Ecology of social search for learning resources

Riina Vuorikari, Rob Koper

    Research output: Contribution to journalArticleAcademicpeer-review

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    Purpose: This paper deals with user-generated Interest indicators (ratings, bookmarks and tags). We answer two research questions: can search strategies based on Social Information Retrieval (SIR) make the discovery of learning resources more efficient for users, and can Community search help users discover a wider variety of cross-boundary resources. By cross-boundary we mean that the user and resource come from different countries and that the language of the resource is different from that of the user’s mother tongue. Design: We focus on a portal that access a federation of multilingual learning resource repositories. We collect users’ attentional metadata based on a server-side logging scheme and use this empirical data to answer two hypotheses. Findings: The search-play-annotation ratio is more efficient with Social Information Retrieval strategies, but Community browsing alone does not help users to discover more cross-boundary resources. Practical implications: By social tagging and bookmarking resources from a variety of repositories, users create underlying connections between resources that otherwise do not cross-reference, for example, via hyperlinks. This is important for bringing them under the umbrella of SIR methods. Future studies should include testing wider range of SIR methods to leverage these user-made connections between resources that originate from a number of countries and are in a variety of languages. Originality: The use of attentional metadata to model the ecology of social search adds value to the actors of learning object economy, e.g. educational institutions, digital libraries and their managers, content providers, policy makers, educators and learners.
    Original languageEnglish
    Pages (from-to)272-286
    Number of pages14
    JournalCampus-Wide Information Systems
    Issue number4
    Publication statusPublished - 28 Aug 2009


    • Social information retrieval forlearning resources
    • learning resources
    • social information retrieval
    • social tagging
    • efficiency


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