Main Article Content


Computer-Based Mathematics Learning (CBML) has gone global in the last decade and is making a substantial impact for educational purposes. But the fact is that in the scientific literature, it is found that studies aimed at testing these theoretical assumptions have inconsistent results. In this regard, this meta-analysis was conducted to determine the effect of CBML and to analyze categorical variables to consider the implications. Data were retrieved from the Scopus database using Publish or Perish between 2010 and 2023. This study examined 29 independent samples from 28 eligible primary studies with 1179 subjects. The population estimate was based on a random effects model, and the CMA software was used as a calculation aid. The study's results provide an overall effect size of 1.03 (large effect). This indicates that applying CBML significantly affects students' mathematical abilities. The four categorical variables considered in the study are discussed to clarify research trends. Furthermore, the research implications are outlined and contribute to future CBML implementation arrangements.


Computer-Based Mathematics Learning Mathematical Abilities Meta-Analysis Scopus Database

Article Details


  1. Apriatna, E. J., Budiyono, & Indriati, D. (2020). The effectiveness of problem based learning assisted by cabri 3D on student’s mathematical communication writing and drawing skills. Journal of Physics: Conference Series, 1581(1), 012060.

  2. Arik, S., & Yilmaz, M. (2020). The effect of constructivist learning approach and active learning on environmental education: a meta-analysis study. International Electronic Journal of Environmental Education, 10(2), 44-84.

  3. Aungamuthu, Y. (2013). Towards a responsive pedagogy: Using ICT as a tool to engage access students’ academic identities in mathematics. Alternation, 8, 66-85.

  4. Çoğaltay, N., & Karadağ, E. (2015). Introduction to Meta-Analysis. In E. Karadağ (Ed.), Leadership and Organizational Outcomes: Meta-Analysis of Empirical Studies (pp. 19-28). Springer International Publishing.

  5. Cohen, L., Manion, L., & Morrison, K. (2017). Research methods in education. Routledge.

  6. Cooper, H. (2017). Research synthesis and meta-analysis: A step-by-step approach (Fifth Edition ed.).

  7. Das, A. K., Das, S., & Mukherjee, J. (2021). Largest triangle inside a terrain. Theoretical Computer Science, 858, 90-99.

  8. de Mendivil, Í. S. M., Crespo, R. G., González-Castaño, A., Ruiz, A. A. M., & Palma, L. O. (2019). Herramienta pedagógica basada en el desarrollo de una aplicación informática para la mejora del aprendizaje en matemática avanzada [A pedagogical tool based on the development of a computer application to improve learning in advanced mathematics]. Revista Española de Pedagogía, 77(274), 457-485.

  9. del Cerro Velázquez, F., & Morales Méndez, G. (2021). Application in augmented reality for learning mathematical functions: A study for the development of spatial intelligence in secondary education students. Mathematics, 9(4), 369.

  10. Demir, S., & Basol, G. (2014). Effectiveness of computer-assisted mathematics education (CAME) over academic achievement: A meta-analysis study. Educational Sciences: Theory and Practice, 14(5), 2026-2035.

  11. Dockendorff, M., & Solar, H. (2018). ICT integration in mathematics initial teacher training and its impact on visualization: the case of GeoGebra. International Journal of Mathematical Education in Science and Technology, 49(1), 66-84.

  12. Fadhli, M., Brick, B., Setyosari, P., Ulfa, S., & Kuswandi, D. (2020). A meta-analysis of selected studies on the effectiveness of gamification method for children. International Journal of Instruction, 13(1), 845-854.

  13. Foster, M. E., Anthony, J. L., Clements, D. H., Sarama, J., & Williams, J. M. (2016). Improving mathematics learning of kindergarten students through computer-assisted instruction. Journal for Research in Mathematics Education JRME, 47(3), 206-232.

  14. Granberg, C., & Olsson, J. (2015). ICT-supported problem solving and collaborative creative reasoning: Exploring linear functions using dynamic mathematics software. The Journal of Mathematical Behavior, 37, 48-62.

  15. Hallinger, P., & Chatpinyakoop, C. (2019). A bibliometric review of research on higher education for sustainable development, 1998–2018. Sustainability, 11(8), 2401.

  16. Hallinger, P., & Nguyen, V.-T. (2020). Mapping the landscape and structure of research on education for sustainable development: A bibliometric review. Sustainability, 12(5), 1947.

  17. Hamid, H., Angkotasan, N., Jalal, A., Muhtadi, D., & Sukirwan. (2020). Students’ mathematical proficiency in solving calculus problems after Maple implementation. Journal of Physics: Conference Series, 1613(1), 012025.

  18. Hartatiana, H., Darhim, D., & Nurlaelah, E. (2017). Student’s spatial reasoning through model eliciting activities with Cabri 3D. Journal of Physics: Conference Series, 895(1), 012075.

