with special reference to teaching and research. Policy Futures in Education, 13(3), 375–394. https://doi. org/10.1177/1478210315569041

Demir, K., Gul, T., & Czerniak, C. (2019). Recruitment of science and mathematics teachers: Review of literature and analysis of findings from three years efforts of a recruitment. Journal of Research in STEM Education, 5(2), 119–137. https://doi.org/10.51355/jstem.2019.78

Dinham, S., Ingvarson, L., & Kleinhenz, E. (2008). Teaching talent: The best teachers for Australia’s classrooms. The Business Council of Australia. https://research.acer.edu.au/teaching_standards/12/

Eccles (Parsons), J., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L.,& Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Eds.), Achievement and achievement motivation (pp. 75–146). Freeman.

Eccles, J. S. (2005). Studying gender and ethnic differences in participation in math, physical science, and information technology. New Directions in Child and Adolescent Development, 2005(110), 7– 14. https://doi.org/10.1002/cd.146

Fazio, X. (2020, September 18). Reorienting curriculum for the Anthropocene. UNESCO Futures of Education Ideas LAB. https://en.unesco.org/futuresofeducation/fazio-reorienting-curriculum-for- anthropocene

Feng, L., & Sass, T. R. (2016). Teacher quality and teacher mobility (Working Paper No. 57). National Center for Analysis of Longitudinal Data in Education Research. https://files.eric.ed.gov/fulltext/ ED529180.pdf

Fray, L., & Gore, J. (2018). Why people choose teaching: A scoping review of empirical studies, 2007– 2016. Teaching and Teacher Education, 75(1), 153–163. https://doi.org/10.1016/j.tate.2018.06.009

Fuchs, T. (2016). LGBTQ inclusivity in the science classroom. Canadian Teacher Magazine, Jan/Feb, 6. http://www.canadianteachermagazine.com/issues/2016/CTM_JanFeb16/docs/CTM_JanFeb16_ web.pdf

Fuchs, T. T., Sadler, P. M., & Sonnert, G. (2015). High school predictors of a career in medicine. Journal of Career and Technical Education,30(1), 9–28. http://doi.org/10.21061/jcte.v30i1.711

Garcia, E., & Weiss, E. (2019). The teacher shortage is real, large and growing, and worse than we thought. Economic Policy Institute. https://www.epi.org/publication/the-teacher-shortage-is-real- large-and-growing-and-worse-than-we-thought-the-first-report-in-the-perfect-storm-in-the-tea cher-labor-market-series/

Giersch, J. (2021). Motivations to enter teaching: An investigation with non-education univer-sity students. Journal of Education for Teaching, 1–14. https://doi.org/10.1080/02607476.2021. 1880870

Gist, C. D., Bianco, M., & Lynn, M. (2019). Examining grow your own programs across the teacher development continuum: Mining research on teachers of color and non-traditional educator pipelines. Journal of Teacher Education, 70(1), 13–25. https://doi.org/10.1177/0022487118787504

Gitomer, D., Latham, A. S., & Ziomek, R. (1999). The academic quality of prospective teachers: The impact of admissions and licensure testing. Educational Testing Service. https://www.ets.org/Media/ Research/pdf/RR-03-35.pdf

Goldhaber, D., Gross, B., & Player, D. (2011). Teacher career paths, teacher quality, and persistence in the classroom: Are public schools keeping their best? Journal of Policy Analysis and Management, 30(1), 57–87. https://doi.org/10.1002/pam.20549

Goldstein, R. A. (2011). Imaging the frame: Media representations of teachers, their unions, NCLB, and education reform. Educational Policy, 25(4), 543–576. https://doi.org/10.1177/ 0895904810361720

Gordon, J. A. (2000). The color of teaching. Routledge.Gore, J., Barron, R. J., Holmes, K., & Smith, M. (2016). Who says we are not attracting the best and

brightest? Teacher selection and the aspirations of Australian school students. The Australian Educational Researcher, 43(5), 527–549. https://doi.org/10.1007/s13384-016-0221-8

