HomeJournal of Interdisciplinary Perspectivesvol. 3 no. 3 (2025)

Structural Equation Model of Students' Interest, Motivation, Self-Efficacy, Persistence, and Perceived Teaching Quality in Mathematics

Keith A. Madrilejos

Discipline: Education

 

Abstract:

Understanding the psychological variables of learning mathematics at the high school level is important to designing effective teaching strategies that improve their active engagement and academic achievement in mathematics. This study validated the assumptions of the Self-Determination Theory and Social Cognitive Theory in the context of mathematics classroom learning. This research aims to fill the gap in the literature by confirming a structural equation model based on two combined theories, incorporating the latent variables of self-efficacy, persistence, interest, motivation to learn mathematics, and the perceived teaching quality, areas that have already been explored in previous studies. Data were gathered from 325 selected public high school students (junior and senior high school) using a multivariate-correlational research design. Results revealed that self-efficacy strongly influences motivation (β=0.823, p<0.001) and weakly influences persistence (β=0.267, p=0.003), while perceived teaching quality significantly impacts interest (β=0.769, p<0.001) and self-efficacy (β=0.328, p<0.001). Additionally, student interest enhanced self- efficacy moderately (β=0.471, p<0.001), further reinforcing its critical role in fostering confidence. Motivation to learn mathematics was found to be strongly associated with students' persistence (β=0.557, p<0.001). These findings led to developing a valid and reliable structural equation model characterized by strong psychometric properties and excellent model fit indices. The results profoundly emphasize the interplay of psychological and instructional factors in promoting engagement, motivation, and resilience among high school mathematics learners, offering valuable insights for enhancing teaching strategies and educational policies. High school students should cultivate self-efficacy and motivation in mathematics through self- reflection and goal-setting. Teachers are encouraged to adopt interactive, real-world strategies that enhance interest and confidence, while school leaders should focus on professional development to sustain student engagement. Future research can explore digital tools, gamification, and advanced statistical modeling to investigate non-cognitive factors shaping mathematics learning.



References:

