Massive Open Online Courses Temporal Profiling for Dropout Prediction

Massive Open Online Courses (MOOCs) are attracting the attention of people all over the world. Regardless the platform, numbers of registrants for online courses are impressive but in the same time, completion rates are disappointing. Understanding the mechanisms of dropping out based on the learner profile arises as a crucial task in MOOCs, since it will allow intervening at the right moment in order to assist the learner in completing the course. In this paper, the dropout behaviour of learners in a MOOC is thoroughly studied by first extracting features that describe the behavior of learners within the course and then by comparing three classifiers (Logistic Regression, Random Forest and AdaBoost) in two tasks: predicting which users will have dropped out by a certain week and predicting which users will drop out on a specific week. The former has showed to be considerably easier, with all three classifiers performing equally well. However, the accuracy for the second task is lower, and Logistic Regression tends to perform slightly better than the other two algorithms. We found that features that reflect an active attitude of the user towards the MOOC, such as submitting their assignment, posting on the Forum and filling their Profile, are strong indicators of persistence.Comment: 8 pages, ICTAI1

GR2ASP: Guided re-identification risk analysis platform

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2019, Tutor: Francesco Bonchi, Rohit Kumar i Jordi Vitrià[en] Data privacy has been gaining considerable momentum in the recent years. The combination of numerous data breaches with the increasing interest for data sharing is pushing policy makers to impose stronger regulations to protect user data. In the E.U, the GDPR, in place since since May 2018, is forcing countless small companies to de-identify their datasets. Numerous privacy policies developed in the last two decades along with several tools are available for doing so. However, both the policies and the tools are relatively complex and require the user to have strong foundations in data privacy. In this paper, I describe the development of GR 2 ASP, a tool aimed at guiding users through de-identifying their dataset in an intuitive manner. To do so, the user is shielded from almost all the complexity inherent to data privacy, and interacts with simplified notions. Our tool differentiates itself from state-of-the-art similar tools by providing explainable recommendations in an intuitive interface, and having a human-in-the-loop approach towards data de-identification. We therefore think that it represents a considerable improvement over currently available tools, and we expect it to be frequently used, especially in the context of the SMOOTH project for which it has been commissioned