Simulating light-weight Personalised Recommender Systems in Learning Networks: A case for Pedagogy-Oriented and Rating-based Hybrid Recommendation Strategies

Rob Nadolski, Bert Van den Berg, Adriana Berlanga, Hendrik Drachsler, Hans Hummel, Rob Koper, Peter Sloep

    Research output: Contribution to journalArticleAcademicpeer-review

    1 Downloads (Pure)

    Abstract

    Recommender systems for e-learning demand specific pedagogy-oriented and hybrid recommendation strategies. Current systems are often based on time-consuming, top down information provisioning combined with intensive data-mining collaborative filtering approaches. However, such systems do not seem appropriate for Learning Networks where distributed information can often not be identified beforehand. Sound way-finding for lifelong learners in Learning Networks requires dedicated personalised recommender systems (PRS) which should also be practically feasible with minimized effort. Currently, such light-weight PRS systems are scarcely available. This study shows that simulations can support defining PRS requirements prior to starting the costly process of development, implementation, testing, revision, and before conducting field experiments with real learners. This study confirms that providing recommendations leads towards more effective, more satisfied, and faster goal achievement. Furthermore, this simulation study reveals that a rating-based light-weight hybrid PRS-system is a good alternative for ontology-based recommendations, in particular for low-level goal achievement. Finally, it is found that rating-based light-weight hybrid PRS-systems enable more effective, more satisfied, and faster goal attainment than peer-based light-weight hybrid PRS-systems (incorporating collaborative techniques without rating).
    Original languageEnglish
    Pages (from-to)1-4
    Number of pages4
    JournalJournal of Artificial Societies and Social Simulation
    Volume12
    Issue number1
    Publication statusPublished - 31 Jan 2009

    Keywords

    • recommendation strategy
    • simulation study
    • way-finding
    • collaborative filtering
    • rating

    Fingerprint

    Dive into the research topics of 'Simulating light-weight Personalised Recommender Systems in Learning Networks: A case for Pedagogy-Oriented and Rating-based Hybrid Recommendation Strategies'. Together they form a unique fingerprint.

    Cite this