The Community of Inquiry Framework
Bibliographic record
Abstract
Many in the online learning field now promote the need for a social presence online, in addition to cognitive and teaching presence, in order to fully realize benefits of online learning. In this regard, two important concepts arise when adopting e-learning: community and collaboration. Within healthcare there has been a recent push towards interprofessional education (IPE). IPE is an approach in which health and social care professionals come together to learn “with, from and about each other”. In this chapter the authors discuss the background of healthcare IPE and online learning. They examine the potential benefits and limitations of both IPE and e-learning as well as issues related to combining these approaches. They will discuss the theory of ‘communities of inquiry’ and apply a modified version of it as a way to think about and create online IPE. Lastly, the authors introduce an ongoing innovative healthcare e-learning project in Canada that was based on this theory and has focused on bringing together both IPE and online learning using the “build-a-case” method.
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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 | 0.032 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.013 | 0.065 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".