Using Data-Driven Insights to Understand Factors Shaping STEM Interest in Secondary Education

Using Data-Driven Insights to Understand Factors Shaping STEM Interest in Secondary Education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Using Data-Driven Insights to Understand Factors Shaping STEM Interest in Secondary Education Nur Syakilah Laege, Dr Nasir Abdul Jalil, Sandra John Mampi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8211254/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the factors influencing Malaysia’s secondary school students’ interest in Science, Technology, Engineering, and Mathematics (STEM) by adapting the Social Cognitive Career Theory (SCCT) as foundation. The key constructs which include self-efficacy, outcome expectations, self-input, and learning experience are explored to understand their role in shaping students’ motivation towards STEM fields (Lent et al., 1994 ; Byars-Winston et al., 2010 ). A quantitative research design is employed, involving a structured questionnaire consisting of 38 items on a 5-point Likert scale distributed online to 456 secondary school students in Kuala Lumpur and Sabah. Using purposive sampling, respondents were selected from participants of a STEM outreach programme conducted in Malaysia, and the survey was administered in a single day during the event. The data will be analysed using SmartPLS to identify significant predictors of STEM interest (Razali et al., 2018 ). While the sample is limited to two regions, the findings offer practical implications for enhancing STEM education, particularly in under-resourced settings (Halim & Subahan, 2010 ; Chng et al., 2023 ). This research could offer valuable insights for the teachers, counsellors, policymakers, and private sector stakeholders particularly those involved in Corporate Social Responsibility (CSR) to develop more thoughtful and strategic approach to increase student interest in STEM subjects and also supporting national goals related to future high-tech development. This research is also aligned with Sustainable Development Goal 4, which emphasise the important of quality and fair education for everyone (UNESCO, 2017 ). STEM education factor influencing STEM interest Social Cognitive Career Theory (SCCT) data analytics secondary school students Figures Figure 1 Figure 2 Figure 3 1.0 Introduction STEM education is notably recognized as a key driver of innovation, economic growth, and sustainable development (Bybee, 2013 ; UNESCO, 2017 ). It integrates science, technology, engineering, and mathematics to foster critical thinking, problem solving, and teamwork, skills which are essential for a technology-driven global economy (Billiark et al., 2014 ; Daugherty, 2013 ). Additionally, modern technologies such as Artificial Intelligence and Augmented Reality further enhance STEM learning by improving digital literacy and career readiness (Chng et al., 2023 ; Çoban et al., 2022 ). In Malaysia, STEM education is one of the national priorities as outlined in the Malaysia Education Blueprint (MEB) 2013–2025 with a focus on curriculum reform, teacher training, and public engagement (Ministry of Education Malaysia, 2013 ). Despite progress, where STEM enrolment rose from 41.84% in 2019 to 50.83% in 2024, Malaysia still struggles to achieve its 60:40 STEM to non-STEM enrolment target (Ministry of Education, 2024). Challenges include student perceptions of STEM difficulty and limited parental awareness of STEM career benefits (Halim et al., 2018; Said et al., 2016). Thus, this study seeks to address this critical gap by identifying both personal and environmental determinants that shape student engagement with STEM disciplines. Grounded in the Social Cognitive Career Theory (SCCT) (Lent et al., 1994 ), this study also contributes theoretically by applying and testing SCCT constructs in the Malaysian secondary school context, where research remains limited. The study extends SCCT’s application by integrating both personal determinants such as self-efficacy, outcome expectations, and self-input, together with environmental factors such as learning experiences. In doing so, it provides new insights into how SCCT explains STEM interest in a developing country setting and offers implications for improving theoretical models of career and educational choice. 2.0 Literature Review 2.1 Self-Efficacy Self-efficacy is a central construct in Social Cognitive Career Theory (SCCT), which posits that career interest is influenced by individual beliefs about one’s ability to perform tasks (Lent et al., 1994). Self-efficacy is a key predictor of academic motivation and career interest (Bandura, 1986; Artino, 2012). Numerous studies have shown that students with high self-efficacy are more likely to choose STEM subjects, persist in challenging coursework, and pursue STEM careers (Nugent et al., 2015; Wang, 2013; Heilbronner, 2011). Gallagher (2012) describes self-efficacy as an agentic value, enabling individuals to set and achieve goals. Students with strong self-efficacy are more passionate about STEM learning (Zainuddin & Kutty, 2021) and are better equipped to overcome perceived difficulties and high failure rates in STEM subjects (Vitali et al., 2020). High self-efficacy also supports perseverance, allowing students to face setbacks and maintain engagement (Hidajat et al., 2023; Fazilah et al., 2020). Buday et al. (2012) further note that students’ confidence in their abilities, as well as their beliefs about balancing career and personal life, are critical for STEM career selection. Recent studies (Msambwa et al., 2024; Sellami et al., 2023) have found that self-efficacy, along with environmental and behavioral factors, as well as gender and nationality, can predict students' interest in STEM careers. Research in Southeast Asia and other developing countries, especially in collectivist cultures, has explored how self-efficacy and outcome expectations influence STEM interest. In both Malaysian and other Asian student groups, self-efficacy has been shown to strongly predict interest in general STEM fields, as well as in physics and science specifically (Halim et al., 2018, 2023; Mohtar et al., 2019; Nguyen, 2021). However, most of these studies focused on only one motivational factor rather than looking at a combination of influences. Given the above findings, this research suggests the following hypotheses: H1: Self-efficacy has a positive impact on students’ interest in STEM. 2.2 Outcome Expectations According to Social Cognitive Career Theory (SCCT) developed by Lent et al. (1994), outcome expectations are one of the key personal factors that influence career interests and choices. Outcome expectations refer to students’ beliefs about the rewards and benefits associated with STEM participation, such as career prospects, societal impact, and personal fulfilment (Lent et al., 1994; Byars-Winston et al., 2010; Li et al., 2022). Research demonstrates that positive outcome expectations enhance students’ motivation to pursue STEM education and careers (Han et al., 2021a). Diekman et al. (2017) and Fuesting et al. (2021) found that students are more likely to engage in STEM if they perceive these fields as meaningful and capable of helping others. Several studies have explored outcome expectations as a direct or indirect factor influencing STEM career interest. For example, Turner et al. (2019) found that outcome expectations can directly predict students' interest in STEM fields. Similarly, Luo et al. (2021) showed that outcome expectations have a direct effect on STEM career interest, though the strength of this effect varies. In their findings, outcome expectations had a moderate predictive value (β = 0.30). However, other research such as Garriott et al. (2014) found that outcome expectations did not significantly predict STEM career interest when measured alongside other factors, suggesting that their influence may depend on the context or population studied. Given the above findings, this research suggests the following hypotheses: H2: Outcome expectations have a positive impact on students’ interest in STEM. 2.3 Self-Input Personal inputs or Self-input, including demographic factors such as gender, socioeconomic status (SES), academic performance, and personal goals, play a pivotal role in shaping STEM interest (Wang & Degol, 2017; Fan et al., 2018; Liou et al., 2014). Students from higher socioeconomic backgrounds often have greater access to STEM resources and opportunities (OECD, 2016). Gender disparities persist, with boys more likely to pursue STEM pathways than girls, due in part to societal stereotypes and a lack of female role models (Wang & Degol, 2013; Kricorian et al., 2020). Students’ personal goals and time investment also matter; those with clear aspirations and who dedicate more effort to STEM activities are more likely to sustain interest (Dönmez & İdin, 2020; Huang et al., 2022). Students from higher SES backgrounds often receive more parental support, access to learning resources, and better preparation, which boosts their interest in STEM (Koyunlu Ünlü & Dökme, 2020; Turner et al., 2019; Yerdelen et al., 2016). Sahin et al. (2018) found that income and parental education directly influence STEM career interest. Other studies noted that students with high SES face fewer barriers and have stronger confidence and expectations about STEM (Turner et al., 2019). For instance, boys in private schools with high SES and high self-efficacy are more likely to choose STEM paths (Ketenci et al., 2020). In contrast, students from low-income or minority backgrounds are less likely to pursue STEM (Saw et al., 2018; Mau & Li, 2018). A review by Chiu (2024) and Msambwa et al. (2024) showed that 61% of studies reported SES as a key factor in STEM interest. Similar findings were reported in Southeast Asia, where studies linked parents' education