Abstract
Nowadays, web-based courses are popular as students can learn at their convenience and according to their free time. One advantage is the availability of study materials that can be used in a blended learning program. However, it suffers from a high withdrawal problem. This article contributes to the research by proposing a withdrawal prediction framework based on the Open University, a large distance-learning institution. The study contribution is a dropout prediction framework. The prediction process includes handling missing data using the MissForest algorithm, tackling imbalanced data issues using SOMTEFUNA, reducing dimensions using PCA, and training different classifiers. The experiments show Decision Tree classifier as the best predictive model with an F1-score of 0.99. The proposed framework outperforms other researches by 12% when compared to the previous research work.
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Jawthari, M., Stoffa, V. (2022). OULAD Learners’ Withdrawal Prediction Framework. In: Singh, P.K., Singh, Y., Chhabra, J.K., Illés, Z., Verma, C. (eds) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 855. Springer, Singapore. https://doi.org/10.1007/978-981-16-8892-8_52
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DOI: https://doi.org/10.1007/978-981-16-8892-8_52
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