Abstract
Current educational recommender systems (RS) represent an essential support tool and the most used system to interpret patterns and human interaction, which supports deeper learning and provides users with fast and accurate data.
In an e-learning environment supported by RS, the learner often needs additional educational resources to enrich his learning scenario to meet his needs and deepen his knowledge and skills. So he spends a lot of time identifying his need, selecting the most convenient data sources and finding the appropriate resources to the current content of his activity.
However, in the era of Big Data, apart from the services offered by RS and other data filtering tools, data sources are currently experiencing a significant evolution in terms of volume and variety of available resources. Given the importance of these data, the quality of the recommended content is decreasing significantly, which implies poor knowledge and a failed learning experience.
To enhance the quality of student’s learning, we propose an approach of recommending quality educational resources, in accordance to the learner’s learning progress and his individual needs.
The quality assessment module is integrated into the recommendation process to judge the level of quality of the resources. To help the quality assessment module make a better decision and improve analytics, we used artificial intelligence technique, Fuzzy Logic to simulate the human reasoning process and aid to deal with the uncertain data in engineering.
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Bel Hadj Ammar, W., Chaabouni, M., Ben Ghezala, H. (2020). Recommender System for Quality Educational Resources. In: Kumar, V., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science(), vol 12149. Springer, Cham. https://doi.org/10.1007/978-3-030-49663-0_39
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DOI: https://doi.org/10.1007/978-3-030-49663-0_39
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