and jobs to student interest (Siregar & Rosli, 2021; Nguyen, 2021). Given the above findings, this research suggests the following hypotheses: H3: Self-input has a positive impact on students’ interest in STEM. 2.4 Learning Experience Learning experiences, both formal and informal, significantly shape students’ interest in STEM (Fan et al., 2020; Chng et al., 2023). Participation in STEM projects, robotics programs, and outreach activities has been shown to deepen students’ understanding and enthusiasm for STEM (Vennix et al., 2018; Chen & Chang, 2018). Integrated curricular approaches, such as those implemented in Malaysia’s KSSM curriculum, aim to make STEM learning more relevant and engaging (MoE, 2016a, 2016b). However, research by Ali et al. (2018) and the Academy of Sciences Malaysia (2018) suggests that an overemphasis on theoretical content, without sufficient contextual application, may fail to spark student interest. Application-based and problem-solving tasks are more effective in maintaining engagement and nurturing a positive attitude toward STEM (McIntyre et al., 2021). Shahali et al. (2017) found that after participating in a hands-on, engineering design STEM program, students showed a significant increase in interest toward both STEM subjects (p<0.05) and STEM careers. In general, educational experts widely recommend hands-on activities in STEM classes, as they effectively boost student interest and motivation (Holstermann et al., 2010; Bergin, 1999). Research shows that students involved in practical tasks, especially using tools or technology, show better academic outcomes and a stronger desire to pursue STEM (VanMeter-Adams et al., 2015; Swarat et al., 2012). Palmer (2009) found that experiments sparked more interest than written tasks, driven by novelty, independence, and social interaction. Overall, engaging students in real-world, professional-like tasks greatly enhances STEM engagement (Blumenfeld et al., 2006). Given the above findings, this research suggests the following hypotheses: H4: Learning experiences have a positive impact on students’ interest in STEM. 3.0 Theoretical Framework and Research Questions This study adopts the Social Cognitive Career Theory (SCCT) developed by Lent et al. (1994) Lent, Brown, and Hackett (1994, 2000), and grounded in Bandura’s (1977) social cognitive theory. The theory explains how individuals developed academic and career interests by integrates external influences such as social, socioeconomic status (SES), and individual cognitive factors (e.g., self-efficacy, outcome expectations, self-input, learning experience) to dynamically illustrates how individuals make career decisions and is recognized as one of the most comprehensive and widely applied theory in STEM career research (Bahar & Adiguzel, 2016; Kang & Keinonen, 2017; Kier et al., 2014; Maiorca et al., 2021; Mohtar et al., 2019; Nugent et al., 2015; Wang et al., 2021). The Social Cognitive Career Theory (SCCT) has been widely applied in research to understand factors influencing students’ interest in STEM careers. It has been shown to effectively explain middle school students’ interest in science-related fields through personal beliefs and motivations (Kier et al., 2014). Students who possess stronger self-efficacy and hold more positive expectations about their future are more likely to pursue STEM pathways (Nugent et al., 2015). Environmental supports such as parental encouragement, school support systems, and access to learning resources also play a crucial role in shaping students’ aspirations toward STEM careers (Mohd et al., 2010; Murcia et al., 2020). The theory also helps to identify key motivational elements that influence students’ interest in science (Sahin et al., 2015). Components of SCCT have been found to significantly predict high school students’ motivation to engage in STEM fields (Bahar and Adiguzel, 2016). Learning experiences and contextual supports are equally important in increasing science engagement among students (Kang and Keinonen, 2017). In Malaysia, students’ personal background and family influence have been identified as key factors contributing to their interest in STEM education (Mohtar et al., 2019). Other studies continue to show that self-beliefs and perceived environmental supports are essential in developing students’ career goals in STEM, reinforcing the applicability of SCCT across educational contexts (Maiorca et al., 2021; Wang et al., 2021). Overall, based on SCCT, this study explored the four key constructs which selected as independent variables in this research (self-efficacy, outcome expectations, learning experience, and self-input) towards students’ interest in STEM within the context of Malaysian secondary schools. Thus, four research questions (RQ) that guided this study are as follows: RQ 1: To what extent does self-efficacy influence students’ interest in STEM? RQ 2: To what extent do outcome expectations influence students’ interest in STEM? RQ 3: To what extent do learning experiences influence students’ interest in STEM? RQ 4: To what extent do self-input influence students’ interest in STEM? 4.0 Methodology The main objective of this study is to examine the factors influencing students’ interest in STEM. This study adopts a quantitative research approach using a descriptive survey design. The descriptive approach is appropriate for investigating the influence of several key variables, including self-efficacy, outcome expectations, self-input, and learning experience, on students’ interest in STEM. These variables were derived from the Social Cognitive Career Theory (SCCT), which explains the dynamic interaction of personal and contextual factors in shaping individuals’ academic interests and career development. A structured questionnaire was employed as the main instrument for data collection in this study; Self-Input (SI1-SI7), Self-Efficacy (SE1-SE6), Outcome Expectation (OE1-OE6), Learning Experience (LE1-LE5), and the dependent variable STEM Interest (STEM1-STEM7) . The questionnaire was conducted online using Google Form to selected secondary schools in Kuala Lumpur and Sabah. The structured format of the instrument ensured consistency in data collection and allowed for direct comparison across responses. All constructs in the questionnaire were developed based on the STEM Career Interest Survey (STEM CIS) and previously validated research instruments, (Buday et al., 2012; Kier et al., 2014; Mohtar et al., 2019; Nugent et al., 2015). A five-point Likert scale, ranging from “Strongly Disagree” to “Strongly Agree”, was used to assess all variables, consistent with established recommendations for validity and reliability (Davis & Venkatesh, 1996). The questionnaire consisted of two sections: demographic information and variable measurements, which included four independent variables and one dependent variable. The sample size was determined based on the guidelines by Uma Sekaran and Bougie (2016), which recommended a minimum of 384 respondents for populations exceeding 100,000 to ensure sufficient statistical power and generalizability. Based on this guideline, the study involved 456 secondary school students aged between 13 and 17. Schools from Kuala Lumpur and Sabah were selected to reflect diverse geographic and socioeconomic backgrounds. This approach aimed to capture a broader range of student experiences related to access to STEM resources, exposure to science and technology, and the influence of environmental support. The goal was not to compare specific groups but to offer a well-rounded understanding of the factors shaping STEM interest among Malaysian students from varied contexts. Permission to conduct the study was obtained from the Ministry of Education Malaysia and the participating schools. Participation was voluntary, with informed consent obtained from parents or guardians and assent secured from students prior to data collection. Respondents were assured of anonymity and confidentiality, and all data were collected solely for academic purposes using secure online forms. Students were also informed of their right to withdraw from the study at any stage without penalty. The collected data were analysed using SMART PLS version 3.0, a statistical software designed specifically for Partial Least Squares Structural Equation Modelling (PLS-SEM). The analysis followed a two-step evaluation process involving (i) the measurement model, which assessed indicator reliability and validity, and (ii) the structural model, which examined the relationships between constructs. This methodological approach was selected due to its suitability for handling complex models and ensuring both theoretical alignment and contextual relevance. In this study, the PLS-SEM evaluation procedure was applied as illustrated in Figure 2 . 