Hancox, Z. orcid.org/0000-0003-0473-5971 and Relton, S.D. orcid.org/0000-0003-0634-4587 (2022) Temporal Graph-Based CNNs (TG-CNNs) for Online Course Dropout Prediction. In: ISMIS 2022: Foundations of Intelligent Systems. Foundations of Intelligent Systems, 26th International Symposium, ISMIS 2022, 2022-10-3 - 2022-10-5, Cosenza, Italy. Lecture Notes in Computer Science, 13515 . Springer , pp. 357-367. ISBN 978-3-031-16563-4
Abstract
Due to the global pandemic, the use of online courses is increasing significantly; yet the rate of student dropout from online courses is rising. The Accessible Culture & Training Massive Open Online Course (ACT MOOC) dataset is comprised of a temporal sequence of student actions and subsequent dropout information. We introduce a novel approach based upon temporal graphs, which uses the sequence of (and time between) events to predict dropout. The dataset consists of 7,047 users, with a dropout rate of 57.7%. The Temporal Graph-Based Convolutional Neural Network (TG-CNN) models developed in this study are compared against baseline models and existing models in the literature. Performance is assessed using the AUC, accuracy, precision, recall, and F1 score. Our novel TG-CNN model achieves an AUC score of 0.797, which improves upon previous literature: JODIE 0.756, TGN + MeTA 0.794, TGN 0.777, and CoPE 0.762. Our model offers a novel and intuitive formulation of this problem, with state-of-the-art performance.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG. This version of the conference paper has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-16564-1_34 |
Keywords: | Temporal graphs; Dropout prediction; Neural networks |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Health Sciences (Leeds) > Centre for Health Services Research (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Nov 2023 16:05 |
Last Modified: | 15 Nov 2023 16:05 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-031-16564-1_340 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205293 |