Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study

• URL: http://arxiv.org/abs/2406.11847v1
• Date: Thu, 28 Mar 2024 03:29:02 GMT
• Title: Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study
• Authors: Jin Yuan, Xuelan Qiu, Jinran Wu, Jiesi Guo, Weide Li, You-Gan Wang,
• Abstract summary: This study proposes an integration framework that blends learning behavior analysis with ML algorithms to enhance the prediction accuracy of students' online learning performance. Results show that the framework yields nearly perfect prediction performance for autonomous students and satisfactory performance for motivated students.
• Score: 6.133369217932887
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The interest in predicting online learning performance using ML algorithms has been steadily increasing. We first conducted a scientometric analysis to provide a systematic review of research in this area. The findings show that most existing studies apply the ML methods without considering learning behavior patterns, which may compromise the prediction accuracy and precision of the ML methods. This study proposes an integration framework that blends learning behavior analysis with ML algorithms to enhance the prediction accuracy of students' online learning performance. Specifically, the framework identifies distinct learning patterns among students by employing clustering analysis and implements various ML algorithms to predict performance within each pattern. For demonstration, the integration framework is applied to a real dataset from edX and distinguishes two learning patterns, as in, low autonomy students and motivated students. The results show that the framework yields nearly perfect prediction performance for autonomous students and satisfactory performance for motivated students. Additionally, this study compares the prediction performance of the integration framework to that of directly applying ML methods without learning behavior analysis using comprehensive evaluation metrics. The results consistently demonstrate the superiority of the integration framework over the direct approach, particularly when integrated with the best-performing XGBoosting method. Moreover, the framework significantly improves prediction accuracy for the motivated students and for the worst-performing random forest method. This study also evaluates the importance of various learning behaviors within each pattern using LightGBM with SHAP values. The implications of the integration framework and the results for online education practice and future research are discussed.

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