دانلود رایگان مقاله انگلیسی استراتژی های یادگیری شخصی برای آموزش عالی - امرالد 2017

Abstract

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.

4. Conclusion

In this paper we used empirical data from server-side logging to study and model the ecology of social search of a learning resource portal integrated with a social bookmarking and tagging system. We conclude that explicit Interest indicators have an important role as a part of the social search ecology and studies into inter-relations of these variables will offer interesting further insights. By studying the cross-boundary discoveries, we find that users create underlying connections between resources that come from a number of countries and are in a variety of languages, which is important for bringing them under the umbrella of SIR methods.

H1 was approved showing that the search taking advantage of social information retrieval (SIR) methods yield more relevant resources with less effort from the user. Despite this edge, users have a strong search preference for explicit search methods (two-thirds of all executed searches). These conventional search methods strongly proved their role in discovering new resources. This also led us to refute our second hypothesis (H2): most cross-boundary resources are discovered using explicit search. Encouraged by the H1, though, we believe that enhancing explicit interest indicators to support cross-boundary discovery (e.g. indicating the cross-boundary nature of resource discoveries, tag clouds filtered by language and by the country of users) and collaborative filtering methods for like-minded users are worth studying further, once the new item problem has been overcome.