Embracing Self-Determined Professional Learning: The Role of the Digital Technology Coach
Bibliographic record
Abstract
It is well established that a “one-size fits all” approach to professional development does not meet teachers’ differentiated needs and diminishes their confidence (Hibbert et. al, 2008). This highlights the need in teacher professional learning for individualized coaching sessions, which were enacted by the digital technology coach in this study. This presentation will profile a digital coach’s experience as she established this new role in an Ontario school board. Over eight months, one digital technology coach delivered professional learning meetings (n=4) and individual coaching sessions (n=16) surrounding the implementation of educational technology to teacher participants (n=18). Data included observational field notes, interviews and artifacts. Through case study methods, this study describes the perceived factors affecting the implementation of digital technology coaching from the perspectives of the coach and teacher participants. Findings include forging the role of digital technology coach, being responsive to teachers, technology across the curriculum, resources and professional learning facilitation. This study illuminates the need for differentiated digital technology coaching and the potential that effective digital coaching may later support the development of on-site teacher leaders in this area.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".