Student Learning Behavior Analysis and Early Warning System Based on Machine Learning

This paper proposes a machine learning-based student learning behavior analysis and early warning system to identify students in advance who may have learning difficulties and to issue early warnings in time through real-time monitoring of student learning behavior data. The system uses a learning management system and an online learning platform to collect data on attendance, homework submission, class participation, and online learning activities. After data cleaning, feature reduction, standardization, logistic regression, decision tree, random forest, and support vector machine are used to train and evaluate the models. The experimental results indicate, classically, good performance of a random forest model at 90% accuracy, 88% precision, and 86% recall, further giving F1 of 87%. In addition, the classification performance is also verified by a confusion matrix and ROC curve. The early warning system can be adjusted according to different thresholds to achieve a balance between precision and recall, providing an effective decision support tool for education managers. The research in this paper provides an effective technical means to improve the quality of education and student learning outcomes.