ISIDE: Proactively Assist University Students at Risk of Dropout

In this work, we present ISIDE, the prototype of a student dropout alert system integrated within Infostud, i.e., the online student portal of the Sapienza University of Rome. Our proposed solution is based on a student dropout prediction (SDP) module built from a large dataset of academic records using advanced machine learning techniques. Offline experiments show that the best-performing SDP model can detect students prone to leave the school with an F1 score of 0.92. To further validate our prototype online, we run a pilot study on a subset of students from our School of Information Engineering, Informatics, and Statistics. This study shows that our prototype can detect students who are most likely to drop out early, as it clearly separates them from those with higher key engagement indicators.