  1. Ariff, S. S. M., Kumar, S. V., Azizi, M. N. B., & Hilmi, F. (2022). Relationship between self-efficacy and academic motivation among university and college students enrolled in Kuala Lumpur during Movement Control Period (MCO). Journal of Positive School Psychology, 6(3), 3362–3374. https://tinyurl.com/mr2nye3p
  2. Bandala, M. (2023). Factors affecting students’ academic performance in mathematics: Basis for development of guidance intervention program. International Journal of Advance Research and Innovative Ideas in Education, 9(4). https://tinyurl.com/39s4yd2c
  3. Branzuela, N., Namoco, S., Duero, J., & Walag, A. M. (2023). Descriptive analysis of the National Achievement Test performance of primary and secondary school students in Misamis Oriental, Philippines. Sci.Int.Lahore., 35(6), 809-813. https://tinyurl.com/yrdxuf46
  4. Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). New York, United States: Routledge. https://tinyurl.com/rddee77j
  5. Calmorin, L. P., & Calmorin, M. A. (2007). Research methods and thesis writing. Rex Bookstore. Philippines,
  6. Casinillo, L. F. (2019). Factors affecting the failure rate in mathematics: The case of Visayas State University (VSU). Review of Socio-Economic Research and Development Studies, 3(1), 1-18. https://tinyurl.com/2pd83xm2
  7. Cherewick, M., Hipp, E., Njau, P., & Dahl, R. E. (2023). Growth mindset, persistence, and self-efficacy in early adolescents: Associations with depression, anxiety, and externalizing behaviors. Global Public Health, 18(1). https://doi.org/10.1080/17441692.2023.2213300
  8. Cherry, K. (n.d.). Cognitive development: Stages and theories. Positive Psychology. Retrieved from: https://positivepsychology.com/cognitive-development/
  9. Çiftçi, Ş., & Karadağ, E. (2016). Developing a mathematics education quality scale. Africa Education Review, 13(3-4), 211-234. https://doi.org/10.1080/18146627.2016.1224590
  10. Civelek, M.E. (2018). Essentials of Structural Equation Modeling. University of Nebraska – Lincoln, United States: Zea E-Books. https://tinyurl.com/43fy463v
  11. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media. https://doi.org/10.1007/978-1-4899-2271-7
  12. Detrina, D. (2016). The correlation between self-efficacy and motivation learning with mathematics learning outcomes students class XI IPS SMA Negeri 5 Batam academic year 2013/2014. Jurnal Mercumatika: Jurnal Penelitian Matematika dan Pendidikan Matematika, 1(1). https://doi.org/10.26486/mercumatika.v1i1.187
  13. Dullas, A. R. (2018). The development of academic self-efficacy scale for Filipino junior high school students. Frontiers in Education, 3. https://doi.org/10.3389/feduc.2018.00019
  14. Engel, K., Moosbrugger, H., and Muller H. (2003). Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness Fit Measures. MRP-Online. 8, 23-74. https://tinyurl.com/4sppjwas
  15. Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., & Shao, C. (2016). Applications of structural equation modeling (SEM) in ecological studies: An updated review. Ecological Processes, 5(19). https://doi.org/10.1186/s13717-016-0063-3
  16. Firdaus, Ismail Kailani, Md. Nor Bin Bakar, Bakry. (2015). Developing Critical Thinking Skills of Students in Mathematics Learning. Journal of Education and Learning. 9(3), 226-236. https://tinyurl.com/5e9yyh39
  17. Fornell, C., & Larcker, D.F. (1981). Evaluating Structural Equation Models with Unobserved variables and Measurement error. JMR. Journal of Marketing Research, 18(1), 39. https://www.scirp.org/reference/ReferencesPapers?ReferenceID=1927929
  18. Great Minds. (n.d.). High-leverage teaching practices that positively impact student learning. Retrieved from https://tinyurl.com/yck3j67v
  19. Gurnon, L. (2020). What you need to understand about Generation Z students. Retrieved from https://tinyurl.com/3s7rxwvc
  20. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). New York, United States: Pearson Education. https://tinyurl.com/4znme4dk
  21. Hair, J. F. Jr., Matthews, L., Matthews, R., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107. https://doi.org/10.1504/IJMDA.2017.087624
  22. Halper, L. R., & Vancouver, J. B. (2016). Self-efficacy’s influence on persistence on a physical task: Moderating effect of performance feedback ambiguity. Psychology of Sport and Exercise, 22, 170–177. https://doi.org/10.1016/j.psychsport.2015.08.007
  23. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  24. Kamayubonye, E., & Mutarutinya, V. (2023). Investigating the effects of teachers’ quality on students’ performance in mathematics in Kamonyi District, Rwanda. Rwandan Journal of Education, 6(2), 198. https://tinyurl.com/2s3sfwxp
  25. Kenny, D., Kaniskan, B., & McCoach, D. B. (2014). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486–507. https://doi.org/10.1177/0049124114543236
  26. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York, United States: A Division of Guilford Publications, Inc, Guilford Press. https://tinyurl.com/9pha9yw6
  27. Kock, N. (2020). WarpPLS User Manual: Version 7.0. Laredo, TX: ScriptWarp Systems. Retrieved from https://tinyurl.com/5fccme3c
  28. LaMorte, W. W. (2022). The Social Cognitive Theory. Retrieved from https://tinyurl.com/59dn4xup
  29. Leon, J., Medina-Garrido, E., & Núñez, J. L. (2017). Teaching Quality in Math Class: The Development of a Scale and the Analysis of Its Relationship with Engagement and Achievement. Frontiers in Psychology, 8, 895. https://doi.org/10.3389/fpsyg.2017.00895
