Carlo Blundo

Nowadays, Massive Open Online Courses (MOOCs) are adopted by students worldwide. One of the main critical issues often associated to MOOCs is the dropout phenomenon. In other words, the percentage of students abandoning a MOOC-based study path is considered still too high. Therefore, an increasing number of scientific works, coming from several and heterogeneous communities (e.g., computer science, data science, statistics, education) proposes approaches trying to mitigate such a problem. The majority of the aforementioned works focuses on machine learning methods to define classifiers able to be trained and, subsequently, to predict students who are going to abandon a course before it ends. Among such approaches, the ones achieving the best performance use enriched sets of features (to train their models) and produce results that cannot be used to easily clearly characterize the different behaviours of dropping-out and non-dropping-out students. The present work proposes the design of a novel process to train a set of dropout predictors leveraging on a reduced set of features. The underlying idea is to exploit weekly data in order to classify, with acceptable levels of precision, students who are likely going towards dropout or not. In cases of uncertainty, the classification decision is deferred to the next week, when new data is available. Such an approach offers several advantages. The first one is the chance to build a real-time educational decision support systems able to support decision as sufficient information is available. The second one is to preserve resources and avoiding wasting them with students erroneously classified at risk of dropout. The third one is to allow explicit characterization of dropout-conducing behaviour by using a rule mining approach.

Role-based access control (RBAC) is the most popular access control model currently adopted in several contexts to define security management. Constraints play a crucial role since they can drive the selection of the best representation of the organization’s security policies when migrating towards an RBAC system. In this paper, we examine different types of constraints addressing both theoretical aspects and practical considerations. On one side, we define the constrained role mining problem for each constraint type, showing its complexity. On the other hand, we present efficient heuristics adapted to each class of constraints, all derived from the specialization of a general approach for role mining. We show that our techniques improve over previous proposals, offering a complete set of experimentations obtained after the application of the heuristics to standard real-world datasets.