Validating the S-STEM among Malaysian Pre-University Students
Abstract
The purpose of this study is to validate the measure of student attitudes toward science, technology, engineering, and mathematics (S-STEM). This study used the cross-sectional design to employ translation and cultural adaptation as well as providing evidence of the reliability and validity of the S-STEM. The instrument was administered to 748 pre-university students in Penang, Malaysia. Data were analyzed using confirmatory factor analysis (CFA) with AMOS 19.0. Results support S-STEM as a three-factor multidimensional construct, namely attitude towards science, attitude towards technology/engineering, and attitude towards mathematics. All statistics such as factor loadings, average variance explained, construct reliability, evidence of discriminant validity, and goodnessof-fit indices were found to be at acceptable values. These positive results are significant because although the instrument has undergone numerous modifications, such as translation and others, the generalizability of the instrument is still preserved in pre-university Malaysian students. Counselors may administer the instrument to facilitate the choice of courses to enroll at university. The research may utilize the instrument to gather data in providing measures to improve students’ participation in learning STEM. The practical implications, as well as the methodological limitations of the present study, are discussed.
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Bell, P., Lewenstein, B., Shouse, A. W., & Feder, M. A. (2009). Learning science in informal environments: People, places, and pursuits (Vol. 140). Washington, DC: National Academies Press.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit In: Bollen KA, Long JS, eds. Testing Structural Equation Models. Beverly Hills, CA: Sage, 136-162.
Breiner, J. M., Harkness, S. S., Johnson, C. C., & Koehler, C. M. (2012). What is STEM? A discussion about conceptions of STEM in education and partnerships. School Science and Mathematics, 112(1), 3-11.
Bybee, R. W. (2010). Advancing STEM education: A 2020 vision. Technology and Engineering Teacher, 70(1), 30-35.
Capobianco, B. M., Diefes-Dux, H. A., Mena, I., & Weller, J. (2011). What is an engineer? Implications of elementary school student conceptions for engineering education, Journal of Engineering Education, 100(2), 304-328.
Ceci, S. J., Ginther, D. K., Kahn, S., & Williams, W. M. (2014). Women in academic science: A changing landscape. Psychological Science in the Public Interest, 15(3), 75-141.
Cheryan, S., Ziegler, S. A., Montoya, A. K., & Jiang, L. (2017). Why are some STEM fields more gender balanced than others?. Psychological Bulletin, 143(1), 1-35.
Chua, K. E., & Karpudewan, M. (2017). The role of motivation and perceptions about science laboratory environment on lower secondary students’ attitude towards science. In Asia-Pacific Forum on Science Learning and Teaching (Vol. 18, No. 2, pp. 1-16). Hong Kong Institute of Education. 10 Lo Ping Road, Tai Po, New Territories, Hong Kong.
Dasgupta, N., & Stout, J. G. (2014). Girls and women in science, technology, engineering, and mathematics: STEMing the tide and broadening participation in STEM careers. Policy Insights from the Behavioral and Brain Sciences, 1(1), 21-29.
Faber, M., Unfried, A., Wiebe, E. N., Corn, J., Townsend, L. W., & Collins, T. L. (2013). Student attitudes toward STEM: The development of upper elementary school and middle/high school student surveys. In the Proceedings of the 120th American Society of Engineering Education Conference.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Gunderson, E. A., Ramirez, G., Levine, S. C., & Beilock, S. L. (2012). The role of parents and teachers in the development of gender-related math attitudes. Sex roles, 66(3-4), 153-166.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2013). Multivariate data analysis (7th international ed.). Harlow, UK: Pearson.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
Karpudewan, M., & Meng, C. K. (2017). The effects of classroom learning environment and laboratory learning environment on the attitude towards learning science in the 21st-century science lessons. Malaysian Journal of Learning and Instruction, 25-45.
Kelley, T. R., & Knowles, J. G. (2016). A conceptual framework for integrated STEM education. International Journal of STEM Education, 3(1), 11.
