Track 12. Recommender and Decision Support Systems for Learning (ReSyL@ICALT2017)

Track Program Chairs


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 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; 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

Track Program Committee

Chi-Cheng Chang, National Taiwan Normal University, Taiwan

Soude Fazeli, Open Universiteit Nederland, Netherlands

Peter Sloep, Open Universiteit Nederland, Netherlands

Katrien Verbert, Katholieke Universiteit Leuven, Belgium

Rory Sie, EPFL, Switzerland

Maren Scheffel, FIT Fraunhofer, Germany

Felix Mödritscher, Vienna University of Economics and Business (WU), Austria

Stefan Dietze, L3S, Germany

Mojisola Erdt, TU Darmstadt, Germany

Kris Jack, Chef Engeneer Mendeley, UK

Xavier Ochoa, Escuela Superior Politecnica del Litoral, Ecuador

Miguel-Angel Sicilia, University of Alcala, Spain

Julien Broisin, IRIT Universite Paul Sabatier, France

Katrin Borcea-Pfitzmann, Dresden University of Technology, Germany

Jesus G. Boticario, Spanish National University for Distance Education, Spain

Tiffany Tang, Kean University, USA

Sergey Sosnovsky, Utrecht University, Netherlands

Beatriz Eugenia Florián Gaviria, Universidad del Valle, Colombia

Amine Chatti, RWTH Aachen, Germany

Mercedes Gomez Albarran, Universidad Complutense de Madrid, Spain

Estefanía Martin, Universidad Rey Juan Carlos, Spain

Regina Motz, Universidad de la República, Uruguay

Pythagoras Karampiperis, National Centre for Scientific Research “Demokritos”, Greece

Ig Ibert Bittencourt, Federal University of Alagoas, Brazil

Alicia Diaz, UNLP, Argentina

Guillermo Jiménez Díaz, Universidad Complutense de Madrid, Spain

Miguel Arevalillo-Herráez, Univeridad de Valencia, Spain

Carla Limongelli, University of Roma Tre, Italy

Important Dates about ICALT 2017 can be found here.

The ICALT 2017 Author Guidelines can be found here.

The ICALT 2017 CfP can be found here.