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Home⇛International Journal of Multidisciplinary Educational Research and Innovation⇛vol. 3 no. 3 (2025)

Determinants of Mathematical Proficiency in Algebra Among Mathematics Education Students: A Structural Equation Model

Noel R. Tan Jr | Erika Mae P. Asoy

Discipline: Education

 

Abstract:

The main aim of this research was to examine the roles of teaching quality, mathematics interest, and the learning environment as determinants of mathematical proficiency in algebra, as well as to develop a best-fit Structural Equation Model (SEM) showing the relationships among the mentioned variables. This research employed a quantitative approach, using both descriptive-correlational methods and SEM analysis. The criteria used for model selection in SEM included the following: CMIN/ DF (0<value<2); P-value (>0.05); P-close (>0.05); RMSEA (<0.05); CFI (>0.95); GFI (>0.95); NFI (>0.95); and TLI (>0.95). Data were collected from 193 mathematics education students at Kapalong College of Agriculture, Sciences, and Technology through total enumeration sampling. Results showed significant positive relationships between mathematics interest, teaching quality, and the learning environment concerning mathematical proficiency in algebra, with teaching quality identified as the strongest predictor. The study generated a total of six models, one of which was identified as the best fit. This final model revealed that teaching quality directly influences mathematical proficiency, while the learning environment strongly correlates with teaching quality and indirectly contributes to proficiency. Meanwhile, mathematics interest and its indicators were omitted from the final model. The proposed model may guide educators, policymakers, and higher education institutions in advancing students’ algebra skills through meaningful teacher training and resources to enhance learning space.



References:

  1. Chand, S., Chaudhary, K., Prasad, A., & Chand, V. (2021). Perceived causes of students’ poor performance in mathematics: a case study at BA and Tavua Secondary Schools. Frontiers in Applied Mathematics and Statistics, 7,1–13. 
  2. Çorrêa, P., & Haslam, D. (2021). Mathematical proficiency as the basis for assessment: A literature review and its potentialities. Mathematics Teaching Research Journal, 12(4),1–18. 
  3. Geniston, G. V., Busa, C., Nofies, B., Moslares, M. E., Cepeda, M., Cepeda, M., Bano, J., Tegio, J. A., & Cebrero, P. O. (2024). Factors related to the poor numeracy level in mathematical fundamental operations among Grade 7 students. EPRA International Journal of Multidisciplinary Research (IJMR), 10(12),1–7. 
  4. Gijsbers, D., De Putter-Smits, L., & Pepin, B. (2019). Changing students’ beliefs about the relevance of mathematics in an advanced secondary mathematics class. International Journal of Mathematical Education in Science and Technology, 51(1), 87–102. 
  5. Hancock, G. R., Mueller, R. O., & Stapleton, L. M. (2018). The reviewer’s guide to Quantitative Methods in the Social Sciences. In Routledge eBooks. 
  6. Junpeng, P., Marwiang, M., Chinjunthuk, S., Suwannatrai, P., Chanayota, K., Pongboriboon, K., Tang, K. N., & Wilson, M. (2020). Validation of a digital tool for diagnosing mathematical proficiency. International Journal of Evaluation and Research in Education (IJERE), 9(3), 665. 
  7. Jupri, A., & Sispiyati, R. (2020). Students’ Algebraic Proficiency from the Perspective of Symbol Sense. Indonesian Journal of Science and Technology, 5(1), 86–94. 
  8. Lazarides, R., Gaspard, H., & Dicke, A. (2019). Dynamics of classroom motivation: Teacher enthusiasm and the development of math interest and teacher support. Learning and Instruction, 60, 126–137. 
  9. Mabena, N., Mokgosi, P. N., & Ramapela, S. S. (2021). Factors Contributing to Poor Learner Performance in Mathematics: a Case of Selected Schools in Mpumalanga province, South Africa. Problems of Education in the 21st Century, 79(3), 451–466. 
  10. Mahulae, A. V., Lumbanraja, P., & Siahaan, E. (2020). Effect of professionalism and competence of teachers on teacher performance and its impact on student learning outcomes at Harapan Mandiri College. International Journal of Research, 7(11), 38–46. 
  11. Mokhtar, M. A. M., Ayub, A. F. M., Said, R. R., & Mustakim, S. S. (2019). Analysis of year four pupils’ difficulties in solving mathematical problems involving fractions. International Journal of Academic Research in Business and Social Sciences, 9(11),1569–1560. 
  12. Monteiro, V., Carvalho, C., & Santos, N. N. (2021). Creating a supportive classroom environment through effective feedback: effects on students’ school identification and behavioral engagement. Frontiers in Education, 6, 1–14. 
  13. Munna, A. S., & Kalam, M. A. (2021). Teaching and Learning Process to Enhance Teaching Effectiveness: Literature Review. International Journal of Humanities and Innovation (IJHI), 4(1), 1–4. 
  14. OECD (2023), PISA 2022 Results (Volume I): The State of Learning and Equity in Education, PISA, OECD Publishing, Paris, .
  15. Olufsen, M., Karlsen, S., Sæleset, J., & Thorvaldsen, S. (2021). The impact of specialised content courses on student teaching in a Norwegian teacher education programme. Education Inquiry, 13(3), 269–286. 
  16. Pramesti, T. I., & Retnawati, H. (2019b). Difficulties in learning algebra: An analysis of students’ errors. Journal of Physics Conference Series, 1320(1), 012061. 
  17. Purwanto, A., & Sudargini, Y. (2022). Partial Least Squares Structural Squation Modeling (PLS-SEM) Analysis for Social and Management Research: A literature review. Journal of Industrial Engineering & Management Research (JIEMAR), 2(4), 113–125. 
  18. Razaga, K. (2024). Factors Affecting the Mathematics Performance of the College Students: A Discriminant Analysis. International Journal of Scientific and Management Research, 7(7), 60–77. 
  19. Salifu, A. S., & Bakari, A. (2022). Exploring the relationship between students’ perception, interest and mathematics achievement. Mediterranean Journal of Social & Behavioral Research, 6(1), 13–20. 
  20. Seeram, E. (2019). An overview of correlational research. PubMed, 91(2), 176–179. 
  21. Snow, G. M. (2011). Development of a Math Interest Inventory to Identify Gifted Students from Underrepresented and Diverse Populations. Digital Commons. 
  22. Soper, D. S. (2020). A-priori sample size calculator for structural equation models [Software]. 
  23. TIMSS & PIRLS International Study Center at Boston College. (2019). TIMSS 2019 International Results in Mathematics and Science. 
  24. Vandecandelaere, M., Speybroeck, S., Vanlaar, G., De Fraine, B., & Van Damme, J. (2012). Learning environment and students’ mathematics attitude. Studies in Educational Evaluation, 38(3–4), 107–120. 
  25. Van Der Scheer, E. A., Bijlsma, H. J. E., & Glas, C. A. W. (2018). Validity and reliability of student perceptions of teaching quality in primary education. School Effectiveness and School Improvement, 30(1), 30–50. 
  26. Werang, B. R., Agung, A. A. G., Jampel, I. N., Gading, I. K., Jim, E. L., Asaloei, S. I., & Sianturi, M. (2024). Exploring the simultaneous impact of parental support, school environment, teacher morale, and student learning motivation on student academic performance. International Journal of Religion, 5(2), 510–520. 
  27. Weyant, E. (2022). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th edition. Journal of Electronic Resources in Medical Libraries, 19(1–2), 54–55. 

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