Corrigan, Owen 
ORCID: 0000-0002-1840-982X and Smeaton, Alan F. ORCID: 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
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Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Event Type: | Conference |
| Refereed: | Yes |
| Uncontrolled Keywords: | learning analytics; student intervention; machine learning |
| Subjects: | Computer Science > Machine learning Social Sciences > Educational technology |
| DCU Faculties and Centres: | Research Initiatives and Centres > INSIGHT Centre for Data Analytics DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing |
| Published in: | Lavoué, Élise and Drachsler, Hendrik and Verbert, K. and Broisin, J. and Pérez-Sanagustín, S., (eds.) Data Driven Approaches in Digital Education. Lecture Notes in Computer Science (LNCS) 10474. Springer International Publishing. ISBN 978-3-319-66609-9 |
| Publisher: | Springer International Publishing |
| Official URL: | http://dx.doi.org/10.1007/978-3-319-66610-5-59 |
| Copyright Information: | © 2017 Springer International Publishing |
| Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
| Funders: | Science Foundation Ireland, grant no. SFI/12/RC/2289, Dublin City University |
| ID Code: | 21870 |
| Deposited On: | 13 Jul 2017 09:01 by Mr Owen Corrigan . Last Modified 05 Jan 2022 14:21 |
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