The value of Jarvis for studying coach learning: Explaining high school teacher-coaches' learning pathways
Bibliographic record
Abstract
The approximately 52,000 volunteer Canadian teacher-coaches do not require formal coach education and remain relatively unstudied. Thirty-one Ontario high school teacher-coaches were interviewed. In Jarvis' theory of human learning, learning occurs when our life experiences transform our biographical repertoires. Individuals receive information via the senses, the perceived content of which can be transformed cognitively, emotively, and/or practically into knowledge and/or skills. Our analysis revealed three groups of teacher-coaches, each sharing important biographical experiences affecting their learning pathways. Rookie coaches lack experience in sport to reflect upon and practically transform into coaching knowledge and skills. Their learning is mainly future directed and intentional as they seek to cognitively transform the information they seek from interactions with colleagues and other sources like the Internet. Ex-varsity athlete coaches often learn to coach through emotive transformation elicited by reflection on their former coaches' behaviours. Previously experienced coaches learn through practical experience, often as assistant coaches and by observation. They use cognitive and practical transformation to learn through creative variation based on reflection and information gained in informal situations. Jarvis helps us understand the learning pathways of these teacher-coaches, and plan for appropriate learning opportunities.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".