STEM career expectations across four diverse countries: motivation to learn mathematics mediates the effects of gender and math classroom environments

Abstract

Background
We tested the broad generality of a model for predicting 9th–10th grade students’ STEM career expectations by age 30, focusing on hard science, mathematics and engineering professions only, known for driving innovation, research and development. The model’s predictors included motivation to learn mathematics, gender, and math classroom environments (disciplinary climate, teacher support and instructional strategies fostering conceptual understanding).

Methods
We used data from the Programme for International Student Assessment (PISA) 2022. Four countries were selected based on the percentage of students expecting STEM careers, representing high vs. low groups (Qatar and Morocco vs. Czech Republic and Lithuania, respectively). Analysis began with computing correlations between the variables, followed by path analyses for each country to determine both direct and indirect effects of the predictors on students’ STEM career expectations.

Results
We found that motivation to learn mathematics not only directly predicted STEM career expectations but also mediated the influence of the remaining variables: gender (boys show higher motivation to learn math), and math classroom environments (students in well-disciplined math classes with supportive teachers who employ instructional strategies fostering math reasoning also demonstrate higher motivation to learn math). Remarkably, our model consistently demonstrated robustness across all four countries, despite their significant economic, ethnic, and religious diversity.

Conclusions
Theoretically, the model reveals that 9th–10th grade students’ transitory long-term STEM career expectations are shaped by their interest in mathematics, their perceived importance of the subject, confidence in their self-efficacy to succeed in math tasks, perceptions of classroom disciplinary climate, teacher support, and their exposure to instructional strategies aimed at enhancing math reasoning. Practically, it suggests widespread potential for informing interventions aimed at increasing student motivation to pursue STEM careers through improved mathematics education practices.

Full text

Caspiand Gorsky
International Journal of STEM Education (2024) 11:52
https://doi.org/10.1186/s40594-024-00511-5
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International Journal of
STEM Education
STEM career expectations acrossfour diverse
countries: motivation tolearn mathematics
mediates theeects ofgender andmath
classroom environments
Avner Caspi1 and Paul Gorsky1*
Abstract
Background We tested the broad generality of a model for predicting 9th–10th grade students’ STEM career
expectations by age 30, focusing on hard science, mathematics and engineering professions only, known for driv-
ing innovation, research and development. The model’s predictors included motivation to learn mathematics, gender,
and math classroom environments (disciplinary climate, teacher support and instructional strategies fostering concep-
tual understanding).
Methods We used data from the Programme for International Student Assessment (PISA) 2022. Four countries
were selected based on the percentage of students expecting STEM careers, representing high vs. low groups
(Qatar and Morocco vs. Czech Republic and Lithuania, respectively). Analysis began with computing correlations
between the variables, followed by path analyses for each country to determine both direct and indirect effects
of the predictors on students’ STEM career expectations.
Results We found that motivation to learn mathematics not only directly predicted STEM career expectations
but also mediated the influence of the remaining variables: gender (boys show higher motivation to learn math),
and math classroom environments (students in well-disciplined math classes with supportive teachers who employ
instructional strategies fostering math reasoning also demonstrate higher motivation to learn math). Remarkably,
our model consistently demonstrated robustness across all four countries, despite their significant economic, ethnic,
and religious diversity.
Conclusions Theoretically, the model reveals that 9th–10th grade students’ transitory long-term STEM career expec-
tations are shaped by their interest in mathematics, their perceived importance of the subject, confidence in their self-
efficacy to succeed in math tasks, perceptions of classroom disciplinary climate, teacher support, and their exposure
to instructional strategies aimed at enhancing math reasoning. Practically, it suggests widespread potential for inform-
ing interventions aimed at increasing student motivation to pursue STEM careers through improved mathematics
education practices.
Keywords Adolescents’ STEM career expectations, Motivation, Gender, Situated expectancy-value theory, PISA 2022
Introduction
For decades, research has explored the factors shap-
ing students’ aspirations and expectations for careers
in Science, Technology, Engineering, and Mathematics
(STEM)—fields vital to global economic sustainability
*Correspondence:
Paul Gorsky
paulgorsky@gmail.com
1 Research Associate, Dept. of Education and Psychology, The Open
University of Israel, 1 University Way, Ra’anana, Israel
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Caspiand Gorsky International Journal of STEM Education (2024) 11:52
(Regan & DeWitt, 2015). is study leverages PISA 2022
data on students’ perceptions of mathematics instruction
to assess its influence on motivation to learn math and
subsequent STEM career expectations. As a cornerstone
of STEM pathways, mathematics is a prerequisite for all
STEM professions (Shumow, 2023). We begin by review-
ing the definition of a STEM career.
Definitions of STEM careers differ across and within
nations. PISA 2022 categorizes STEM and related careers
into four groups: (1) science, math, and engineering pro-
fessionals; (2) health professionals; (3) science techni-
cians and associate professionals; and (4) information
and communication technology (ICT) professionals. is
study adopts a narrow definition, focusing exclusively on
the first category—Scientists, Mathematicians, and Engi-
neers. Although this excludes “Technology” in the strict
sense, ABET (Accreditation Board for Engineering and
Technology) notes the close relationship between engi-
neering and technology, with subtle distinctions (ABET,
2024):
• Engineering: e profession in which knowledge of
mathematical and natural sciences gained by study,
experience, and practice is applied with judgment to
develop ways to utilize, economically, the materials
and forces of nature for the benefit of mankind.
• Technology: e profession in which knowledge of
mathematical and natural sciences… is applied with
judgment to develop ways to utilize, economically,
the materials and forces of nature for the benefit of
mankind.
is study evaluates the broad applicability of a model
based on situated expectancy value theory (Eccles & Wig-
field, 2020) across four countries with two extreme lev-
els of student expectations for STEM careers. Qatar and
Morocco represent high-expectation groups, while the
Czech Republic and Lithuania represent low-expectation
groups. ese countries also differ significantly in eco-
nomic, ethnic, and religious diversity. e model includes
key variables that shape students’ STEM career expec-
tations. While we expected to confirm the influence of
widely reported factors such as gender, interest and self-
efficacy, particular emphasis was placed on the effects
of specific variables linked to mathematics instruction,
which are amenable to various school-based interven-
tions. Our model stands out for its focus on examining
students’ STEM career expectations through the prism of
mathematics education.
Before discussing the theoretical framework, it is
important to distinguish between STEM career aspira-
tions and expectations. During middle-childhood (ages
6–11), aspirations often reflect vague notions of future
success (Cochran et al., 2011). By middle-adolescence
(ages 14–17), expectations become more realistic, shaped
by self-assessment and societal norms (Oliveira et al.,
2020). PISA 2022 surveys these adolescent expectations.
Theoretical framework
Situated expectancy-value theory (SEVT, Eccles & Wig-
field, 2020) and its predecessor, expectancy-value theory
(EVT, Eccles (Parsons) etal., 1983), offer a robust frame-
work for predicting and explaining individuals’ achieve-
ment-related choices, including academic decisions and
career expectations. is framework has been widely
applied to examine the factors influencing academic
choices, such as high school and college majors (e.g.,
Andersen & Ward, 2014; Caspi etal., 2019; Harackiewicz
etal., 2016; Watt etal., 2017), as well as career aspirations
and expectations (e.g., Ahmed & Mudrey, 2019; Carrico
etal., 2016; Lv etal., 2022; Wang & Degol, 2013).
According to SEVT, achievement-related choices are
primarily driven by two motivational factors: subjective
task value and expectation of success. ese factors also
mediate the effects of 16 secondary variables within the
theory (Eccles & Wigfield, 2020). In some cases, second-
ary variables may also directly influence the dependent
variable.
Our study explores (1) how two key predictors from
PISA 2022—gender and math teaching environments—
affect students’ motivation to learn math and (2) the
potential link between that motivation and their expecta-
tions for STEM careers. If this connection is confirmed,
it suggests that improving math classroom practices
could enhance both student motivation and STEM career
expectations.
