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Record W2505759296 · doi:10.1123/jce.6.2.103

Applying Kolb’s Theory of Experiential Learning to Coach Education

2013· article· en· W2505759296 on OpenAlex
Ashley Stirling

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Coaching Education · 2013
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsExperiential learningConceptualizationCoachingPsychologyCertificationExperiential educationPedagogyLearning theoryEngineering ethicsManagementEngineeringComputer sciencePsychotherapistArtificial intelligence

Abstract

fetched live from OpenAlex

Coach education is the key to improved coaching. In order for coach education initiatives to be effective though, the conceptualization of those initiatives must be developed based on empirical learning theory. It is suggested that Kolb’s theory of experiential learning may be an appropriate learning theory to apply to coach education. This paper outlines how Kolb’s theory of experiential learning was used in the development of Canada’s National Coaching Certification Program coach education module entitled “Empower +: Creating Positive and Healthy Sport Experiences.” The module is summarized briefly, and Kolb’s six key tenets of experiential learning are reviewed. Applications of each tenet within the coach education module are highlighted, and recommendations are made for future evaluation and research.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.017
GPT teacher head0.351
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it