Title

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

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.

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Link to publisher version (DOI)

http://dx.doi.org/10.1109/TALE.2018.8615370