A Response to the Review of the Community of Inquiry Framework
Bibliographic record
Abstract
The Community of Inquiry (CoI) framework has become a prominent model of teaching and learning in online and blended learning environments. Considerable research has been conducted which employs the framework with promising results, resulting in wide use to inform the practice of online and blended teaching and learning. For the CoI model to continue to grow and evolve, constructive critiques and debates are extremely beneficial, in so much as they identify potential problems and weaknesses in the model or its application, as well as provide direction for further research. In this context, the CoI framework was recently reviewed and critiqued by Rourke and Kanuka in their JDE article entitled “Learning in Communities of Inquiry: A Review of the Literature.” This paper is a response to this article and focuses on two main issues. The first issue is the focus of the review and critique on learning outcomes. The second issue concerns the representation, comprehensiveness, and methodology of the review.
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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 | 0.070 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".