Analysis of Psychological Factors Influencing Mathematical Achievement and Machine Learning Classification

Juhyung Park, Sungtae Kim and Beakcheol Jang ()
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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

Mathematics, 2023, vol. 11, issue 15, 1-13

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.

Keywords: machine learning; linear regression; psychological test; mathematical achievement (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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