What are student inservice teachers talking about in their online Communities of Practice? Investigating student inservice teachers’ experiences in a double-layered CoP
Bibliographic record
Abstract
This qualitative case study is the first phase of a large-scale design-based research project to implement a theoretically derived double-layered CoP model within real-world teacher development practices. The main goal of this first iteration is to evaluate the courses and test and refine the CoP model for future implementations. This paper demonstrates the potential synergies between two major approaches to teacher professional development practices: i) teachers’ CoPs development and ii) online teacher education courses. The double-layered CoP model could provide a practical integration of the two approaches by providing student inservice teachers in an online graduate course with meaningful opportunities to participate in two different teachers’ CoPs: i) an internal course CoP and ii) an external professional CoP. Our analysis of student inservice teachers’ CoPs experiences shows that the two layers of CoPs supported each other iteratively through the course period. Several design considerations for the second iteration of the online course design are also addressed.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".