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Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data

Drousiotis, Efthyvoulos and Pentaliotis, Panagiotis and Shi, Lei and Cristea, Alexandra I. (2022) 'Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data.', in Artificial Intelligence in Education. , pp. 256-268. Lecture Notes in Computer Science., 13355

Abstract

Along with the exponential increase of students enrolling in MOOCs [26] arises the problem of a high student dropout rate. Researchers worldwide are interested in predicting whether students will drop out of MOOCs to prevent it. This study explores and improves ways of handling notoriously challenging continuous variables datasets, to predict dropout. Importantly, we propose a fair comparison methodology: unlike prior studies and, for the first time, when comparing various models, we use algorithms with the dataset they are intended for, thus ‘like for like.’ We use a time-series dataset with algorithms suited for time-series, and a converted discrete-variables dataset, through feature engineering, with algorithms known to handle discrete variables well. Moreover, in terms of predictive ability, we examine the importance of finding the optimal hyperparameters for our algorithms, in combination with the most effective pre-processing techniques for the data. We show that these much lighter discrete models outperform the time-series models, enabling faster training and testing. This result also holds over fine-tuning of pre-processing and hyperparameter optimisation.

Item Type:Book chapter
Full text:Publisher-imposed embargo until 27 July 2023.
(AM) Accepted Manuscript
File format - PDF
(423Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/978-3-031-11644-5_21
Publisher statement:The final authenticated version is available online at https://doi.org/10.1007/978-3-031-11644-5_21
Date accepted:25 April 2022
Date deposited:01 September 2022
Date of first online publication:27 July 2022
Date first made open access:27 July 2023

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