Type

Conference Proceedings

Authors

Alan F. Smeaton
Owen Corrigan

Subjects

Computer Science

Topics
neural networks student intervention educational technology learning analytics success machine learning neural network virtual learning environment

A course agnostic approach to predicting student success from VLE log data using recurrent neural networks (2017)

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.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Status = In Press
Ireland -> Dublin City University -> Subject = Social Sciences: Educational technology
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning

Full list of authors on original publication

Alan F. Smeaton, Owen Corrigan

Experts in our system

1
Alan F. Smeaton
Dublin City University
Total Publications: 492
 
2
Owen Corrigan
Dublin City University
Total Publications: 9