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.
CITATION STYLE
Corrigan, O., & Smeaton, A. F. (2017). A course agnostic approach to predicting student success from vle log data using recurrent neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10474 LNCS, pp. 545–548). Springer Verlag. https://doi.org/10.1007/978-3-319-66610-5_59
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