Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation

International audienceSearching and recommendation are basic functions that effectively assist learners to approach their favorite learning resources. Several searching and recommendation techniques in the Information Retrieval (IR) domain have been proposed to apply in the Technology Enhanced Learning (TEL) domain. However, few of them pay attention on particular properties of e-learning resources, which potentially improve the quality of searching and recommendation. In this paper, we propose an approach that studies relations between e-learning resources, which is a particular property existing in online educational systems, to support resource searching and recommendation. Concretely, we rank e-learning resources based on their relations by adapting the Google's PageRank algorithm. We integrate this ranking into a text-matching search engine to refine the search results. We also combine it with a content-based recommendation technique to compute the similarity between user profile and e-learning resources. Experimental results on a shared dataset showed the efficiency of our approach