4.1 Convergent Validity The assessment of the measurement model represents the initial phase in PLS-SEM analysis, aimed at confirming that each construct is measured both reliably and validly before proceeding to the evaluation of structural relationships. According to Hair et al. (2012), convergent validity refers to the extent to which indicators of a construct are correlated and consistently represent the same underlying concept. Peng and Lai (2012) outlined three key criteria for assessing convergent validity: (i) outer loadings should ideally exceed 0.70, (ii) composite reliability (CR) should be greater than 0.70, and (iii) average variance extracted (AVE) should be above 0.50. However, Hair et al. (2011, 2014) also noted that items with loadings between 0.40 and 0.70 may be retained if their removal does not improve CR or AVE significantly. Items with loadings below 0.40 should be removed. In this study, although not all outer loadings surpassed the 0.70 threshold, many fell within the acceptable range of 0.50–0.70, and the CR values ranged from 0.50 to 0.80, which are still considered acceptable in exploratory research (Hair et al., 2014). Wong (2013) emphasized that a careful evaluation of the measurement model strengthens the credibility of structural model analysis. The results, as presented in Figure 2 , reflect acceptable levels of indicator reliability and convergent validity, supporting the adequacy of the measurement model used in this study. Furthermore, as shown in Table 1 , all constructs reported Cronbach's alpha values ranging from 0.795 to 0.836, indicating acceptable levels of internal consistency. The composite reliability (CR) values ranged from 0.859 to 0.876, all exceeding the recommended threshold of 0.70, which supports the overall reliability of the measurement model. In terms of convergent validity, the average variance extracted (AVE) values varied between 0.503 and 0.550, all above the minimum acceptable level of 0.50. These results collectively demonstrate that the constructs exhibit adequate internal consistency, reliability, and convergent validity, confirming that the measurement model is psychometrically sound and suitable for further structural analysis. Table 1: Construct Reliability and Validity Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE) LE 0.795 0.807 0.859 0.550 OE 0.810 0.819 0.863 0.513 SE 0.810 0.850 0.863 0.521 SI 0.836 0.843 0.876 0.504 STEM 0.831 0.845 0.874 0.503 4.2 Discriminant Validity Analysis Discriminant validity ensures that each construct in a model is clearly different from the others and measures a unique concept (Pan and Jang, 2008). The indicators within a construct should not show high correlations with indicators from different constructs. Therefore, items belonging to separate constructs should remain distinct and divergence rather than convergence. There are two approaches used to evaluate discriminant validity. The first is the cross-loadings approach, which involves comparing an item's loading on its intended construct against its loadings on all other constructs. The second is the Fornell and Larcker criterion, which requires that the square root of the average variance extracted for each construct be greater than its correlation with any other construct (Fornell and Larcker, 1981; Hair et al., 2012). Based on the results in Table 2 , the square roots of the AVE values for each construct are higher than their corresponding inter-construct correlations, confirming that the constructs are empirically distinct and the measurement model demonstrates acceptable discriminant validity. To further support this, the Heterotrait-Monotrait (HTMT) ratio was also assessed. As shown in Table 4 , all HTMT values are below the recommended threshold of 0.85, with the highest being 0.847. These findings confirm that the constructs are sufficiently distinct and not overly overlapping in measurement. Table 2: Discriminant Validity Fornell & Larcker Criterion LE OE SE SI STEM LE 0.741 OE 0.443 0.716 SE 0.563 0.451 0.722 SI 0.708 0.579 0.570 0.710 STEM 0.575 0.634 0.652 0.671 0.709 Table 3: Heterotrait-monorait Ratio (HTMT) LE OE SE SI STEM LE OE 0.531 SE 0.703 0.544 SI 0.847 0.690 0.671 STEM 0.688 0.764 0.771 0.791 4.3 Analysis of the Constructs The structural model illustrates the relationships between the constructs, which are aligned with the hypotheses proposed in this study. These relationships represent the theoretical framework underlying the path model. Therefore, it is essential to evaluate the strength and significance of these hypothesized connections. According to Sarstedt (2014), structural model assessment involves testing hypotheses by examining the links between constructs, which helps determine the relevance and significance of these relationships. This evaluation ultimately reflects the model’s predictive accuracy. To ensure reliable estimates, a bootstrapping procedure with 5000 resamples was performed. As shown in Figure 3 and Table 4 , the bootstrapping analysis generated T-values and corresponding path coefficients, from which P-values were derived. Table 4: Bootstrapping Result: Hypothesis Testing Original sample (O) Sample mean (M) Standard deviation (STDEV) T..statistics (|O/STDEV|) Hypothesis LE -> STEM 0.075 0.076 0.052 1.441 Not supported OE -> STEM 0.306 0.306 0.041 7.504 Supported SE -> STEM 0.327 0.327 0.049 6.682 Supported SI -> STEM 0.255 0.256 0.054 4.698 Supported 5.0 Discussion and Managerial Implications 5.1 Discussion According to the structural model analysis, three of the four hypothesized relationships were found to have a significant impact on students' interest in STEM. With p-values of 0.000 and T-values above the 1.96 threshold, outcome expectations (OE), self-efficacy (SE), and social influence (SI) all demonstrated strong and statistically significant effects, providing strong support for the hypotheses presented. Self-efficacy (β = 0.327) showed the strongest influence among these, indicating that students' interest in STEM is significantly shaped by their confidence in their ability to excel in the field. Additionally, outcome expectations (β = 0.306) had a major effect, suggesting that students are more inclined to study STEM when they believe there would be real benefits like employment or personal development. Social influence (β = 0.255) further demonstrated a significant effect, indicating how crucial role models, teachers, and peers are in encouraging interest in STEM. However, there was no statistically significant relationship between STEM interest and environmental support (LE) (β = 0.075, p = 0.150). Although supportive surroundings are advantageous in theory, our research indicates that they might not have a direct impact on students' interest unless they are combined with individual motivation and an understanding of its usefulness. This finding is consistent with the understanding that, although environmental influences play a significant role, self-awareness and social factors may have a greater impact on the development of career-related interests. 5.2 Managerial Implications Knowing which important factors have the most effects on students' interest in STEM provides a useful basis for different stakeholders to develop proactive, focused plans that will result in maximum impact. These findings have significant effects for stakeholders in the private sector, educators, legislators, and institutions of higher learning involved in STEM education and talent development in Malaysia. These findings offer educators and school counsellors, especially those working in under resourced environments, a good starting point for creating educational methods and career guidance programs that boost students' self-esteem and interest for STEM-related subjects. These improvements can encourage more productive learning settings and provide students the ability to make informed decisions about their future academic and career paths. The results can be implemented by policymakers to develop initiatives that increase STEM education accessibility in different parts of the country. This supports Malaysia's national goals for technological advancement by ensuring that students from all backgrounds have equal opportunities to participate and benefit from STEM programs. Higher education institutions can also benefit from these insights by enhancing their outreach, recruitment, and curriculum development efforts. Aligning university programs with the aspirations, motivations, and learning needs of secondary school students can help attract and retain more students in STEM-related fields. Private sector stakeholders, especially those involved in Corporate Social Responsibility (CSR) initiatives, can use this research to develop impactful STEM engagement programs. These may involve industrial seminar, science workshops, mentoring programs, and other initiatives that promote student growth and align with national educational priorities. Collectively, these efforts can strengthen Malaysia’s STEM pipeline by ensuring that education, policy, and industry initiatives work together to support student growth and future innovation. 6.0 Research Contributions and Conclusion 6.1 Research Contributions This study contributes to the growing body of knowledge on STEM education by empirically examining the determinants of Malaysian secondary school students’ interest in STEM through the lens of the Social Cognitive Career Theory (SCCT). Specifically, it highlights the roles of self-efficacy, learning experiences, outcome expectations, and self-input as critical constructs in shaping students’ academic and career motivations. The findings enrich the theoretical understanding of SCCT by demonstrating that while environmental support (learning experiences) may provide important background conditions, personal and social factors such as confidence, perceived benefits, and influence from peers or role models exert stronger direct effects. This nuanced insight advances the theory by emphasizing how the balance of personal versus contextual factors may differ in developing countries compared to Western settings where SCCT is most frequently applied. Additionally, by testing the SCCT framework on a sample of 456 secondary school students in Malaysia, this study broadens the theory’s applicability to a non-Western and multi-ethnic context, addressing calls for greater cultural diversity in career development research. The results show that SCCT remains a robust explanatory framework but requires contextual sensitivity, particularly when applied to regions where educational infrastructure, social support, and career awareness differ from advanced economies. Thus, the study not only validates but also extends the theoretical boundaries of SCCT, offering an evidence-based model that may be adapted for future STEM education research in similar developing or transitional economies. 6.2 Conclusion This study contributes to the growing body of knowledge on STEM education by analyzing the key variables of Malaysian secondary school students' interest in STEM. It is based on the Social Cognitive Career Theory and highlights self-efficacy, learning experiences, outcome expectations, and self-input. The results provide further insight on how these factors interact to influence students' interest in STEM-related fields. In addition, the study supports national education and development goals, including the Ministry of Education Malaysia’s target to achieve a 60 to 40 ratio of science to arts enrolment and the Ministry of Science, Technology and Innovation’s plan to develop Malaysia into a high technology nation by 2030. It also aligns with the global agenda under Sustainable Development Goal 4, which promotes inclusive and quality education to prepare future generations for a skilled workforce. By applying and extending the Social Cognitive Career Theory in the Malaysian setting, the study provides a useful framework for understanding how individual, social, and environmental factors influence students’ STEM interest, particularly among youth in developing countries. Declarations Author Contribution N.S.L., as the corresponding author, conceptualized and designed the study, prepared the manuscript draft, and coordinated the research activities. N.A.J. contributed to methodology design, data analysis planning, and interpretation of results. S.J.M. assisted with literature review, data collection coordination, and manuscript editing. All authors reviewed and approved the final manuscript. Data Availability The datasets generated and analyzed during the current study are not publicly available due to privacy and ethical restrictions involving secondary school students. However, the data are available from the corresponding author (N.S.L.) upon reasonable request and with permission from the relevant educational authorities. References Abdul Jalil, N., Hwang, H. and Mat Dawi, N., 2019. Machines learning trends, perspectives and prospects in education sector . In: Proceedings of the 2019 3rd International Conference on Education and Multimedia Technology (ICEMT 2019) . pp.201–205. https://doi.org/10.1145/3345120.3345147 Abdul Jalil, N. and Leen, M., 2021. 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Fornell, C. and Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104 Fuesting, M.A., Diekman, A.B. and Hudiburgh, L. (2021). From classroom to career: The role of communal experiences in predicting interest in STEM careers. Social Psychology of Education, 24(4), 1045-1067. https://doi.org/10.1007/s11218-021-09642-x Gallagher, A.M. (2012). Self-efficacy and STEM career interest. Journal of Career Assessment, 20(3), 345-355. https://doi.org/10.1177/1069072711434410 Garriott, P.O., Hultgren, K.M. and Frazier, J. (2014). STEM outcome expectations and career plans of rural high school students. Journal of Career Assessment, 22(3), 562-578. https://doi.org/10.1177/1069072713514933 Hair, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage. Halim, L. and Subahan, M. (2010). Factors influencing interest in STEM among Malaysian students. Asia-Pacific Forum on Science Learning and Teaching, 11(2), 1-18. Han, S., Capraro, R. and Capraro, M.M. (2021). How science, technology, engineering, and mathematics project-based learning affects high-need students in the U.S. Learning and Instruction, 71, 101395. https://doi.org/10.1016/j.learninstruc.2020.101395 Heilbronner, N.N. (2011). Stepping onto the STEM pathway: Factors affecting talented students' declaration of STEM majors in college. Journal for the Education of the Gifted, 34(6), 876-899. https://doi.org/10.1177/0162353211425100 Hidajat, F.A., Sa'dijah, C. and Sudirman. (2023). STEM interest and self-efficacy in Indonesian students. International Journal of Instruction, 16(1), 901-918. Holstermann, N., Grube, D. and Bögeholz, S. (2010). Hands-on activities and their influence on students' interest. Research in Science Education, 40(5), 743-757. https://doi.org/10.1007/s11165-009-9142-0 Huang, X., Wang, C. and Wang, J. (2022). The role of self-input in STEM career choices. Journal of Career Development, 49(3), 456-470. https://doi.org/10.1177/08948453211014567 Kang, J. and Keinonen, T. (2017). The effect of inquiry-based learning experiences on adolescents' science-related career aspirations. International Journal of Science Education, 39(6), 836-853. https://doi.org/10.1080/09500693.2017.1310409 Kier, M.W., Blanchard, M.R., Osborne, J.W. and Albert, J.L. (2014). The development of the STEM career interest survey (STEM-CIS). Research in Science Education, 44(3), 461-481. https://doi.org/10.1007/s11165-013-9389-3 Kricorian, K., Seu, M., Lopez, D., Ureta, E. and Equils, O. (2020). Factors influencing participation of underrepresented students in STEM fields. International Journal of STEM Education, 7(1), 1-12. https://doi.org/10.1186/s40594-020-00219-2 Lent, R.W., Brown, S.D. and Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79-122. https://doi.org/10.1006/jvbe.1994.1027 Li, Y., Wang, K., Xiao, Y. and Froyd, J.E. (2022). Research and trends in STEM education: A systematic review. International Journal of STEM Education, 9(1), 1-16. https://doi.org/10.1186/s40594-022-00377-5 Ministry of Education Malaysia. (2013). Malaysia Education Blueprint 2013-2025. Mohtar, L.E., Halim, L. and Samsudin, M.A. (2019). The impact of self-efficacy and outcome expectations on STEM career interest. Research in Science & Technological Education, 37(1), 71-89. https://doi.org/10.1080/02635143.2018.1463981 Nugent, G., Barker, B., Welch, G., Grandgenett, N., Wu, C. and Nelson, C. (2015). A model of factors contributing to STEM learning and career orientation. International Journal of Science Education, 37(7), 1067-1088. https://doi.org/10.1080/09500693.2015.1017863 OECD. (2016). PISA 2015 results: Excellence and equity in education. OECD Publishing. https://doi.org/10.1787/9789264266490-en Palmer, D. (2009). Student interest generated during an inquiry skills lesson. Journal of Research in Science Teaching, 46(2), 147-165. https://doi.org/10.1002/tea.20263 Razali, N., Wah, Y.B. and Zain, A.M. (2018). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors, and Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21-33. Sahin, A., Ayar, M.C. and Adiguzel, T. (2018). Factors influencing STEM career interest among high school students. Journal of STEM Education, 19(3), 34-47. Sellami, A., Kimmel, L. and Husman, J. (2023). The role of self-efficacy and outcome expectations in STEM persistence. Journal of Engineering Education, 112(1), 45-63. https://doi.org/10.1002/jee.20512 UNESCO. (2017). Education for sustainable development goals: Learning objectives. UNESCO Publishing. Wang, M.T. and Degol, J.L. (2017). Gender gap in STEM: Current knowledge and implications for policy. Educational Researcher, 46(8), 406-420. https://doi.org/10.3102/0013189X17735323 Zainuddin, N. and Kutty, F.M. (2021). STEM education in Malaysia: A systematic literature review. Journal of Science Learning, 4(2), 156-167. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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2","display":"","copyAsset":false,"role":"figure","size":64740,"visible":true,"origin":"","legend":"\u003cp\u003ePLS Algorithm Path Diagram for the Research Model\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8211254/v1/4563b59752365b8b5bf6d0b5.png"},{"id":98429049,"identity":"b5e1cb6e-954f-4f27-a3d5-5a992eb804bd","added_by":"auto","created_at":"2025-12-17 16:42:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67306,"visible":true,"origin":"","legend":"\u003cp\u003eBootstrapping Result\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8211254/v1/3bc15cc8901cc5d737c44c95.png"},{"id":98444663,"identity":"7ee7a2b8-0a01-4d8b-8b40-7d04a7b3448c","added_by":"auto","created_at":"2025-12-17 17:16:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":822177,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8211254/v1/e0de7c70-dc7f-425a-9e66-2c195d9aafed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":" Using Data-Driven Insights to Understand Factors Shaping STEM Interest in Secondary Education","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eSTEM education is notably recognized as a key driver of innovation, economic growth, and sustainable development (Bybee, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; UNESCO, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It integrates science, technology, engineering, and mathematics to foster critical thinking, problem solving, and teamwork, skills which are essential for a technology-driven global economy (Billiark et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Daugherty, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, modern technologies such as Artificial Intelligence and Augmented Reality further enhance STEM learning by improving digital literacy and career readiness (Chng et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; \u0026Ccedil;oban et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Malaysia, STEM education is one of the national priorities as outlined in the Malaysia Education Blueprint (MEB) 2013\u0026ndash;2025 with a focus on curriculum reform, teacher training, and public engagement (Ministry of Education Malaysia, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Despite progress, where STEM enrolment rose from 41.84% in 2019 to 50.83% in 2024, Malaysia still struggles to achieve its 60:40 STEM to non-STEM enrolment target (Ministry of Education, 2024). Challenges include student perceptions of STEM difficulty and limited parental awareness of STEM career benefits (Halim et al., 2018; Said et al., 2016). Thus, this study seeks to address this critical gap by identifying both personal and environmental determinants that shape student engagement with STEM disciplines.