  19. Higgins, S., & Katsipataki, M. (2015). Evidence from meta-analysis about parental involvement in education which supports their children’s learning. Journal of Children's Services, 10(3), 280-290.

  20. Ishartono, N., Nurcahyo, A., Waluyo, M., Prayitno, H. J., & Hanifah, M. (2022). Integrating GeoGebra into the flipped learning approach to improve students' self-regulated learning during the COVID-19 pandemic. Journal on Mathematics Education, 13(1), 69-86.

  21. Jacinto, H., & Carreira, S. (2017). Mathematical problem solving with technology: The techno-mathematical fluency of a student-with-GeoGebra. International Journal of Science and Mathematics Education, 15(6), 1115-1136.

  22. Jelatu, S., Sariyasa, S., & Ardana, I. M. (2018). Effect of GeoGebra-Aided REACT Strategy on Understanding of Geometry Concepts. International Journal of Instruction, 11(4), 325-336.

  23. Juandi, D., Kusumah, Y. S., & Tamur, M. (2022). A meta-analysis of the last two decades of realistic mathematics education approaches. International Journal of Instruction, 15(1), 381-400.

  24. Juandi, D., Kusumah, Y. S., Tamur, M., Perbowo, K. S., Siagian, M. D., Sulastri, R., & Negara, H. R. P. (2021). The effectiveness of dynamic geometry software applications in learning mathematics: A meta-analysis study. International Journal of Interactive Mobile Technologies (iJIM), 15(02), 18-37.

  25. Juandi, D., Kusumah, Y. S., Tamur, M., Perbowo, K. S., & Wijaya, T. T. (2021). A meta-analysis of Geogebra software decade of assisted mathematics learning: what to learn and where to go? Heliyon, 7(5), e06953.

  26. Kariadinata, R., Yaniawati, R. P., Juariah, J., Sugilar, H., & Muthmainah, A. (2019). Spatial thinking ability and mathematical character students through Cabri 3D with a scientific approach. Journal of Physics: Conference Series, 1402(7), 077094.

  27. Kartal, B., & Çınar, C. (2022). Preservice mathematics teachers’ TPACK development when they are teaching polygons with geogebra. International Journal of Mathematical Education in Science and Technology, 1-33.

  28. Khalil, M., Farooq, R., Çakıroğlu, E., Khalil, U., & Khan, D. (2018). The development of mathematical achievement in analytic geometry of grade-12 students through GeoGebra activities. Eurasia Journal of Mathematics Science and Technology Education, 14(4), 1453-1463.

  29. Kim, S. J., Kastberg, S. E., Xin, Y. P., Lei, Q., Liu, B., Wei, S., & Chen, Y. (2022). Counting strategies of students struggling in mathematics in a computer-based learning environment. The Journal of Mathematical Behavior, 68, 101007.

  30. McLaren, B. M., Adams, D. M., Mayer, R. E., & Forlizzi, J. (2017). A computer-based game that promotes mathematics learning more than a conventional approach. International Journal of Game-Based Learning (IJGBL), 7(1), 36-56.

  31. Mendrek, M., Grzesik, N., Krzyżak, A., & Kuźma, K. (2018). Different defuzzification methods in Guimbal Cabri G2 helicopter takeoff possibility evaluation. Transport Problems, 13(2), 27-38.

  32. Muntazhimah, M., & Miatun, A. (2018). Cabri 3D - assisted collaborative learning to enhance junior high school students’ spatial ability. Journal of Physics: Conference Series, 948(1), 012042.

  33. Ningsih, Y. L., & Paradesa, R. (2018). Improving students’ understanding of mathematical concept using maple. Journal of Physics: Conference Series, 948(1), 012034.

  34. Nurjanah, N., Latif, B., Yuliardi, R., & Tamur, M. (2020). Computer-assisted learning using the Cabri 3D for improving spatial ability and self- regulated learning. Heliyon, 6(11), e05536.

  35. Ochkov, V. F., & Bogomolova, E. P. (2015). Teaching mathematics with mathematical software. Journal of Humanistic Mathematics, 5(1), 265-285.

  36. Park, S., & Hong, S. (2016). The empirical review of meta-analysis published in Korea. Asia Pacific Education Review, 17(2), 313-324.

  37. Pereira, J., Tang, J., Wijaya, T. T., Chen, J., Hermita, N., & Tamur, M. (2021). Modeling the interior angles of a triangle using Hawgent Dynamic Mathematics Software. In 2021 Universitas Riau International Conference on Education Technology (URICET).

  38. Phan, T. T., Do, T. T., Trinh, T. H., Tran, T., Duong, H. T., Trinh, T. P. T., Do, B. C., & Nguyen, T.-T. (2022). A bibliometric review on realistic mathematics education in scopus database between 1972-2019. European Journal of Educational Research, 11(2), 1133-1149.

