Machine Learning-Driven Student Performance Prediction for Enhancing Tiered Instruction

• URL: http://arxiv.org/abs/2502.03143v1
• Date: Wed, 05 Feb 2025 13:13:25 GMT
• Title: Machine Learning-Driven Student Performance Prediction for Enhancing Tiered Instruction
• Authors: Yawen Chen, Jiande Sun, Jinhui Wang, Liang Zhao, Xinmin Song, Linbo Zhai,
• Abstract summary: Student performance prediction is one of the most important subjects in educational data mining. Despite extensive prediction experiments, machine learning methods have not been effectively integrated into practical teaching strategies. This study integrates the results of machine learning-based student performance prediction with tiered instruction, aiming to enhance student outcomes in target course.
• Score: 11.564820268803619
• License:
• Abstract: Student performance prediction is one of the most important subjects in educational data mining. As a modern technology, machine learning offers powerful capabilities in feature extraction and data modeling, providing essential support for diverse application scenarios, as evidenced by recent studies confirming its effectiveness in educational data mining. However, despite extensive prediction experiments, machine learning methods have not been effectively integrated into practical teaching strategies, hindering their application in modern education. In addition, massive features as input variables for machine learning algorithms often leads to information redundancy, which can negatively impact prediction accuracy. Therefore, how to effectively use machine learning methods to predict student performance and integrate the prediction results with actual teaching scenarios is a worthy research subject. To this end, this study integrates the results of machine learning-based student performance prediction with tiered instruction, aiming to enhance student outcomes in target course, which is significant for the application of educational data mining in contemporary teaching scenarios. Specifically, we collect original educational data and perform feature selection to reduce information redundancy. Then, the performance of five representative machine learning methods is analyzed and discussed with Random Forest showing the best performance. Furthermore, based on the results of the classification of students, tiered instruction is applied accordingly, and different teaching objectives and contents are set for all levels of students. The comparison of teaching outcomes between the control and experimental classes, along with the analysis of questionnaire results, demonstrates the effectiveness of the proposed framework.

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