The WSSC and the TIDE research groups (TESI research unit) at UPF have studied the effects of first year workload on the dropout rate of undergraduate students. The work has been presented at the Artificial Intelligence in Education international conference, where it was selected as a Best paper nominee. 

The research evaluates 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. All study programs at the eight faculties/schools of UPF are considered in the study. 

Machine Learning methods are used to predict university dropout and underperformance. The study has found that among important drivers of dropout over which the first-year students have some control, first year workload (i.e., the number of credits taken) is a key one. Moreover, the study determines the effect of taking a relatively lighter workload in the first year on dropout risk using causal inference methods. The results show that a reduction in workload reduces dropout risk.

Karimi-Haghighi, M., Castillo, C., Hernández-Leo, D. (2022). A Causal Inference Study on the Effects of First Year Workload on the Dropout Rate of Undergraduates. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_2 (Best paper nominee)