Shared Metacognition, Collaboration and the Community of Inquiry Framework in Action
Bibliographic record
Abstract
This study explored the application of the Community of Inquiry (CoI) framework in a large enrollment online course, which focuses specifically on collaborative learning and student engagement. The CoI framework, which comprises social, cognitive, and teaching presence, provides a theoretical foundation for understanding how students construct personal meaning and confirm mutual understanding. While metacognition has been an important factor in learning, its role in collaborative online learning environments is poorly understood. This mixed-methods study investigates the impact of shared metacognition on collaboration in a large-enrollment online Academic Writing course at an open distance learning university in South Africa. The study employed the Shared Metacognition survey, which was developed from the CoI framework, to collect data from 1200 students at three stages: pre-module, midpoint, and post-module. Statistical analysis and qualitative content analysis were used to examine self-regulation and co-regulation dimensions of metacognition. The findings highlight the significance of teaching presence in predicting student success and satisfaction. Shared metacognition emerged as a significant factor in developing collaborative learning environments. Students’ awareness of their thinking and learning processes improved through critical discourse and peer interaction. This research contributes to the understanding of collaborative learning in large courses and emphasises the importance of metacognitive awareness and shared regulatory functions. The study has significant implications for lecturers, practitioners, and instructional designers in large modules who seek to enhance collaborative learning experiences in online education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".