Multilevel Analysis of Academic Factors Influencing Academic Outcomes in Mathematics

Arvin M. Larobis; Marlon S. Frias

doi:10.11594/ijmaber.07.05.32

Authors

Arvin M. Larobis Department of Education , Bukidnon State University
Marlon S. Frias Bukidnon State University

DOI:

https://doi.org/10.11594/ijmaber.07.05.32

Keywords:

Instructional capacity, Multilevel modeling, Pedagogical expertise, School leadership, Secondary education

Abstract

Mathematics skill is a significant indicator of a school’s quality as well as a nation’s competitive edge globally. Despite its significance, academic achievement gaps among nations and school settings remain prominent. The current study investigated how institutional and teacher-related factors predict mathematics proficiency among secondary students in junior high schools in Region XI, Philippines. A quantitative multilevel approach was employed to analyse data from 114 licensed junior high school mathematics teachers across 30 public and private junior high schools in five city divisions of the region. A 70-item researcher-developed instrument was used to assess institutional factors (professional development engagement, supportive school administration, and availability and use of school resources) and teacher-related factors (pedagogical content knowledge, mathematics teaching self-efficacy, teaching practices, and students’ classroom engagement). Students’ mathematics proficiency was measured using their quarterly grades from the end of the second academic year.

This study applies hierarchical linear models (HLM) to examine student, teacher, and school-level predictors of mathematics proficiency. Findings reveal that both pedagogical content knowledge and teacher self-efficacy are strong predictors of student achievement. Moreover, findings suggest that teacher practices and student engagement are significant predictors of mathematics proficiency. At the institutional level, findings show that supportive school leadership and professional development engagement are significant predictors of teacher practices and mathematics student proficiency.

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References

Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.

Bartlett, M. S. (1954). A note on the multiply-ing factors for various chi-square approx-imations. Journal of the Royal Statistical Society: Series B, 16(2), 296–298.

Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., Klusmann, U., Krauss, S., Neubrand, M., & Tsai, Y. (2010). Teachers’ mathematical knowledge, cog-nitive activation in the classroom, and student progress. American Educational Research Journal, 47(1), 133–180. https://doi.org/10.3102/0002831209345157

Bronfenbrenner, U. (1979). The ecology of human development: Experiments by na-ture and design. Harvard University Press.

Bryk, A. S., Raudenbush, S. W., & Congdon, R. (2010). HLM 7 for Windows [Computer software]. Scientific Software Interna-tional.

Caprara, G. V., Barbaranelli, C., Steca, P., & Malone, P. S. (2006). Teachers’ self-efficacy beliefs as determinants of job satisfaction and students’ academic achievement. Journal of School Psycholo-gy, 44(6), 473–490. https://doi.org/10.1016/j.jsp.2006.09.001

Cheema, J. R., & Kitsantas, A. (2014). Influ-ences of disciplinary classroom climate on high school student self-efficacy and mathematics achievement: A look at gen-der and immigrant status. International Journal of Science and Mathematics Edu-cation, 12(5), 1261–1279. https://doi.org/10.1007/s10763-013-9454-4

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psy-chometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555

Department of Education. (2023). National achievement test results report. Depart-ment of Education, Philippines.

Desimone, L. M., & Garet, M. S. (2015). Best practices in teachers’ professional devel-opment in the United States. Psychology, Society & Education, 7(3), 252–263.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.

Kaiser, H. F. (1974). An index of factorial sim-plicity. Psychometrika, 39(1), 31–36.

Lee, J., & Shute, V. J. (2021). The role of con-textual and instructional factors in stu-dent achievement: A multilevel perspec-tive. Educational Psychologist, 56(2), 88–104.

Leithwood, K., Harris, A., & Hopkins, D. (2017). Seven strong claims about successful school leadership. School Leadership & Management, 28(1), 27–42. https://doi.org/10.1080/13632430701800060

Middleton, J. A., Jansen, A., & Goldin, G. A. (2017). The complexities of mathematical motivation. In J. Cai (Ed.), Compendium for research in mathematics education (pp. 667–699). National Council of Teachers of Mathematics.

Mullis, I. V. S., & Martin, M. O. (2019). TIMSS 2019 international results in mathematics and science. TIMSS & PIRLS International Study Center.

OECD. (2019). PISA 2018 results (Volume I): What students know and can do. OECD Publishing. https://doi.org/10.1787/5f07c754-e

Schleicher, A. (2019). PISA 2018: Insights and interpretations. OECD Publishing.

Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educa-tional Researcher, 15(2), 4–14.

Shulman, L. S. (1987). Knowledge and teach-ing: Foundations of the new reform. Har-vard Educational Review, 57(1), 1–22.

Tschannen-Moran, M., & Hoy, A. W. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher

Education, 17(7), 783–805. https://doi.org/10.1016/S0742-051X(01)00036-1

Wang, M. T., Degol, J. L., Amemiya, J., Parr, A., & Guo, J. (2016). Classroom climate and children’s academic and psychological well-being: A systematic review. Educa-tional Psychology Review, 28(4), 1–45.