Recommender System for Online Teaching.

This paper focuses on the support process within the online teaching environment, which is currently unsatisfactory because of the very limited size of the course trainers or teachers compared to the massive number of the enrolled learners who need support. Indeed, many of the learners can not appropriate the information they receive and must therefore be guided. Thus, in order 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 online environment. The recommendation phase is based on a multicriteria decision making approach to periodically predict the set of “Leader learners”. Moreover, since the learners’ profiles are very heterogeneous, we recommend to each learner the leaders who are most appropriate to his profile in order to ensure a good understanding between them. The recommendation we propose is based on the demographic filtering and the Euclidean distance to identify the neighbourhood of the target learner. This method concerns only the higher-education teaching.