• DocumentCode
    259534
  • Title

    Learning Analytics in Linked Open Online Courses

  • Author

    Hover, Kai Michael ; Muhlhauser, Max

  • Author_Institution
    Dept. of Comput. Sci., Tech. Univ. Darmstadt, Darmstadt, Germany
  • fYear
    2014
  • fDate
    10-12 Dec. 2014
  • Firstpage
    369
  • Lastpage
    374
  • Abstract
    Learning offerings and learners´ activities are increasingly moving to decentralized learning environments in the cloud. The advent of MOOC platforms gives a good example for this ongoing evolution. However, most learning applications use proprietary data models. As learners nowadays not only use only one e-learning offering, the analysis of learner activities is challenging in such decentralized and heterogeneous learning environments. To address this problem, we present three different learning applications and show the potential of exploiting Semantic Web technologies.
  • Keywords
    computer aided instruction; educational courses; semantic Web; MOOC platforms; decentralized learning environments; e-learning offering; learner activities; learning analytics; learning applications; linked open online courses; proprietary data models; semantic Web technologies; Data models; Discussion forums; Distributed databases; Electronic learning; Interoperability; Ontologies; Semantic Web; Interoperability; Learning Analytics; Lecture Recording; Ontology; Semantic Web;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia (ISM), 2014 IEEE International Symposium on
  • Conference_Location
    Taichung
  • Print_ISBN
    978-1-4799-4312-8
  • Type

    conf

  • DOI
    10.1109/ISM.2014.51
  • Filename
    7033053