Figure1 presents our proposed model, which accounts
for potential direct effects of secondary variables on the
dependent variable. e aim of this study is to evaluate
the model’s robustness using data from students in coun-
tries with significant differences in STEM career expec-
tations, as well as high levels of economic, ethnic, and
religious diversity.
INTERPRETATION OF
EXPERIENCE
Mathemacs Class-
room Environments
Disciplinary climate,
Teacher support,
Instruconal strategies
for math reasoning
MOTIVATION TO
LEARN MATHEMATICS
Subjecve Task Value
(Interest, Aainment)
Expectaon of Success
(Self-Efficacy)
ACHIEVEMENT
RELATED CHOICES
STEM Career
Expectaons
PERSONAL
CHARACTERISTICS
Gender
Fig. 1 SEVT theoretical model for adolescents’ STEM career
expectations
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Caspiand Gorsky International Journal of STEM Education (2024) 11:52
The model’s predictors
Motivation
Studies have shown that students motivated to learn
mathematics are more likely to express interest in STEM
careers than those who are not (e.g., Andersen & Ward,
2014; Gottlieb, 2018; Wang, 2012). According to SEVT,
two key beliefs predict students’ STEM career expec-
tations (Eccles & Wigfield, 2020); high subjective task
values and high expectations of success (self-efficacy).
Students with high levels of these beliefs have higher
expectations for STEM careers than their peers with
lower levels (e.g., Guo etal., 2016; Lauermann etal., 2017;
Pagkratidou etal., 2024; Rosenzweig etal., 2019).
Subjective task value includes four variables: interest,
attainment, utility, and cost. In the PISA student ques-
tionnaire only the first two were surveyed and only they
are included in the model. Eccles and Wigfeld (2002)
defined interest as “the enjoyment the individual gets
from performing the activity or the subjective interest
the individual has in the subject” (p.120). Wigfield and
Cambria (2010) defined attainment as the “personal
importance of doing well on a given task” (p.4). Expecta-
tion of success was defined by Eccles and Wigfield (2002)
as “individuals’ beliefs about how well they will do on
upcoming tasks, either in the immediate or longer‐term
future” (p.119).
Gender
Research suggests that during primary school, boys and
girls show equally positive attitudes towards science,
math and engineering (e.g., Caspi et al., 2023; DeWitt
et al., 2013; Xu & Jack, 2023). However, upon entering
middle-school, a gender disparity emerges in attitudes,
interests, and aspirations for future STEM education and
careers, despite balanced performance levels in STEM
subjects (e.g., Caspi etal., 2019; Else-Quest etal., 2013).
Boys typically display higher motivation towards pursu-
ing STEM paths (Else-Quest etal., 2013; Stoet & Geary,
2018; Su & Rounds, 2015), while adolescent girls, though
equally proficient in STEM fields, often hesitate to pur-
sue STEM professions, particularly in male-dominated
disciplines like physics, mathematics, computer science
and engineering (Hamer etal., 2023; Han, 2016; Moote
et al., 2020; Nitzan-Tamar & Kohen, 2022; Sax et al.,
2017). If so, these findings highlight that for 9th–10th
grade students, boys will express greater motivation to
learn math and have higher expectations of working in
STEM careers.
Key variables inthemathematics classroom environment
We investigated three key variables in the math class-
room environment which may influence adolescent
students’ STEM career expectations, namely, classroom
disciplinary climate, teacher support and instructional
strategies that foster mathematical reasoning including
the frequency of encountering specified math reasoning
tasks relevant to the twenty-first century in courses other
than math.
Classroom disciplinary climate is variable refers to
the everyday atmosphere perceived by students within
the math classroom, encompassing factors such as dis-
ruption, noise, disorder, and students’ attentiveness to
the teacher’s instructions (Sortkær & Reimer, 2018).
Research has consistently shown that a positive class-
room disciplinary climate is conducive to student learn-
ing and achievement in mathematics (e.g., Cheema &
Kitsantas, 2014; López et al., 2023; Wang et al., 2022,
2023). Our study will clarify whether and to what extent
it positively influences students’ motivation to learn math
and to expect STEM careers, outcomes not generally
explored in the literature.
Teacher support is variable includes the provision of
adaptive explanations, constructive responses to errors,
perception of class pace adequacy, and the quality of
teacher–student interactions characterized by respect
and care (e.g., Dietrich etal., 2015; Lazarides etal., 2019).
In culturally diverse settings worldwide, research consist-
ently underscores the link between teacher support and
students’ math achievement. Of particular relevance to
our study are findings indicating that math teacher sup-
port predicts students’ motivation to learn mathemat-
ics, especially their interest and self-efficacy (e.g., Marsh
etal., 2024; Wang, 2012; Yu & Singh, 2018). Accordingly,
we anticipate that increased levels of teacher support
will not only boost students’ motivation to learn math
but also potentially bolster their expectations for STEM
careers.
Instructional strategy is variable was broadly defined
by Gorsky etal. (2008) as ‘the approach a teacher takes to
achieve learning objectives’ (p.53). Over the past century,
extensive research has explored the impact of instruc-
tional strategies on learning outcomes, initially focusing
on academic achievement. However, contemporary stud-
ies now extend this inquiry to include outcomes in the
affective domain and career expectations (Hattie, 2009).
We next summarize issues regarding a crucial topic
in mathematics education: the debate between lessons
emphasizing conceptual understanding vs. procedural
knowledge. is longstanding discourse seeks to deter-
mine the most effective strategy for fostering the skills
and motivation essential for success in mathematical dis-
ciplines and STEM careers. e discourse on this issue
has persisted for decades, and it is of such significance
that the authors of the PISA 2022 survey incorporated
numerous items addressing it.
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Caspiand Gorsky International Journal of STEM Education (2024) 11:52
e National Council of Teachers of Mathematics
(NCTM, 2014) defines procedural knowledge as “the
ability to apply procedures accurately, efficiently, and
flexibly; to transfer procedures to different problems
and contexts; to build or modify procedures from other
procedures; and to recognize when one strategy or pro-
cedure is more appropriate to apply than another” (p. 1).
Instructional strategies for developing procedural knowl-
edge typically include fast-paced lessons where teachers
demonstrate, guide student activities, and require exten-
sive practice until mastery is attained (NCTM, 2014).
e National Assessment of Educational Progress
(NAEP, 2003) describes students’ conceptual understand-
ing in mathematics as their “ability to reason in settings
involving the careful application of concept definitions,
relations, or representations of either” (p.1). Accord-
ingly, students show conceptual understanding in math
when they can (1) recognize, label, and create examples
of concepts, (2) use various models, diagrams, and tools
to understand concepts, (3) apply principles and use
facts and definitions, (4) compare, contrast, and connect
related concepts, and (5) understand and apply signs,
symbols, and terms in math.
e three instructional strategy variables we selected
from the PISA 2022 questionnaire gauge the degree to
which students perceive math lessons as focusing on
the acquisition of conceptual understanding skills. e
first two variables involve reasoning, applying principles,
comparing related concepts, and using various math-
ematical models.
e third variable involves the extent to which students
reported encountering math reasoning skills relevant
to twenty-first century tasks (such as extracting math-
ematical information from diagrams, graphs, or simula-
tions, and using statistical variation to make decisions)
in various contexts like physics, computer science, or
social science, not just in math classes. It is important to
emphasize that our model aims to determine the extent,
if any, to which these perceived teaching practices con-
tribute to students’ motivation to learn math and their
STEM career expectations.