  30. Luo, Z., Dang, Y., & Xu, W. (2019). Academic interest scale for adolescents: Development, validation, and measurement invariance with Chinese students. Frontiers in Psychology, 10, 2301 https://doi.org/10.3389/fpsyg.2019.02301
  31. Magno, F. (2021). Factor Analysis. Proceedings of 15-Day Capacity Building on Data Analysis, STAR Training Center, Philippines
  32. Mananghaya, A. K. (2020). It’s Not Complicated: Analysis of Data from A Single, Two, or More Populations. University of the Philippines Los Baños, Institute of Statistics. Retrieved from https://www.youtube.com/watch?v=eoafxBJdfa4&t=1499s
  33. Mattan S., Ben-Shachar, Dominique, M., Daniel, L., Indrajeet, P., Brenton, W., Remi, T., & Wagonner, P. (n.d.). Interpret of CFA / SEM Indices of Goodness of Fit. effect size. Retrieved from: https://easystats.github.io/effectsize/reference/interpret_gfi.html
  34. Meti, H., Hamdiyanti, M., Ivta, R., Laelasari, L., & Subroto, T. (2024). Systematic literature review: Mathematical literacy skills in terms of mathematics learning motivation. IJCER (International Journal of Chemistry Education Research. 8(2),1-9. https://doi.org/10.20885/ijcer.vol8.iss2.art3
  35. Nazareth-Tanaid, M. & Osic, M. A. (2023). Influence of Academic and Emotional Self-Efficacy on Students Mathematics Motivation in the New Normal Learning. Psychology and Education: A Multidisciplinary Journal, 15(9), 874-885. http://doi.org/10.5281/zenodo.10405764
  36. Nickerson, C. (2024). Social cognitive theory. Retrieved from: https://www.simplypsychology.org/social-cognitive-theory.html
  37. Nurkarim, A., Qonita, W., & Monterroza, D. (2023). The students’ mathematics motivation scale: A measure of intrinsic, extrinsic, and perceptions of mathematics. International Journal on Teaching and Learning Mathematics, 6(1), 42-51. https://doi.org/10.18860/ijtlm.v6i1.23610
  38. Nuutila, K., Tapola, A., Tuominen, H., Kupiainen, S., Pásztor, A., & Niemivirta, M. (2020). Reciprocal predictions between interest, self-efficacy, and performance during a task. Frontiers in Education, 5(36), 1-13. https://doi.org/10.3389/feduc.2020.00036
  39. Oamen, T. E. (2024). Competing confirmatory factor analysis models in management research: Bifactor modeling of the employee work assessment tool. Management Dynamics in the Knowledge Economy Journal, 12(2), 101-115. https://doi.org/10.2478/mdke-2024-0007
  40. Resnick, B., & Boltz, M. (2019). Optimizing function and physical activity in hospitalized older adults to prevent functional decline and falls. Clinics in Geriatric Medicine, 35(2), 237-251. https://doi.org/10.1016/j.cger.2019.01.004
  41. Rusczyk, R. (n.d.). Why it’s so important to learn a problem-solving approach to mathematics. Retrieved from https://tinyurl.com/4pdba2tb
  42. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78. https://doi.org/10.1037/0003-066X.55.1.68
  43. Sakirudeen, A. O., & Sanni, K. B. (2017). Study habits and academic performance of secondary school students in mathematics: A case study of selected secondary schools in Uyo Local Education Council. Research in Pedagogy, 7(2), 283-297. https://doi.org/10.17810/2015.65
  44. Schweder, S., & Raufelder, D. (2022). Students’ interest and self-efficacy and the impact of changing learning environments. Contemporary Educational Psychology, 70(3), 1-43. https://doi.org/10.1016/j.cedpsych.2022.102082
  45. Selden, A., & Selden, J. (2013). Persistence and Self-Efficacy in Proving. Retrieved from https://tinyurl.com/2e6j8h8f
  46. Shamim, A. (2021). Composite reliability, convergent validity, and discriminant validity in AMOS (Video 3). Retrieved from: https://www.youtube.com/watch?v=3XJFuQOJR6s
  47. Sudiyatno, Wu, M., Budiman, A., Purwantoro, D., Mahfud, T., & Siswanto, I. (2019). The effect of instructional quality on vocational students’ academic achievement and career optimism. International Journal of Innovation, Creativity and Change, 7(10). 244-260. http://www.ijicc.net
  48. Syafril, S., Aini, N. R., Netriwati, Pahrudin, A., Yaumas, N. E., & Engkizar. (2019). Spirit of Mathematics Critical Thinking Skills (CTS). Journal of Physics: Conference Series, 1467, Proceedings of Young Scholar Symposium on Science Education and Environment Lampung, Indonesia. Retrieved from https://doi.org/10.1088/1742-6596/1467/1/012069
  49. Tambunan, H., Sinaga, B., & Widada, W. (2021). Analysis of teacher performance to build student interest and motivation towards mathematics achievement. International Journal of Evaluation and Research in Education, 10(1), 42-47. https://doi.org/10.11591/ijere.v10i1.20711
  50. Velez, A. J., & Abuzo, E. (2024). Mathematics self-efficacy and motivation as predictors of problem-solving skills of students. Twist, 19, 417-430. https://doi.org/10.5281/zenodo.10049652#108
  51. Vollmeyer, R., & Rheinberg, F. (2000). Does motivation affect performance via persistence? Learning and Instruction, 10(4), 293-309. https://doi.org/10.1016/S0959-4752(99)00031-6
  52. Yambot, J.L. (2020). Are We Correlated? University of the Philippines Los Baños, Institute of Statistics. Retrieved from https://www.youtube.com/watch?v=lM9Ztjwquww&t=5174s
  53. Young, J. C. (2003). Problem solving reviewer in algebra. Luminaire Printing and Publishing Corp. Paranaque, Philippines.
  54. Zhu, Y., & Kaiser, G. (2022). Impacts of classroom teaching practices on students’ mathematics learning interest, mathematics self-efficacy and mathematics test achievements: A secondary analysis of Shanghai data from the international video study Global Teaching Insights ZDM Mathematics Education, 54(3), 581–593. https://doi.org/10.1007/s11858-022-01343-9