20 T. T. FUCHS ET AL.

Gore, J., Holmes, K., Smith, M., Fray, L., McElduff, P., Weaver, N., & Wallington, C. (2017). Unpacking the career aspirations of Australian school students: Towards an evidence base for university equity initiatives in schools. Higher Education Research & Development, 36(7), 1383– 1400. https://doi.org/10.1080/07294360.2017.1325847

Graham, A., & Erwin, K. D. (2011). “I don’t think black men teach because how they get treated as students”: High-achieving African American boys’ perceptions of teaching as a career option. The Journal of Negro Education, 80(3), 398–416. https://www.jstor.org/stable/41341142

Han, S. W., Borgonovi, F., & Guerriero, S. (2018). What motivates high school students to want to be teachers? The role of salary, working conditions, and societal evaluations about occupations in a comparative perspective. American Educational Research Journal, 55(1), 3–39. https://doi.org/10. 3102/0002831217729875

Heinz, M. (2013). Why choose teaching in the Republic of Ireland?– Student teachers’ motivations and perceptions of teaching as a career and their evaluations of Irish second-level education. European Journal of Educational Studies, 5(1), 1–17.

Heinz, M. (2015). Why choose teaching? An international review of empirical studies exploring student teachers’ career motivations and levels of commitment to teaching. Educational Research and Evaluation, 21(3), 258–297. https://doi.org/10.1080/13803611.2015.1018278

Henry, G. T., Bastian, K. C., & Smith, A. A. (2012). Scholarships to recruit the “best and brightest” into teaching: Who is recruited, where do they teach, how effective are they, and how long do they stay? Educational Researcher, 41(3), 83–92. https://doi.org/10.3102/ 0013189X12437202

Hodson, D. (2011). Looking to the future: Building a curriculum for social activism. Sense Publishers.Holmlund, H., & Sund, K. (2008). Is the gender gap in school performance affected by the sex of the

teacher? Labour Economics, 15(1), 37–53. https://doi.org/10.1016/j.labeco.2006.12.002 Humes, K. R., Jones, N. A., & Ramirez, R. R. (2010). Overview of race and Hispanic origin:2010US

census briefs. U.S. Census Bureau. https://www.census.gov/prod/cen2010/briefs/c2010br-02.pdf Hutchison, G., & Johnson, B. (1993/1994). Teaching as a career: Examining high school students’

perspectives. Action in Teacher Education, 15(4), 61–67. https://doi.org/10.1080/01626620.1994. 10463179

Ingersoll, R., Merrill, E., Stuckey, D., & Collins, G. (2018). Seven trends: The transformation of the teaching force, updated October 2018. Consortium for Policy Research in Education. https:// repository.upenn.edu/cpre_researchreports/108/

Ingersoll, R. M., & Mitchell, E. (2011). The status of teaching as a profession. In J. Ballantine & J. Spade (Eds.), Schools and society: A sociological approach to education (4th ed., pp. 185–189). Pine Forge Press/Sage Publications.

Kelly, S., & Northrop, L. (2015). Early career outcomes for the “best and the brightest” selectivity, satisfaction, and attrition in the beginning teacher longitudinal survey. American Educational Research Journal, 52(4), 624–656. https://doi.org/10.3102/0002831215587352

Kieschke, U., & Schaarschmidt, U. (2008). Professional commitment and health among teachers in Germany: A typological approach. Learning and Instruction, 18(5), 429–437. https://doi.org/10. 1016/j.learninstruc.2008.06.005

Kind, V. (2014). Science teachers’ content knowledge. In H. Venkat, M. Rollnick, M. Askew, & J. Loughran (Eds.), Exploring mathematics and science teachers’ knowledge: Windows into teacher thinking (pp. 15–28). Routledge.

