Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods
41 Pages Posted: 31 Oct 2018
Date Written: 2018
To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and a private university to predict student success as a basis for a targeted intervention. The EDS uses regression analysis, neural networks, decision trees, and the AdaBoost algorithm to identify student characteristics which distinguish potential dropouts from graduates. Prediction accuracy at the end of the first semester is 79% for the state university and 85% for the private university of applied sciences. After the fourth semester, the accuracy improves to 90% for the state university and 95% for the private university of applied sciences.
Keywords: student attrition, machine learning, administrative student data, AdaBoost
JEL Classification: I230, H420, C450
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