2 research outputs found
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 Mà ster de Fonaments de Ciència de Dades, Facultat de matemà tiques, 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