A MULTILEVEL STRUCTURAL EQUATION MODEL OF FACTORS AFFECTING TO SCIENTIFIC LITERACY OF 10TH GRADE STUDENTS UNDER THE SECONDARY EDUCATIONAL SERVICE AREA OFFICE 33
Article Sidebar
Main Article Content
Abstract
The objective of this research were: 1) to develop and validate the multilevel structural equation model with empirical data; and 2) to study causal factors at student and school level effect on students scientific literacy. The sample consisted of 1,357 students and 595 teachers from 64 schools were randomly by multistage sampling method. Instruments used were a test and rating scales, with reliability from 0.856 – 0.984. Statistical analyses were using factor analysis and multilevel structural equation model. The research results showed that:
1. The proposed multilevel structural equation model fits quite well with empirical data set (2 = 323.820, df = 235, CFI = 0.993, TLI = 0.992, RMSEA = 0.017, SRMRW = 0.038, SRMRB = 0.108 and
2 /df = 1.378)
2. Student-level variables, such as science-self believe and motivation for learning science were significance affected the scientific literacy. For school-level variables, only teacher’s science instruction was significance. The predictor variables at student and school levels accounted for variance of the student’s scientific literacy about 13.800% and 15.700%, respectively.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The owner of the article does not copy or violate any of its copyright. If any copyright infringement occurs or prosecution, in any case, the Editorial Board is not involved in all the rights to the owner of the article to be performed.
References
Artino, A. R. (2012). Academic self-efficacy: From educational theory to instructional practice. Perspective on Medical Education, 1(2), 76-85.
Dumrongpanit, S. (2012). Mplus program with data analysis in behavioral and social sciences. Mahasarakham: Mahasarakham University. [in Thai]
Education for All Global Monitoring Report Team (EFA-GMR Team). (2015). Investing in Teacher is Investing in Learning: A Prerequisite for the Transformative Power of Education. Background Paper for the Oslo Summit on Education for Development; 6-7 July 2015, Oslo: Norway.
Faikhamta, C. (2020). Strategies for teaching chemistry. Bangkok: Chulalongkorn University press. [in Thai]
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). New Jersey: Pearson Education.
Hayat, A. A., Shateri, K., Amini, A., & Shokrpour, N. (2020). Relationship Between Academic Self-Efficacy, Learning-related Emotions, and Metacognitive Learning Strategies with Academic Performance in Medical Students: A Structural Equation Model. BMC Medical Education, 20(76), 1-11.
Jiang, F., & McComas, W. F. (2015). The effects of inquiry teaching on students science achievement and attitudes: Evidence from propensity score analysis of PISA data. International Journal of Education. 37(3), 554-576.
Kanjanawasee, S. (2011). Multilevel Analysis. Bangkok: Chulalongkorn University Press. [in Thai]
Keefe, J., & Jenkins, J. (1993). Eye on education: Instruction and the learning environment. In Zenzen, T. G. (2002). Achievement Motivation (pp. 7-8). Wisconsin, USA: University of Wisconsin-Stout.
Khemmani, T. (2017). The science of teaching: Knowledge for effective learning (21st ed.). Bangkok: Chulalongkorn University Press. [in Thai]
Khongkaew, P. (2016). Strategies of educational resources mobilization of basic education school (Doctoral dissertation). Ubon Ratchathani: Ubon Ratchathani Rajabhat University. [in Thai]
Kowtrakul, S. (2016). Educational psychology (12th ed.). Bangkok: Chulalongkorn University press. [in Thai]
Kyllonen, P. C., Walters, A. M., & Kaufman, J. C. (2011). The role of noncognitive constructs and other background variables in graduate education (Research report). Princeton, NJ: Educational Testing Service.
Ladachart, L. (2018). Teaching science as Science: History philosophy and education. Bangkok: Chulalongkorn University Press. [in Thai]
Margarida, R., & Federico, B. (2017). Digital technologies and learning outcomes of students from low socio-economic background: An analysis of PISA 2015. Luxembourg: Publications Office of the European Union.
Mason, L., Boscolo, P., Tornatora, M. C., & Ronconi, L. (2013). Besides knowledge: A cross-sectional study on the relations between epistemic beliefs, achievement goals, self-beliefs, and achievement in science. Instructional Science, 41(1), 49-79.
Mass, C. J. M., & Hox, J. J. (2005). Sufficient sample sizes for multilevel modeling. Methodology, 1(3), 86-92.
Muthén, B. O. (1991). Multilevel factor analysis of class and student achievement components. Journal of Educational measurement, 28(4), 338-354.
National Academies of Sciences, Engineering and Medicine (NASEM). (2016). Science Literacy: Concepts, Contexts, and Consequences. Washington, DC: The National Academies Press.
National Research Council (NRC). (2000). How People Learn: Brain, Mind, Experience, and School: Expanded Edition. Washington, DC: The National Academies Press.
Niltheun, A. (2010). Effect of learning styles and matching of learning styles – teaching styles on ninth grade student’s science learning achievement with attitude towards science as mediator (Master thesis). Bangkok: Chulalongkorn University. [in Thai]
Nuangchalerm, P. (2014). Science learning in the 21st century. Mahasarakham: Apichart Printing. [in Thai]
Organization for Economic Co-operation and Development (OECD). (2015). Students, Computers and Learning: Making the Connection. Paris: OECD Publish.
Organization for Economic Co-operation and Development (OECD). (2016). PISA 2015 Results (Volume I): Excellent and Equity in Education. Paris: OECD Publish.
Organization for Economic Co-operation and Development (OECD). (2017). The Funding of School Education: Connecting Resources and Learning. Paris: OECD Publish.
Organization for Economic Co-operation and Development (OECD). (2018). Education Policy Outlook: Mexico. Paris: OECD Publish.
Panich, V. (2013). How learning works? Bangkok: S. R. Printing Mass Product. [in Thai]
Sharma, H. L., & Nasa, G. (2014). Academic self-efficacy: A reliable predictor of educational performances, British Journal of Education, 2(3), 57-64.
Taksino, P., Promabun, T., Suraset, Ch., Jogsatit, T., & Kadchaturat, J. (2016). Measurement and assessment model of students’ desirable characteristics based on national education standards and developing battery test of attitudinal constructs for basic educational students. Bangkok: National Institute of Educational Testing Service (Public Organization). [in Thai]
Tantichuwet, P. (2017). Learner development to grow to their full potential. In Sinlarat, P., & Meesan, N. (Editor), Educational Leadership (pp. 303-312). Bangkok: Chulalongkorn University press. [in Thai]
The Institute for the Promotion of Teaching Science and Technology (IPST). (2018). PISA 2015 results (Volume I): Excellent and equity in education. Bangkok: Success Publication. [in Thai]
Viorel, M., & Viorel, D. (2015). Scientific literacy in school. Procedia – Social and Behavioral Sciences, (209), 167-172.
Zenzen, T. G. (2002). Achievement motivation. Wisconsin: University of Wisconsin-Stout.