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The “Grey Area”: A Computational Approach to Model the Zone of Proximal Development Read chapter Read first chapter

2017 | Supplement | Chapter

A Course Agnostic Approach to Predicting Student Success from VLE Log Data Using Recurrent Neural Networks

Authors : Owen Corrigan, Alan F. Smeaton

Published in: Data Driven Approaches in Digital Education

Publisher: Springer International Publishing

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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.

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previous chapter ASR in Classroom Today: Automatic Visualization of Conceptual Network in Science Classrooms
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Literature
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Corrigan, O., Smeaton, A.F., Glynn, M., Smyth, S.: Using educational analytics to improve test performance. In: Conole, G., Klobučar, T., Rensing, C., Konert, J., Lavoué, É. (eds.) EC-TEL 2015. LNCS, vol. 9307, pp. 42–55. Springer, Cham (2015). doi:10.​1007/​978-3-319-24258-3_​4 CrossRef
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Metadata
Title
A Course Agnostic Approach to Predicting Student Success from VLE Log Data Using Recurrent Neural Networks
Authors
Owen Corrigan
Alan F. Smeaton
Copyright Year
2017
Book
Data Driven Approaches in Digital Education

Print ISBN: 978-3-319-66609-9

Electronic ISBN: 978-3-319-66610-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-66610-5_59

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