Klassen, R. M., Al-Dhafri, S., Hannok, W., & Betts, S. M. (2011). Investigating pre- service teacher motivation across cultures using the Teachers’ Ten Statements Test. Teaching and Teacher Education, 27(3), 579–588. https://doi.org/10.1016/j.tate.2010.10.012

Kuncel, N., Credé, M., & Thomas, L. (2005). The validity of self-reported grade point averages, RICs, and test scores: A meta-analysis and review of the literature. Review of Educational Research, 75(1), 63–82. https://doi.org/10.3102/00346543075001063

Kyriacou, C., & Coulthard, M. (2000). Undergraduates’ views of teaching as a career choice. Journal of Education for Teaching: International Research and Pedagogy, 26(2), 117–126. https://doi.org/10. 1080/02607470050127036

JOURNAL OF SCIENCE TEACHER EDUCATION 21

Lai, K. C., Chan, K. W., Ko, K. W., & So, K. S. (2005). Teaching as a career: A perspective from Hong Kong senior secondary students. Journal of Education for Teaching, 31(3), 153–168. https://doi.org/ 10.1080/02607470500168974

Lankford, H., Loeb, S., McEachin, A., Miller, L. C., & Wyckoff, J. (2014). Who enters teaching? Encouraging evidence that the status of teaching is improving. Educational Researcher, 43(9), 444– 453. https://doi.org/10.3102/0013189X14563600

Lawrence, S., Johnson, T., & Small, C. (2019). Watering our own lawn: Exploring the impact of a collaborative approach to recruiting African Caribbean STEM majors into teaching. Journal of Negro Education, 88(3), 391–406. https://doi.org/10.7709/jnegroeducation.88.3.0391

Leech, N. L., Haug, C. A., & Bianco, M. (2019). Understanding urban high school students of color motivation to teach: Validating the FIT-Choice Scale. Urban Education, 54(7), 957–983. https:// doi.org/10.1177/0042085915623338

Lortie, D. C. (1975/2002). Schoolteacher: A sociological study (2nd ed.). University of Chicago Press.Luft, J. A., Fletcher, S., & Fortney, B. (2005). Early recruitment of science teachers: Promising or

problematic strategy. Science Educator, 14(1), 41–48. https://eric.ed.gov/?id=EJ740957 Luft, J. A., Wong, S. S., & Semken, S. (2011). Rethinking recruitment: The comprehensive and

strategic recruitment of secondary science teachers. Journal of Science Teacher Education, 22(5), 459–474. https://doi.org/10.1007/s10972-011-9243-2

MacMahon, B. (1965). Epidemiologic methods in cancer research. The Yale Journal of Biology and Medicine, 37(6), 508–522. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2604763/pdf/ yjbm00598-0082.pdf

Mantel, N., & Haenszel, W. (1959). Statistical aspects of the analysis of data from retrospective studies. Journal of the National Cancer Institute, 22(4), 719–748. http://www.med.mcgill.ca/epide miology/hanley/c634/stratified/Mantel_Haenszel_1.pdf

Marso, R., & Pigge, F. (1997). A longitudinal study of persisting and nonpersisting teachers’ academic and personal characteristics. The Journal of Experimental Education, 65(3), 243–254. https://doi. org/10.1080/00220973.1997.9943457

Nesje, K., Brandmo, C., & Berger, J. L. (2018). Motivation to become a teacher: A Norwegian validation of the Factors Influencing Teaching Choice Scale. Scandinavian Journal of Educational Research, 62(6), 813–831. https://doi.org/10.1080/00313831.2017.1306804

Niemi, R., & Smith, J. (2003). The accuracy of students’ reports of course taking in the 1994 National Assessment of Educational Progress. Educational Measurement: Issues and Practice, 22(1), 15–21. https://doi.org/10.1111/j.1745-3992.2003.tb00112.x

