Enhancing mathematics achievement through self-regulated learning and problem-based learning: The mediating role of learning motivation
DOI:
https://doi.org/10.58524/jasme.v6i2.1358Keywords:
Learning Motivation, Mathematics Achievement, Partial Least Squares Structural Equation Modeling (PLS-SEM), Problem-Based Learning, Self-Regulated LearningAbstract
Background: Improving mathematics achievement remains a major challenge in secondary education, particularly in developing countries where students continue to demonstrate relatively low performance in mathematics. Self-Regulated Learning (SRL) and Problem-Based Learning (PBL) have been widely recognized as effective approaches for enhancing academic performance; however, the psychological mechanism through which these approaches influence mathematics achievement remains insufficiently understood.
Aim: This study examined the direct and indirect effects of SRL and PBL on mathematics achievement through the mediating role of learning motivation.
Method: A quantitative correlational survey design was employed involving 270 Indonesian secondary school students. Data were collected using validated questionnaires measuring SRL, PBL, and learning motivation, alongside a mathematics achievement test. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).
Results: The findings revealed that the model explained 70.1% of the variance in learning motivation and 69.7% of the variance in mathematics achievement. Learning motivation emerged as the strongest predictor of mathematics achievement, followed by PBL and SRL. Furthermore, learning motivation significantly mediated the relationships between SRL, PBL, and mathematics achievement.
Conclusion: Mathematics achievement can be enhanced through the combined influence of SRL and PBL, with learning motivation serving as a key mechanism that strengthens their impact. These findings highlight the importance of integrating motivational support and student-centered learning strategies in mathematics education.
References
Acosta-Gonzaga, E., & Ramirez-Arellano, A. (2021). The influence of motivation, emotions, cognition, and metacognition on students’ learning performance: A comparative study in higher education in blended and traditional contexts. SAGE Open, 11(2), 21582440211027561. https://doi.org/10.1177/21582440211027561
Agbi, A., & Yuangsoi, P. (2022). Enhancement of critical thinking skills in students using mobile-blended learning with a collaborative inquiry-based approach. Humanities, Arts and Social Sciences Studies, 9–20. https://doi.org/10.14456/hasss.2022.2
Alhadabi, A., & Karpinski, A. C. (2020). Grit, self-efficacy, achievement orientation goals, and academic performance in university students. International Journal of Adolescence and Youth, 25(1), 519–535. https://doi.org/10.1080/02673843.2019.1679202
Amerstorfer, C. M., & Freiin von Münster-Kistner, C. (2021). Student perceptions of academic engagement and student-teacher relationships in problem-based learning. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.713057
Amland, T., Grande, G., Scherer, R., Lervåg, A., & Melby-Lervåg, M. (2025). Cognitive factors underlying mathematical skills: A systematic review and meta-analysis. Psychological Bulletin, 151(1), 88–129. https://doi.org/10.1037/bul0000457
Balci, S., Secaur, J. M., & Morris, B. J. (2022). Comparing the effectiveness of badges and leaderboards on academic performance and motivation of students in fully versus partially gamified online physics classes. Education and Information Technologies, 27(6), 8669–8704. https://doi.org/10.1007/s10639-022-10983-z
Barroso, C., Ganley, C. M., McGraw, A. L., Geer, E. A., Hart, S. A., & Daucourt, M. C. (2021). A meta-analysis of the relation between math anxiety and math achievement. Psychological Bulletin, 147(2), 134–168. https://doi.org/10.1037/bul0000307
Burlacu, A., Iftene, A., Busoiu, E., Cogean, D., & Covic, A. (2020). Challenging the supremacy of evidence-based medicine through artificial intelligence: The time has come for a change of paradigms. Nephrology Dialysis Transplantation, 35(2), 191–194. https://doi.org/10.1093/ndt/gfz203
Chen, C., Bian, F., & Zhu, Y. (2023). The relationship between social support and academic engagement among university students: The chain mediating effects of life satisfaction and academic motivation. BMC Public Health, 23(1), 2368. https://doi.org/10.1186/s12889-023-17301-3
