We begin by discussing the study's findings and conclusions, and their relation to EVT. We do so briefly because they hold no surprises. We next discuss, in depth, the highly unexpected relationship between participation in advanced middle‐school science programs and the choice to major in STEM disciplines, which we neither hypothesized nor foresaw; we discuss its implications vis‐à‐vis science education practice, policy, and research. To conclude, we review the study's limitations.

5.1 Findings and conclusions from the current study and their relation to EVT

We found that students who chose to major in STEM disciplines had significantly higher scores for all the relevant predictor variables than their counterparts who majored in non‐STEM disciplines. These findings fully support and corroborate those reported in the extensive research literature reviewed above.

As hypothesized regarding gender (e.g., Else‐Quest et al., 2013; Lavonen & Laaksonen, 2009; OECD, 2007; Taskinen et al., 2008), we found no significant relationship with choosing a high school STEM major. In addition, as hypothesized, we found a significant relationship between gender and choosing particular majors; in physics and computer science, boys held significant majorities (1.5:1 and 2.6:1, respectively) while in biology girls held a more than 4:1 majority. No significant differences were found for math and chemistry. These findings corroborate those previously reported by several researchers (Beyer, 2014; Cheryan, Ziegler, Montoya, & Jiang, 2017). We note, however, that the kinds of predictions made by EVT are not relevant for discerning the underlying gender‐based reasons for choosing particular majors. To capture specific discipline choice, further research which will fine‐tune the expectancy and value items is needed.

Regarding the proposed model of academic choice (see Figure 2), here too no surprises emerged. As hypothesized, “subjective task value” and “expectation of success” predicted academic choice and mediated the effects of the perceived environmental factors. These findings fully corroborate EVT (Eccles (Parsons) et al., 1983; Eccles, 2005aa). Regarding this corroboration, we raise two relevant points. First, it may be contended that corroboration is simply a result of the starting assumption that EVT will explain the academic choice. We counter this possible argument by citing support from the other prominent theories of motivation discussed above. For example, “interest” is a key predictor of academic choice in SDT (Deci & Ryan, 2002) where it is named “intrinsic motivation” and in several theories of interest (Berlyne, 1949; Dewey, 1913; Hidi & Renninger, 2006; Schiefele, 1991). Similarly, “self‐efficacy” is a key predictor of academic choice in Self‐Efficacy Theory (Bandura, 1977) and in SDT where it is named “competence.” Utility value is also a key predictor of academic choice in SDT where it is named “extrinsic motivation.” Given the congruence with these additional theories of motivation, we believe our findings that corroborate EVT are not the result of circular reasoning or tautology. Second, from an epistemological framework, we note Popper's (2005) principle of falsifiability: theories not falsifiable are unscientific. Although our data corroborated the basic tenets of EVT, opposite findings could plausibly have been recorded. For example, if most students did not cite interest or self‐efficacy as reasons to major in STEM, then EVT would, at best, be rejected as being an appropriate framework or, at worst, its theoretical foundations would be questioned. This, however, was not the case.

We next discuss two methodological issues (the relationship between the qualitative and the quantitative data, and dealing with multicollinearity) that we dealt with in this study.

5.1.1 The relationship between the qualitative and quantitative data The study's qualitative findings (the major roles played by interest and utility value) are the ninth‐grade students' own perceptions of the factors that ostensibly caused them to major in high school STEM disciplines. We write ostensibly because the qualitative data presented in Section 4.1 reflect certain methodological weaknesses associated with using open‐ended questions, especially among 15‐year‐old adolescents. First, as predicted by Krosnick and Presser (2010), we found a relatively low‐response rate: 29.5% of the students who chose a high school STEM major declined to cite any reasons at all (these students did, however, fully respond to the closed‐ended questions). This weakness relates to representativeness. Second, 26.5% cited one reason only. These respondents, either well‐intentioned or motivated by a desire to fill out the questionnaire as quickly as possible, provided at best a superficial response that seemed acceptable or, at worst, any reasonable response even if it was not personally applicable (Krosnick & Presser, 2010). This weakness may reflect availability bias. Third, only 44% of the students who chose a STEM major provided two or more reasons. In contrast to these findings, Caspi et al. (under review) obtained qualitative data from younger students (12‐year‐old primary‐school graduates) who chose to enroll in advanced middle‐school science programs; in addition to closed‐ended questions, they answered one nearly identical open‐ended question: “What led you to enroll? List all the reasons you can think of, both important and seemingly unimportant.” In this study, 89.2% cited two or more reasons, a significantly higher rate than found in the present study, χ2(2) = 206.95, p < 0.0001, that may reflect age differences in willingness to cooperate. Despite these possible weaknesses, one important pattern of results deserves attention. Many students (78%) cited utility value in the form of either short, medium‐ and/or long‐terms goals as their major reason for choosing STEM learning. We may therefore speculate that ninth‐grade students have developed a utilitarian view of science learning adopted from the societal argument for STEM majoring that is common in Western cultures where neoliberalism positions STEM careers as central to national security and economic prosperity. For example, Pres. Obama saw STEM studies as a matter of national security. In his National Security Strategy (Obama, 2010) of the United States, he wrote: “America's long‐term leadership depends on educating and producing future scientists and innovators. We will invest more in STEM education… so American students are no longer outperformed by those in other nations…” (p. 29). In a similar way, the Israeli Minister of Education Naftali Bennett, during his 2015–2019 term in office, also viewed STEM learning as the path to national security and economic prosperity. As a result, the Israeli Education Ministry prioritized STEM topics, especially math, in the country's high schools. Thus, we may reasonably infer that students' answers reflect an internalization of these national goals and priorities. To summarize, the two data types fully complement and reinforce each other. The qualitative data point to perceived causality while the statistically significant quantitative data, grounded in theory, fill in the students' blind spots. They point to important correlations among the many factors (the roles of schools, parents, peers, etc.)—beyond the ken of the students—that led them to choose STEM majors.