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A course agnostic approach to predicting student success from VLE log data using recurrent neural networks
Corrigan, Owen; Smeaton, Alan F.
We describe a method of improving the accuracy of a learning analytics system through the application of a Recurrent Neural Network over all students in a University, regardless of course. Our target is to discover how well a student will do in a class given their interaction with a virtual learning environment. We show how this method performs well when we want to predict how well students will do, even if we do not have a model trained based on their specific course.
Keyword(s): Machine learning; Educational technology; learning analytics; student intervention; machine learning
Publication Date:
2017
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Contributor(s): Lavoué, Élise; Drachsler, Hendrik; Verbert, K.; Broisin, J.; Pérez-Sanagustín, S.
Institution: Dublin City University
Citation(s): Corrigan, Owen ORCID: 0000-0002-1840-982X <https://orcid.org/0000-0002-1840-982X> and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 <https://orcid.org/0000-0003-1028-8389> (2017) A course agnostic approach to predicting student success from VLE log data using recurrent neural networks. In: 12th European Conference on Technology Enhanced Learning, 12-15 Sep, 2017, Tallinn, Estonia. ISBN 978-3-319-66609-9 (In Press)
Publisher(s): Springer International Publishing
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/21870/1/main.pdf,
http://dx.doi.org/10.1007/978-3-319-66610-5-59
First Indexed: 2017-10-02 06:19:05 Last Updated: 2020-08-14 06:11:42