LEARNING ANALYTICS FOR STUDENT SUCCESS PREDICTION
DOI:
https://doi.org/10.69980/2qeq8n66Keywords:
Academic performance, Early-warning systems, Learning analytics, Machine learning, Virtual Learning EnvironmentAbstract
Learning analytics enables higher education institutions to use demographic, academic, assessment, registration, and Virtual Learning Environment data to identify students who may need early support. This retrospective predictive modeling study analyzed 32,593 student–module–presentation enrollments representing 28,785 unique students from the Open University Learning Analytics Dataset. Student success was defined as Pass or Distinction, while Fail or Withdrawn outcomes were classified as unsuccessful. Only information available during the first 30 course days was used to reduce data leakage. Predictors included early assessment scores, assessment completion, Virtual Learning Environment clicks, active learning days, unique resources accessed, educational background, credits studied, and module characteristics. The data were split into 26,122 training and 6,471 independent test observations using student-grouped partitioning. Logistic regression and random forest models were evaluated using accuracy, balanced accuracy, sensitivity, specificity, precision, F1-score, receiver operating characteristic area under the curve, precision-recall area under the curve, and Brier score. The random forest achieved the best performance, with 76.03% accuracy and a receiver operating characteristic area under the curve of 0.843, while logistic regression produced comparable results. Academic module, early assessment score, educational qualification, active learning days, and total platform activity were the leading predictors. These findings support transparent, ethically governed early-warning systems linked to timely, student-centered interventions in online higher education.
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