Students at Risk: Detection and Remediation.

Detecting students at risk of failing is particularly useful and desirable when it is done in a timely manner and accompanied with practical information that can help with remediation. In this paper we investigate ways to detect students at risk of failing early in the semester for timely intervention. The context of our study is a first year computer programming course. We explore whether the use of several student data sources can improve the process: submission steps and outcomes in an automatic marking system that provides instant feedback, student activity in the discussion forum Piazza and assessment marks during the semester. We built a decision tree classifier that is able to predict whether students will pass or fail their final exam with an accuracy of 87% mid semester. The obtained rules are useful and actionable for teachers and students, and can be used to drive remediation.