Track 12. Recommender and Decision Support Systems for Learning (ReSyL@ICALT2016)

Track Chairs

Draschler Hendrik Drachsler, Open University of Netherlands, The Netherlands
Sabine Graf, Athabasca University, Canada
Xiao Hu, The University of Honk Kong, Hong Kong
Li Yanyan Li, Beijing Normal University, China [Coordinator -]
Demetrios G Sampson, Curtin University, Australia
Santos Olga Santos, Spanish National University for Distance Education, Spain

Track Program Committee

  • Alicia Diaz, UNLP, Argentina
  • Amine Chatti, RWTH Aachen, Germany
  • Beatriz Eugenia Florián Gaviria, Universidad del Valle, Colombia
  • Carla Limongelli , University of Roma Tre, Italy
  • Chi-Cheng Chang, National Taiwan Normal University, Taiwan
  • Dr. Pythagoras Karampiperis. National Centre for Scientific Research "Demokritos". Greece
  • Estefanía Martin - Universidad Rey Juan Carlos, Spain
  • Julien Broisin, IRIT Universite Paul Sabatier, France
  • Katrin Borcea-Pfitzmann, Dresden University of Technology, Germany
  • Maria Bielikova Slovak University of Technology. Slovakia
  • MERCEDES GOMEZ ALBARRAN. Universidad Complutense de Madrid. Spain
  • Miguel-Angel Sicilia, University of Alcala, Spain
  • Milos Kravcik RWTH Aachen University, Germany
  • Mojisola Erdt, TU Darmstadt, Germany
  • Peter Sloep, Open Universiteit Nederland
  • Regina Motz. Universidad de la República. Uruguay
  • Rita Kuo, Academic and Industrial Research Centre, Knowledge Square, Inc.
  • Rory Sie, EPFL, Switzerland
  • Ryan Baker, University Columbia
  • Sergey Sosnovsky, CeLTech, DFKI, Germany
  • Soude Fazeli, Open Universiteit Nederland
  • Tiffany Tang, Kean University, USA

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. 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 is to be 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; and 6) New evaluation methods that cover technical and educational criteria.

In this sense, 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 learning 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 2017 submissions can be found here.

The ICALT 2017 Author Guidelines can be found here.

The Track 12 CfP can be downloaded from here:

  • .txt version
  • .doc version