Call for paper - Vol.10, n.1, 2014

RECOMMENDER SYSTEMS FOR LEARNING

Guest Editors

Demetrios Sampson, Antonella Carbonaro

Recommender systems have become an important research area since the emergence of the first research paper on collaborative filtering in the mid-1990s. In general, recommender systems directly help users to find content, products, or services by aggregating and analyzing suggestions from other users, and turning data on users and their preferences into predictions of users' possible future likes and interests.
Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, much remains to be understood. For further advances intuition alone is no longer enough and a multidisciplinary approach will surely bring powerful tools that may help innovative matchmakers to turn the immense potential of recommendations into real life applications. For example, applying data mining techniques to recommender systems has been effective in providing personalized information to the user by analyzing his or her preferences.
A recommender system in an e-learning context tries to intelligently recommend actions to a learner based on the actions of previous learners. In educational domains, recommendations should be made not only to suit learners' interests, but also to keep them engaged and pedagogically motivated throughout the learning process, as understanding learners' pedagogical needs is key to delivering individualized learning materials. In addition, recommender systems can be used to guide collaborative interactions in learning settings, namely by supporting group members' metacognitive learning activities.
In this special issue, we solicit original research and experience papers on challenging as well as novel issues concerning recommender systems in collaborative social learning environments. We are especially interested in papers documenting how the integration of recommender systems in e-learning context can promote the overall acceptance of given recommendations, which in turn encourages learner participation in the learning process.

Topics of interest
The topics of interest include but are not limited to:

• recommandation in the educational domain
• use of web mining techniques to recommend learning activities
• use of semantic web techniques in e-learning recommender systems
• semantic information to improve recommender strategies
• group recommendation in education and learning settings
• comparisons of different recommender systems
• location-based recommendation
• intelligent recommender systems
• case studies of educational recommender system implementations


All submitted papers will be subject to a selection mechanism based on a double blind review.

Language: all contributions/papers must be written in English
Author Guidelines: http://www.je-lks.org/ojs/index.php/Je-LKS_EN/about/submissions

The Journal of e-Learning and Knowledge Society (Je-LKS) (eISSN 1971-8829) is published by the Italian Society of e-Learning since 2005 and in 2013 is publishing its ninth volume, consisting of three numbers (their output is four months). Je-LKS is indexed, among other things, on AACE-EdITLib, Scopus, Elsevier, DOAJ, IET Inspec, CiteFactor and in 2013 reached a h-index of 13 compared to Google Scholar database.