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Automatic Classification of Online Discussions and Other Learning Traces to Detect Cognitive Presence
Verena Dornauer1, Michael Netzer1, Éva Kaczkó1,2
1Institute of Medical Informatics, UMIT TIROL - Private University for Health Sciences and Health Technology, Eduard Wallnöfer Zentrum 1, Hall in Tirol, 6060 Austria.
International Journal of Artificial Intelligence in Education
|June 3, 2024
Summary
Researchers developed a German-language classifier to automatically detect students' cognitive presence in online courses using linguistic analysis. This tool, based on the Community of Inquiry framework, achieved 82% accuracy without needing additional learning traces.
Area of Science:
- Educational Technology
- Online Learning
- Artificial Intelligence in Education
Background:
- Cognitive presence is vital for meaningful online learning within the Community of Inquiry (CoI) framework.
- Real-time dashboards visualizing cognitive presence can aid instructors in monitoring and supporting student progress.
- Current classifiers often rely solely on linguistic analysis of student posts, with limited exploration of other learning traces.
Purpose of the Study:
- To develop and evaluate a German-language classifier for cognitive presence in online courses.
- To investigate whether incorporating additional learning traces (e.g., file attachments, tagging) improves classifier accuracy compared to linguistic analysis alone.
- To create a foundational dataset for future research on cognitive presence in German online learning environments.
Main Methods:
- Utilized a dataset of 1,521 manually coded meaningful units from a German online university course.
- Employed the Linguistic Inquiry and Word Count (LIWC) tool for linguistic feature extraction.
- Included additional learning traces such as file attachments, tagging, and glossary term usage.
- Applied k-nearest neighbor, random forest, and multilayer perceptron classification models.
Main Results:
- Achieved a maximum accuracy of 82% and a Cohen's κ of 0.76 for the cognitive presence classifier.
- The inclusion of additional learning traces did not significantly enhance the predictive performance of the classifier.
- Successfully developed an automatic classifier for cognitive presence in German online courses based on linguistic analysis.
Conclusions:
- An automatic classifier for detecting cognitive presence in German online courses has been developed, primarily using linguistic analysis.
- The developed classifier represents a step towards creating real-time teacher dashboards for monitoring student engagement.
- This study provides the first fully CoI-coded German dataset, valuable for future research in cognitive presence and online learning.
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