A course agnostic approach to predicting student success from VLE log data using recurrent neural networks
Refereed Conference Meeting Proceeding
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.
12th European Conference on Technology Enhanced Learning,
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Dublin City University (DCU)
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