Drop out risk prediction in on-line learning environments
Online learning environments (OLEs) have seen a continuous increase over the past decade and asudden surge in the last year, due to the coronavirus outbreak. The widespread use of OLEs hasled to an increasing number of enrolments, even from students who had previously left educationsystems, but it also resulted in a much higher dropout rate than in traditional classrooms. This is acrucial problem since online courses have rapidly expanded from individual MOOCs to entire studyprograms, also due to the pandemic.Early detection of students in difficulty is a challenging problem that can be mitigated with the support of state-of-the-art methods for data analytics and machine learning. In this talk, we present a novel strategy that exploits both hidden space information and time-related data from student trajectories and copes with unbalanced data and time-series sparsity issues to solve the student dropoutprediction problem. The proposed approach outperforms state-of-the-art methods, particularly in the complex case of full-length curricula (such as online degrees), a scenario that was thought to be lesscommon before the pandemic, but is now particularly relevant.
Paola Velardi is a Full Professor of computer science at Sapienza University in Rome, Italy. Her research encompasses natural language processing, machine learning, business intelligence and semantic web, web information extraction in particular. Velardi is one of the hundred female scientists included in the database "100esperte.it" (translated from Italian with "100 female experts"). This online, open database champions the recognition of top-rated female scientists in the Science, Technology, Engineering and Mathematics (STEM) area.