Improving final grade prediction accuracy in blended learning environment using voting ensembles

This paper deals with the comparative analysis of prediction classifiers in the blended learning environment. The model proposed in this paper predicts students’ final grades based on activities within different educational environments. A comparative study of classifier performance has been performed in order to determine the classifier most suitable for multiclass feature dataset. Important results for different classes have been obtained using different classifiers, and the majority vote scheme is subsequentially used to form an ensemble based on Naïve Bayes, Hidden Naïve Bayes, J48 decision tree and Random Forest. According to experimental evaluation, there is a significant improvement of proposed model's precision and accuracy regarding the students’ grades prediction in blended learning environment scenario. The major contribution of the research presented in this paper is an efficient multi‐class prediction model applicable to aforementioned environment.

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