Machine Learning Based Engagement Prediction for Online Courses - Wang Wanning (ITM Web of Conferences)
Within the constraints of the epidemic, the demand for distance learning in education is growing rapidly, and technological advances are opening up new possibilities for online education. This study investigates the performance of three machine learning models (decision trees. SVMs, and random forests) in predicting online course participation. To ensure the accuracy and generalizability of the results, the paper evaluated the models using k-fold cross-validation. Performance metrics such as accuracy, precision, recall and F1 score were used for comparison. The results show that the Random Forest model outperforms the other models on all metrics while the SVM model performs the weakest among the three models. Therefore, this study conducted a feature importance analysis specifically for the decision tree and random forest models to gain insight into the predictive power of individual features. This helps educators and course designers to develop strategies to improve engagement and retention. In summary, this study emphasizes the effectiveness of random forests in predicting engagement in online courses and highlights the potential of machine learning in improving the quality of e-learning environments. The findings can help optimize ongoing online education discussions and can guide future research in the field of e-learning.