Track 12. Recommender Systems for Learning (ReSyL@ICALT2014)

Track Chairs

Li Yanyan Li, Beijing Normal University, China [Coordinator - liyy@bnu.edu.cn]
Draschler Hendrik Drachsler, Open University of Netherlands, The Netherlands
Li Olga Santos, Spanish National University for Distance Education, Spain

Track Program Committee

  • Hendrik Drachsler, Open University of Netherlands, The Netherlands
  • Yanyan Li, Beijing Normal University, China
  • Olga C. Santos, Spanish National University for Distance Education, Spain
  • Rachid Anane, Coventry University, UK
  • Chi-Cheng Chang, National Taiwan Normal University, Taiwan
  • Su Cai, Beijing Normal University, China
  • Soude Fazeli, Open Universiteit Nederland, Netherlands
  • Peter Sloep, Universiteit Nederland, Netherlands
  • Nikos Manouselis, Agro-Know Technologies & ARIADNE Foundation, Greece
  • Rory Sie, Ecole Polytechnique Federal de Lausanne, Switzerland
  • Maren Scheffel, FIT Fraunhofer, Germany
  • Felix Mödritscher, University of Vienna, Austria
  • Mojisola Erdt, TU Darmstadt, Germany
  • Kris Jack, Mendeley, UK
  • Christoph Rensing, TU Darmstadt, Germany
  • Rob Nadolski, Open University of Netherlands, Netherlands
  • Julien Broisin, IRIT Universite Paul Sabatier, France
  • Katrin Borcea-Pfitzmann, Dresden University of Technology, Germany
  • Rita Kuo, Academic and Industrial Research Centre & Knowledge Square, Taiwan
  • Tiffany Tang, Kean University, USA
  • Beatriz Eugenia Florián Gaviria, Universidad del Valle, Colombia
  • Mohamed Amine Chatti, RWTH Aachen, Germany
  • Ralf Klamma, RWTH Aachen, Germany

Track Description and Topics of Interest

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 (e.g. learners, experts, tutors), 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.

Topics of interest include but are not limited to:

  • User modeling for learning recommender systems
  • Affective computing in educational recommender systems
  • Multimedia information retrieval and recommendation for learning
  • Semantic Web technologies for recommendation
  • Data Mining and Web Mining for recommendation
  • Machine Learning for recommendation
  • Context modeling techniques for recommender systems
  • Recommendation algorithms and systems for learning
  • Data sets for learning recommender systems
  • Explanation and visualization of recommendations
  • Evaluation criteria and methods for learning recommender systems

Important dates about ICALT 2014 submissions can be found here.

The ICALT 2014 Author Guidelines can be found here.

The Track 12 CfP can be downloaded from here: