Learning in Communities of Inquiry: A Review of the Literature (Winner 2009 Best Research Article Award)
Bibliographic record
Abstract
The purpose of this study was to investigate learning in communities of inquiry (CoI) as the terms are defined in Garrison, Anderson, and Archer's (2000) framework. We identified 252 reports from 2000—2008 that referenced the framework, and we reviewed them using Ogawan and Malen's (1991) strategy for synthesizing multi-vocal bodies of literature. Of the 252 reports, 48 collected and analyzed data on one or more aspects of the CoI framework; only five included a measure of student learning. Predominantly, learning was defined as perceived learning and assessed with a single item on a closed-form survey. Concerns about the soundness of such measures pervade the educational measure m e n t community; in addition, we question the validity of the particular items employed in the CoI literature. Bracketing these concerns, the review indicates that it is unlikely that deep and meaningful learning arises in CoI. Students associate the surface learning that does occur with independent activities or didactic instruction; not sustained communication in critical CoI. We encourage researchers to conduct more, substantial investigations into the central construct of the popular framework for e-learning and theorists to respond to the mounting body of disconfirming evidence.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".