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Personalized recommender system for e-Learning environment

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Abstract

Traditional e-Learning environments are based on static contents considering that all learners are similar, so they are not able to respond to each learner’s needs. These systems are less adaptive and once a system that supports a particular strategy has been designed and implemented, it is less likely to change according to student’s interactions and preferences. New educational systems should appear to ensure the personalization of learning contents. This work aims to develop a new personalization approach that provides to students the best learning materials according to their preferences, interests, background knowledge, and their memory capacity to store information. A new recommendation approach based on collaborative and content-based filtering is presented: NPR_eL (New multi-Personalized Recommender for e Learning). This approach was integrated in a learning environment in order to deliver personalized learning material. We demonstrate the effectiveness of our approach through the design, implementation, analysis and evaluation of a personal learning environment.

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Authors and Affiliations

  1. Computer Science Department, University of Badji Mokhtar, 12, Annaba, 23000, Algeria

    Soulef Benhamdi

  2. LGEG Laboratory, University of 8 Mai 1945, 401, Guelma, 24000, Algeria

    Soulef Benhamdi & Abdesselam Babouri

  3. ISEP, Paris, France

    Raja Chiky

Authors
  1. Soulef Benhamdi
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  2. Abdesselam Babouri
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  3. Raja Chiky
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Correspondence to Abdesselam Babouri.

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Benhamdi, S., Babouri, A. & Chiky, R. Personalized recommender system for e-Learning environment. Educ Inf Technol 22, 1455–1477 (2017). https://doi.org/10.1007/s10639-016-9504-y

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