| Autor: |
Hlioui, Fedia, Aloui, Nadia, Gargouri, Faiez |
| Zdroj: |
International Journal of Service Science Management Engineering & Technology (IJSSMET); July 2020, Vol. 11 Issue: 3 p47-64, 18p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
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