A causal inference study on the effects of first year workload on the dropout rate of undergraduates

Karimi-Haghighi, Marzieh; Castillo, Carlos; Hernández Leo, Davinia

doi:http://dx.doi.org/10.1007/978-3-031-11644-5_2

A causal inference study on the effects of first year workload on the dropout rate of undergraduates

Citació

Karimi-Haghighi M, Castillo C, Hernández-Leo D. A causal inference study on the effects of first year workload on the dropout rate of undergraduates. In: Mercedes Rodrigo M, Matsuda N, Cristea AI, Dimitrova V, editors. Artificial Intelligence in Education. 23rd International Conference, AIED 2022; 2022 Jul 27-31; Durham, United Kingdom. Cham: Springer; 2022. p. 15-27. DOI: 10.1007/978-3-031-11644-5_2

Resum

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.

Descripció

Comunicació presentada a 23rd International Conference on Artificial Intelligence in Education (AIED 2022), celebrat del 27 al 31 de juliol de 2022 a Durham, Regne Unit.