From Presences to Linked Influences Within Communities of Inquiry
Bibliographic record
Abstract
Much research has identified and confirmed the core elements of the well-known Community of Inquiry Framework (CoIF): Social, Cognitive and Teaching Presence (Garrison, 2011). The overlap of these Presences, their definitions and roles, and their subsequent impact on the educational experience, has received less attention. This article is prompted by the acceptance of that omission (Garrison, Anderson, & Archer, 2010). It proposes enrichment to the Framework, by entitling the overlapping spaces uniting pairs of Presences as “Influences.” These three spaces, linking pairings of Social, Teaching, and Cognitive Presences, can be labelled as “trusting,” “meaning-making,” and “deepening understanding.” Their contribution to the educational experience is to address constructively some of the challenges of online learning, including learner isolation, limited learner experience of collaborative group work and underdeveloped higher-level abilities. For these purposes we also envisage “cognitive maps” as supporting learners to assess progress to date and identify pathways forward (Garrison & Akyol, 2013). Such maps, developed by a course team, describe the territory that learners may wish to explore, signpost possible activities, and encourage the development of cognitive and interpersonal abilities required for online learning. We hope that considering the Influences may also assist tutor conceptualisations of online community-based learning. Our proposals call on both learners and tutors to conceive of the Presences and Influences as working together, in unison, to enhance the educational experience whilst fostering deep learning. Our suggestions are presented to stimulate scholarly debate about the potential of these interwoven sections, constructively extending the Framework.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".