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

Drousiotis, Efthyvoulos and Pentaliotis, Panagiotis and Shi, Lei and Cristea, Alexandra I. (2021) 'Capturing Fairness and Uncertainty in Student Dropout Prediction – A Comparison Study.', in Artificial Intelligence in Education. , pp. 139-144. Lecture Notes in Computer Science., 12749

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.

Item Type:Book chapter
Full text:Publisher-imposed embargo until 12 June 2022.
(AM) Accepted Manuscript
File format - PDF
(386Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/978-3-030-78270-2_25
Publisher statement:This a post-peer-review, pre-copyedit version of a chapter published in Artificial Intelligence in Education. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-78270-2_25
Date accepted:05 April 2021
Date deposited:21 June 2021
Date of first online publication:12 June 2021
Date first made open access:12 June 2022

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