1. Supporting Users of Open Online Courses with Recommendations: An Algorithmic Study
- Author
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Fazeli, Soude, Rajabi, Enayat, Lezcano, Leonardo, Drachsler, Hendrik, Sloep, Peter, Spector, J. Michael, Tsai, Chin Chung, Sampson, Demetrios, Kinshuk, Huang, Ronghai, Chen, Nian-Shing, Resta, Paul, RS-Research Line Technology Enhanced Learning Innovations for teaching and learning (TELI) (part of WO program), Department TELI, RS-Research Line Clinical psychology (part of IIESB program), Department Clinical Psychology, Spector, J. Michael, Tsai, Chin Chung, Sampson, Demetrios, Kinshuk, Huang, Ronghai, Chen, Nian-Shing, and Resta, Paul
- Subjects
accuracy ,Computer science ,02 engineering and technology ,Open university ,Recommender system ,matrix factorization ,Electronic mail ,collabortive filtering ,World Wide Web ,Metadata ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,open online course ,020201 artificial intelligence & image processing ,recommender systems ,performance - 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.
- Published
- 2016