The growth of online learning in higher education, particularly after the COVID-19 pandemic, has fostered the advancement of learning analytics, which nowadays relies greatly on capturing and mining data derived from systems such as Blackboard and Moodle. However, it remains difficult to identify all the variables having a direct bearing on academic success, and drawing advice from machine learning models trained to support data-driven decision making is challenging. Therefore, we have endeavoured to pair a descriptive model, which characterises the profiles of computer science students, with a predictive model, which relies on Bayesian networks to forecast academic success. To achieve this, we have looked for the factors directly influencing the academic performance of computing science students, and the common patterns of behaviour which characterise higher education students individually and as part of a cohort. Our approach has been tested with data provided by a Chilean institution—University of Bío-Bío. We have enhanced and supplemented the data employed in our investigation by means of two surveys distributed among all the different cohorts of the student population. Our predictive model can determine student outcomes with an accuracy rate above 97%.

Learning Analytics; Bayes Nets; Educational Data Mining; Higher Education