Nixon, R. S., Campbell, B. K., & Luft, J. A. (2016). Effects of subject-area degree and classroom experience on new chemistry teachers’ subject matter knowledge. International Journal of Science Education, 38(10), 1636–1654. https://doi.org/10.1080/09500693.2016.1204482

Organization for Economic Co-operation and Development. (2005). Teaching matters: Attracting, developing and retaining effective teachers. OECD Publishing. https://www.oecd. org/education/school/34990905.pdf

Organization for Economic Co-operation and Development. (2012). What kind of careers do boys and girls expect of themselves? OECD Publishing. https://www.oecd.org/pisa/pisaproducts/pisainfocus/ 49829595.pdf

Organization for Economic Co-operation and Development. (2019). TALIS 2018 results (Volume 1): Teachers and school leaders as lifelong learners. OECD Publishing.

Pace, C. R., Barahona, D., & Kaplan, D. (1985). The credibility of student self-reports. Center for the Study of Evaluation. https://files.eric.ed.gov/fulltext/ED266174.pdf

Putman, H., Hansen, M., Walsh, K., & Quintero, D. (2016). High hopes and harsh realities: The real challenges to building a diverse workforce. Brown Center on Education Policy at Brookings. https:// www.brookings.edu/wp-content/uploads/2016/08/browncenter_20160818_teacherdiversityre portpr_hansen.pdf

Reeves, T. D., & Lowenhaupt, R. J. (2016). Teachers as leaders: Pre-service teachers’ aspirations and motivations. Teaching and Teacher Education, 57(1), 176–187. https://doi.org/10.1016/j.tate. 2016.03.011

22 T. T. FUCHS ET AL.

Roness, D. (2011). Still motivated? The motivation for teaching during the second year in the profession. Teaching and Teacher Education, 27(3), 628–638. https://doi.org/10.1016/j.tate.2010. 10.016

Ross, J. A., Cousins, J. B., & Gadalla, T. (1996). Within-teacher predictors of teacher efficacy. Teaching and Teacher Education, 12(4), 385–400. https://doi.org/10.1016/0742-051X(95)00046-M

Sadler, P. M., Sonnert, G., Hazari, Z., & Tai, R. (2012). Stability and volatility of STEM career interest in high school: A gender study. Science Education,96(3), 411–427. https://doi.org/10.1002/sce. 21007

Scott, T. P., Milam, J. L., Stuessy, C. L., Blount, K. P., & Bentz, A. (2006). Math and science scholars (MASS) program: A model program for the recruitment and retention of preservice mathematics and science teachers. Journal of Science Teacher Education, 17(4), 389–411. https://doi.org/10. 1007/s10972-006-9026-3

Seeger, F., & Waschescio, U. (Eds.). (1998). The culture of the mathematics classroom. Cambridge University Press.

Simmons, A. (2018, February 12). Teacher recruitment starting in high school. Edutopia. https://www. edutopia.org/article/teacher-recruitment-starting-high-school

Sinclair, C. (2008). Initial and changing student teacher motivation and commitment to teaching. Asia-Pacific Journal of Teacher Education, 36(2), 79–104. https://doi.org/10.1080/ 13598660801971658

Smith, V., Mack, F., & Akyea, S. (2004). African- American male honor students’ views of teaching as a career choice. Teacher Education Quarterly, 31(2), 75–88. http://www.teqjournal.org/backvols/ 2004/31_2/smith.pmd.pdf

Summerhill, A., Matranga, M., Peltier, G., & Hill, G. (1998). High school seniors’ perceptions of a teaching career. Journal of Teacher Education, 49(3), 228–234. https://doi.org/10.1177/ 0022487198049003009

Sutcher, L., Darling-Hammond, L., & Carver-Thomas, D. (2016). A coming crisis in teaching? Teacher supply,demand, and shortages in the U.S. Learning Policy Institute.