Khor, L. K., & Zakaria, S. F. (2019). Predicting student’s STEM subject performance by using the Malay version of S-STEM. Universal Journal of Educational Research, 7(10), 2037-2044.
Langdon, D., McKittrick, G., Beede, D., Khan, B., & Doms, M. (2011). STEM: Good Jobs Now and for the Future. ESA Issue Brief# 03-11. US Department of Commerce.
Lay, Y. F. & Khoo, C. H. (2012). Relationships between Actual and Preferred Science Learning Environment at Tertiary Level and Attitudes towards Science among Pre-Service Science Teachers. Pertanika Journal of Social Sciences & Humanities, 20(4), 1117-1142.
Long, C. Y., & Jiar, Y. K. (2014). Mathematical thinking and physics achievement of secondary school students. Sains Humanika, 2(4), 231-237.
Luo, W., Wei, H. R., Ritzhaupt, A. D., Huggins-Manley, A. C., & Gardner-McCune, C. (2019). Using the S-STEM Survey to Evaluate a Middle School Robotics Learning Environment: Validity Evidence in a Different Context. Journal of Science Education and Technology, 28(4), 429-443.
Moore, T. J., Stohlmann, M. S., Wang, H. H., Tank, K. M., Glancy, A. W., & Roehrig, G. H. (2014). Implementation and integration of engineering in K-12 STEM education. In Engineering in precollege settings: Synthesizing research, policy, and practices (pp. 35-60). Purdue University Press.
Naadiah Mohamed, A., & Razak, F. A. (2018). Effects of gender and school type on attitudes towards mathematics. JPhCS, 1132(1), 012038.
Navarro, M., Förster, C., González, C., & GonzálezPose, P. (2016). Attitudes toward science: Measurement and psychometric properties of the Test of Science-Related Attitudes for its use in Spanish-speaking classrooms. International Journal of Science Education, 38(9), 1459-1482.
Nugent, G., Barker, B., Welch, G., Grandgenett, N., Wu, C., & Nelson, C. (2015). A model of factors contributing to STEM learning and career orientation. International Journal of Science Education, 37(7), 1067-1088.
Reeve, E. M. (2015). STEM Thinking!. Technology and Engineering Teacher, 75(4), 8-16.
Reinking, A., & Martin, B. (2018). The Gender Gap in STEM Fields: Theories, Movements, and Ideas to Engage Girls in STEM. Journal of New Approaches in Educational Research, 7(2), 148-153.
Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling. Psychology Press.
Singh, P., Moin, M. A. A. A., Veloo, P. K., Han, C. T., & Hoon, T. S. (2019). The relationship between self-regulated learning and mathematics attitude towards college students’ development of mathematical thinking. Universal Journal of Educational Research, 7(10A), 48-53.
Thomas, B., & Watters, J. J. (2015). Perspectives on Australian, Indian, and Malaysian approaches to STEM education. International Journal of Educational Development, 45, 42-53.
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 (SSTEM). Journal of Psychoeducational Assessment, 33(7), 622-639.
Villafañe, S. M., & Lewis, J. E. (2016). Exploring a measure of science attitude for different groups of students enrolled in introductory college chemistry. Chemistry Education Research and Practice, 17(4), 731-742.
Wang, X. (2013). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50(5), 1081-1121.
Wong, S. L., & Wong, S. L. (2019). Relationship between interest and mathematics performance in a technology-enhanced learning context in Malaysia. Research and Practice in Technology Enhanced Learning, 14(1), 21.
Ximénez, C. (2015). Recovery of Weak Factor Loadings When Adding the Mean Structure in Confirmatory Factor Analysis: A Simulation Study. Frontiers in Psychology, 6, 1943-1943.
Zakaria, E., & Nordin, N. M. (2008). The effects of mathematics anxiety on matriculation students as related to motivation and achievement. Eurasia Journal of Mathematics, Science and Technology Education, 4(1), 27-30
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