ERIC Number: EJ1348084
Record Type: Journal
Publication Date: 2022
Pages: 21
Abstractor: As Provided
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EISSN: EISSN-2365-9440
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A Revised Application of Cognitive Presence Automatic Classifiers for MOOCs: A New Set of Indicators Revealed?
Hu, Yuanyuan; Donald, Claire; Giacaman, Nasser
International Journal of Educational Technology in Higher Education, v19 Article 48 2022
Automatic analysis of the myriad discussion messages in large online courses can support effective educator-learner interaction at scale. Robust classifiers 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 classifier 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 classifier using MOOC discussion data from three other disciplines: Medicine, Education, and Humanities. Our results suggest that the cognitive classifier trained using MOOC data in only one discipline cannot yet be applied to other disciplines with sufficient accuracy.
Descriptors: MOOCs, Philosophy, Artificial Intelligence, Computer Mediated Communication, Group Discussion, Intellectual Disciplines, Medicine, Humanities, Education, Classification, Automation
BioMed Central, Ltd. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://www-springer-com.bibliotheek.ehb.be/gp/biomedical-sciences
Publication Type: Journal Articles; Reports - Research
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Language: English
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