\u003c/p\u003e\u003cp\u003eGrounded in the Social Cognitive Career Theory (SCCT) (Lent et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), this study also contributes theoretically by applying and testing SCCT constructs in the Malaysian secondary school context, where research remains limited. The study extends SCCT\u0026rsquo;s application by integrating both personal determinants such as self-efficacy, outcome expectations, and self-input, together with environmental factors such as learning experiences. In doing so, it provides new insights into how SCCT explains STEM interest in a developing country setting and offers implications for improving theoretical models of career and educational choice.\u003c/p\u003e"},{"header":"2.0 Literature Review","content":"\u003ch2\u003e\u003cstrong\u003e2.1 Self-Efficacy\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eSelf-efficacy is a central construct in Social Cognitive Career Theory (SCCT), which posits that career interest is influenced by individual beliefs about one\u0026rsquo;s ability to perform tasks (Lent et al., 1994). Self-efficacy is a key predictor of academic motivation and career interest (Bandura, 1986; Artino, 2012). Numerous studies have shown that students with high self-efficacy are more likely to choose STEM subjects, persist in challenging coursework, and pursue STEM careers (Nugent et al., 2015; Wang, 2013; Heilbronner, 2011). Gallagher (2012) describes self-efficacy as an agentic value, enabling individuals to set and achieve goals. Students with strong self-efficacy are more passionate about STEM learning (Zainuddin \u0026amp; Kutty, 2021) and are better equipped to overcome perceived difficulties and high failure rates in STEM subjects (Vitali et al., 2020). High self-efficacy also supports perseverance, allowing students to face setbacks and maintain engagement (Hidajat et al., 2023; Fazilah et al., 2020). Buday et al. (2012) further note that students\u0026rsquo; confidence in their abilities, as well as their beliefs about balancing career and personal life, are critical for STEM career selection. Recent studies (Msambwa et al., 2024; Sellami et al., 2023) have found that self-efficacy, along with environmental and behavioral factors, as well as gender and nationality, can predict students\u0026apos; interest in STEM careers. Research in Southeast Asia and other developing countries, especially in collectivist cultures, has explored how self-efficacy and outcome expectations influence STEM interest. In both Malaysian and other Asian student groups, self-efficacy has been shown to strongly predict interest in general STEM fields, as well as in physics and science specifically (Halim et al., 2018, 2023; Mohtar et al., 2019; Nguyen, 2021). However, most of these studies focused on only one motivational factor rather than looking at a combination of influences.\u003c/p\u003e\n\u003cp\u003eGiven the above findings, this research suggests the following hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1:\u003c/strong\u003e Self-efficacy has a positive impact on students\u0026rsquo; interest in STEM.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e2.2 Outcome Expectations\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAccording to Social Cognitive Career Theory (SCCT) developed by Lent et al. (1994), outcome expectations are one of the key personal factors that influence career interests and choices. Outcome expectations refer to students\u0026rsquo; beliefs about the rewards and benefits associated with STEM participation, such as career prospects, societal impact, and personal fulfilment (Lent et al., 1994; Byars-Winston et al., 2010; Li et al., 2022). Research demonstrates that positive outcome expectations enhance students\u0026rsquo; motivation to pursue STEM education and careers (Han et al., 2021a). Diekman et al. (2017) and Fuesting et al. (2021) found that students are more likely to engage in STEM if they perceive these fields as meaningful and capable of helping others. Several studies have explored outcome expectations as a direct or indirect factor influencing STEM career interest. For example, Turner et al. (2019) found that outcome expectations can directly predict students\u0026apos; interest in STEM fields. Similarly, Luo et al. (2021) showed that outcome expectations have a direct effect on STEM career interest, though the strength of this effect varies. In their findings, outcome expectations had a moderate predictive value (\u0026beta; = 0.30). However, other research such as Garriott et al. (2014) found that outcome expectations did not significantly predict STEM career interest when measured alongside other factors, suggesting that their influence may depend on the context or population studied.\u003c/p\u003e\n\u003cp\u003eGiven the above findings, this research suggests the following hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2:\u003c/strong\u003e Outcome expectations have a positive impact on students\u0026rsquo; interest in STEM.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e2.3 Self-Input\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003ePersonal inputs or Self-input, including demographic factors such as gender, socioeconomic status (SES), academic performance, and personal goals, play a pivotal role in shaping STEM interest (Wang \u0026amp; Degol, 2017; Fan et al., 2018; Liou et al., 2014). Students from higher socioeconomic backgrounds often have greater access to STEM resources and opportunities (OECD, 2016). Gender disparities persist, with boys more likely to pursue STEM pathways than girls, due in part to societal stereotypes and a lack of female role models (Wang \u0026amp; Degol, 2013; Kricorian et al., 2020). Students\u0026rsquo; personal goals and time investment also matter; those with clear aspirations and who dedicate more effort to STEM activities are more likely to sustain interest (D\u0026ouml;nmez \u0026amp; İdin, 2020; Huang et al., 2022). Students from higher SES backgrounds often receive more parental support, access to learning resources, and better preparation, which boosts their interest in STEM (Koyunlu \u0026Uuml;nl\u0026uuml; \u0026amp; D\u0026ouml;kme, 2020; Turner et al., 2019; Yerdelen et al., 2016). Sahin et al. (2018) found that income and parental education directly influence STEM career interest. Other studies noted that students with high SES face fewer barriers and have stronger confidence and expectations about STEM (Turner et al., 2019). For instance, boys in private schools with high SES and high self-efficacy are more likely to choose STEM paths (Ketenci et al., 2020). In contrast, students from low-income or minority backgrounds are less likely to pursue STEM (Saw et al., 2018; Mau \u0026amp; Li, 2018). A review by Chiu (2024) and Msambwa et al. (2024) showed that 61% of studies reported SES as a key factor in STEM interest. Similar findings were reported in Southeast Asia, where studies linked parents\u0026apos; education and jobs to student interest (Siregar \u0026amp; Rosli, 2021; Nguyen, 2021).\u003c/p\u003e\n\u003cp\u003eGiven the above findings, this research suggests the following hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3:\u003c/strong\u003e Self-input has a positive impact on students\u0026rsquo; interest in STEM.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e2.4 Learning Experience\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eLearning experiences, both formal and informal, significantly shape students\u0026rsquo; interest in STEM (Fan et al., 2020; Chng et al., 2023). Participation in STEM projects, robotics programs, and outreach activities has been shown to deepen students\u0026rsquo; understanding and enthusiasm for STEM (Vennix et al., 2018; Chen \u0026amp; Chang, 2018). Integrated curricular approaches, such as those implemented in Malaysia\u0026rsquo;s KSSM curriculum, aim to make STEM learning more relevant and engaging (MoE, 2016a, 2016b). However, research by Ali et al. (2018) and the Academy of Sciences Malaysia (2018) suggests that an overemphasis on theoretical content, without sufficient contextual application, may fail to spark student interest. Application-based and problem-solving tasks are more effective in maintaining engagement and nurturing a positive attitude toward STEM (McIntyre et al., 2021). Shahali et al. (2017) found that after participating in a hands-on, engineering design STEM program, students showed a significant increase in interest toward both STEM subjects (p\u0026lt;0.05) and STEM careers. In general, educational experts widely recommend hands-on activities in STEM classes, as they effectively boost student interest and motivation (Holstermann et al., 2010; Bergin, 1999). Research shows that students involved in practical tasks, especially using tools or technology, show better academic outcomes and a stronger desire to pursue STEM (VanMeter-Adams et al., 2015; Swarat et al., 2012). Palmer (2009) found that experiments sparked more interest than written tasks, driven by novelty, independence, and social interaction. Overall, engaging students in real-world, professional-like tasks greatly enhances STEM engagement (Blumenfeld et al., 2006).\u003c/p\u003e\n\u003cp\u003eGiven the above findings, this research suggests the following hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4:\u003c/strong\u003e Learning experiences have a positive impact on students\u0026rsquo; interest in STEM.