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Educational recommender systems and their application in lifelong learning

Author

Listed:
  • Maria-Iuliana Dascalu
  • Constanta-Nicoleta Bodea
  • Monica Nastasia Mihailescu
  • Elena Alice Tanase
  • Patricia Ordoñez de Pablos

Abstract

The almost unlimited access to educational information plethora came with a drawback: finding meaningful material is not a straightforward task anymore. Recommender algorithms can be used to make smart decisions in complex information systems and help the users decide upon useful materials; therefore, they become a promising area in academia and industry. The current paper presents a survey on educational recommender systems (RS): a set of analysis criteria are exposed and the technological specifications and challenges of each analysed system are provided, in the context of the main trends in the development of RS. Also, an ontology-based educational recommendation mechanism is proposed and its application to lifelong learning is highlighted, proving that RS can successfully support new learning paradigms.

Suggested Citation

  • Maria-Iuliana Dascalu & Constanta-Nicoleta Bodea & Monica Nastasia Mihailescu & Elena Alice Tanase & Patricia Ordoñez de Pablos, 2016. "Educational recommender systems and their application in lifelong learning," Behaviour and Information Technology, Taylor & Francis Journals, vol. 35(4), pages 290-297, April.
    • Handle: RePEc:taf:tbitxx:v:35:y:2016:i:4:p:290-297
    DOI: 10.1080/0144929X.2015.1128977
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    Cited by:

    1. Yazhi Yang & Jiandong Shi & Siwei Zhou & Shasha Yang, 2024. "Geometric Matrix Completion via Graph-Based Truncated Norm Regularization for Learning Resource Recommendation," Mathematics, MDPI, vol. 12(2), pages 1-21, January.

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