deep learning

In this tutorial, we explored challenges in retention, and presented machine learning solutions to the difficulties in predicting student dropout rates from online courses.

In this paper, we compare several deep and surface state-of-the-art machine learning methods for risk prediction in problems that can …

Online courses and e-degrees, although present since the mid-1990, have received enormous attention only in the last decade. Moreover, …

This paper compares machine learning methods for risk prediction in event trajectories and provides insights on their reproducibility and applicability.

The recent diffusion of online education (both MOOCs and e-courses) has led to an increased economic and scientific interest in …

An Italian presentation about the current research projects of the lab. As research project for the “AI for Education” workshop, I presented the state-of-the-art in EDM, and proposed to solve the student dropout problem with Hierarchical Attention Networks (HANs).

This paper compares machine learning methods for risk prediction in event trajectories and provides insights on their reproducibility and applicability.