Balancing Accuracy and Transparency in Early Alert Identification of Students at Risk

Authors

Flynn R. Hill, University of WollongongFollow
David P. Fulcher, University of WollongongFollow
Rory L. Sie, University of WollongongFollow
Maarten F. de Laat, University of WollongongFollow

RIS ID

133731

Publication Details

Hill, F., Fulcher, D., Sie, R. & de Laat, M. (2019). Balancing Accuracy and Transparency in Early Alert Identification of Students at Risk. Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018 (pp. 1125-1128). United States: IEEE.

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.