Student dropout in Engineering Education is an important problem which has been studied from different perspectives, as well as using different techniques. This manuscript describes the methodology used in order to address this question in the context of learning analytics. Bayesian networks (BNs) have been used as they provide adequate methods for the representation, interpretation and contextualisation of data. The proposed approach is illustrated through a case study about Computer Science (CS) dropout at the University of Castilla-La Mancha (Spain), which is close to 40%. To that end, several BNs were obtained from a database which contained 383 records representing both academic and social data of the students enrolled in the CS degree during four courses. Then, these probabilistic models were interpreted and evaluated. The results obtained revealed that the best model that fits the data is provided by the K2 algorithm although the great heterogeneity of the data studied did not permit the adjustment of the dropout profile of the student too accurately. Nonetheless, the methodology described here can be taken as a reference for future works.

This paper presents the analysis of recent research on mobile learning and usability areas, applying a systematic mapping study. The aim is to understand the tendencies and needs in the m-learning field. The results demonstrate that research in the area has grown significantly since 2013, and we identify a necessity when we see that not all the m-learning applications have used usability tests; we also did not find guidelines or frameworks to evaluate them. With these results and tendencies, we propose an evaluation framework for m-learning applications, considering pedagogical usability and user interface usability, to improve the quality of m-learning applications.