Personalizing access to learning networks

Author:

Dolog Peter1,Simon Bernd2,Nejdl Wolfgang3,Klobučar Tomaž4

Affiliation:

1. Aalborg University, Aalborg, Denmark

2. Vienna University of Economics and Business Administration, Vienna, Austria

3. University of Hannover, Hannover, Germany

4. Jozef Stefan Institute, Ljubljana, Slovenia

Abstract

In this article, we describe a Smart Space for Learning™ (SS4L) framework and infrastructure that enables personalized access to distributed heterogeneous knowledge repositories. Helping a learner to choose an appropriate learning resource or activity is a key problem which we address in this framework, enabling personalized access to federated learning repositories with a vast number of learning offers. Our infrastructure includes personalization strategies both at the query and the query results level. Query rewriting is based on learning and language preferences; rule-based and ranking-based personalization improves these results further. Rule-based reasoning techniques are supported by formal ontologies we have developed based on standard information models for learning domains; ranking-based recommendations are supported through ensuring minimal sets of predicates appearing in query results. Our evaluation studies show that the implemented solution enables learners to find relevant learning resources in a distributed environment and through goal-based personalization improves relevancy of results.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

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