Relevant to our research, Ekmekci and Serrano (2022)
examined how math teachers’ instructional strategies
influence the academic achievements and STEM career
expectations of 10th grade students. eir study revealed
that teachers who emphasized connecting mathematical
concepts and prioritized the development of problem-
solving abilities, mathematical reasoning, and concep-
tual comprehension yielded higher levels of motivational
factors (such as self-efficacy, utility, and interest) among
students compared to those who did not prioritize these
aspects. Notably, the positive impact of these instruc-
tional strategies on STEM career expectations was found
to be mediated by motivation. ese findings align with
similar observations reported by numerous researchers
over more than two decades (e.g., Anthony & Walshaw,
2023; Mainali, 2021; Marsh etal., 2024; Rittle-Johnson &
Jordan, 2016; Sinay & Nahornick, 2016; Wang, 2012; Yu
& Singh, 2018).
The current study
Our study aims to explore the robustness of a theory
based general model for predicting 9th–10th grade ado-
lescents’ likelihood of expecting careers in science, math-
ematics and engineering. To achieve this, we selected
four countries having very dissimilar percentages of stu-
dents expecting STEM careers at age 30; specifically, we
compared Qatar and Morocco, which have high percent-
ages, with the Czech Republic and Lithuania, which have
low percentages. ese countries also exhibit significant
economic, ethnic and cultural diversity.
Assuming the model’s broad applicability, this study
holds both theoretical and practical significance by show-
ing how gender and specific factors in math classroom
environments influence adolescents’ motivation to learn
mathematics, which in turn shapes their current long-
term expectations for STEM careers.
Methods
Research question andhypotheses
e key research question is whether the model holds
across four diverse countries, which differ significantly in
the proportion of students expecting STEM careers and
in their economic, ethnic, religious, and cultural diver-
sity. In short, is the model broadly valid?
To address this question, we will test the model’s good-
ness-of-fit and validity within each country. Analyzing
the results across all four countries will help determine
the model’s overall validity. e specific hypotheses are as
follows:
H1: Motivation to learn mathematics will have a
direct positive influence on STEM career expecta-
tions (e.g., Andersen & Ward, 2014; Gottlieb, 2018;
Wang, 2012).
H2: Gender will have a direct effect on students’
STEM career expectations, with a greater propor-
tion of boys than girls expressing such expectations.
We further hypothesize that this effect will be medi-
ated by motivation to learn mathematics, suggest-
ing that boys will exhibit higher motivation to learn
math compared to girls (e.g., Guo, 2022; Hamer
etal., 2023; Han, 2016; Moote etal., 2020; Sax etal.,
2017).
H3: Math classroom environment includes five vari-
ables (disciplinary climate, teacher support, two
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Caspiand Gorsky International Journal of STEM Education (2024) 11:52
instructional strategies that foster math reasoning
and one that assesses the extent to which students
encountered specific math reasoning skills pertinent
to 21st century tasks); each will have positive effects
on students’ STEM career expectations when medi-
ated by motivation to learn mathematics, assuming
positive relations between the variable and motiva-
tion to learn math (e.g.,Cheema & Kitsantas, 2014;
Ekmekci & Serrano, 2022; Marsh etal., 2024).
Participants
We selected four countries that participated in the Pro-
gramme for International Student Assessment (PISA)
2022 program based on the percentages and numbers of
students expecting STEM careers in science, math and
engineering only. Based on student records, we chose
the two countries with the highest percentages (Qatar
and Morocco) and two countries with very low per-
centages (Lithuania and Czech Republic). As shown in
Table1, the high group average percentage (8.06%) is 5.5
times greater than the low group (1.47%). Our decision to
investigate four countries only reflects our goal to test for
the robustness of the model using the criterion of maxi-
mum variance between the high and low extremes. For
this purpose, two pairs of countries suffice to test for sig-
nificant differences.
ere were countries with even lower percentages than
the two chosen; however, for carrying out statistical anal-
yses, we selected two countries where more than 1% of
the students expected a STEM career and this percentage
contains at least 100 students. Table1 displays these data
along with the countries’ geographic locations, ethnic
and religious demographics, and gross domestic prod-
ucts (GDP), illustrating their notable diversity.
Measures
Students’ job/career expectations
In the PISA 2022 questionnaire, students were asked to
specify the ‘job’ they anticipated having at age 30, either
by title or description. e terms job, occupation, and
career were used interchangeably throughout the ques-
tionnaire, and we will do the same where appropriate.
PISA 2022 staff categorized the responses using four-
digit codes from the ‘International Standard Classifi-
cation of Careers’ (ISCO-08) detailed in the Technical
Report (OECD, 2024). We focused on careers classified as
(1) Science and Engineering Professionals, (2) Mathema-
ticians, Actuaries, and Statisticians, and (3) Engineering
Professions (see Appendix A for the STEM occupations
and ISCO-08 codes). All other occupations were classi-
fied as non-STEM.
Gender
e PISA 2022 format elicited one of two responses, ‘boy’
or ‘girl’.
Disciplinary climate in math classroom (DISCLIM)
Students used a four-point scale (“Every lesson”, “Most
lessons”, “Some lessons”, “Never or almost never”) to
assess the occurrence of seven hypothetical situations
during their mathematics lessons (e.g., “ere is noise
and disorder”; “Students do not start working for a long
time after the lesson begins”). For each of the four coun-
tries, reliability was tested by McDonald’s omega (Ω);
values ranged from 0.92 to 0.95.
Math teacher support (TEACHSUP)
Students used the same four-point scale listed above
(data were reverse-coded before averaging)to assess the
incidence of four situations during their math lessons
(e.g., “e teacher gives extra help when students need
it”; “e teacher continues teaching until the students
understand”). McDonald’s omega (Ω) ranged from 0.89
to 0.92.
Cognitive activation inmath: foster reasoning (COGACRCO)
Students used a five-point scale (“Never or almost never”,
“Less than half of the lessons”, “About half of the lessons”,
“More than half of the lessons”, “Every lesson or almost
every lesson”) to assess the incidence of nine situations
Table 1 Participating countries: selection criteria and background data
Countries Students
expecting STEM
careers
Geographic location Ethnic demographics Religious
demographics GDP/per capita 2022
Qatar (N = 7676) N = 636 (8.29%) Middle East 12% Qatari/Arab; 88%
other 66% Muslim; 16%
Christian $88,046 [for Qataris only]
Morocco (N = 6867) N = 537 (7.82%) North Africa 99% Arab 99% Muslim $3570
Lithuania (N = 7257) N = 115 (1.58%) North Europe 85% Lithuanian 93% Christian $23,962
Czech Republic (N = 8460) N = 115 (1.36%) Central Europe 57% Czech; 32% other 60% atheist/agnostic $27,566
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 6 of 15
Caspiand Gorsky International Journal of STEM Education (2024) 11:52
where their math teacher fostered mathematics reason-
ing over the entire school year (e.g., “e teacher asked
us to explain what assumptions we were making when
solving a mathematics problem”; “e teacher asked
us to explain how we solved a mathematics problem”).
McDonald’s omega (Ω) values ranged from 0.89 to 0.93.
Cognitive activation inmath: encourage mathematical
thinking (COGACMCO)
Students used the same five-point scale listed above
to assess the frequency of nine situations where their
math teacher fostered mathematics thinking over the
entire school year (e.g., “e teacher asked us to think
of problems from everyday life that could be solved with
new mathematics knowledge we learned”; “e teacher
encouraged us to think mathematically”). McDonald’s
omega (Ω) values ranged from 0.94 to 0.96.
Exposure tomathematics reasoning andtwenty‑rst
century math tasks (EXPO21ST)
Students used a four-point scale whose items (“Fre-
quently”, “Sometimes”, “Rarely”, “Never”) were reverse-
coded before averaging to assess the frequency of
encountering different types of math tasks during the
school year (e.g., “Representing a situation mathemati-
cally using variables, symbols, or diagrams”; “Identifying
mathematical aspects of a real-world problem”). McDon-
ald’s omega (Ω) values ranged from 0.89 to 0.92.
