An Ensemble Approach for Predicting Student Performance using Learning Activities
DOI:
https://doi.org/10.15662/IJEETR.2026.0802017Keywords:
Student Performance Prediction, Ensemble Learning, Machine Learning, Learning AnalyticsAbstract
One important field of study in current educational research is knowledge tracking. Knowledge tracking models that exhibit better prediction capabilities than conventional methods have been developed as a result of recent developments in deep learning. This paper presents a novel ensemble-based knowledge tracking model to address the issues of restricted interpretability and the complexity of lengthy sequence dependencies in current deep knowledge tracking frameworks. In order to better identify important characteristics at different temporal points during the learning process and improve predictive performance, this model uses the features of exercises in addition to students' learning capacities as input layer feature information
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