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Title: Supporting Users of Open Online Courses with Recommendations: an Algorithmic Study
Authors: Fazeli, Soude
Rajabi, Enayat
Lezcano, Leonardo
Drachsler, Hendrik
Sloep, Peter
Keywords: recommender systems
collabortive filtering
open online course
matrix factorization
Issue Date: 2016
Publisher: IEEE
Citation: Fazeli, S., Rajabi, E., Lezcano, L., Drachsler, H., & Sloep, P. B. (2016). Supporting Users of Open Online Courses with Recommendations: An Algorithmic Study. In 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT), pp. 423-427, doi:10.1109/ICALT.2016.119
Abstract: Almost all studies on course recommenders in online platforms target closed online platforms that belong to a University or other provider. Recently, a demand has developed that targets open platforms. Such platforms lack rich user profiles with content metadata. Instead they log user interactions. We report on how user interactions and activities tracked in open online learning platforms may generate recommendations. We use data from the OpenU open online learning platform in use by the Open University of the Netherlands to investigate the application of several state-of-the-art recommender algorithms, including a graph-based recommender approach. It appears that user-based and memory-based methods perform better than model-based and factorization methods. Particularly, the graph-based recommender system outperforms the classical approaches on prediction accuracy of recommendations in terms of recall.
ISSN: 2161-377X
Appears in Collections:1. TELI Publications, books and conference papers

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