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Automated Content Analysis of Students’ Cognitive Presence In Asynchronous Online Discussion
PROCEEDINGS

, Syracuse University, United States

EdMedia + Innovate Learning, in Montreal, Quebec, Canada ISBN 978-1-939797-16-2 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC

Abstract

This study investigated the use of text mining techniques in qualitatively analyzing online students’ cognitive presence in their discussion. Multinomial Naïve Bayes and Support Vector Machines were adopted as the text classification algorithms to automate content analysis of students’ discussion transcription. The results demonstrated the potential of text mining in exploring students’ online discourse and learning process. To develop the optimal classification model, the effectiveness of the two algorithms were compared, different classifiers were examined, and the use of different lexical/vectorization features were also discussed.

Citation

Chen, Y. (2015). Automated Content Analysis of Students’ Cognitive Presence In Asynchronous Online Discussion. In S. Carliner, C. Fulford & N. Ostashewski (Eds.), Proceedings of EdMedia 2015--World Conference on Educational Media and Technology (pp. 38-43). Montreal, Quebec, Canada: Association for the Advancement of Computing in Education (AACE). Retrieved October 22, 2019 from .

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