\u003c/p\u003e"},{"header":"3.0 Theoretical Framework and Research Questions","content":"\u003cp\u003eThis study adopts the Social Cognitive Career Theory (SCCT) developed by Lent et al. (1994) Lent, Brown, and Hackett (1994, 2000), and grounded in Bandura\u0026rsquo;s (1977) social cognitive theory. The theory explains how individuals developed academic and career interests by integrates external influences such as social, socioeconomic status (SES), and individual cognitive factors (e.g., self-efficacy, outcome expectations, self-input, learning experience) to dynamically illustrates how individuals make career decisions and is recognized as one of the most comprehensive and widely applied theory in STEM career research (Bahar \u0026amp; Adiguzel, 2016; Kang \u0026amp; Keinonen, 2017; Kier et\u0026nbsp;al., 2014; Maiorca et\u0026nbsp;al., 2021; Mohtar et\u0026nbsp;al., 2019; Nugent et\u0026nbsp;al., 2015; Wang et\u0026nbsp;al., 2021).\u003c/p\u003e\n\u003cp\u003eThe Social Cognitive Career Theory (SCCT) has been widely applied in research to understand factors influencing students\u0026rsquo; interest in STEM careers. It has been shown to effectively explain middle school students\u0026rsquo; interest in science-related fields through personal beliefs and motivations (Kier et al., 2014). Students who possess stronger self-efficacy and hold more positive expectations about their future are more likely to pursue STEM pathways (Nugent et al., 2015). Environmental supports such as parental encouragement, school support systems, and access to learning resources also play a crucial role in shaping students\u0026rsquo; aspirations toward STEM careers (Mohd et al., 2010; Murcia et al., 2020). The theory also helps to identify key motivational elements that influence students\u0026rsquo; interest in science (Sahin et al., 2015). Components of SCCT have been found to significantly predict high school students\u0026rsquo; motivation to engage in STEM fields (Bahar and Adiguzel, 2016). Learning experiences and contextual supports are equally important in increasing science engagement among students (Kang and Keinonen, 2017). In Malaysia, students\u0026rsquo; personal background and family influence have been identified as key factors contributing to their interest in STEM education (Mohtar et al., 2019). Other studies continue to show that self-beliefs and perceived environmental supports are essential in developing students\u0026rsquo; career goals in STEM, reinforcing the applicability of SCCT across educational contexts (Maiorca et al., 2021; Wang et al., 2021).\u003c/p\u003e\n\u003cp\u003eOverall, based on SCCT, this study explored the four key constructs which selected as independent variables in this research (self-efficacy, outcome expectations, learning experience, and self-input) towards students\u0026rsquo; interest in STEM within the context of Malaysian secondary schools.\u003c/p\u003e\n\u003cp\u003eThus, four research questions (RQ) that guided this study are as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ 1:\u003c/strong\u003e To what extent does self-efficacy influence students\u0026rsquo; interest in STEM?\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ 2:\u003c/strong\u003e To what extent do outcome expectations influence students\u0026rsquo; interest in STEM?\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ 3:\u003c/strong\u003e To what extent do learning experiences influence students\u0026rsquo; interest in STEM?\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ 4:\u003c/strong\u003e To what extent do self-input influence students\u0026rsquo; interest in STEM?\u0026nbsp;\u003c/p\u003e"},{"header":"4.0 Methodology","content":"\u003cp\u003eThe main objective of this study is to examine the factors influencing students\u0026rsquo; interest in STEM. This study adopts a quantitative research approach using a descriptive survey design. The descriptive approach is appropriate for investigating the influence of several key variables, including self-efficacy, outcome expectations, self-input, and learning experience, on students\u0026rsquo; interest in STEM. These variables were derived from the Social Cognitive Career Theory (SCCT), which explains the dynamic interaction of personal and contextual factors in shaping individuals\u0026rsquo; academic interests and career development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA structured questionnaire was employed as the main instrument for data collection in this study; \u003cstrong\u003eSelf-Input (SI1-SI7), Self-Efficacy (SE1-SE6), Outcome Expectation (OE1-OE6), Learning Experience (LE1-LE5),\u003c/strong\u003e and the dependent variable \u003cstrong\u003eSTEM Interest (STEM1-STEM7)\u003c/strong\u003e. The questionnaire was conducted online using Google Form to selected secondary schools in Kuala Lumpur and Sabah. The structured format of the instrument ensured consistency in data collection and allowed for direct comparison across responses. All constructs in the questionnaire were developed based on the STEM Career Interest Survey (STEM CIS) and previously validated research instruments, (Buday et al., 2012; Kier et al., 2014; Mohtar et al., 2019; Nugent et al., 2015). A five-point Likert scale, ranging from \u0026ldquo;Strongly Disagree\u0026rdquo; to \u0026ldquo;Strongly Agree\u0026rdquo;, was used to assess all variables, consistent with established recommendations for validity and reliability (Davis \u0026amp; Venkatesh, 1996). The questionnaire consisted of two sections: demographic information and variable measurements, which included four independent variables and one dependent variable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sample size was determined based on the guidelines by Uma Sekaran and Bougie (2016), which recommended a minimum of 384 respondents for populations exceeding 100,000 to ensure sufficient statistical power and generalizability. Based on this guideline, the study involved 456 secondary school students aged between 13 and 17. Schools from Kuala Lumpur and Sabah were selected to reflect diverse geographic and socioeconomic backgrounds. This approach aimed to capture a broader range of student experiences related to access to STEM resources, exposure to science and technology, and the influence of environmental support. The goal was not to compare specific groups but to offer a well-rounded understanding of the factors shaping STEM interest among Malaysian students from varied contexts.\u003c/p\u003e\n\u003cp\u003ePermission to conduct the study was obtained from the Ministry of Education Malaysia and the participating schools. Participation was voluntary, with informed consent obtained from parents or guardians and assent secured from students prior to data collection. Respondents were assured of anonymity and confidentiality, and all data were collected solely for academic purposes using secure online forms. Students were also informed of their right to withdraw from the study at any stage without penalty.\u003c/p\u003e\n\u003cp\u003eThe collected data were analysed using SMART PLS version 3.0, a statistical software designed specifically for Partial Least Squares Structural Equation Modelling (PLS-SEM). The analysis followed a two-step evaluation process involving (i) the measurement model, which assessed indicator reliability and validity, and (ii) the structural model, which examined the relationships between constructs. This methodological approach was selected due to its suitability for handling complex models and ensuring both theoretical alignment and contextual relevance. In this study, the PLS-SEM evaluation procedure was applied as illustrated in \u003cstrong\u003eFigure 2\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e4.1 Convergent Validity\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe assessment of the measurement model represents the initial phase in PLS-SEM analysis, aimed at confirming that each construct is measured both reliably and validly before proceeding to the evaluation of structural relationships. According to Hair et al. (2012), convergent validity refers to the extent to which indicators of a construct are correlated and consistently represent the same underlying concept.\u003c/p\u003e\n\u003cp\u003ePeng and Lai (2012) outlined three key criteria for assessing convergent validity: (i) outer loadings should ideally exceed 0.70, (ii) composite reliability (CR) should be greater than 0.70, and (iii) average variance extracted (AVE) should be above 0.50. However, Hair et al. (2011, 2014) also noted that items with loadings between 0.40 and 0.70 may be retained if their removal does not improve CR or AVE significantly. Items with loadings below 0.40 should be removed. In this study, although not all outer loadings surpassed the 0.70 threshold, many fell within the acceptable range of 0.50\u0026ndash;0.70, and the CR values ranged from 0.50 to 0.80, which are still considered acceptable in exploratory research (Hair et al., 2014).\u003c/p\u003e\n\u003cp\u003eWong (2013) emphasized that a careful evaluation of the measurement model strengthens the credibility of structural model analysis. The results, as presented in \u003cstrong\u003eFigure 2\u003c/strong\u003e, reflect acceptable levels of indicator reliability and convergent validity, supporting the adequacy of the measurement model used in this study.