Multilevel Analysis of Academic Factors Influencing Academic Outcomes in Mathematics
Arvin M. Larobis | Marlon S. Frias
Discipline: Mathematics
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 re- main prominent. The current study investigated how institutional and teacher-related factors predict mathematics proficiency among sec- ondary 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 pub- lic and private junior high schools in five city divisions of the region. A 70-item researcher-developed instrument was used to assess insti- tutional factors (professional development engagement, supportive school administration, and availability and use of school resources) and teacher-related factors (pedagogical content knowledge, mathe- matics teaching self-efficacy, teaching practices, and students' class- room 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 profi- ciency. 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 engage- ment are significant predictors of mathematics proficiency. At the in- stitutional level, findings show that supportive school leadership and professional development engagement are significant predictors of teacher practices and mathematics student proficiency. Notably, find- ings indicate that the availability and use of school and classroom re- sources predict mathematics student proficiency, but only slightly (β = 0.12, p = .182), and that the direct effects of these resources are *Corresponding author: E-mail: arvin.larobis001@deped.gov.ph Larobis & Frias., 2026 / Multilevel Analysis of Academic Factors Influencing Academic Outcomes in Mathematics IJMABER 2350 Volume 7 | Number 5 | May | 2026 largely mediated by other variables, serving as enabling conditions. In additional analyses, findings show that leadership strengthens the ef- fect of teacher self-efficacy on student achievement. This study develops the ISICE (Institutional Support-Instructional Support for Broad and Specific Instructional Capacity-Engagement) Model of instructional improvement, which posits that support for in- struction (enhancing instructional capacity) increases student en- gagement, which, in turn, improves mathematics proficiency for all students. The findings contribute to multilevel educational theories of change and provide a theoretical basis supporting particular profes- sional development approaches for strengthening mathematics in- struction, as well as particular roles for instructional leaders and school-level improvement initiatives.
References:
- Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
- Bartlett, M. S. (1954). A note on the multiplying factors for various chi-square approxima-tions. 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, cognitive activation in the classroom, and student progress. American Educational Research Journal, 47(1), 133–180. https://doi.org/10.3102/0002831209345157">https://doi.org/10.3102/0002831209345157">https://doi.org/10.3102/0002831209345157
- Bronfenbrenner, U. (1979). The ecology of hu-man development: Experiments by nature and design. Harvard University Press.
- Bryk, A. S., Raudenbush, S. W., & Congdon, R. (2010). HLM 7 for Windows [Computer software]. Scientific Software International.
- Caprara, G. V., Barbaranelli, C., Steca, P., & Malone, P. S. (2006). Teachers’ self-effi-cacy beliefs as determinants of job satisfaction and students’ academic achievement. Journal of School Psychology, 44(6), 473–490. https://doi.org/10.1016/j.jsp.2006.09.001">https://doi.org/10.1016/j.jsp.2006.09.001">https://doi.org/10.1016/j.jsp.2006.09.001
- Cheema, J. R., & Kitsantas, A. (2014). Influences of disciplinary classroom climate on high school student self-efficacy and mathe-matics achievement: A look at gender and immigrant status. International Journal of Science and Mathematics Education, 12(5), 1261–1279. https://doi.org/10.1007/s10763-013-9454-4">https://doi.org/10.1007/s10763-013-9454-4">https://doi.org/10.1007/s10763-013-9454-4
- Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555">https://doi.org/10.1007/BF02310555">https://doi.org/10.1007/BF02310555
- Department of Education. (2023). National achievement test results report. Department 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 simplicity. Psychometrika, 39(1), 31–36.
- Lee, J., & Shute, V. J. (2021). The role of contextual and instructional factors in student achievement: A multilevel perspective. 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">https://doi.org/10.1080/13632430701800060">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">https://doi.org/10.1787/5f07c754-e">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. Educational Researcher, 15(2), 4–14.
- Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard 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">https://doi.org/10.1016/S0742-051X(01)00036-1">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. Educational Psychology Review, 28(4), 1–45.