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A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed?

  • Autores: Yuanyuan Hu, Claire Donald, Nasser Giacaman
  • Localización: International Journal of Educational Technology in Higher Education, ISSN 2365-9440, Nº. 19, 2022
  • Idioma: inglés
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  • Resumen
    • Automatic analysis of the myriad discussion messages in large online courses can support efective educator-learner interaction at scale. Robust classifers are an essential foundation for the use of automatic analysis of cognitive presence in practice. This study reports on the application of a revised machine learning approach, which was originally developed from traditional, small-scale, for-credit, online courses, to automatically identify the phases of cognitive presence in the discussions from a Philosophy Massive Open Online Course (MOOC). The classifer performed slightly better on the MOOC discussions than similar previous studies have found. A new set of indicators to identify cognitive presence was revealed in the MOOC discussions, unlike those in the traditional courses. This study also cross-validated the classifer using MOOC discussion data from three other disciplines: Medicine, Education, and Humanities. Our results suggest that the cognitive classifer trained using MOOC data in only one discipline cannot yet be applied to other disciplines with sufcient accuracy.


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