This study aims to explore a new feature model and a set of machine learning classifiers to predict student performance by monitoring his/her activities on the Virtual Learning Environment (VLE). The features are used for training the classifier to predict the final result of each student. The proposed model is evaluated by using a dataset built at the Open University of London. The results show good performance (80% of accuracy) of the proposed approach compared to other similar studies.
An Empirical Study to Predict Student Performance Using Information of the Virtual Learning Environment
Aversano L.;Bernardi M. L.;Iammarino M.;Montano D.;Verdone C.
2023-01-01
Abstract
This study aims to explore a new feature model and a set of machine learning classifiers to predict student performance by monitoring his/her activities on the Virtual Learning Environment (VLE). The features are used for training the classifier to predict the final result of each student. The proposed model is evaluated by using a dataset built at the Open University of London. The results show good performance (80% of accuracy) of the proposed approach compared to other similar studies.File in questo prodotto:
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