Track 12. Recommender and Decision Support Systems for Learning

With the increasingly growth of multimedia resources in the various e-learning systems and online learning communities, how to find and access useful information for learning and teaching has become a big challenge. Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The focus is to develop, deploy and evaluate recommender systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources, both in terms of digital learning content and people resources, from a potentially overwhelming variety of choices. This track aims to bring together researchers and practitioners around the topics of designing, developing and evaluating recommender systems in educational settings as well as present the current status of research in this area. We welcome papers describing work in progress and encourage submissions that make datasets available to the community. In addition, we look forward contributions that move the field forward the challenges in the field, which have been identified in a recent review chapter on the panorama of recommender systems for technology enhanced learning scenarios that has been published in the second handbook on recommender systems by Springer.

These identified challenges are the following:

1) Pedagogical needs and expectations to recommenders;

2) Context-based recommender systems;

3) Visualisation and explanation of recommendations;

4) Demands for more diverse educational datasets;

5) Distributed datasets; 

6) New evaluation methods that cover technical and educational criteria.