The 2nd Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2012) presents the current status related to the design, development and evaluation of recommender systems in educational settings. It emphasizes the importance of recommender systems for Technology Enhanced Learning (TEL) to support learners with personalized learning resources and suitable peer learners to improve their learning process. 6 full papers and 3 short papers were accepted for publication, and 1 keynote speaker was invited to the workshop.
self-regulated learning. Information retrieval is a pivotal activity in TEL, and the deployment of recommender systems has attracted increased interest during the past years as it addresses the information overload problem in TEL scenarios with a low cost approach.
As already confirmed at RecSysTEL 2010, recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. There are plenty a resource available on the Web, in terms of digital learning content, services and people resources (e.g. other learners, experts, tutors) that can be used to facilitate teaching and learning tasks. The challenge is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices.
The previous edition of the workshop moved a step forward in this research line, but there is still need for joining the ever increasing number of researchers working on TEL recommenders to share our progress and go further. By using recommendation technology, this workshop contributes to answer this edition ECTEL research questions, refined as follows: How can TEL recommenders support people for the technology-rich workplace after they have left school? How can TEL recommenders promote informal and independent learning outside traditional educational settings? How can TEL recommenders apply next generation social and mobile technologies to promote informal and responsive learning? In this context, several questions are being researched around the application of recommender systems in TEL, such as: Which are the user tasks that may be supported by recommender systems in
TEL settings? What should be the focus of recommendation in TEL - resources, people or both? What are the requirements for the deployment of recommender systems in a
TEL setting? What is needed to create a set of public available data sets ranging from formal to non-formal learning settings for TEL recommender systems? Can successful recommendation algorithms and systems from other application areas be applied in TEL and what should be the education related requirements taken into account when doing so? How to define evaluation criteria for TEL recommender systems? How can the success of SIR systems can be evaluated in the context of teaching, learning and/or TEL community building? Next, we comment on the contributions of the workshop and acknowledge the support received both from organizations and people.
The call for papers was disseminated in relevant lists and communities. We received
13 submissions, and each of them was reviewed using a blind refereeing process by 3
members of the Program Committee with expertise from both the RecSys and TEL
communities. The reviewing process was carried out using Ginkgo submission system
and took into account the following criteria: relevance, sound, organization and
readability. In the end, 6 full papers and 3 short papers were accepted. Moreover,
Stefan Dietze was invited as keynote speaker by the workshop organizers to share his
experience with the participants on linked data as a facilitator for TEL recommender
systems in research and practice. More specifically, his contribution focuses on
providing an overview of most relevant linked data sources and techniques together
with a discussion of their potential for the TEL domain in general and TEL
recommender systems based on insights from related European projects, including
mEducator and LinkedUp [
The accepted contributions covered several topics, such as recommendations in learning objects repositories, recommendations in learning scenarios and recommendations of human resources, the consideration of trust and affective issues in the recommendation process and the usage of different data formats in TEL recommenders. Moreover, the recommenders address both the needs of learners and educators.
In particular, the full papers address the following issues. Cechinel et al. describe
the results of an experiment for automatically generating quality information about
learning resources inside repositories in order to pursuit the automatic generation of
internal quality information about resources inside repositories [
In turn, the short papers deal with the following topics. Anjorin et al. present a
framework to support the development of cross-platform recommender systems for
TEL ecosystems and discuss challenges faced, which was effectively applied to
develop a cross-platform recommender system in a TEL ecosystem having Moodle as
the Learning Management System, Mahara as the Social Networking Service and
Ariadne as Learning Object Repository [
We would like to take this opportunity to thank all the authors who submitted valuable contributions to this workshop.
We would also like to thank in particular our Steering Committee members for their work and assistance over the last few months in helping to shape this workshop (listed in alphabetical order): Jesus G. Boticario (Spanish National University for Distance Education, Spain), Peter Brusilovsky (University of Pittsburgh, USA), Erik Duval (KU Leuven, Belgium), Denis Gillet (Swiss Federal Institute of Lausanne, Switzerland), Stefanie Lindstaedt (Know-Center Graz, Austria), Wolfgang Nejdl (L3S and Leibniz Universitat, Germany), Miguel-Angel Sicilia (University of Alcala, Spain), Martin Wolpers (Fraunhofer FIT, Germany) and Riina Vuorikari (European Schoolnet, Belgium).
Moreover, we would like to thank the workshop Program Committee members for their valuable review comments, which significantly contributed to the high quality of the accepted papers (listed in alphabetical order): Katrin Borcea-Pfitzmann (Dresden University of Technology, Germany), Julien Broisin (Université Paul Sabatier, France), Cristian Cechinel (Fundação Universidade Federal do Pampa, Brazil), Hannes Ebner (Royal Institute of Technology, Sweden), Wolfgang Greller (Open University of the Netherlands, Netherlands), Joris Klerkx (KU Leuven, Belgium), Rita Kuo (Academic and Industrial Research Centre, Knowledge Square, Inc., Taiwan), Paul Libbrecht (Center for Educational Research in Mathematics and Technology, Karlsruhe University of Education and Martin Luther University of Halle, Germany), Martin Memmel (German Research Center for Artificial Intelligence, Germany), Jad Najjar (Eummena, Belgium), Xavier Ochoa (Escuela Superior Politécnica del Litoral, Ecuador), Mimi Recker (Instructional Technology & Learning Sciences, Utah State University, USA), Wolfgang Reinhardt (University of Paderborn, Germany), Christoph Rensing (Multimedia Communications Lab. Technische Universität Darmstadt, Germany), Cristóbal Romero (University of Cordoba, Spain), Christina Schwind (Knowledge Media Research Center, Germany), Tomislav Šmuc (Rudjer Bošković Institute, Croatia), Sergey Sosnovsky (CeLTech, DFKI, Germany).
Finally, we would like to thank the Open Discovery Space CIP PSP project and TELSpain for their support. Katrien Verbert is a Postdoctoral Fellow of the Research Foundation – Flanders (FWO).