Chen, C., Sonnert, G., Sadler, P. M., Sasselov, D., & Fredericks, C. (2020). The impact of student misconceptions on student persistence in a MOOC. Journal of Research in Science Teaching, 57(6), 879–910. https://doi.org/10.1002/tea.21616
Chen, R. H. (2021). Fostering students’ workplace communicative competence and collaborative mindset through an inquiry-based learning design. Education Sciences, 11(1). https://doi.org/10.3390/educsci11010017
Chou, C.-Y., & Zou, N.-B. (2020). An analysis of internal and external feedback in self-regulated learning activities mediated by self-regulated learning tools and open learner models. International Journal of Educational Technology in Higher Education, 17(1), 55. https://doi.org/10.1186/s41239-020-00233-y
Daucourt, M. C., Napoli, A. R., Quinn, J. M., Wood, S. G., & Hart, S. A. (2021). The home math environment and math achievement: A meta-analysis. Psychological Bulletin, 147(6), 565–596. https://doi.org/10.1037/bul0000330
de Ruig, N. J., de Jong, P. F., & Zee, M. (2023). Stimulating elementary school students’ self-regulated learning through high-quality interactions and relationships: A narrative review. Educational Psychology Review, 35(3), 71. https://doi.org/10.1007/s10648-023-09795-5
E., S., & Benjamin, A. E. W. (2024). Studying the student’s perceptions of engagement and problem-solving skills for academic achievement in chemistry at the higher secondary level. Education and Information Technologies, 29(7), 8347–8368. https://doi.org/10.1007/s10639-023-12165-x
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544
Ferrer, J., Ringer, A., Saville, K., A Parris, M., & Kashi, K. (2022). Students’ motivation and engagement in higher education: The importance of attitude to online learning. Higher Education, 83(2), 317–338. https://doi.org/10.1007/s10734-020-00657-5
Fong, C. J., Altan, S., Gonzales, C., Kirmizi, M., Adelugba, S. F., & Kim, Y. (2024). Stay motivated and carry on: A meta-analytic investigation of motivational regulation strategies and academic achievement, motivation, and self-regulation correlates. Journal of Educational Psychology, 116(6), 997–1018. https://doi.org/10.1037/edu0000886
Gebremeskel, A. A., Ayele, M. A., & Wondimuneh, T. E. (2025). Student engagement, conceptual-understanding, and problem-solving ability in learning plane geometry through an integrated instructional approach. Eurasia Journal of Mathematics, Science and Technology Education, 21(5), em2634. https://doi.org/10.29333/ejmste/16391
Gillies, R. M. (2023). Using cooperative learning to enhance students’ learning and engagement during inquiry-based science. Education Sciences, 13(12). https://doi.org/10.3390/educsci13121242
Greenberg, A., Olvet, D. M., Brenner, J., Zheng, B., Chess, A., Schlegel, E. F. M., & Ginzburg, S. B. (2023). Strategies to support self-regulated learning in integrated, student-centered curricula. Medical Teacher, 45(12), 1387–1394. https://doi.org/10.1080/0142159X.2023.2218538
He, A., Yuan, W., Lee, L. S., & Tian, T. (2025). AI-driven predictive models for optimizing mathematics education technology: Enhancing decision-making through educational data mining and meta-analysis. Smart Learning Environments, 12(1), 64. https://doi.org/10.1186/s40561-025-00415-z
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hidayatullah, A., & Csíkos, C. (2024). The role of students’ beliefs, parents’ educational level, and the mediating role of attitude and motivation in students’ mathematics achievement. The Asia-Pacific Education Researcher, 33(2), 253–262. https://doi.org/10.1007/s40299-023-00724-2
Hossain, K. I. (2024). Literature-based language learning: Challenges, and opportunities for English learners. Ampersand, 13, 100201. https://doi.org/10.1016/j.amper.2024.100201
Hsbollah, H. M., & Hassan, H. (2022). Creating meaningful learning experiences with active, fun, and technology elements in the problem-based learning approach and its implications. Malaysian Journal of Learning and Instruction, 19(1), 147–181. https://doi.org/10.32890/mjli2022.19.1.6