Data analyses
Variables were sourced from the PISA 2022 ‘student
questionnaire’ only (since the data are publically avail-
able, ethical clearance was waived by the university’s eth-
ics committee). While variables in the PISA database are
normalized for international comparisons, we utilized
raw data for each country which are openly accessible at
https:// www. oecd. org/ pisa/ data/ 2022d ataba se. Further-
more, we included only records with complete data for all
the variables in the model. us, data included 60.4% of
the original records in Morocco, 67.1% in Qatar, 86.2% in
Czech Republic, and 88.4% in Lithuania. In all four coun-
tries a large enough sample was available to detect small
effects.
For each country, analysis began with computing cor-
relations between each variable, followed by path analysis
to determine both direct and indirect effects of the pre-
dictors on students’ STEM career expectations. Corre-
lations were calculated using SPSS 24; path analysis was
carried out using the “Lavaan” package in the statistics
environment R (Rosseel, 2012). For the mediation effects
in the path analysis that included both direct and indirect
effects, we used z statistics and the 95% confidence inter-
vals. at is, direct and indirect effects were considered
significant if the 95% confidence intervals did not include
zero. Each model’s goodness-of-fit was assessed using
conventional cutoff values: RMSEA (Root Mean Square
Error of Approximation) below 0.05, SRMR (Standard-
ized Root Mean Square Residual) below 0.08, and both
CFI (Comparative Fit Index) and TLI (Tucker Lewis
Index) above 0.90 (Wang & Wang, 2012).
Results
Results for each country are presented in a table show-
ing the number of respondents by gender, the percent-
age of students expecting STEM careers, and the means,
standard deviations, and correlations for each variable.
A figure follows, displaying the path analysis, goodness-
of-fit indices, and R2 values for the two endogenous vari-
ables: motivation to learn mathematics and STEM career
expectations. e section concludes by highlighting simi-
larities and differences across the four countries.
Qatar
Table2 shows data about Qatar, its participants, the vari-
ables and their correlations.
Figure2 shows the path analysis for Qatar (solid lines
are significant relationships, while dashed lines are not).
e data-to-model-fit indices are robust: CFI = 0.970;
TLI = 0.830; RMSE A = 0.077; SRMR = 0.033. Total stand-
ard indirect effect = 0.052 (p < 0.001). R2 for the two
endogenous variables were 0.177 and 0.027 for motiva-
tion and for STEM career expectation, respectively.
In full accord with the theoretical model being exam-
ined, all predictor variables are statistically significant
either directly or when mediated through motivation to
learn mathematics. Specifically, we found full support
for H1 which hypothesized the direct significant effect of
motivation on STEM career expectations. In partial sup-
port of H2, the direct effect of gender on STEM career
expectations is significant; however, its effect is not
mediated by motivation to learn mathematics. Regard-
ing the hypotheses subsumed under H3, the effects of
disciplinary climate (DISCLIM), math teacher support
(TEACHSUP) and the use of instructional strategies that
foster mathematical reasoning (COGACRCO) and math-
ematical thinking (COGACMCO) were significant when
mediated through motivation to learn mathematics. e
only somewhat contrary result was EXPO21ST whose
direct effect was significant, while its indirect effect was
not.
Morocco
Table3 shows data about Morocco, its participants, the
variables and their correlations.
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Caspiand Gorsky International Journal of STEM Education (2024) 11:52
Figure 3 shows the path analysis for Morocco (solid
lines are significant relationships, while dashed lines are
not). e indices are robust: CFI = 0.982; TLI = 0.899;
RMSEA = 0.052; SRMR = 0.021. Total standard indirect
effect = 0.030 (p < 0.001). R2 for the two endogenous vari-
ables were 0.077 and 0.031 for motivation and for STEM
career expectation, respectively.
In almost full accord with the theoretical model, the
predictor variables are statistically significant either
directly or when mediated through motivation to learn
mathematics. e one exception is exposure to math rea-
soning (EXPO21ST) whose direct and indirect effects
were insignificant.
Specifically, we found full support for H1 which
hypothesized the direct significant effect of motivation
to learn mathematics on STEM career expectations.
In partial support of H2, the direct effect of gender on
STEM career expectations is significant; however, the
indirect effect of gender is not mediated by motivation.
In support of H3, the effects of classroom disciplinary
climate (DISCLIM) and two instructional strategies
that foster math reasoning and thinking (COGACRCO
and COGACMCO) attained significance when medi-
ated through motivation to learn math. Contrary to
the hypothesis, both the direct and indirect effects of
EXPO21ST were not significant.
Czech Republic
Table4 shows data for the Czech Republic, its partici-
pants, the variables and their correlations.
Figure 4 shows the path analysis (solid lines are sig-
nificant relationships, while dashed lines are not).
Fig. 2 Path analysis: Qatar
Table 2 Data about Qatar’s participants, variables and correlations
* < 0.05; ** < 0.001
Students:5152
F = 2842
M = 2310
Students
expecting a STEM
career
N = 587, 11.39%
Gender
1 = F
2 = M
Motivation
Scales: 1–4
M = 2.84
SD = 0.78
DISCLIM
Scales: 1–4
M = 2.99
SD = 0.81
TEACHSUP
Scales: 1–5
M = 3.20
SD = 0.87
COGACRCO
Scales: 1–5
M = 3.20
SD = 1.102
COGACMCO
Scales: 1–4
M = 3.09
SD = 1.20
EXPO21ST
Scales: 1–4;
M = 2.60
SD = 0.77
Gender 0.072**
Motivation 0.133** − 0.045**
DISCLIM 0.044** − 0.158** 0.293**
TEACHSUP 0.034* − 0.086** 0.190** 0.110**
COGACRCO 0.048** − 0.053** 0.307** 0.294** 0.102**
COGACMCO 0.043** − 0.035* 0.328** 0.266** 0.209** 0.627**
EXPO21ST 0.035* − 0.007 0.020 − 0.022 0.269** 0.003 0.100**
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Caspiand Gorsky International Journal of STEM Education (2024) 11:52
Fig. 3 Path analysis: Morocco
Table 4 Data about Czech Republic’s participants, variables and correlations
* < 0.05; ** < 0.001
Students: 7295
F = 3687
M = 3608
Students
expecting a STEM
career
N = 114, 1.60%
Gender
1 = F
2 = M
Motivation
Scales: 1–4
M = 2.55
SD = 0.66
DISCLIM
Scales: 1–4
M = 2.97
SD = 0.76
TEACHSUP
Scales: 1–5
M = 2.56
SD = 0.87
COGACRCO
Scales: 1–5
M = 2.87
SD = 0.97
COGACMCO
Scales: 1–4
M = 2.54
SD = 1.02
EXPO21ST
Scales: 1–4
M = 2.32
SD = 0.70
Gender 0.037**
Motivation 0.071** 0.095**
DISCLIM 0.031** − 0.034** 0.159**
TEACHSUP 0.000 0.076** 0.256** 0.125**
COGACRCO 0.035** 0.048** 0.173** 0.123** 0.294**
COGACMCO 0.016 0.101** 0.213** 0.093** 0.377** 0.543**
EXPO21ST 0.006 0.086** 0.069** − 0.008 0.203** 0.205** 0.334**
Table 3 Data about Morocco’s participants, variables and correlations
* < 0.05; ** < 0.001
Students:
4151
F = 2054
M = 2097
Students
expecting a STEM
career
N = 483, 11.64%
Gender
1 = F
2 = M
Motivation
Scales: 1–4
M = 2.70
SD = 0.72
DISCLIM Scales: 1–4
M = 2.66
SD = 0.81
TEACHSUP
Scales: 1–5
M = 3.00
SD = 0.91
COGACRCO
Scales: 1–5
M = 2.99
SD = 1.12
COGACMCO
Scales: 1–4
M = 2.77
SD = 1.20
EXPO21ST
Scales: 1–4;
M = 2.60
SD = 0.77
Gender 0.033*
Motivation 0.164** − 0.043**
DISCLIM 0.029 − 0.095** 0.152**
TEACHSUP − 0.022 − 0.010 0.106** 0.129**
COGACRCO 0.043** − 0.043** 0.227** 0.231** 0.223**
COGACMCO 0.019 0.002 0.234** 0.215** 0.325** 0.555**
EXPO21ST − 0.011 0.066** 0.015 − 0.041** 0.212** 0.093** 0.202**
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Caspiand Gorsky International Journal of STEM Education (2024) 11:52
e indices are robust: CFI = 0.980; TLI = 0.887;
RMSEA = 0.057; SRMR = 0.028. Total standard indirect
effect = 0.011 (p < 0.001). R2 for the two endogenous vari-
ables were 0.101 and 0.008 for motivation and for STEM
career expectation, respectively.
