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Full-Text Articles in Education
An Optimized Bagging Ensemble Learning Approach Using Bestrees For Predicting Students’ Performance, Edmund Evangelista
An Optimized Bagging Ensemble Learning Approach Using Bestrees For Predicting Students’ Performance, Edmund Evangelista
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Every academic institution's goal is to identify students who require additional assistance and take appropriate actions to improve their performance. As such, various research studies have focused on developing prediction models that can detect correlated patterns influencing students' performance, dropout, collaboration, and engagement. Among the influential predictive models available, the bagging ensemble has captured the interest of researchers seeking to improve prediction accuracy over single classifiers. However, prior work in this area has focused mainly on selecting single classifiers as the base classifier of the bagging ensemble, with little to no further optimization of the proposed framework. This study aims …
A Hybrid Machine Learning Framework For Predicting Students’ Performance In Virtual Learning Environment, Edmund Evangelista
A Hybrid Machine Learning Framework For Predicting Students’ Performance In Virtual Learning Environment, Edmund Evangelista
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Virtual Learning Environments (VLE), such as Moodle and Blackboard, store vast data to help identify students' performance and engagement. As a result, researchers have been focusing their efforts on assisting educational institutions in providing machine learning models to predict at-risk students and improve their performance. However, it requires an efficient approach to construct a model that can ultimately provide accurate predictions. Consequently, this study proposes a hybrid machine learning framework to predict students' performance using eight classification algorithms and three ensemble methods (Bagging, Boosting, Voting) to determine the best-performing predictive model. In addition, this study used filter-based and wrapper-based feature …