A Cooperative Learning, Technology, and Motivation in Mathematics Achievement: An Ordinal Logistic Regression Analysis
Abstract
Mathematics achievement among Indonesian senior secondary students remains a persistent national concern, prompting renewed attention to the pedagogical and psychological factors that can be strengthened in the classroom. This study analyzed the simultaneous and partial effects of cooperative learning, technology use, and learning motivation on the mathematics achievement of students at SMA Negeri 1 Kedungpring , Lamongan , and situations of cooperative learning within the Islamic educational value of cooperative learning(mutual cooperation in goodness). A quantitative survey design was employed with ordinal logistic regression as the analytical technique. The sample consisted of 100 students of Grades X, XI, and XII selected through random sampling. Data were collected through validated Likert -scale questionnaires and documented mathematics scores, categorized into five ordinal achievement levels. All instrument items were valid (r > 0.197) and reliable ( Cronbach's Alpha > 0.70), and no multicollinearity was found (Tolerance > 0.10; VIF < 10). Simultaneously, the three variables significantly affected students' mathematics achievement category (Chi-Square = 15.257; Sig. = 0.002). Partially, only learning motivation contributed significantly (Sig. = 0.011; Exp ( B) = 1.249), while cooperative learning and technology use did not reach significance individually; the model explained 14.9% of the variance ( Nagelkerke R² = 0.149). These findings suggest that nurturing sincere, intrinsically driven motivation, together with well-implemented cooperative and technology-based strategies rooted in cooperative learning, can meaningfully support mathematics learning outcomes.
References
Agresti, A. (2002). Categorical data analysis (2nd ed.). John Wiley & Sons.
Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s educational objectives. Longman.
Areepattamannil, S., Freeman, J. G., & Klinger, D. A. (2011). Intrinsic motivation, extrinsic motivation, and academic achievement among Indian adolescents in Canada and India. Social Psychology of Education, 14(3), 427–439. https://doi.org/10.1007/s11218-011-9155-1
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: Handbook I: Cognitive domain. David McKay.
Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13. https://doi.org/10.3102/0013189X032001009
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications.
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Drijvers, P., Doorman, M., Boon, P., Reed, H., & Gravemeijer, K. (2010). The teacher and the tool: Instrumental orchestrations in the technology-rich mathematics classroom. Educational Studies in Mathematics, 75(2), 213–234. https://doi.org/10.1007/s10649-010-9254-5
Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference (4th ed.). Allyn & Bacon.
Gottfried, A. E. (1990). Academic intrinsic motivation in young elementary school children. Journal of Educational Psychology, 82(3), 525–538. https://doi.org/10.1037/0022-0663.82.3.525
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Pearson Education.
Harini, S., Murtafi’ah, W., & Prayitno, H. J. (2022). Faktor-faktor yang mempengaruhi hasil belajar matematika siswa SMA: Sebuah kajian empiris. Jurnal Riset Pendidikan Matematika, 9(1), 41–55.
Hiebert, J., & Grouws, D. A. (2007). The effects of classroom mathematics teaching on students’ learning. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 371–404). Information Age Publishing.
Hohenwarter, M., & Jones, K. (2007). Ways of linking geometry and algebra: The case of GeoGebra. Proceedings of the British Society for Research into Learning Mathematics, 27(3), 126–131.
Johnson, D. W., & Johnson, R. T. (1994). Learning together and alone: Cooperative, competitive, and individualistic learning (4th ed.). Allyn & Bacon.
Johnson, D. W., & Johnson, R. T. (2017). The use of cooperative procedures in teacher education and professional development. Journal of Education for Teaching, 43(3), 284–295. https://doi.org/10.1080/02607476.2017.1328023
Johnson, D. W., Johnson, R. T., & Stanne, M. B. (2000). Cooperative learning methods: A meta-analysis. University of Minnesota.
Kemendikdasmen. (2025). Hasil Tes Kemampuan Akademik (TKA) Matematika 2025: Analisis dan implikasi kebijakan. Kementerian Pendidikan Dasar dan Menengah Republik Indonesia.
Mayer, R. E. (2014). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 43–71). Cambridge University Press.
McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B, 42(2), 109–127.
Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1–47.
National Council of Teachers of Mathematics (NCTM). (2014). Principles to actions: Ensuring mathematical success for all. NCTM.
OECD. (2023). PISA 2022 results (Volume I and II): Country notes – Indonesia. OECD Publishing. https://doi.org/10.1787/ed6fbcc5-en
Pajares, F., & Miller, M. D. (1994). Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path analysis. Journal of Educational Psychology, 86(2), 193–203. https://doi.org/10.1037/0022-0663.86.2.193
Rohendi, D., Setiawan, D., & Wihardi, Y. (2010). Pengaruh pembelajaran kooperatif tipe STAD berbantuan media komputer terhadap motivasi dan hasil belajar matematika siswa SMA. Jurnal Pendidikan Teknologi Informasi dan Komunikasi, 3(1), 1–8.
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020
Schunk, D. H. (2012). Learning theories: An educational perspective (6th ed.). Pearson.
Slavin, R. E. (1995). Cooperative learning: Theory, research, and practice (2nd ed.). Allyn & Bacon.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Wardani, S. (2019). Hubungan motivasi belajar dengan hasil belajar matematika siswa SMA di Malang. Jurnal Ilmiah Pendidikan Matematika, 7(2), 88–96.
Yuanita, P., Zulnaidi, H., & Zakaria, E. (2018). The effectiveness of Realistic Mathematics Education approach: The role of mathematical representation as mediator between mathematical belief and problem solving. PLOS ONE, 13(9), e0204847. https://doi.org/10.1371/journal.pone.0204847
Zakaria, E., Chin, L. C., & Daud, M. Y. (2010). The effects of cooperative learning on students’ mathematics achievement and attitude towards mathematics. Journal of Social Sciences, 6(2), 272–275. https://doi.org/10.3844/jssp.2010.272.275
Copyright (c) 2026 ENDANG Setyowati Mas Ula

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

