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Analysis of Psychological Factors Influencing Mathematical Achievement and Machine Learning Classification

Author

Listed:
  • Juhyung Park

    (Graduate School of Information, Yonsei University, Seoul 03722, Republic of Korea)

  • Sungtae Kim

    (Able Edutech Inc., Seoul 04081, Republic of Korea)

  • Beakcheol Jang

    (Graduate School of Information, Yonsei University, Seoul 03722, Republic of Korea)

Abstract

This study analyzed the psychological factors that influence mathematical achievement in order to classify students’ mathematical achievement. Here, we employed linear regression to investigate the variables that contribute to mathematical achievement, and we found that self-efficacy, math-efficacy, learning approach motivation, and reliance on academies affect mathematical achievement. These variables are derived from the Test of Learning Psychology (TLP), a psychological test developed by Able Edutech Inc. specifically to measure students’ learning psychology in the mathematics field. We then conducted machine learning classification with the identified variables. As a result, the random forest model demonstrated the best performance, achieving accuracy values of 73% (Test 1) and 81% (Test 2), with F1-scores of 79% (Test 1) and 82% (Test 2). Finally, students’ skills were classified according to the TLP items. The results demonstrated that students’ academic abilities could be identified using a psychological test in the field of mathematics. Thus, the TLP results can serve as a valuable resource to develop personalized learning programs and enhance students’ mathematical skills.

Suggested Citation

  • Juhyung Park & Sungtae Kim & Beakcheol Jang, 2023. "Analysis of Psychological Factors Influencing Mathematical Achievement and Machine Learning Classification," Mathematics, MDPI, vol. 11(15), pages 1-13, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3380-:d:1209023
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    References listed on IDEAS

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    1. Gwo-Jen Hwang & Yun-Fang Tu, 2021. "Roles and Research Trends of Artificial Intelligence in Mathematics Education: A Bibliometric Mapping Analysis and Systematic Review," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
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