Like its predecessor, all predictor variables are statisti-
cally significant either directly or when mediated through
motivation to learn mathematics except for exposure to
math reasoning (EXPO21ST); again, the direct and indi-
rect effects for this variable were insignificant.
Specifically, we found full support for H1, the direct
significant effect of motivation on STEM career expec-
tations. In partial support of H2, the direct effect of
gender on STEM career expectations is significant.
However, while the direct effect of gender on motiva-
tion to learn math is significant, the effect of gender on
STEM career expectations does not achieve statistical
significance when mediated by motivation. In support
of H3, the positive effects of disciplinary climate (DIS-
CLIM), teacher support (TEACHSUP) and the use
of instructional strategies that foster math reasoning
(COGACRCO and COGACMCO) were indirect, medi-
ated through motivation to learn math. In addition, the
effects of three variables (DISCLIM, TEACHSUP and
COGACRCO) were direct as well as indirect. Again, no
support was found for EXPO21ST.
Lithuania
Table5 shows data about Lithuania, its participants, the
variables and their correlations.
Figure 5 shows the path analysis for Lithuania (solid
lines are significant relationships, while dashed lines are
not). e indices are robust: CFI = 0.992; TLI = 0.956;
RMSEA = 0.033; SRMR = 0.013. Total standard indi-
rect effect = 0.012 (p < 0.001). R2 for the two endogenous
Fig. 4 Path analysis: Czech Republic
Table 5 Data about Lithuania’s participants, variables and correlations
* < 0.05; ** < 0.001
Students: 6412
F = 3289
M = 3123
Students
expecting a STEM
career
N = 113, 1.79%
Gender
1 = F
2 = M
Motivation
Scales: 1–4
M = 2.58
SD = 0.65
DISCLIM
Scales:
1–4
M = 3.10
SD = 0.73
TEACHSUP
Scales: 1–5
M = 2.80
SD = 0.83
COGACRCO
Scales: 1–5
M = 3.10
SD = 0.96
COGACMCO
Scales: 1–4
M = 2.74
SD = 1.06
EXPO21ST
Scales: 1–4
M = 2.46
SD = 0.72
Gender 0.024**
Motivation 0.080** 0.025*
DISCLIM 0.012 − 0.032* 0.142**
TEACHSUP 0.020 0.011 0.223** 0.095**
COGACRCO 0.012 − 0.055** 0.188** 0.153** 0.263**
COGACMCO 0.007 − 0.007 0.207** 0.119** 0.312** 0.537**
EXPO21ST − 0.002 0.037** 0.077** − 0.002 0.215** 0.167** 0.299**
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Caspiand Gorsky International Journal of STEM Education (2024) 11:52
variables were 0.088 and 0.008 for motivation and for
STEM career expectation, respectively.
For the fourth time, all predictor variables are statisti-
cally significant either directly or when mediated through
motivation to learn mathematics except for exposure to
math reasoning (EXPO21ST) where again its direct and
indirect effects were insignificant.
Specifically, we found full support for H1, the direct
significant effect of motivation to learn mathematics on
STEM career expectations. In partial support of H2, only
the direct effect of gender on STEM career expectations
is marginally significant (p = 0.06). However, while the
direct effect of gender on motivation to learn math is sig-
nificant, its effect on STEM career expectations does not
achieve statistical significance when mediated by moti-
vation. In support of H3, the positive effects of discipli-
nary climate (DISCLIM), teacher support (TEACHSUP)
and the use of instructional strategies that foster math
reasoning (COGACRCO and COGACMCO) were indi-
rect, mediated through motivation to learn math. Again,
the direct and indirect effects of EXPO21ST were not
significant.
Assessing model equivalency acrosscountries
As can be inferred from the data shown in Table6, the
predictors of students’ STEM career expectations were
equivalent across countries with very different levels
of expectations, diverse economies and cultures. One
variable only seems to be irrelevant in three of the four
countries, namely, Exposure to math reasoning and
twenty-first century math tasks (EXPO21ST) whose
significant direct influence was observed in one case only
and whose indirect effects were nowhere else observed.
Discussion
Our path analysis supports a concise model explaining
adolescents’ motivation to learn mathematics and their
STEM career expectations. is model is generalizable
across four nations with highlydiverse student expecta-
tions for STEM careers, as well as varyingeconomic, eth-
nic, and religious backgrounds.
Before examining the model’s individual factors, we
discuss its robustness and significance. Strong goodness-
of-fit indices (CFI, TLI, RMSEA, and SRMR) across all
countries confirm that gender and certain math class-
room factors directly influence students’ motivation
to learn math, which in turn affects their STEM career
expectations. However, the model’s R2 values for motiva-
tion to learn math and STEM career expectations are rel-
atively low. We recall that R2 indicates the proportion of
variance in an outcome variable explained by some inde-
pendent variables.
e relatively low R2 values reveal the model’s limita-
tions, suggesting that additional factors influence stu-
dents’ motivation to learn math and their expectations
for STEM careers. Two key points address these low val-
ues. First, when a binary outcome is highly imbalanced—
as in our case, where only 5.64% of participants expected
STEM careers and 94.36% did not—correlations with the
model’s predictors tend to be weak. is occurs because
limited variability in the binary dependent variable
Fig. 5 Path analysis: Lithuania
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Caspiand Gorsky International Journal of STEM Education (2024) 11:52
Table 6 Model equivalency across countries
Predictors Direct eects Meaning of direct eects Mediated eects
(via motivation) Meaning of indirect eects Predictors
found in all 4
models
Gender 3/4 Boys expect STEM jobs more than girls 0/4 Boys are more motivated to learn math; conse-
quently they expect STEM jobs more than girls (3/4)
Motivation [Interest, Importance, Self-efficacy] 4/4 Students with high motivation to learn math
expect STEM jobs more than students with low
motivation
Not relevant Not relevant (4/4)
DISCLIM [Math classroom disciplinary climate] 1/4 Students in well-disciplined math classes
expect STEM jobs more than students who
do not
4/4 Students in well-disciplined math classes
are more motivated to learn and more likely
to expect STEM jobs than those who are not
(4/4)
TEACHSUP [Math teacher support] 0/4 Students who experience their math teacher
as supportive expect STEM jobs more than stu-
dents that experience no such support
3/4 Students who experience their math teacher
as supportive are more motivated to learn
math, and more likely to expect STEM jobs
compared to those without such support
(3/4)
COGACRCO [Math teacher fosters math
reasoning] 1/4 Students whose math teacher fosters math
thinking are more likely to expect STEM jobs
than those without such experiences
4/4 Students who experience their math teacher
as fostering math reasoning and thinking are
more highly motivated learn math; conse-
quently they expect STEM jobs more than stu-
dents without such experiences
(4/4)
COGACMCO [Math teacher fosters math
thinking] 0/4 Students whose math teacher fosters math
reasoning are more likely to expect STEM jobs
than those without such experiences
4/4 (4/4)
EXPO21ST [Confidence about doing specified
math tasks] 1/4 Students who encounter different types
of twenty-first century math tasks in their class
expect STEM jobs more than students who
do not
0/4 Students who encounter different types
of twenty-first century math tasks are more
motivated to learn math; thus they expect
STEM jobs more than students who do not
(1/4)
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Caspiand Gorsky International Journal of STEM Education (2024) 11:52
makes it harder to detect strong associations with other
variables (King & Zeng, 2001).
