Balancing Fined-tuned Machine Learning Models between Continuous and Discrete Variables - A Comprehensive Analysis using Educational Data



Drousiotis, Efthyvoulos, Pentaliotis, Panagiotis ORCID: 0000-0002-4623-7623, Shi, Lei and Cristea I, Alexandra
(2022) Balancing Fined-tuned Machine Learning Models between Continuous and Discrete Variables - A Comprehensive Analysis using Educational Data. In: AIED2022, Durham.

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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: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: Neural networks, Tree-based algorithms, Educational data mining, Feature engineering, MOOCs
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 25 May 2022 15:30
Last Modified: 18 Jan 2023 21:01
DOI: 10.1007/978-3-031-11644-5_21
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3155445