This study aims to examine the psychometric properties of the Malay language version of the STEM Efficacy for Children Scale (SECS). This initiative involved 389 primary school children aged 10–11 in Klang Valley, Selangor, Malaysia. Exploratory factor analysis (EFA) was conducted to identify the underlying factors within 16 items in SECS, followed by confirmatory factor analysis (CFA) to determine the model and reliability of the scale. Based on EFA, SECS managed to capture three factors related to STEM, namely, efficacy in learning science and mathematics, as well as efficacy in the application of engineering. SECS obtained a high Cronbach’s alpha index (>0.8), and CFA confirmed that the model provided a good fit for the data collected. The average variance extracted demonstrated that all constructs in the model were >.50, while the composite reliability was >.80. These findings verify that the scale obtained good internal consistency. Therefore, the analysis proved that SECS is considered reliable and valid in capturing STEM efficacy among primary school children. The scale is expected to offer useful insights for educators, schools, and the government in their policy planning and execution concerning STEM teaching and learning at the primary school level.
Keywords: STEM education, primary school, efficacy, STEM Efficacy for Children Scale (SECS), confirmatory factor analysis (CFA), exploratory factor analysis (EFA)Adnam, M., Puteh, M., Tajuddin, N. M., Maat, S. M., & Ng, C. H. (2018) Integrating STEM education through project-based inquiry learning in topic Space among Year One children [Special edition]. Turkish Online Journal of Design, Art and Communication, 1383-–1390. https://doi.org/10.7456/1080SSE/185 https://doi.org/10.7456/1080SSE/185
Australia Government, Department of Education, Skills and Employment. (2020). STEM jobs are growing almost twice as fast as other jobs. https://www.employment.gov.au/newsroom/stem-jobs-growing-almost-twice-fast-other-jobs https://www.employment.gov.au/newsroom/stem-jobs-growing-almost-twice-fast-other-jobs
Awang, Z. (2015). SEM made simple: A gentle approach to learning structural equation modeling. MPWS Rich Publication.
Bahrum, S., Wahid, N., & Ibrahim, N. (2017). Integration of STEM education in Malaysia and why to STEAM. International Journal of Academic Research in Business and Social Sciences, 7(6), 645-–654. https://doi.org/10.6007/IJARBSS/v7-i6/3027 https://doi.org/10.6007/IJARBSS/v7-i6/3027
Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman.
Bandura. A. (1986). Social foundation of thought and action: A social cognitive theory. Prentice Hall.
Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman.
Behling, O., & Law, K. S. (2000). Translating questionnaires and other research instruments: Problems and solutions. Sage Publications.
Bentler, P. M. (1990). Comparative fit indexes in structural model. Psychological Bulletin, 107, 238-–246. https://doi.org/10.1037/0033-2909.107.2.238 https://doi.org/10.1037/0033-2909.107.2.238
Brislin, R. W. (1986). The wording and translation of research instruments. In W. J. Lonner & J. W. Berry (Eds.), Field methods in cross-cultural research (pp. 137-–164). Sage Publications.
Britner, S. L., & Pajares, F. (2006). Sources of science self‐efficacy beliefs of middle school students. Journal of Research in Science Teaching, 43(5), 485-–499. https://doi.org/10.1002/tea.20131 https://doi.org/10.1002/tea.20131
Byrne, B. M. (2016). Structural equation modeling with AMOS: bBasic concepts, applications, and programming (3rd ed.). Routledge.
