Paper Info
Title
UMAP 2017 EdRecSys Workshop Organizers' Welcome & Organization.
Abstract
Welcome to the 4th International Workshop on Educational Recommender Systems (EdRecSys) held in conjunction with the 25th ACM Conference on User Modeling, Adaptation, and Personalization (UMAP 2017). 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: (i) suitable degrees and courses, based on their background, preferences and prior experience; (ii) project and thesis topics, and appropriate supervisors; (iii) internships and jobs; (iv) other students to work with (to do group work or peer learning); (v) suitable learning resources based on their knowledge, skills and learning style, e.g. books, tutorials, activities, etc. This workshop has brought together researchers and practitioners from the areas of user modeling and personalisation, 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.
Year
DOI
Venue
2017
10.1145/3099023.3099033
UMAP (Adjunct Publication)
Field
DocType
Citations 
Recommender system,Open research,World Wide Web,Learning analytics,Learning Management,Computer science,Group work,User modeling,Peer learning,Multimedia,Personalization
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Kurt Driessens148934.75
Irena Koprinska278364.00
Olga C. Santos333348.18
Evgueni N. Smirnov42420.38
Kalina Yacef579880.57
Osmar R. Zaïane63143285.09