Capturing Fairness and Uncertainty in Student Dropout Prediction – A Comparison Study

Drousiotis, Efthyvoulos; Pentaliotis, Panagiotis; Shi, Lei; Cristea, Alexandra I.

Abstract

This study aims to explore and improve ways of handling a continuous variable dataset, in order to predict student dropout in MOOCs, by implementing various models, including the ones most successful across various domains, such as recurrent neural network (RNN), and tree-based algorithms. Unlike existing studies, we arguably fairly compare each algorithm with the dataset that it can perform best with, thus ‘like for like’. I.e., we use a time-series dataset ‘as is’ with algorithms suited for time-series, as well as a conversion of the time-series into a discrete-variables dataset, through feature engineering, with algorithms handling well discrete variables. We show that these much lighter discrete models outperform the time-series models. Our work additionally shows the importance of handing the uncertainty in the data, via these ‘compressed’ models.

Citation

Drousiotis, E., Pentaliotis, P., Shi, L., & Cristea, A. I. (2021). Capturing Fairness and Uncertainty in Student Dropout Prediction – A Comparison Study. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V. Dimitrova (Eds.), Artificial Intelligence in Education (139-144). Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_25

• Online Publication Date: Jun 12, 2021
• Publication Date: 2021
• Deposit Date: Jun 20, 2021
• Publicly Available Date: Jun 12, 2022
• Pages: 139-144
• Series Title: Lecture Notes in Computer Science
• Series Number: 12749
• Book Title: Artificial Intelligence in Education
• DOI: https://doi.org/10.1007/978-3-030-78270-2_25
• Public URL: https://durham-repository.worktribe.com/output/1624992
• Contract Date: Apr 5, 2021