A latent profile analysis and structural equation modeling of the instructional quality of mathematics classrooms based on the PISA 2012 results of Korea and Singapore
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Teachers’ classroom behaviors and their effects on student learning have received significant attention from educators, because the quality of instruction is a critical factor closely tied to students’ learning experiences. Based on a theoretical model conceptualizing the quality of instruction, this study examined the characteristics of instructional quality represented by cognitive activation, student-oriented teacher behavior, class management, and learning support and investigated the relationships between instructional quality and students’ affective and cognitive outcomes. The PISA 2012 survey, administered to students in Korea and Singapore, was used to conduct a latent profile analysis and structural equation modeling. It was found that using more student-oriented instruction and less strategies of cognitive activation was positively associated with lower performance in math, while well-managed classroom and learning support were positively associated with higher performance. The level of instructional quality was generally higher for Singapore than Korea in every index at all achievement levels. Most affective characteristics and the math teachers’ instructional focus were positively associated with higher profiles of instructional quality. However, discrepant results were found between the two countries: Cognitive activation had positive effects on interest and self-concept in math as well as math performance for Korean students, whereas it only had a positive effect on math performance for Singaporean students. In contrast, student-oriented instruction had negative effects on interest in math as well as math performance in Korea, but a positive effect on interest in math in Singapore. The implications of each finding were discussed in detail.
KeywordsPISA 2012 mathematics Teachers’ classroom behaviors Instructional quality Math performance
- Brophy, J. E. (2000). Teaching: Educational practices series-1. Geneva: International Academy of Education/International Bureau of Education (IAE).Google Scholar
- Burns, D., & Darling-Hammond, L. (2014). Teaching around the world: What can TALIS tell us? Stanford, CA: Stanford Center for Opportunity Policy in Education.Google Scholar
- Hiebert, J., & Grouws, D. A. (2007). The effects of classroom mathematics teaching on students’ learning. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 371–404). Greenwich, CT: Information Age.Google Scholar
- Kim, H. S., & Ham, E. H. (2014). What school characteristics affect Korean students’ non-cognitive outcomes in mathematics? Journal of Educational Evaluation, 27(5), 1311–1335.Google Scholar
- Klieme, E. (2013). The role of large-scale assessments in research on educational effectiveness and school development. In M. von Davier, E. Gonzalez, I. Kirsch, & K. Yamamoto (Eds.), The role of international large-scale assessments: Perspectives from technology, economy, and educational research (pp. 115–147). Berlin: Springer.CrossRefGoogle Scholar
- Klieme, E., Pauli, C., & Reusser, K. (2009). The Pythagoras study: Investigating effects of teaching and learning in Swiss and German mathematics classrooms. In T. Janik & T. Seidel (Eds.), The power of video studies in investigating teaching and learning in the classroom (pp. 137–160). Münster: Waxmann.Google Scholar
- Ku, J., Kim, S., Rim, H., Park, H., & Han, J. (2015). Comparative analysis on the achievement characteristics and effects of educational contextual variables in high ranking countries of PISA 2012 results. Research report (RRE 2015-6-1), Korea Institute for Curriculum and Evaluation.Google Scholar
- Muthen, L. K., & Muthen, B. (2012). 1998–2012. Mplus user’s guide (7th ed.). Los Angeles, CA: Muthen & Muthen. https://www.statmodel.com/download/usersguide/Mplus%20user%20guide%20Ver_7_r6_web.pdf.
- OECD (2009). Creating effective teaching and learning environments: First results from TALIS. Paris: OECD Publishing.Google Scholar
- Scheerens, J., & Bosker, R. J. (1997). The foundations of educational effectiveness. Oxford: Pergamon.Google Scholar
- Sohn, W., Shin, Y., & Bae, J. (2014). Effects of teachers’ classroom assessment practices on student math confidence, enjoyment and achievement: Comparative analysis of TIMSS 2011 data of Korea, Singapore and Finland. Journal of Educational Evaluation, 27(5), 1337–1359.Google Scholar
- Vieluf, S., Lee, J., & Kyllonen, P. (2009). The predictive power of variables from the PISA 2003 Student Questionnaire. Paper presented at the QEG Meeting, Offenbach, Germany, 19–21 October.Google Scholar