Dropout Prediction

Axis 3 of the +Sucesso@UPortucalense project focuses on the development and implementation of an innovative predictive model, based on academic and behavioural data, which allows for the early identification of students at risk of failure or dropout. This axis integrates a data-driven approach, combining data science and pedagogy, to promote more effective and personalised monitoring of students.

By integrating existing computer systems at Universidade Portucalense (SIUPT and Moodle), this model uses machine learning algorithms to analyse behaviour patterns, academic performance and socio-demographic factors. The aim is to provide reliable, useful and timely information to students, teachers and course coordinators, enabling them to take preventive action.

Predicting academic dropout contributes to a better understanding of the factors that influence student success and strengthens the institutional capacity to respond to the needs of the academic community, ensuring the quality of the educational path and promoting successful retention in higher education.