\u003c/p\u003e\n\u003cp\u003eFurthermore, as shown in \u003cstrong\u003eTable 1\u003c/strong\u003e, all constructs reported Cronbach\u0026apos;s alpha values ranging from 0.795 to 0.836, indicating acceptable levels of internal consistency. The composite reliability (CR) values ranged from 0.859 to 0.876, all exceeding the recommended threshold of 0.70, which supports the overall reliability of the measurement model. In terms of convergent validity, the average variance extracted (AVE) values varied between 0.503 and 0.550, all above the minimum acceptable level of 0.50. These results collectively demonstrate that the constructs exhibit adequate internal consistency, reliability, and convergent validity, confirming that the measurement model is psychometrically sound and suitable for further structural analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Construct Reliability and Validity\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"616\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eCronbach\u0026apos;s alpha\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eComposite reliability (rho_a)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eComposite reliability (rho_c)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eAverage variance extracted (AVE)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eLE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e0.795\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.807\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.859\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.550\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eOE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e0.810\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.819\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.863\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.513\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eSE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e0.810\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.850\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.863\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.521\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eSI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e0.836\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.843\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.876\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.504\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eSTEM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e0.831\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0.845\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.874\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.503\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e4.2 Discriminant Validity Analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eDiscriminant validity ensures that each construct in a model is clearly different from the others and measures a unique concept (Pan and Jang, 2008). The indicators within a construct should not show high correlations with indicators from different constructs. Therefore, items belonging to separate constructs should remain distinct and divergence rather than convergence.\u003c/p\u003e\n\u003cp\u003eThere are two approaches used to evaluate discriminant validity. The first is the cross-loadings approach, which involves comparing an item\u0026apos;s loading on its intended construct against its loadings on all other constructs. The second is the Fornell and Larcker criterion, which requires that the square root of the average variance extracted for each construct be greater than its correlation with any other construct (Fornell and Larcker, 1981; Hair et al., 2012).\u003c/p\u003e\n\u003cp\u003eBased on the results in \u003cstrong\u003eTable 2\u003c/strong\u003e, the square roots of the AVE values for each construct are higher than their corresponding inter-construct correlations, confirming that the constructs are empirically distinct and the measurement model demonstrates acceptable discriminant validity. To further support this, the Heterotrait-Monotrait (HTMT) ratio was also assessed. As shown in \u003cstrong\u003eTable 4\u003c/strong\u003e, all HTMT values are below the recommended threshold of 0.85, with the highest being 0.847. These findings confirm that the constructs are sufficiently distinct and not overly overlapping in measurement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Discriminant Validity Fornell \u0026amp; Larcker Criterion\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSTEM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.741\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.443\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.716\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.563\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.451\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.722\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.708\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.579\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.570\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.710\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSTEM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.575\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.634\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.652\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.671\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.709\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Heterotrait-monorait Ratio (HTMT)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSTEM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.531\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.703\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.544\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.847\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.690\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.671\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSTEM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.688\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.764\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.771\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.791\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e4.3 Analysis of the Constructs\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe structural model illustrates the relationships between the constructs, which are aligned with the hypotheses proposed in this study. These relationships represent the theoretical framework underlying the path model. Therefore, it is essential to evaluate the strength and significance of these hypothesized connections. According to Sarstedt (2014), structural model assessment involves testing hypotheses by examining the links between constructs, which helps determine the relevance and significance of these relationships. This evaluation ultimately reflects the model\u0026rsquo;s predictive accuracy. To ensure reliable estimates, a bootstrapping procedure with 5000 resamples was performed. As shown in \u003cstrong\u003eFigure 3\u003c/strong\u003e and \u003cstrong\u003eTable 4\u003c/strong\u003e, the bootstrapping analysis generated T-values and corresponding path coefficients, from which P-values were derived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Bootstrapping Result: Hypothesis Testing\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOriginal sample (O)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSample mean (M)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard deviation (STDEV)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT..statistics (|O/STDEV|)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHypothesis \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLE -\u0026gt; STEM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.075\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.076\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.052\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.441\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNot supported\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOE -\u0026gt; STEM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.306\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.306\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.041\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.504\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSupported\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSE -\u0026gt; STEM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.327\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.327\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.049\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.682\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSupported\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSI -\u0026gt; STEM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.255\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.256\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.054\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.698\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSupported\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"5.0 Discussion and Managerial Implications","content":"\u003ch2\u003e\u003cstrong\u003e5.1 Discussion\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAccording to the structural model analysis, three of the four hypothesized relationships were found to have a significant impact on students\u0026apos; interest in STEM. With p-values of 0.000 and T-values above the 1.96 threshold, outcome expectations (OE), self-efficacy (SE), and social influence (SI) all demonstrated strong and statistically significant effects, providing strong support for the hypotheses presented. Self-efficacy (\u0026beta; = 0.327) showed the strongest influence among these, indicating that students\u0026apos; interest in STEM is significantly shaped by their confidence in their ability to excel in the field. Additionally, outcome expectations (\u0026beta; = 0.306) had a major effect, suggesting that students are more inclined to study STEM when they believe there would be real benefits like employment or personal development. Social influence (\u0026beta; = 0.255) further demonstrated a significant effect, indicating how crucial role models, teachers, and peers are in encouraging interest in STEM. However, there was no statistically significant relationship between STEM interest and environmental support (LE) (\u0026beta; = 0.075, p = 0.150). Although supportive surroundings are advantageous in theory, our research indicates that they might not have a direct impact on students\u0026apos; interest unless they are combined with individual motivation and an understanding of its usefulness. This finding is consistent with the understanding that, although environmental influences play a significant role, self-awareness and social factors may have a greater impact on the development of career-related interests.