Jeno, L. M., Nylehn, J., Hole, T. N., Raaheim, A., Velle, G., & Vandvik, V. (2023). Motivational determinants of students’ academic functioning: The role of autonomy-support, autonomous motivation, and perceived competence. Scandinavian Journal of Educational Research, 67(2), 194–211. https://doi.org/10.1080/00313831.2021.1990125
Jonsson, B., Granberg, C., & Lithner, J. (2020). Gaining mathematical understanding: The effects of creative mathematical reasoning and cognitive proficiency. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.574366
Kim, Y., Brady, A. C., & Wolters, C. A. (2020). College students’ regulation of cognition, motivation, behavior, and context: Distinct or overlapping processes? Learning and Individual Differences, 80, 101872. https://doi.org/10.1016/j.lindif.2020.101872
King, N., & Bunce, L. (2020). Academics’ perceptions of students’ motivation for learning and their own motivation for teaching in a marketized higher education context. British Journal of Educational Psychology, 90(3), 790–808. https://doi.org/10.1111/bjep.12332
Kohen, Z., & Orenstein, D. (2021). Mathematical modeling of tech-related real-world problems for secondary school-level mathematics. Educational Studies in Mathematics, 107(1), 71–91. https://doi.org/10.1007/s10649-020-10020-1
Korpershoek, H., Canrinus, E. T., Fokkens-Bruinsma, M., & de Boer, H. (2020). The relationships between school belonging and students’ motivational, social-emotional, behavioural, and academic outcomes in secondary education: A meta-analytic review. Research Papers in Education, 35(6), 641–680. https://doi.org/10.1080/02671522.2019.1615116
Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1). https://doi.org/10.3390/ijerph18010271
Letterie, G. (2021). Three ways of knowing: The integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies. Journal of Assisted Reproduction and Genetics, 38(7), 1617–1625. https://doi.org/10.1007/s10815-021-02159-4
Li, L., Hew, K. F., & Du, J. (2024). Gamification enhances student intrinsic motivation, perceptions of autonomy and relatedness, but minimal impact on competency: A meta-analysis and systematic review. Educational Technology Research and Development, 72(2), 765–796. https://doi.org/10.1007/s11423-023-10337-7
Liu, Y., Ma, S., & Chen, Y. (2024). The impacts of learning motivation, emotional engagement and psychological capital on academic performance in a blended learning university course. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1357936
Lo, K. W. K., Ngai, G., Chan, S. C. F., & Kwan, K. (2022). How students’ motivation and learning experience affect their service-learning outcomes: A structural equation modeling analysis. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.825902
Manfreda Kolar, V., & Hodnik, T. (2021). Mathematical literacy from the perspective of solving contextual problems. European Journal of Educational Research, 10(1), 467–483. https://doi.org/10.12973/eu-jer.10.1.467
Marini, A., Muawanah, U., & Marfu, A. (2026). Enhancing critical thinking through problem-based learning: The role of student engagement and technology for education sustainability in Indonesia. Sustainable Futures, 11, 101846. https://doi.org/10.1016/j.sftr.2026.101846
Mejias, P. P., McAllister, D. E., Diaz, K. G., & Ravest, J. (2021). A longitudinal study of the gender gap in mathematics achievement: Evidence from Chile. Educational Studies in Mathematics, 107(3), 583–605. https://doi.org/10.1007/s10649-021-10052-1
van der Merwe, R. L., Groenewald, M. E., Venter, C., Scrimnger-Christian, C., & Bolofo, M. (2020). Relating student perceptions of readiness to student success: A case study of a mathematics module. Heliyon, 6(11). https://doi.org/10.1016/j.heliyon.2020.e05204
Munatsi, R. (n.d.). Using artificial intelligence to enhance evidence informed-decision-making. South African Journal of Information Management, 27(1), 2004. https://doi.org/10.4102/sajim.v27i1.2004
Nie, Y., Sun, B., & Xiong, F. (2024). Motivation and self-regulated learning profiles: A person-centered perspective of English learning and achievement in an Asia context. System, 125, 103448. https://doi.org/10.1016/j.system.2024.103448
Nilimaa, J. (2023). New examination approach for real-world creativity and problem-solving skills in mathematics. Trends in Higher Education, 2(3), 477–495. https://doi.org/10.3390/higheredu2030028