Second, small but significant correlations and path
coefficients can still be meaningful. Even minor effects
may have substantial implications given the large global
student population. Complex psychological phenomena
are often driven by many small factors, offering valu-
able insights even when below traditional significance
thresholds. Recent methodological discussions stress the
importance of small effect sizes and advocate for revisit-
ing conventional cutoffs established years ago (e.g., Bak-
ker etal., 2019; Funder & Ozer, 2019; Götz etal., 2022,
2024; Kraft, 2020).
e importance of our model lies in identifying three
key math classroom features—disciplinary climate,
teacher support, and instructional strategies that pro-
mote mathematical reasoning—which predict motivation
to learn mathematics. e R2 values show these features’
modest but significant contribution to motivation, which
we consider important. In addition, we highlight the sur-
prisingly strong relationship between motivation to learn
math and STEM career expectations.
In the following discussion, we will examine each factor
and show how, given the model’s transferability, they can
be applied in any country to boost students’ motivation
to learn mathematics and enhance their STEM career
expectations. is approach aims to help students suc-
ceed in future STEM endeavors while avoiding pitfalls
along their educational paths.
Factors thatinuence adolescents tohave STEM
career expectations
We identified a theoretical model with five variables pre-
dicting 9th–10th grade students’ STEM career expecta-
tions. One variable, gender, has been widely studied, with
more boys than girls typically expecting to pursue careers
in hard sciences, mathematics, and engineering. What
stands out, however, is the predictive power of students’
perceptions of math classroom environments, especially
pedagogy, and how these perceptions influence both
their motivation to learn math and their STEM career
expectations. Before exploring these factors, we first take
a closer look at gender.
Gender
In all four countries, significant gender differences were
observed, with more boys expecting STEM occupations
than girls. ese findings hold true across countries
with diverse economic, religious, and cultural back-
grounds, despite extensive efforts spanning decades to
address gender gaps in schools and workplaces. In two
countries (Morocco and Qatar) girls were more moti-
vated to learn mathematics than boys (see Tables2 and
3). Nevertheless, expectations for careers in STEM were
still higher for boys than for girls as in all other countries.
e obstinate persistence of gender stereotypes and roles
emphasizes the need for continued efforts as we progress
into the second quarter of the twenty-first century.
Math class environments, students’ motivation tolearn
math andtheir expectations forSTEM careers
As hypothesized, certain factors within math classroom
environments predicted students’ STEM career expecta-
tions across culturally and economically diverse nations,
either directly or through their influence on motivation
to learn math. We begin our discussion with factors con-
cerning classroom learning conditions—disciplinary cli-
mate, teacher support and instructional strategies. We
conclude with a brief summary.
Math Classroom Disciplinary Climate and Teacher
Support ese variables predicted students’ STEM career
expectations both directly and indirectly, with their
effects mediated by motivation to learn mathematics.
e reasons for their impact on motivation are intuitive
and have high face validity: they create optimal learn-
ing conditions that enhance motivation. However, we
encountered challenges in explaining why and how these
factors directly predict adolescents’ STEM career expec-
tations 15 years in the future. Some research has utilized
these PISA variables without delving into the nature of
their impact (e.g., Cheema & Kitsantas, 2014; Wang etal.,
2022). To better understand how these factors influ-
ence long-term career choices, we recommend further
research.
Instructional Strategies Strategies that promote math-
ematical reasoning indirectly influence adolescents’
STEM career expectations by enhancing their motivation
to learn mathematics. is includes fostering interest in
math, emphasizing its importance, and building expec-
tations of success. ese findings highlight the benefits
of math education grounded in conceptual understand-
ing, which facilitates flexible problem-solving (e.g., Hong
et al., 2023; Ye etal., 2024), knowledge retention (e.g.,
Bartlett, 1932; Wilder & Berry, 2016), and the transfer of
learning (e.g., Hattie, 2009; Mayer, 2002).
An additional instructional variable related to math-
ematical reasoning, EXPO21ST, asked, “How often have
you encountered the following types of mathematics
tasks during your time at school?” is variable showed
no significant impact on students’ motivation to learn
mathematics or their STEM career expectations, except
in Qatar. Unlike the other two variables, EXPO21ST
measures students’ reported exposure to specific math
reasoning tasks across various contexts, including phys-
ics, computer science, and social science classes, rather
than solely in math classes.
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Caspiand Gorsky International Journal of STEM Education (2024) 11:52
Although we cannot fully explain this finding, we can
suggest possible reasons for the lack of a positive corre-
lation between this variable and students’ motivation to
learn mathematics. Applied mathematics tasks in other
disciplines might have been uninteresting, irrelevant, or
too difficult, which could lower motivation. In addition,
classroom disciplinary climates and teacher support—
unmeasured in these other disciplines—might have
been suboptimal. While some students may find diverse
applied mathematics tasks motivating, others might
experience a negative association. Further investigation is
needed to clarify this finding.
Summary We emphasize the pivotal role of mathemat-
ics teachers in shaping students’ motivation to learn
math and their subsequent STEM career expectations.
is influence manifests through classroom disciplinary
climate, levels of teacher support, and instructional strat-
egies that integrate the development of mathematical
reasoning skills.
Limitations
Despite its theoretical and empirical implications, this
study has certain limitations. First, our analyses relied on
cross-sectional data from PISA 2022, primarily establish-
ing correlational relationships rather than strong causal
inferences. Conducting more extensive longitudinal stud-
ies would better uncover potential causality between pre-
dictors and STEM career expectations, as well as track
changes in such expectations over time.
Second, the PISA ’student questionnaire’ comprised
only three items corresponding to subjective task value
and expectation of success. A more comprehensive vari-
able would include additional items for ascertaining if
current student interest in math classes reflects a broader
interest in mathematics, as opposed to being influenced
by specific topics or external factors such as teaching
quality or course difficulty. e same would be true for
self-efficacy.
ird, previous research has identified other signifi-
cant factors impacting students’ STEM career expecta-
tions such as parental encouragement to learn STEM
disciplines (e.g., Caspi et al., 2020; Nugent etal., 2015),
peer influence (e.g., Nugent etal., 2015) and instrumen-
tal motivation (e.g., Guo, 2022). ese factors were not
surveyed in PISA 2022. Adding them to the model would
most likely augment its robustness.
Conclusions
e model predicts 9th–10th grade students’ likelihood
of pursuing STEM careers in science, mathematics
and engineering. Even with substantial diversity across
countries, it consistently exhibited strong robustness.
erefore, we maintain that the model is significant,
relevant and generalizable; it complements previous
research spanning from K-12 to beyond, encompassing
both younger and older students. However, to establish
more robust causal inference, we suggest conducting
longitudinal studies which could reveal potential and
actual causality between key factors and STEM career
expectations at specific benchmarks over time.
To conclude, we echo Voltaire’s Candide (1759), pro-
posing a contemporary interpretation: ‘We must cul-
tivate our math classes’. By optimizing mathematics
learning environments at every educational stage, we
can nurture and cultivate the scientists, mathemati-
cians and engineers needed to address the pressing sci-
entific and engineering challenges facing humanity.
Appendix
See here Table7.
Acknowledgements
We thank the reviewers for their extended efforts and especially for their
constructive comments which significantly improved the paper.
Author contributions
Both authors contributed equally to all aspects of the research, its conceptual-
ization, methodology, data analyses and writing.
