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Learning Analytics and Deep Learning in Large Virtual Learning Environments (VLEs)

Wechie, Nkesi; Brayshaw, Mike; Gordon, Neil

Authors

Nkesi Wechie

Mike Brayshaw



Abstract

In this paper we look at the use of Deep Learning as a technique for Education Data Mining and Learnng Analytics. We discuss existing approaches and how Deep Learning can be used in a complimentary manner in order to provide new and insightful perspectives to existing Learning Analytics Tools and Machine Learning Algorithms. The paper first outlines the context, before considering the use of Big Data. A case study of a Large Virtual Learning Environment (VLE) is introduced. The paper presents a series of Deep Learning Experiments with this Data Set and the new insights this has led to. The paper concludes with a discussion of how this approach compliments other Learning Analytic work in a similar context.

Citation

Wechie, N., Brayshaw, M., & Gordon, N. (2022). Learning Analytics and Deep Learning in Large Virtual Learning Environments (VLEs). International Journal on Engineering Technologies and Informatics, 3(1), 1-3. https://doi.org/10.51626/ijeti.2022.03.00029

Journal Article Type Article
Acceptance Date Jun 6, 2022
Online Publication Date Jun 8, 2022
Publication Date Jun 8, 2022
Deposit Date Jun 16, 2022
Publicly Available Date Oct 27, 2022
Journal International Journal on Engineering Technologies and informatics
Peer Reviewed Peer Reviewed
Volume 3
Issue 1
Pages 1-3
DOI https://doi.org/10.51626/ijeti.2022.03.00029
Keywords Learning analytics; Educational data mining; Deep learning
Public URL https://hull-repository.worktribe.com/output/4014812
Publisher URL https://skeenapublishers.com/journals/ijeti/

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Publisher Licence URL
http://creativecommons.org/licenses/by-nc/3.0/

Copyright Statement
©2022 Wechie, et al. This work is published and licensed by Example Press Limited. The full terms of this license are available at https://skeenapublishers.com/terms-conditions and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Emample Press, provided the work is properly attributed.







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