查看文章

In higher education, providing personalized feedback and support to students is a significant challenge. Early warning systems can help by identifying both at-risk and high-performing students, allowing for timely interventions and enhanced learning opportunities. In our study, we used a year's worth of data from an information management school to build predictive models for two binary classification problems: identifying at-risk students and high-performing students. We employed traditional machine learning classifiers and long-short term memory units (LSTM), testing them at various stages of course completion. The best performance was achieved using all course data, with an AUC of 0.756 for at-risk students and 78.2% accuracy for high-performing students using Random Forest and Extremely Randomized Trees, respectively. We found that early prediction was possible as early as 25% course completion …