Ecology of social search for learning resources
Riina Vuorikari, Rob Koper · Campus-Wide Information Systems · 2009
Purpose This paper deals with user‐generated interest indicators (ratings, bookmarks and tags). The authors aim to answer two research questions: Can search strategies based on social information retrieval (SIR) make the discovery of learning resources more efficient for users? Can Community search help users discover a wider variety of cross‐boundary resources? Design/methodology/approach Cross‐boundary is defined as 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. The authors focus on a portal that accesses a federation of multilingual learning resource repositories. The authors 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/value 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.