While the growing number of learning resources increases the choice for learners on how, what and when to learn, it also makes it more and more difficult to find the learning resources that best match the learners’ preferences and needs. The same applies to learning systems that aim to adapt or recommend suitable courses and learning resources according to a learner’s wishes and requirements. Improved representations for a learner’s preferences as well as improved search capabilities that take these preferences into account leverage these issues. In this paper, we propose an approach for selecting optimal learning resources based on preference-enabled queries. A preference-enabled query does not only allow for hard constraints (like ’return lectures about Mathematics’) but also for soft constraints (such as ’I prefer a course on Monday, but Tuesday is also fine’) and therefore allows for a more fine-grained representation of a learner’s requirements, interests, and wishes. We show how to exploit the representation of a learner’s wishes and interests with preferences and how to use preferences in order to find optimal learning resources. We present the Personal Preference Search Service (PPSS), which offers significantly enhanced search capabilities for learning resources by taking the learner’s detailed preferences into account.
|Journal||IEEE Journal for Transactions on Learning Technologies|
|Publication status||Published - 15 Dec 2008|