Ecology of social search for learning resources

Abstract

Purpose: This paper deals with user-generated Interest indicators (e.g., ratings, bookmarks, 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 browsing help users discover more cross-boundary resources. By crossboundary 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 accesses a federation of multilingual learning resource repositories. A measure for user's efficiency in finding relevant resources was defined. We then collected users' attention metadata and use this empirical data to answer two hypotheses.Findings: We show that users are more efficient with Social Information Retrieval strategies, however, Community browsing alone does not help users discover a wider variety of 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. This is important for bringing them under the umbrella of SIR methods. Future studies should include testing 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 attention 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.

Dataset-driven research for improving recommender systems for learning

“…Additionally, it has metadata on the origin of the educational resource and its language. The dataset thus allows tracking the interests of users on travel well resources, indicating that the user and resource come from different countries and that the language of the resource is different from that of the users mother tongue [34]. Additionally, this dataset is useful for research on extraction of teacher interests and identication of teachers who share common interests, on the basis of their tags and ratings.…”

Personalization of Web Search Using Social Signals

“…Using the above user weight function, the following weights are generated for the five users: Definition Persocial relevance-level 2 between document d j and user u i is defined based on the number and type of social actions between user u i and document d j , the relationships between user u i and other users, the overall importance of each user and the number and type of social actions between user u i 's friends 4 and document d j , as follows:…”

“…As mentioned in McDonnell and Ali [2] there exist several definitions for social search: One definition is the way individuals make use of peers and other available social resources during search tasks [3]. Similarly, Vuorikari et al [4] defines social search as using the behavior of other people to help navigate online, driven by the tendency of people to follow other people's footprints when they feel lost. A third definition is by Amitay et al [5] and is defined as searching for similar-minded users based on similarity of bookmarks.…”

Recommender Systems for Learning

“…Indirect measures like time or learning outcomes, and direct measures like ratings and tags given by users allow to identify paths in a learning network which are faster to complete or more attractive than others (e.g. Drachsler et al 2009a;Vuorikari and Koper 2009). This information can be fed back to other learners in the learning network, providing collective knowledge of the 'swarm of learners' in the learning network.…”