Article

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

Authors:

Efthyvoulos Drousiotis,

Panagiotis Pentaliotis,

Alexandra I. CristeaAuthors Info & Claims

Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021, Proceedings, Part II

Pages 139 - 144

https://doi.org/10.1007/978-3-030-78270-2_25

Published: 14 June 2021 Publication History

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.

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Cited By

Li SZhao YGuo LRen MLi JZhang LLi K(2024)Quantification and prediction of engagementInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10353661:1Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103536
Drousiotis EPhillips ASpirakis PMaskell S(2023)Bayesian Decision Trees Inspired from Evolutionary AlgorithmsLearning and Intelligent Optimization10.1007/978-3-031-44505-7_22(318-331)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1007/978-3-031-44505-7_22
Drousiotis EPentaliotis PShi LCristea A(2022)Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational DataArtificial Intelligence in Education10.1007/978-3-031-11644-5_21(256-268)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-11644-5_21

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

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Published In

cover image Guide Proceedings

Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021, Proceedings, Part II

Jun 2021

535 pages

ISBN:978-3-030-78269-6

DOI:10.1007/978-3-030-78270-2

Editors:
Ido Roll
Technion – Israel Institute of Technology, Haifa, Israel
,
Danielle McNamara
Arizona State University, Tempe, AZ, USA
,
Sergey Sosnovsky
Utrecht University, Utrecht, The Netherlands
,
Rose Luckin
London Knowledge Lab, London, UK
,
Vania Dimitrova
University of Leeds, Leeds, UK

© Springer Nature Switzerland AG 2021.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 14 June 2021

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Cited By

Li SZhao YGuo LRen MLi JZhang LLi K(2024)Quantification and prediction of engagementInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10353661:1Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103536
Drousiotis EPhillips ASpirakis PMaskell S(2023)Bayesian Decision Trees Inspired from Evolutionary AlgorithmsLearning and Intelligent Optimization10.1007/978-3-031-44505-7_22(318-331)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1007/978-3-031-44505-7_22
Drousiotis EPentaliotis PShi LCristea A(2022)Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational DataArtificial Intelligence in Education10.1007/978-3-031-11644-5_21(256-268)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-11644-5_21

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