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

Ngoc Chan Nguyen 1 Azim Roussanaly 1 Anne Boyer 1
1 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : Searching 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.
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Communication dans un congrès
Proceedings of the 7th International Conference on Computer Supported Education, May 2015, Lisbon, Portugal. pp.119-129, 〈10.5220/0005454301190129〉
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Ngoc Chan Nguyen, Azim Roussanaly, Anne Boyer. Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation. Proceedings of the 7th International Conference on Computer Supported Education, May 2015, Lisbon, Portugal. pp.119-129, 〈10.5220/0005454301190129〉. 〈hal-01253030〉

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