Recommender System to Improve Knowledge Sharing in Massive Open Online Courses

摘要

This paper focuses on the support process, within a Massive Open Online Course (MOOC), that is currently unsatisfactory because of the very limited size of the pedagogical team compared to the massive number of the enrolled learners who need support. Indeed, many of the MOOC learners can not appropriate the information they receive and must therefore be assisted in order to not abandon the course. Thus, to help these learners take advantage of the course they follow, we propose a tool to recommend to each of them an ordered list of "Leader learners" who are able to support him throughout his navigation in the MOOC environment. The recommendation phase is based on a multicriteria decision making approach to weekly predict the set of "Leader learners". Moreover, since the MOOC learners' profiles are very heterogeneous, we recommend to each of them the leaders who are most appropriate to his profile in order to ensure a good understanding between them. The recommendation we propose is validated on real data coming from a French MOOC and has proved satisfactory results.