Pedagogical Self-improvement Methods: Lessons from a Master Coach Extrapolated to Developing Educators
Bibliographic record
Abstract
In this article we explore how research on a master coach, deliberate practice, and teaching effectiveness intersect in an effort to suggest key strategies for instructional self-improvement for developing physical educators and sport instructors. Wooden’s reputation as a master coach and teacher is legendary, based upon his lengthy tenure and success as the coach of the men’s basketball team at UCLA. During his career his methods attracted the scrutiny of educational researchers who were interested in the lessons that they could derive from his coaching and apply to a classroom setting. Those lessons continue to resonate, particularly considering the recent emphasis on sustained, effortful, ‘deliberate’ practice as a key component of continual improvement in sports and other fields (e.g., Carter & Bloom, 2009; Ericsson, Krampe, & Tesch-Romer 1993). The theory of deliberate practice exemplifies Wooden’s coaching philosophy, which was to seek small, incremental improvements every day, both in his players, and in his own coaching techniques. Borrowing from teaching effectiveness literature (Siedentop & Tannehill, 2000), we suggest ways by which developing instructors might improve, with a primary focus on personal self-improvement activities Cet article veut etablir en quoi les resultats d’une recherche axee sur un entraineur emerite, sur la pratique deliberee et sur l’efficacite de l’enseignement convergent, menant a la determination de strategies cles sur l’auto-amelioration instructive pour mieux former les enseignants d’education physique et les instructeurs sportifs. La reputation du legendaire Wooden a titre d’entraineur de l’equipe de basket-ball masculine de l’Universite de la Californie a Los Angeles (UCLA) et d’enseignant emerite decoule de ses longues annees de service et de ses reussites hors pair. Tout au long de sa carriere, ses methodes ont suscite un vif interet en raison des grandes lecons qu’on pouvait en tirer et ensuite appliquer a divers contextes de classe. Ces lecons sont plus pertinentes que jamais en raison de la nouvelle attention que suscitent les modes de pratique deliberee soutenue axee sur l’effort qui constitue aujourd’hui l’element cle d’une amelioration permanente dans les domaines du sport et autres (p. ex., Carter et Bloom, 2009; Ericsson, Krampe, et Tesch-Romer 1993). La theorie de la pratique deliberee exemplifie la philosophie d’entrainement de Wooden qui consiste a faire chaque jour de petits progres evolutifs, qu’il s’agisse du rendement de ses joueurs ou de ses propres techniques d’entrainement. S’inspirant de la documentation sur l’enseignement efficace (Siedentop et Tannehill, 2000), les auteurs proposent des approches utiles pour aider les nouveaux entraineurs a s’ameliorer, misant surtout sur des activites d’amelioration personnelle.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".