Table 7 ISCO-08 codes for STEM occupations
211 Physical and earth science professionals
2111 Physicists and Astronomers
2112 Meteorologists
2113 Chemists
2114 Geologists and Geophysicists
212 Mathematicians, Actuaries and Statisticians
2120 Mathematicians, Actuaries and Statisticians
213 Life Science Professionals
2131 Biologists, Botanists, Zoologists and Related Professionals
214 Engineering Professionals (excluding electrotechnology)
2141 Industrial and Production Engineers
2142 Civil Engineers
2143 Environmental Engineers
2144 Mechanical Engineers
2145 Chemical Engineers
2146 Mining Engineers, Metallurgists and Related Professionals
2149 Engineering Professionals Not Elsewhere Classified
215 Electrotechnology Engineers
2151 Electrical Engineers
2152 Electronics Engineers
2153 Telecommunications Engineers
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Caspiand Gorsky International Journal of STEM Education (2024) 11:52
Funding
Not applicable.
Availability of data and materials
Data used in this study are publically available at https:// www. oecd. org/ pisa/
Declarations
Competing interests
The authors declare that they have no potential conflicts of interest.
Received: 22 April 2024 Accepted: 26 September 2024
References
ABET (Accreditation Board for Engineering and Technology) (2024). Retrieved
July 16, 2024 from https:// www. abet. org/.
Ahmed, W., & Mudrey, R. R. (2019). The role of motivational factors in predicting
STEM career aspirations. International Journal of School & Educational
Psychology, 7(3), 201–214.
Andersen, L., & Ward, T. J. (2014). Expectancy-value models for the STEM persis-
tence plans of ninth-grade, high-ability students: A comparison between
Black, Hispanic, and White students. Science Education, 98(2), 216–242.
Anthony, G., & Walshaw, M. (2023). Characteristics of effective teaching of
mathematics: A view from the west. Journal of Mathematics Education, 2,
147–164.
Bakker, A., Cai, J., English, L., Kaiser, G., Mesa, V., & Van Dooren, W. (2019). Beyond
small, medium, or large: Points of consideration when interpreting effect
sizes. Educational Studies in Mathematics, 102, 1–8.
Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology.
Cambridge: Cambridge University Press.
Carrico, C., Murzi, H., & Matusovich, H. (2016). The roles of socializers in career
choice decisions for high school students in rural central Appalachia:
Who’s doing what? In C. Carrico, H. Murzi, & H. Matusovich (Eds.), 2016
IEEE frontiers in education conference (FIE) (pp. 1–6). New York: IEEE.
Caspi, A., Gorsky, P., Nitzani-Hendel, R., & Shildhouse, B. (2023). STEM-oriented
primary school children: Participation in informal STEM programmes
and career aspirations. International Journal of Science Education, 45(11),
923–945. https:// doi. org/ 10. 1080/ 09500 693. 2023. 21779 77
Caspi, A., Gorsky, P., Nitzani-Hendel, R., Zacharia, Z., Rosenfeld, S., Berman, S.,
& Shildhouse, B. (2019). Ninth grade students’ perceptions of the factors
that led them to major in high school STEM disciplines. Science Education,
103(5), 1176–1205.
Caspi, A., Gorsky, P., Nitzani-Hendel, R., Zacharia, Z. C., Rosenfeld, S., Berman,
S., & Shildhouse, B. (2020). Children’s perceptions of the factors that led
to their enrolment in advanced, middle-school science programmes.
International Journal of Science Education, 42(11), 1915–1939.
Cheema, J. R., & Kitsantas, A. (2014). Influences of disciplinary classroom
climate on high school student self-efficacy and mathematics achieve-
ment: A look at gender and racial-ethnic differences. International Journal
of Science and Mathematics Education, 12(5), 1261–1279.
Cochran, D. B., Wang, E. W., Stevenson, S. J., Johnson, L. E., & Crews, C. (2011).
Adolescent occupational aspirations: Test of Gottfredson’s theory of
circumscription and compromise. The Career Development Quarterly, 59(5),
412–427.
DeWitt, J., Osborne, J., Archer, L., Dillon, J., Willis, B., & Wong, B. (2013). Young
children’s aspirations in science: The unequivocal, the uncertain and the
unthinkable. International Journal of Science Education, 35(6), 1037–1063.
Dietrich, J., Dicke, A. L., Kracke, B., & Noack, P. (2015). Teacher support and its
influence on students’ intrinsic value and effort: Dimensional comparison
effects across subjects. Learning and Instruction, 39, 45–54.
Eccles, J. S., 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 (Ed.), Achievement and achievement motivation (pp. 75–146). San
Francisco: Freeman.
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual
Review of Psychology, 53(1), 109–132.
Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated
expectancy-value theory: A developmental, social cognitive, and socio-
cultural perspective on motivation. Contemporary Educational Psychology,
61, 101859.
Ekmekci, A., & Serrano, D. M. (2022). The impact of teacher quality on student
motivation, achievement, and persistence in science and mathematics.
Education Sciences, 12(10), 649.
Else-Quest, N. M., Mineo, C. C., & Higgins, A. (2013). Math and science attitudes
and achievement at the intersection of gender and ethnicity. Psychology
of Women Quarterly, 37(3), 293–309.
Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychological
research: Sense and nonsense. Advances in Methods and Practices in
Psychological Science, 2(2), 156–168.
Gorsky, P., Caspi, A., & Chajut, E. (2008). The theory of instructional dialogue:
Toward a unified theory of instructional design. In R. Zheng & S. Pixy-
Ferris (Eds.), Understanding online instructional modeling: Theories and
practices (pp. 47–68). Hershey: Idea Group Inc.
Gottlieb, J. J. (2018). STEM career aspirations in Black, Hispanic, and White
ninth-grade students. Journal of Research in Science Teaching. https:// doi.
org/ 10. 1002/ tea. 21456
Götz, F. M., Gosling, S. D., & Rentfrow, P. J. (2022). Small effects: The indispen-
sable foundation for a cumulative psychological science. Perspectives on
Psychological Science, 17(1), 205–215. https:// doi. org/ 10. 1177/ 17456 91620
984483
Götz, F. M., Gosling, S. D., & Rentfrow, P. J. (2024). Effect sizes and what to make
of them. Nature Human Behaviour, 8, 798–800. https:// doi. org/ 10. 1038/
s41562- 024- 01858-z
Guo, J., Nagengast, B., Marsh, H. W., Kelava, A., Gaspard, H., Brandt, H., Cambria,
J., Flunger, B., Dicke, A. L., Häfner, I., Brisson, B., & Trautwein, U. (2016). Prob-
ing the unique contributions of self-concept, task values, and their inter-
actions using multiple value facets and multiple academic outcomes.
AERA Open, 2, 1–20. https:// doi. org/ 10. 1177/ 23328 58415 626884
Guo, L. (2022). Factors affecting adolescents’ science career expectations in
Asian and Western top-performing educational systems. Journal of Voca-
tional Behavior, 135, 103718.
Hamer, J. M., Kemp, P. E., Wong, B., & Copsey-Blake, M. (2023). Who wants to be
a computer scientist? The computing aspirations of students in English
secondary schools. International Journal of Science Education. https:// doi.
org/ 10. 1080/ 09500 693. 2023. 21793 79
Han, S. W. (2016). National education systems and gender gaps in STEM occu-
pational expectations. International Journal of Educational Development,
49, 175–187.
Harackiewicz, J. M., Canning, E. A., Tibbetts, Y., Priniski, S. J., & Hyde, J. S. (2016).
Closing achievement gaps with a utility-value intervention: Disentan-
gling race and social class. Journal of Personality and Social Psychology,
111, 745–765.
Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses related to
achievement. New York: Routledge.
Hong, W., Star, J. R., Liu, R., Jiang, R., & Fu, X. (2023). A systematic review of
mathematical flexibility: Concepts, measurements, and related research.
Educational Psychology Review, 35(4), 104.
King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political
Analysis, 9(2), 137–163.
Kraft, M. A. (2020). Interpreting effect sizes of education interventions. Educa-
tional Researcher, 49(4), 241–253.
