Collaborative inquiry as a professional learning structure for educators: a scoping review
Bibliographic record
Abstract
Collaborative inquiry (CI) has emerged as a dominant structure for educator professional learning in the twenty-first century. CI engages educators in collaboratively investigating focused aspects of their professional practice by exploring student responses to instruction, leading to new understandings and changes in classroom teaching. However, despite the increased presence of CI, research on CI frameworks has yet to be consolidated and synthesized. The purpose of this systematic, scoping review was to examine literature on the structure, challenges, and benefits of CI as a professional learning structure for educators (i.e. teachers, principals, school district leaders). In total, 42 sources were identified and analysed in relation to characteristics of CI, supports and resources for CI, empirically supported benefits of CI, and enactment challenges. The review found that the majority of texts and research in this field are highly practical, describing CI steps or case-study examples. Accordingly, the current literature reviewed in this paper largely serves a how to function for engaging in CI projects with empirical data collected from primarily case-study work. The literature also provides preliminary theoretical articulations for CI as a professional learning structure for educators. The paper concludes with identified areas for future CI research related to: clarifying the focus of CI initiatives, articulating what ‘inquiry’ means in CI, and sustaining CI within the profession of teaching.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".