  39. Pigott, T. D., & Polanin, J. R. (2020). Methodological guidance paper: High-quality meta-analysis in a systematic review. Review of Educational Research, 90(1), 24-46.

  40. Santos-Trigo, M., & Reyes-Rodriguez, A. (2016). The use of digital technology in finding multiple paths to solve and extend an equilateral triangle task. International Journal of Mathematical Education in Science and Technology, 47(1), 58-81.

  41. Schmidt, F. L., & Hunter, J. E. (2015). Methods of meta-analysis: Correcting error and bias in research findings (Third Edition ed.). London: SAGE Publications, Ltd.

  42. Shyshkina, M., Kohut, U., & Popel, M. (2018). The systems of computer mathematics in the cloud-based learning environment of educational institutions. In CEUR Workshop Proceedings.

  43. Siddaway, A. P., Wood, A. M., & Hedges, L. V. (2019). How to do a systematic review: A best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Annual Review of Psychology, 70(1), 747-770.

  44. Sugandi, B., & Delice, A. (2014). Comparison of Turkish and Indonesian secondary mathematics curricula; Reflection of the Paradigms. Procedia - Social and Behavioral Sciences, 152, 540-545.

  45. Syed, M., & Nelson, S. C. (2015). Guidelines for establishing reliability when coding narrative data. Emerging Adulthood, 3(6), 375-387.

  46. Takači, D., Stankov, G., & Milanovic, I. (2015). Efficiency of learning environment using GeoGebra when calculus contents are learned in collaborative groups. Computers & Education, 82, 421-431.

  47. Tamur, M., Juandi, D., & Kusumah, Y. S. (2020). The effectiveness of the application of mathematical software in Indonesia; A meta-analysis study. International Journal of Instruction, 13(4), 867-884.

  48. Tamur, M., Kusumah, Y. S., Juandi, D., Kurnila, V. S., Jehadus, E., & Samura, A. O. (2021). A meta-analysis of the past decade of mathematics learning based on the computer algebra system (CAS). Journal of Physics: Conference Series, 1882(1), 012060.

  49. Tamur, M., Kusumah, Y. S., Juandi, D., Wijaya, T. T., Nurjaman, A., & Samura, A. O. (2021). Hawthorne effect and mathematical software based learning: a meta-analysis study. Journal of Physics: Conference Series, 1806(1), 012072.

  50. Tamur, M., Weinhandl, R., Sennen, E., Ndiung, S., & Nurjaman, A. (2022). The effect of Cabri Express in geometry learning on students' mathematical communication ability. JTAM (Jurnal Teori Dan Aplikasi Matematika), 6(4), 1027-1033.

  51. Tatar, E., KaÄŸizmanli, T. B., & Akkaya, A. (2014). The effect of a Dynamic Software on the success of analytical analysis of the circle and prospective mathematics teachers opinions. Necatibey Faculty of Education Electronic Journal of Science & Mathematics Education, 8(1), 153-177.

  52. Timmers, C. F., Braber-van den Broek, J., & van den Berg, S. M. (2013). Motivational beliefs, student effort, and feedback behaviour in computer-based formative assessment. Computers & Education, 60(1), 25-31.

  53. Wang, A. I., & Tahir, R. (2020). The effect of using Kahoot! for learning – A literature review. Computers & Education, 149, 103818.

  54. Wicherts, J. (2020). Meta-analysis. In V. Zeigler-Hill & T. K. Shackelford (Eds.), Encyclopedia of Personality and Individual Differences (pp. 2859-2860). Springer International Publishing.

  55. Xin, Y. P., Park, J. Y., Tzur, R., & Si, L. (2020). The impact of a conceptual model-based mathematics computer tutor on multiplicative reasoning and problem-solving of students with learning disabilities. The Journal of Mathematical Behavior, 58, 100762.

  56. Zaldívar-Colado, A., Alvarado-Vázquez, R. I., & Rubio-Patrón, D. E. (2017). Evaluation of using mathematics educational software for the learning of first-year primary school students. Education Sciences, 7(4), 79.

  57. Zhang, Y., & Wang, Q. (2020). Content learning opportunities, computer-based instruction, and students’ mathematics and science achievement. International Journal of Mathematical Education in Science and Technology, 51(8), 1164-1180.

  58. Zulnaidi, H., Oktavika, E., & Hidayat, R. (2020). Effect of use of GeoGebra on achievement of high school mathematics students. Education and Information Technologies, 25(1), 51-72.

  59. Zulnaidi, H., & Zamri, S. N. A. S. (2017). The effectiveness of the GeoGebra software: The intermediary role of procedural knowledge on students’ conceptual knowledge and their achievement in mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 13(6), 2155-2180.