Nurjanah, R. L., Mujiyanto, J., Pratama, H., & Rukmini, D. (2022). Students’ perceptions on learning independence: How self-regulated learning strategy helps? Language Value, 15(2), 29–53. https://doi.org/10.6035/languagev.6930
Okada, R. (2023). Effects of perceived autonomy support on academic achievement and motivation among higher education students: A meta-analysis. Japanese Psychological Research, 65(3), 230–242. https://doi.org/10.1111/jpr.12380
Raza, S. A., Qazi, W., & Yousufi, S. Q. (2020). The influence of psychological, motivational, and behavioral factors on university students’ achievements: The mediating effect of academic adjustment. Journal of Applied Research in Higher Education, 13(3), 849–870. https://doi.org/10.1108/JARHE-03-2020-0065
Rehman, N., Huang, X., Mahmood, A., AlGerafi, M. A. M., & Javed, S. (2024). Project-based learning as a catalyst for 21st-century skills and student engagement in the math classroom. Heliyon, 10(23). https://doi.org/10.1016/j.heliyon.2024.e39988
Shimizu, Y. (2022). Relation between mathematical proof problem solving, math anxiety, self-efficacy, learning engagement, and backward reasoning. Journal of Education and Learning, 11(6), 62–75. https://doi.org/10.5539/jel.v11n6p62
Sholihah, T. M., & Lastariwati, B. (2020). Problem based learning to increase competence of critical thinking and problem solving. Journal of Education and Learning (EduLearn), 14(1), 148–154. https://doi.org/10.11591/edulearn.v14i1.13772
Theobald, M. (2021). Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis. Contemporary Educational Psychology, 66, 101976. https://doi.org/10.1016/j.cedpsych.2021.101976
Twohill, A., NicMhuirí, S., Harbison, L., & Karakolidis, A. (2023). Primary preservice teachers’ mathematics teaching efficacy beliefs: The role played by mathematics attainment, educational level, preparedness to teach, and gender. International Journal of Science and Mathematics Education, 21(2), 601–622. https://doi.org/10.1007/s10763-022-10259-5
Wang, M.-T., Guo, J., & Degol, J. L. (2020). The role of sociocultural factors in student achievement motivation: A cross-cultural review. Adolescent Research Review, 5(4), 435–450. https://doi.org/10.1007/s40894-019-00124-y
Wang, X., & Liu, H. (2026). Exploring the moderating roles of emotions, attitudes, environment, and teachers in the impact of motivation on learning behaviours in students’ English learning. Psychological Reports, 129(1), 492–518. https://doi.org/10.1177/00332941241231714
Wolters, C. A., & Brady, A. C. (2021). College students’ time management: A self-regulated learning perspective. Educational Psychology Review, 33(4), 1319–1351. https://doi.org/10.1007/s10648-020-09519-z
Xu, E., Wang, W., & Wang, Q. (2023). The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature. Humanities and Social Sciences Communications, 10(1), 16. https://doi.org/10.1057/s41599-023-01508-1
Xu, Z., Zhao, Y., Zhang, B., Liew, J., & Kogut, A. (2023). A meta-analysis of the efficacy of self-regulated learning interventions on academic achievement in online and blended environments in K–12 and higher education. Behaviour & Information Technology, 42(16), 2911–2931. https://doi.org/10.1080/0144929X.2022.2151935
Yang, S., & Wang, W. (2022). The role of academic resilience, motivational intensity and their relationship in EFL learners’ academic achievement. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.823537
Zepeda, C. D., Martin, R. S., & Butler, A. C. (2020). Motivational strategies to engage learners in desirable difficulties. Journal of Applied Research in Memory and Cognition, 9(4), 468–474. https://doi.org/10.1016/j.jarmac.2020.08.007
Zhang, J.-H., Zou, L., Miao, J., Zhang, Y.-X., Hwang, G.-J., & Zhu, Y. (2020). An individualized intervention approach to improving university students’ learning performance and interactive behaviors in a blended learning environment. Interactive Learning Environments, 28(2), 231–245. https://doi.org/10.1080/10494820.2019.1636078
Zheng, B., Chang, C., Lin, C.-H., & Zhang, Y. (2021). Self-efficacy, academic motivation, and self-regulation: How do they predict academic achievement for medical students? Medical Science Educator, 31(1), 125–130. https://doi.org/10.1007/s40670-020-01143-4
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Addin Zuhrotul Aini, Vera Septi Andrini, Erdyna Dwi Etika

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.