Katrien Verbert

Nikos Manouselis

Hendrik Drachsler

Erik Duval

Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.

Recommender Systems for Learning

Fabio Sartori

Miguel-Angel Sicilia

This volume constitutes the selected paqpers of the third international conference on Metadata and Semantic Research, MTSR 2009, held in Milan, Italy, in September/October 2009. In order to give a novel perspective in which both theoretical and application aspects of metadata research contribute in the growth of the area, this book mirrors the structure of the Congress, grouping the papers into three main categories: 1) theoretical research: results and proposals, 2) applications: case studies and proposals, 3) special track: metadata and semantics for agriculture, food and environment. The book contains 32 full papers (10 for the first category, 10 for the second and 12 for the third), selected from a preliminary initial set of about 70 submissions.

Nikos Manouselis

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