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ERIC Number: EJ1348084
Record Type: Journal
Publication Date: 2022
Pages: 21
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: EISSN-2365-9440
Available Date: N/A
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.
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
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A