Balancing accuracy and transparency in early alert identification of students at risk
Date
2019
Authors
Hill, F.
Fulcher, D.
Sie, R.
De Laat, M.
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Conference paper
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Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018, 2019, iss.8615370, pp.1125-1128
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International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018 (4 Dec 2018 - 7 Dec 2018 : Woolongong, Australia)
Abstract
One of the challenges in implementing early alert systems to identify students at risk of failure or withdrawal is striking a balance between accuracy and transparency, as there are clear benefits to being able to communicate the reason why a student has been identified. An important predictor of future academic success is past performance, which is generally not available for commencing students. Here, we present a work-in-progress in which the full predictive power of an ensemble-based machine learning approach is employed to make predictions for commencing students, while for ongoing students a simple logistic regression method is used
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Copyright 2018 IEEE