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e5.2 Managerial Implications\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eKnowing which important factors have the most effects on students\u0026apos; interest in STEM provides a useful basis for different stakeholders to develop proactive, focused plans that will result in maximum impact. These findings have significant effects for stakeholders in the private sector, educators, legislators, and institutions of higher learning involved in STEM education and talent development in Malaysia.\u003c/p\u003e\n\u003cp\u003eThese findings offer educators and school counsellors, especially those working in under resourced environments, a good starting point for creating educational methods and career guidance programs that boost students\u0026apos; self-esteem and interest for STEM-related subjects. These improvements can encourage more productive learning settings and provide students the ability to make informed decisions about their future academic and career paths.\u003c/p\u003e\n\u003cp\u003eThe results can be implemented by policymakers to develop initiatives that increase STEM education accessibility in different parts of the country. This supports Malaysia\u0026apos;s national goals for technological advancement by ensuring that students from all backgrounds have equal opportunities to participate and benefit from STEM programs.\u003c/p\u003e\n\u003cp\u003eHigher education institutions can also benefit from these insights by enhancing their outreach, recruitment, and curriculum development efforts. Aligning university programs with the aspirations, motivations, and learning needs of secondary school students can help attract and retain more students in STEM-related fields.\u003c/p\u003e\n\u003cp\u003ePrivate sector stakeholders, especially those involved in Corporate Social Responsibility (CSR) initiatives, can use this research to develop impactful STEM engagement programs. These may involve industrial seminar, science workshops, mentoring programs, and other initiatives that promote student growth and align with national educational priorities. Collectively, these efforts can strengthen Malaysia\u0026rsquo;s STEM pipeline by ensuring that education, policy, and industry initiatives work together to support student growth and future innovation.\u003c/p\u003e"},{"header":"6.0 Research Contributions and Conclusion","content":"\u003ch2\u003e\u003cstrong\u003e6.1 Research Contributions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study contributes to the growing body of knowledge on STEM education by empirically examining the determinants of Malaysian secondary school students\u0026rsquo; interest in STEM through the lens of the Social Cognitive Career Theory (SCCT). Specifically, it highlights the roles of self-efficacy, learning experiences, outcome expectations, and self-input as critical constructs in shaping students\u0026rsquo; academic and career motivations. The findings enrich the theoretical understanding of SCCT by demonstrating that while environmental support (learning experiences) may provide important background conditions, personal and social factors such as confidence, perceived benefits, and influence from peers or role models exert stronger direct effects. This nuanced insight advances the theory by emphasizing how the balance of personal versus contextual factors may differ in developing countries compared to Western settings where SCCT is most frequently applied.\u003c/p\u003e\n\u003cp\u003eAdditionally, by testing the SCCT framework on a sample of 456 secondary school students in Malaysia, this study broadens the theory\u0026rsquo;s applicability to a non-Western and multi-ethnic context, addressing calls for greater cultural diversity in career development research. The results show that SCCT remains a robust explanatory framework but requires contextual sensitivity, particularly when applied to regions where educational infrastructure, social support, and career awareness differ from advanced economies. Thus, the study not only validates but also extends the theoretical boundaries of SCCT, offering an evidence-based model that may be adapted for future STEM education research in similar developing or transitional economies.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e6.2 Conclusion\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study contributes to the growing body of knowledge on STEM education by analyzing the key variables of Malaysian secondary school students\u0026apos; interest in STEM. It is based on the Social Cognitive Career Theory and highlights self-efficacy, learning experiences, outcome expectations, and self-input. The results provide further insight on how these factors interact to influence students\u0026apos; interest in STEM-related fields.\u003c/p\u003e\n\u003cp\u003eIn addition, the study supports national education and development goals, including the Ministry of Education Malaysia\u0026rsquo;s target to achieve a 60 to 40 ratio of science to arts enrolment and the Ministry of Science, Technology and Innovation\u0026rsquo;s plan to develop Malaysia into a high technology nation by 2030. It also aligns with the global agenda under Sustainable Development Goal 4, which promotes inclusive and quality education to prepare future generations for a skilled workforce.\u003c/p\u003e\n\u003cp\u003eBy applying and extending the Social Cognitive Career Theory in the Malaysian setting, the study provides a useful framework for understanding how individual, social, and environmental factors influence students\u0026rsquo; STEM interest, particularly among youth in developing countries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.S.L., as the corresponding author, conceptualized and designed the study, prepared the manuscript draft, and coordinated the research activities. N.A.J. contributed to methodology design, data analysis planning, and interpretation of results. S.J.M. assisted with literature review, data collection coordination, and manuscript editing. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to privacy and ethical restrictions involving secondary school students. However, the data are available from the corresponding author (N.S.L.) upon reasonable request and with permission from the relevant educational authorities.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdul Jalil, N., Hwang, H. and Mat Dawi, N., 2019. \u003cem\u003eMachines learning trends, perspectives and prospects in education sector\u003c/em\u003e. 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Journal of Science Learning, 4(2), 156-167.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"STEM education, factor influencing STEM interest, Social Cognitive Career Theory (SCCT), data analytics, secondary school students","lastPublishedDoi":"10.21203/rs.3.rs-8211254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8211254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the factors influencing Malaysia\u0026rsquo;s secondary school students\u0026rsquo; interest in Science, Technology, Engineering, and Mathematics (STEM) by adapting the Social Cognitive Career Theory (SCCT) as foundation. The key constructs which include self-efficacy, outcome expectations, self-input, and learning experience are explored to understand their role in shaping students\u0026rsquo; motivation towards STEM fields (Lent et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Byars-Winston et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A quantitative research design is employed, involving a structured questionnaire consisting of 38 items on a 5-point Likert scale distributed online to 456 secondary school students in Kuala Lumpur and Sabah. Using purposive sampling, respondents were selected from participants of a STEM outreach programme conducted in Malaysia, and the survey was administered in a single day during the event. The data will be analysed using SmartPLS to identify significant predictors of STEM interest (Razali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While the sample is limited to two regions, the findings offer practical implications for enhancing STEM education, particularly in under-resourced settings (Halim \u0026amp; Subahan, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chng et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This research could offer valuable insights for the teachers, counsellors, policymakers, and private sector stakeholders particularly those involved in Corporate Social Responsibility (CSR) to develop more thoughtful and strategic approach to increase student interest in STEM subjects and also supporting national goals related to future high-tech development. This research is also aligned with Sustainable Development Goal 4, which emphasise the important of quality and fair education for everyone (UNESCO, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e","manuscriptTitle":" Using Data-Driven Insights to Understand Factors Shaping STEM Interest in Secondary Education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 13:24:39","doi":"10.21203/rs.3.rs-8211254/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1131081e-4d72-4608-99a3-f131e519b6ce","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-10T12:40:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 13:24:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8211254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8211254","identity":"rs-8211254","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}