Supporting Users of Open Online Courses with Recommendations: An Algorithmic Study

Soude Fazeli, Enayat Rajabi, Leonardo Lezcano, Hendrik Drachsler, Peter Sloep

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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.
Original languageEnglish
Title of host publication2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT)
EditorsJ.M. Spector, C.C. Tsai, D.M. Sampson, Kinshuk, R. Huang, N.S. Chen, P. Resta
Place of PublicationPiscataway
PublisherIEEE
Pages423-427
Number of pages5
ISBN (Electronic)978-1-4673-9041-5
ISBN (Print)978-1-4673-9042-2
DOIs
Publication statusPublished - 1 Dec 2016
Event2016 IEEE 16th International Conference on Advanced Learning Technologies - Austin, TX, United States
Duration: 25 Jul 201628 Jul 2016
http://www.ask4research.info/icalt/2016/

Conference

Conference2016 IEEE 16th International Conference on Advanced Learning Technologies
Abbreviated titleICALT
CountryUnited States
CityAustin, TX
Period25/07/1628/07/16
Internet address

Keywords

  • recommender systems
  • collabortive filtering
  • open online course
  • performance
  • accuracy
  • matrix factorization

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