Withdrawal Prediction Framework in Virtual Learning Environment

Fedia Hlioui, Nadia Aloui and Faiez Gargouri
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Fedia Hlioui: Multimedia Information System and Advanced Computing Laboratory, University of Sfax, Sfax, Tunisia
Nadia Aloui: CCSE Department SWE, Jeddah University, Saudi Arabia & University of Sfax, Tunisia & ISIMS, Sfax, Tunisia
Faiez Gargouri: Multimedia Information System and Advanced Computing Laboratory, University of Sfax, Sfax, Tunisia

International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 2020, vol. 11, issue 3, 47-64

Abstract: Making the most from virtual learning environments captivates researchers, enhancing the learning experience and reducing the withdrawal rate. In that regard, this article presents a framework for a withdrawal prediction model for the data of the Open University, one of the largest distance-learning institutions. The main contributions of this work cover two main aspects: relational-to-tabular data transformation and data mining for withdrawal prediction. This main steps of the process are: (1) tackling the unbalanced data issue using the SMOTE algorithm; (2) voting over seven different features' selection algorithms; and (3) learning different classifiers for withdrawal prediction. The experimental study demonstrates that the decision trees exhibit better performance in terms of the F-measure value compared to the other tested models. Furthermore, the data balancing and feature selection processes show a crucial role for guiding the predictive model towards a reliable module.

Date: 2020
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