Chong, C. J. (2019). Preliminary review on preparations in Malaysia to improve STEM education. Journal of Sustainability Science and Management, 14(5), 135-–147.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688 https://doi.org/10.2307/249688
Ekmekci, A., Sahin, A., & Waxman, H. (2019). Factors affecting students` STEM choice and persistence: A synthesis of research and findings form the second year of a longitudinal high school stem tracking study. In A. Sahin, & M. J. Mohr-Schroeder (Eds.), STEM Education 2.0: Myths and truths-what has K-12 STEM education research taught us? (pp. 277-–304). Brill Sense. https://doi.org/10.1163/9789004405400_015 https://doi.org/10.1163/9789004405400_015
Falco, L. D., & Summers, J. J. (2019). Improving career decision self-efficacy and STEM self-efficacy in high school girls: Evaluation of an intervention. Journal of cCareer dDevelopment, 46(1), 62-–76. https://doi.org/10.1177/0894845317721651 https://doi.org/10.1177/0894845317721651
Field, A. (2013) Discovering statistics using IBM statistics (4th ed.). Sage Publications.
Fornell, C. & Larcker D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 41-–54. https://doi.org/10.2307/3151312 https://doi.org/10.2307/3151312
Friday Institute for Educational Innovation. (2012). Upper Elementary School Student Attitudes toward STEM Survey. Author.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate data analysis: A global perspective (7th ed.). Pearson Prentice Hall.
Jamali, S. M., Md Zain, A. N., Samsudin, M. A., & Ale Ebrahim, N. (2017). Self-efficacy, scientific reasoning, and learning achievement in the STEM project-based learning literature. The Journal of Nusantara Studies (JONUS), 2(2), 29-–43. https://doi.org/10.24200/jonus.vol2iss2pp29-43 https://doi.org/10.24200/jonus.vol2iss2pp29-43
Jayarajah, K., Saat, R. M., Rauf, A., & Amnah, R. (2014). A review of Science, technology, engineering & mathematics (STEM) education research from 1999-2013: A Malaysian perspective. Eurasia Journal of Mathematics, Science & Technology Education, 10(3), 155-–163. https://doi.org/10.12973/eurasia.2014.1072a https://doi.org/10.12973/eurasia.2014.1072a
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-–36. https://doi.org/10.1007/BF02291575 https://doi.org/10.1007/BF02291575
Kranzler, J. H., & Pajares, F. (1997). An exploratory factor analysis of the Mathematics Self-Efficacy Scale—Revised (MSES-R). Measurement and Evaluation in Counseling and Development, 29(4), 215-–228. https://doi.org/10.1080/07481756.1997.12068906 https://doi.org/10.1080/07481756.1997.12068906
Kukul, V., Gökçearslan, Ş., & Günbatar, M. S. (2017). Computer programming self-efficacy scale (CPSES) for secondary school students: Development, validation and reliability. Eğitim Teknolojisi Kuram ve Uygulama, 7(1), 158-–179. https://doi.org/10.17943/etku.288493
Langdon, D., McKittrick, G., Beede, D., Khan, B., & Doms, M. (2011). STEM: Good jobs now and for the future. U.S. Department of Commerce, Economics & Statistics Administration.
