Facilitating professional learning for technology coaches through cross-district collaboration
Bibliographic record
Abstract
Purpose This two-year study illuminates the experiences of technology coaches (digital learning coaches [DL] and science technology engineering and mathematics/literacy coaches [STEM/L]) as they engaged in their own professional learning (PL) facilitated by a faculty researcher. Design/methodology/approach Technology coaches from different school districts and their respective colleagues participated in book studies as part of their PL. They reflected and debriefed individually and collaboratively with a researcher facilitator. Data were collected through interviews, field notes at meetings, observations, researchers’ reflections and artefacts. Qualitative data analysis methods were employed. Findings The findings offer a glimpse into (1) benefits of cross-district collaboration, (2) challenges finding resources for coaching, (3) career-long desire to learn and (4) time to build and sustain cross-collaborations. Practical implications Conclusions suggest that DL and STEM/L coaches benefit from their own dedicated, differentiated programme of PL supported by each other (as from other districts) and a researcher facilitator. Educational implications are offered for researchers and other school district stakeholders for consideration for them to foster coaches’ collaborative PL. Originality/value Importantly, this project is an exemplar of how to support coaches’ PL and growth through researcher facilitation of cross-district collaborative learning.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".