| Autor: |
MEHENAOUI, Zohra, LAFIFI, Yacine, DIB, Amira, GUENFOUD, Zeyneb |
| Předmět: |
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| Zdroj: |
Proceedings of the International Future-Learning Conference on Innovations in Learning for the Future: e-Learning; Oct2020, p87-88, 2p |
| Abstrakt: |
Within academic communities, to facilitate research and encourage collaboration, it is important that individuals can identify the right expertise or resources and interact with potential collaborators. However, finding a good collaborator may not be easy task for several reasons. The expertise is highly dynamic, difficult to qualify and quantify. The aim of this work is to propose a collaborative filtering approach for recommending relevant learning resources in a collaborative learning environment. Learning resources may include learning materials (learning objects) and relevant collaborators with whom learning activities can take place. The proposed approach is based on the learner's similarity calculation according to their interests. We have integrated the tags into the collaborative filtering process to provide personalized recommendation to the learners according to their interests. Tags allow to better building the learner's profiles by automatic inferring their interests. The proposed approach has been implemented in a collaborative learning system tested on a sample database. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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