Lau, R. (2019). Tough love for STEM threatens the labour market. Borneo Post Online. https://www.theborneopost.com/2019/04/07/tough-love-for-stem-threatens-labour-market/ https://www.theborneopost.com/2019/04/07/tough-love-for-stem-threatens-labour-market/
Lent, R. W., Lopez, F. G., Brown, S. D., & Gore, P. A., Jr. (1996). Latent structure of the sources of mathematics self-efficacy. Journal of Vocational Behavior, 49, 292––308. https://doi.org/10.1006/jvbe.1996.0045 https://doi.org/10.1006/jvbe.1996.0045
Lin, T. J., & Tsai, C. C. (2013). A multi-dimensional instrument for evaluating Taiwanese high school students` science learning self-efficacy in relation to their approaches to learning Science. International Journal of Science and Mathematics Education, 11, 1275–1301. https://doi.org/10.1007/s10763-012-9376-6 https://doi.org/10.1007/s10763-012-9376-6
Luo, T., So, W. W. M., Li, W. C., & Yao, J. (2020). The development and validation of a survey for evaluating primary students’ self-efficacy in STEM activities. Journal of Science Education and Technology, 1-–12. https://doi.org/10.1007/s10956-020-09882-0 https://doi.org/10.1007/s10956-020-09882-0
Majid, F. A. (2018). A comparative study on the current TESL curriculum: Identifying a match for Industry Revolution (IR) 4.0. International Journal of Applied Linguistics & English Literature, 7(6), 214-–222. https://doi.org/10.7575/aiac.ijalel.v.7n.6p.214 https://doi.org/10.7575/aiac.ijalel.v.7n.6p.214
Maltese, A. V., & Tai, R . H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among us students. Science Education, 95(5), 877-–907. https://doi.org/10.1002/sce.20441 https://doi.org/10.1002/sce.20441
Mamaril, N. A., Usher, E. L., Li, C. R., Economy, D. R., & Kennedy, M. S. (2016). Measuring undergraduate students’ engineering self-efficacy: A validation study. Journal of Engineering Education, 105(2), 366–395. https://doi.org/10.1002/jee.20121 https://doi.org/10.1002/jee.20121
Meng, C. C., Idris, N. I., & Eu, L. K. (2014). Secondary students` perceptions of assessment in Science, Technology, Engineering, and Mathematics (STEM). Eurasia Journal of Mathematics, Science and Technology Education, 10(3), 219-–227. https://doi.org/10.12973/eurasia.2014.1070a https://doi.org/10.12973/eurasia.2014.1070a
Meyers, L. S., Gamst, G., & Guarino, A. J. (2017). Applied multivariate research: design and interpretation (3rd ed.). SAGE Publications.
Ministry of Education. (2013). Malaysia education blueprint 2013-2025: Preschool to post-secondary education. Malaysian Ministry of Education.
Ministry of Education. (2020). List of schools in accordance to group, type and state. https://www.moe.gov.my/en/statistik-menu/senarai-sekolah-mengikut-kumpulan-jenis-dan-negeri https://www.moe.gov.my/en/statistik-menu/senarai-sekolah-mengikut-kumpulan-jenis-dan-negeri
Mohd Shahali, E. H., Halim, L., Rasul, M. S., Osman, K., & Arsad, N. M. (2019) Students’ interest towards STEM: a A longitudinal study, Research in Science & Technological Education, 37(1), 71-–89. https://doi.org/10.1080/02635143.2018.1489789 https://doi.org/10.1080/02635143.2018.1489789
Pampaka, M., Kleanthous, I., Hutcheson, G. D., & Wake, G. (2011). Measuring mathematics self-efficacy as a learning outcome. Research in Mathematics Education, 13(2), 169-–190. https://doi.org/10.1080/14794802.2011.585828 https://doi.org/10.1080/14794802.2011.585828
Pintrich, P. R., & de Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33. https://doi.org/10.1037/0022-0663.82.1.33 https://doi.org/10.1037/0022-0663.82.1.33
Randhawa, B. S., Beamer, J. E., & Lundberg, I. (1993). Role of mathematics self-efficacy in the structural model of mathematics achievement. Journal of Educational Psychology, 85(1), 41-–48.
Razali, F., Talib, O., Manaf, U. K. A., & Hassan, S. A. (2017). A measure of students’ motivation, attitude and parental influence towards interest in stem careers among Malaysian form four science stream students. International Journal of Academic Research in Business and Social Sciences, 7, 245-–264. https://doi.org/10.6007/IJARBSS/v7-i14/3665 https://doi.org/10.6007/IJARBSS/v7-i14/3665
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological bBulletin, 138(2), 353. https://doi.org/10.1037/a0026838 https://doi.org/10.1037/a0026838
Rivera, H., & Li, J. T. (2021). Potential factors to enhance students` STEM college learning and career orientation. Front. iers in Education. , 5(25). https://doi.org/10.3389/feduc.2020.00025 https://doi.org/10.3389/feduc.2020.00025
Scepanovič, S. (2019). The Fourth Industrial Revolution and Education. 2019 8th Mediterranean Conference on Embedded Computing (MECO), 1–4. https://doi.org/10.1109/MECO.2019.8760114 https://doi.org/10.1109/MECO.2019.8760114
Tran, T. V., Nguyen, T. H., & Chan, K. T. (2017). Developing cross-cultural measurement in social work research and evaluation (2nd ed.). Oxford University Press.
