Withdrawal Prediction Framework in Virtual Learning Environment
| Autor: | Nadia Aloui, Faiez Gargouri, Fedia Hlioui |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Multidisciplinary
General Computer Science Human–computer interaction Computer science 05 social sciences 0202 electrical engineering electronic engineering information engineering General Engineering 050301 education Virtual learning environment 020201 artificial intelligence & image processing 02 engineering and technology 0503 education General Business Management and Accounting |
| Zdroj: | International Journal of Service Science, Management, Engineering, and Technology. 11:47-64 |
| ISSN: | 1947-9603 1947-959X |
| Popis: | 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: | OpenAIRE |
| Externí odkaz: |
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