Impactful Digital Technology Coaches: Identifying their Characteristics and Competencies while Delineating their Role
Bibliographic record
Abstract
Digital technology coaches (DTCs) often support teachers with integrating technology into their classroom and instructional program, as well as provide ongoing staff development. To be effective, coaches tend to have specific characteristics for instructional coaching and competencies for educational coaching. We investigated if these characteristics and competencies applied to effective DTCs while we observed their proficiency with technology, their interactions with other educators, and the way they provide support for the teacher-professional learning (PL) process. Three DTCs led over 80 K–12 teachers from the same school district in classroom coaching sessions, collaborative planning meetings, PL sessions, and conference presentations. In keeping with generic qualitative methods, multiple data sources including fieldnotes, artifacts, and transcribed interviews were analyzed. Through examining data detailing their role and impact on the learning of their teacher colleagues, it was apparent that these DTCs possess the characteristics and competencies of effective instructional coaches. Importantly, this study adds to the literature on effective coaches by documenting the applicability of these characteristics and competencies to not only instructional coaches, but also DTCs, elucidating their role, and explaining their influence on teacher PL.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".