Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.


Recommender Systems for Learning

Recommender systems (RSs) are extremely popular as a research and application area, but it was only around early 2000 when the first notable applications appeared in the domain of education, since relevant work was generally considered to be connected to the area of adaptive educational systems. Nowadays, RSs research in an educational context has significantly increased. Responding to this growing interest, this short book expands to 76 pages the relevant chapter on RSs for Technology Enhanced Learning (TEL) at the 1st (2011) edition of the RSs Handbook (RSH), by briefly introducing RSs, then providing a wide sample of RSs features, and finally giving a framework for RS practitioners to position their work in the overall 2013 landscape.


Machine Learning, Optimization

Target Group:


Cite as:
N. Manouselis, H. Drachsler, K. Verbert and E. Duval, Recommender Systems for Learning. Springer New York, 2013. ISBN 978-1-4614-4360-5.

Author of the review:
Pablo Guerrero-Garcia
University of Malaga


You have to login to leave a comment. If you are not registered click here