General Information

Recommender systems have become increasingly popular in recent years, helping us to make decisions about what products to buy, what movies to watch, what books to read or who to date. While these systems have shown their effectiveness in e-commerce, music and social networks, the field of education is an emerging and very promising application area. The educational environment is no longer limited to face-to-face classes; it includes online learning and activities using Technology Enhanced Learning (TEL), Learning Management Systems (LMS) and Massive Open Online Courses (MOOC), all of which can benefit from the application of recommender systems to alleviate information overload and improve personalisation, to better meet the needs of the individual student. For example, high school and university students can be provided with recommendations about suitable degrees and courses, based on their background, preferences and prior experience; project and thesis topics and supervisors, internships and jobs, other students to work or study with in a group, suitable learning resources based on their knowledge and skills (e.g. books, tutorials, activities, etc.).

This workshop aims to bring together researchers and practitioners from the areas of user modeling and personalization, recommender systems, education, data mining, learning analytics, intelligent tutoring systems and other related disciplines, to explore the use of recommender systems in education, share their experience and discuss the challenges and open research topics in the design and deployment of effective solutions.

Topics include (but are not limited to):

  • Algorithms for making recommendations in education (e.g. content-based, collaborative filtering, hybrid, context-aware)
  • User modelling for recommendations in education
  • Preference extraction and learning for making recommendations in education
  • Item recommenders (e.g. courses, degrees, thesis topics, learning resources, jobs, internships)
  • People recommenders (teammates, friends, supervisors)
  • Evaluation of recommender systems in education
  • Emotion and personality-based recommender systems in education
  • Data mining and learning analytics of recommender systems in education – e.g. for understanding the student characteristics, expectations, aspirations and behavior
  • Privacy and security of recommender systems in education
  • Case studies of real-world implementations, e.g. in TELs, LMSs and MOOCs
  • Matchmaking between graduating students and potential employers’ ads

Motivation

Recommender Systems are an emerging technology in education and they have a huge potential. Our workshop will provide a dedicated forum to discuss the possibilities, solutions and challenges, bringing together researchers and practitioners from different areas.

Workshop format

We will have paper presentations and panel discussion, and possibly an invited talk, depending on the number of accepted papers.

Workshop history

EdRecSys 2017 builds on the success of the previous EdRecSys 2016, RecSysTEL 2012 and RecSysTEL 2010 workshops, thus, is the fourth workshop in the series.

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