Combining Social- and Information-based Approaches for Personalised Recommendation on Sequencing Learning Activities

Hans Hummel, Hendrik Drachsler, José Janssen, Rob Nadolski, Rob Koper, A.J. Berlanga Flores, Bert Van den Berg

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    Abstract

    Lifelong learners who assign learning activities (from multiple sources) to attain certain learning goals throughout their lives need to know which learning activities are (most) suitable and in which sequence these should be performed. Learners need support in this way finding process (selection and sequencing), and we argue this could be provided by using personalised recommender systems. To enable personalisation, collaborative filtering could use information about learners and learning activities, since their alignment contributes to learning efficiency. A model for way finding has been developed that presents personalised recommendations in relation to information about learning goals, learning activities and learners. A personalised recommender system has been developed accordingly, and recommends learners on the best next learning activities. Both model and system combine social-based (i.e., completion data from other learners) and information-based (i.e., metadata from learner profiles and learning activities) approaches to recommend the best next learning activity to be completed.
    Original languageEnglish
    Pages (from-to)152-168
    Number of pages17
    JournalInternational Journal of Learning Technology
    Volume3
    Issue number2
    DOIs
    Publication statusPublished - 2007

    Keywords

    • collaborative filtering
    • personalised recommender systems
    • sequencing
    • learner profile
    • domain model for way finding
    • learning technology specifications

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