Student interactions with Learning Management Systems (LMS) generate logs, which are usually stored, allowing torecover each student activity. Analysis of these data with data mining and/or learning analytics techniques have beenprovided a better understanding of student behavior and teaching-learning processes. In this context, a number ofstudies have been reporting promising results in the task of predicting student performance, which allows proactiveactions to avoid academic failures. Usually, data mining techniques estimate predictive models by using (past)historical data, assuming the premise that the estimated predictor will make predictions in future contexts that aresimilar to the (past) contexts which were used in its design. Although it is reasonable to assume that the diversity ofexisting educational contexts is reflected in the data, few studies discuss the impact of the aforementioned premisein the area of Educational Data Mining (EDM), resulting in models that may perform poorly when used underunforeseen educational conditions. This paper proposes an empirical analysis to verify evidences of differencesbetween data from different educational contexts in the task of predicting students’ academic failure. Logs of morethan 3,000 distance higher education students are used, and the adopted methodology is based on the supervisedclassification approach, commonly used in prediction tasks. Specifically, we aim to verify if distinct educationalcontexts are in fact separable in terms of the data they generate. Although data scenarios involve activities commonto students in the same subject, the experiments indicate an accuracy of up to 83% in the separation of data fromdifferent academic terms. Although empirical, our results indicate a similar direction to that pointed out by otherstudies, contributing about the need of using transfer learning and/or domain adaptation techniques in the design ofpredictive models that aim to support proactive actions to prevent student failures.
An Educational Data Mining Model for Predicting Student Performance in Programming Course