Tsai, M. J., Wang, C. Y., & Hsu, P. F. (2019). Developing the computer programming self-efficacy scale for computer literacy education. Journal of Educational Computing Research, 56(8), 1345-–1360. https://doi.org/10.1177/0735633117746747 https://doi.org/10.1177/0735633117746747
Uitto, A. (2014). Interest, attitudes and self-efficacy beliefs explaining upper-secondary school students’ orientation towards biology-related careers. International Journal of Science and Mathematics Education, 12(6), 1425-–1444. https://doi.org/10.1007/s10763-014-9516-2 https://doi.org/10.1007/s10763-014-9516-2
UNESCO. (2020). COVID-19 Educational Disruption and Response. . https://en.unesco.org/covid19/educationresponse https://en.unesco.org/covid19/educationresponse.
Unfried, A., Faber, M., Stanhope, D. S., & Wiebe, E. (2015). The development and validation of a measure of student attitudes toward science, technology, engineering, and math (S-STEM). Journal of Psychoeducational Assessment, 33(7), 622-–639. https://doi.org/10.1177/0734282915571160 https://doi.org/10.1177/0734282915571160
Van Aalderen-Smeets, S. I., Walma van der Molen, J. H., & Xenidou-Dervou, I. (2019). Implicit STEM ability beliefs predict secondary school students` STEM self‐efficacy beliefs and their intention to opt for a STEM field career. Journal of Research in Science Teaching, 56(4), 465-–485. https://doi.org/10.1002/tea.21506 https://doi.org/10.1002/tea.21506
Van de Vijver, F. J. R., & Leung, K. (2011). Equivalence and bias: A review of concepts, models and data analytic procedures. In D. Matsumoto, & F. J. R . van de Vijver (Eds.), Cross-cultural research in psychology (pp.. 17-–45). Cambridge University Press.
Wigfield, A., & Eccles, J. S. (2002). The development of competence beliefs, expectancies for success, and achievement values from childhood through adolescence. Development of Achievement Motivation, 91(120), 91-–120. https://doi.org/10.1016/b978-012750053-9/50006-1
Wong, S. Y., Liang, J., & Tsai, C. (2019). Uncovering Malaysian secondary school students` academic hardiness in Science, conceptions of learning Science, and science learning self-efficacy: A structural equation modeling analysis. Research in Science Education, 1-–28. https://doi.org/10.1007/s11165-019-09908-7 https://doi.org/10.1007/s11165-019-09908-7
World Bank. (2020). How countries are using edtech (including online learning, radio, television, texting) to support access to remote learning during the COVID-19 pandemic. https://www.worldbank.org/en/topic/edutech/brief/how-countries-are-using-edtech-to-support-remote-learning-during-the-covid-19-pandemic. https://www.worldbank.org/en/topic/edutech/brief/how-countries-are-using-edtech-to-support-remote-learning-during-the-covid-19-pandemic
Zhu, X., & Liu, J. (2020). Education in and after Covid-19: Immediate responses and long-term visions. Postdigital Science and Education, 2(3), 695-–699. https://doi.org/10.1007/s42438-020-00126-3 https://doi.org/10.1007/s42438-020-00126-3
Zimmerman, B. J. (1995). Self-regulation involves more than metacognition: A social cognitive perspective. Educational Psychologist, 30(4), 217-–221. https://doi.org/10.1207/s15326985ep3004_8 https://doi.org/10.1207/s15326985ep3004_8