In this work, we evaluate the risk of early dropout in undergraduate studies using causal inference methods, and focusing on groups of students who have a relatively higher dropout risk. We use a large dataset consisting of undergraduates admitted to multiple study programs at eight faculties/schools of our university. Using data available at enrollment time, we develop Machine Learning (ML) methods to predict university dropout and underperformance, which show an AUC of 0.70 and 0.74 for each risk ...
In this work, we evaluate the risk of early dropout in undergraduate studies using causal inference methods, and focusing on groups of students who have a relatively higher dropout risk. We use a large dataset consisting of undergraduates admitted to multiple study programs at eight faculties/schools of our university. Using data available at enrollment time, we develop Machine Learning (ML) methods to predict university dropout and underperformance, which show an AUC of 0.70 and 0.74 for each risk respectively. Among important drivers of dropout over which the first-year students have some control, we find that first year workload (i.e., the number of credits taken) is a key one, and we mainly focus on it. We determine the effect of taking a relatively lighter workload in the first year on dropout risk using causal inference methods: Propensity Score Matching (PSM), Inverse Propensity score Weighting (IPW), Augmented Inverse Propensity Weighted (AIPW), and Doubly Robust Orthogonal Random Forest (DROrthoForest). Our results show that a reduction in workload reduces dropout risk.
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