Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning

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    Abstract

    The following article addresses open questions of the discussions in the first SIRTEL workshop at the EC-TEL conference 2007. It argues why personal recommender systems have to be adjusted to the specific characteristics of learning to support lifelong learners. Personal recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system from one context and transfer it to another context or domain. The article describes a number of distinct differences for personalized recommendation to consumers in contrast to recommendations to learners. Similarities and differences are translated into specific demands for learning and specific requirements for personal recommendation systems. It further suggests an evaluation approach for recommender systems in technology-enhanced learning.
    Original languageEnglish
    Pages (from-to)4-24
    JournalJournal of Digital Information
    Volume10
    Issue number2
    Publication statusPublished - 7 Jan 2009

    Keywords

    • information discovery
    • usability of digital information
    • technology-enhanced learning
    • lifelong learning
    • personal recommender
    • collaborative filtering
    • learner profiling
    • evaluation

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