Lauermann, F., Tsai, Y. M., & Eccles, J. S. (2017). Math-related career aspirations
and choices within Eccles et al’.s expectancy value theory of achieve-
ment-related behaviors. Developmental Psychology, 53, 1540–1559.
Lazarides, R., Gaspard, H., & Dicke, A. L. (2019). Dynamics of classroom motiva-
tion: Teacher enthusiasm and the development of math interest and
teacher support. Learning and Instruction, 60, 126–137.
López, V., Salgado, M., & Berkowitz, R. (2023). The contributions of school and
classroom climate to mathematics test scores: A three-level analysis.
School Effectiveness and School Improvement, 34(1), 43–64.
Lv, B., Wang, J., Zheng, Y., Peng, X., & Ping, X. (2022). Gender differences in high
school students’ STEM career expectations: An analysis based on multi-
group structural equation model. Journal of Research in Science Teaching,
59(10), 1739–1764.
Mainali, B. (2021). Representation in teaching and learning mathematics.
International Journal of Education in Mathematics, Science, and Technology,
9(1), 1–21.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 15 of 15
Caspiand Gorsky International Journal of STEM Education (2024) 11:52
Marsh, D. D., Sharpe, S. T., & Graham, S. E. (2024). The role of mathematics and
science expectancy-value attitudes in students’ STEM course-taking and
major choices. Journal for STEM Education Research. https:// doi. org/ 10.
1007/ s41979- 024- 00125-0
Mayer, R. E. (2002). Rote versus meaningful learning. Theory into Practice, 41(4),
226–232.
Moote, J., Archer, L., DeWitt, J., & MacLeod, E. (2020). Comparing students’
engineering and science aspirations from age 10 to 16: Investigating the
role of gender, ethnicity, cultural capital, and attitudinal factors. Journal of
Engineering Education, 109(1), 34–51.
NAEP (National Assessment of Educational Progress). (2003). Mathematical
abilities. Retrieved Feb 6 2024 from https:// nces. ed. gov/ natio nsrep ortca
rd/ mathe matics/ abili ties. aspx.
NCTM (National Council of Teachers of Mathematics). (2014). Procedural fluency
in mathematics. Retrieved February 1, 2024 from https:// www. nctm. org/
Stand ards- and- Posit ions/ Posit ion- State ments/ Proce dural- Fluen cy- in-
Mathe matics/.
Nitzan-Tamar, O., & Kohen, Z. (2022). Secondary school mathematics and
entrance into the STEM professions: A longitudinal study. International
Journal of STEM Education, 9(1), 63.
Nugent, G., Barker, B., Welch, G., Grandgenett, N., Wu, C., & Nelson, C. (2015). A
model of factors contributing to STEM learning and career orientation.
International Journal of Science Education. https:// doi. org/ 10. 1080/ 09500
693. 2015. 10178 63
OECD. (2024). PISA 2022 technical report. Paris: OECD Publishing. https:// doi.
org/ 10. 1787/ 01820 d6d- en
Oliveira, Í. M., Porfeli, E. J., Do Céu Taveira, M., & Lee, B. (2020). Children’s career
expectations and parents’ jobs: Intergenerational (dis)continuities. The
Career Development Quarterly, 68(1), 63–77.
Pagkratidou, M., Michaelides, M. P., Pitsia, V., & Karakolidis, A. (2024). Fac-
tors associated with STEM career expectations of Greek 15 year-old
students. Journal for STEM Education Research. https:// doi. org/ 10. 1007/
s41979- 024- 00124-1
Regan, E., & DeWitt, J. (2015). Attitudes, interest and factors influencing
STEM enrolment behaviour: An overview of relevant literature. In E. K.
Henriksen, J. Dillon, & J. Ryder (Eds.), Understanding student participation
and choice in science and technology education (pp. 63–88). Dordrecht:
Springer.
Rittle-Johnson, B., & Jordan, N. C. (2016). Synthesis of IES-funded research on
mathematics: 2002–2013. NCER 2016–2003. Washington, DC: National
Center for Education Research.
Rosenzweig, E. Q., Wigfield, A., & Eccles, J. S. (2019). Expectancy value theory
and its relevance for student motivation and learning. In K. A. Renninger
& S. Hidi (Eds.), Cambridge handbook on motivation and learning (pp.
617–644). Cambridge: Cambridge University Press.
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling.
Journal of Statistical Software, 48, 1–36.
Sax, L. J., Lehman, K. J., Jacobs, J. A., Kanny, M. A., Lim, G., Monje-Paulson, L.,
& Zimmerman, H. B. (2017). Anatomy of an enduring gender gap: The
evolution of women’s participation in computer science. Journal of Higher
Education, 88(2), 258–293. https:// doi. org/ 10. 1080/ 00221 546. 2016. 12573
06
Shumow, L. (2023). Math matters for careers and jobs. Northern Illinois Univer-
sity, Retrieved January 31 2024 from https:// www. niu. edu/ mathm atters/
caree rs- jobs/ index. shtml.
Sinay, E., & Nahornick, A. (2016). Teaching and learning mathematics research
series l: Effective instructional strategies. (Research report no 16/17–08).
Toronto: Toronto District School Board.
Sortkær, B., & Reimer, D. (2018). Classroom disciplinary climate of schools and
gender -evidence from the Nordic countries. School Effectiveness and
School Improvement, 29(4), 511–528.
Stoet, G., & Geary, D. C. (2018). The gender-equality paradox in science, tech-
nology, engineering, and mathematics education. Psychological Science,
29(4), 581–593.
Su, R., & Rounds, J. (2015). All STEM fields are not created equal: People and
things interest explain gender disparities across STEM fields. Frontiers in
Psychology. https:// doi. org/ 10. 3389/ fpsyg. 2015. 00189
Wang, F., Liu, Y., & Leung, S. O. (2022). Disciplinary climate, opportunity to
learn, and mathematics achievement: An analysis using doubly latent
multilevel structural equation modeling. School Effectiveness and School
Improvement, 33(3), 479–496.
Wang, J., & Wang, X. (2012). Structural equation modeling: Applications using
Mplus. Hoboken: Wiley.
Wang, M.-T. (2012). Educational and career interests in math: A longitudinal
examination of the links between classroom environment, motivational
beliefs, and interests. Developmental Psychology, 48(6), 1643–1657. https://
doi. org/ 10. 1037/ a0027 247
Wang, M. T., & Degol, J. (2013). Motivational pathways to STEM career choices:
Using expectancy–value perspective to understand individual and gen-
der differences in STEM fields. Developmental Review, 33(4), 304–340.
Wang, X. S., Perry, L. B., Malpique, A., & Ide, T. (2023). Factors predicting mathe-
matics achievement in PISA: A systematic review. Large-Scale Assessments
in Education, 11(1), 24.
Watt, H. M., Hyde, J. S., Petersen, J., Morris, Z. A., Rozek, C. S., & Harackiewicz, J.
M. (2017). Mathematics—a critical filter for STEM-related career choices?
A longitudinal examination among Australian and US adolescents. Sex
Roles, 77, 1–18.
Wigfield, A., & Cambria, J. (2010). Students’ achievement values, goal orienta-
tions, and interest: Definitions, development and relations to achieve-
ment outcomes. Developmental Review, 30(1), 1–35.
Wilder, S., & Berry, L. (2016). Emporium model: The key to content retention in
secondary math courses. Journal of Educators Online, 13(2), 53–75.
Xu, T., & Jack, L. (2023). Engineers in young children’s minds: An exploratory
case study of young children’s drawings and narratives. Early Childhood
Education Journal, 52(2), 1–8.
Ye, H., Ng, O. L., & Cui, Z. (2024). Conceptualizing flexibility in programming-
based mathematical problem-solving. Journal of Educational Computing
Research, 62(2), 594–619.
Yu, R., & Singh, K. (2018). Teacher support, instructional practices, student
motivation, and mathematics achievement in high school. The Journal of
Educational Research, 111(1), 81–94.
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