Tai, R. H., Sadler, P. M., & Loehr, J. F. (2005). Factors influencing success in introductory college chemistry. Journal of Research in Science Teaching,42(9), 987–1012. https://doi.org/10.1002/tea. 20082

Thomson, M. M., Turner, J. E., & Nietfeld, J. L. (2012). A typological approach to investigate the teaching career decision: Motivations and beliefs about teaching of prospective teacher candidates. Teaching and Teacher Education, 28(3), 324–335. https://doi.org/10.1016/j.tate.2011.10.007

Tomanek, D. (1996). Creating interest in teaching: Science classroom experiences for academically talented college science majors. Journal of Science Teacher Education, 7(3), 213–225. https://doi. org/10.1007/BF00117036

Tsui, L. (2007). Effective strategies to increase diversity in STEM fields: A review of the research literature. The Journal of Negro Education, 76(4), 555–581. http://www.asee.org/Review_Incr_ Diversity_-_J_Negro_Education.pdf

Van Rooij, E. C. M., Fokkens-Bruinsma, M., & Goedhart, M. J. (2020). Identifying potential secondary school teachers among science university students: A latent profile analysis. Journal of Science Teacher Education, 31(5), 556–577. https://doi.org/10.1080/1046560X.2020.1729478

Vegas, E., Murnane, R. J., & Willett, J. B. (2001). From high school to teaching: Many steps, who makes it? Teachers College Record, 103(3), 427–449. https://doi.org/10.1111/0161-4681.00121

Watt, H. M. G., Richardson, P. W., & Smith, K. (2017). Why teach? How teachers` motivations matter around the world. In H. M. G. Watt, P. W. Richardson, & K. Smith (Eds.), Global perspectives on teacher motivation (pp. 1–21). Cambridge University Press.

Watt, H. M. G., & Richardson, P. W. (2007). Motivational factors influencing teaching as a career choice: Development and validation of the FIT-Choice Scale. The Journal of Experimental Education, 75(3), 167–202. https://doi.org/10.3200/JEXE.75.3.167-202

Watt, H. M. G., & Richardson, P. W. (2008). Motivations, perceptions, and aspirations concerning teaching as a career for different types of beginning teachers. Learning and Instruction, 18(5), 408– 428. https://doi.org/10.1016/j.learninstruc.2008.06.002

JOURNAL OF SCIENCE TEACHER EDUCATION 23

Watt, H. M. G., Richardson, P. W., Klusmann, U., Kunter, M., Beyer, B., Trautwein, U., & Baumert, J. (2012). Motivations for choosing teaching as a career: An international comparison using the FIT- Choice scale. Teaching and Teacher Education, 28(6), 791–805. https://doi.org/10.1016/j.tate.2012. 03.003

Wigfield, A., Rosenzweig, E. Q., & Eccles, J. S. (2017). Achievement values: Interactions, interven-tions, and future directions. In A. J. Elliot, C. S. Dweck, & D. S. Yeager (Eds.), Handbook of competence and motivation: Theory and application (pp. 116–134). The Guilford Press.

Worsham, H. M., Friedrichsen, P., Soucie, M., Barnett, E., & Akiba, M. (2014). Recruiting science majors into secondary science teaching: Paid internships in informal science settings. Journal of Science Teacher Education, 25(1), 53–77. https://doi.org/10.1007/s10972-013-9360-1

Younger, M., Brindley, S., Pedder, D., & Hagger, H. (2004). Starting points: Student teachers’ reasons for becoming teachers and their preconceptions of what this will mean. European Journal of Teacher Education, 27(3), 245–264. https://doi.org/10.1080/0261976042000290787

Zeidler, D. L. (2016). STEM education: A deficit framework for the twenty first century? A socio-cultural socioscientific response. Cultural Studies of Science Education, 11(1), 11–26. https://doi. org/10.1007/s11422-014-9578-z

24 T. T. FUCHS ET AL.