Personalized recommendation for learning resources based-on case reasoning agents

Lina Yang*, Zhijun Yan

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    6 Citations (Scopus)

    Abstract

    Ample online resources for e-learning provide students with choices and initiative, which however results in much challenge in matching the needs of students with different backgrounds and learning preferences due to information overload. Facing diverse learning resources, students have difficulties in making appropriate choices to meet their learning objectives. This paper proposes a framework of multi-agents collaboration case-based reasoning (MACBR) for personalized recommendations of e-learning resources, taking into account of characteristics of the learner. The paper firstly presents a workflow of Case-based Reasoning (CBR) for learning resource recommendation, and then proposes the collaboration framework of MACBR, finally illustrates the application of MACBR for personalized recommendation in e-learning.

    Original languageEnglish
    Title of host publication2011 International Conference on Electrical and Control Engineering, ICECE 2011 - Proceedings
    Pages6689-6692
    Number of pages4
    DOIs
    Publication statusPublished - 2011
    Event2nd Annual Conference on Electrical and Control Engineering, ICECE 2011 - Yichang, China
    Duration: 16 Sept 201118 Sept 2011

    Publication series

    Name2011 International Conference on Electrical and Control Engineering, ICECE 2011 - Proceedings

    Conference

    Conference2nd Annual Conference on Electrical and Control Engineering, ICECE 2011
    Country/TerritoryChina
    CityYichang
    Period16/09/1118/09/11

    Keywords

    • Case-Based Reasoning
    • E-learning
    • Multi-agent System
    • Resource Recommendation

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