Facilitating professional learning for technology coaches through cross-district collaboration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it