Alignment of Masters Swim Coaches’ Approaches With the Andragogy in Practice Model
Bibliographic record
Abstract
Coaches working with Masters Athletes (MAs) are tasked with facilitating learning and enhancing performance and quality of experience specifically for an adult cohort. In education, the Andragogy in Practice Model (APM) characterizes adult learners and provides teachers with principles for how to best facilitate learning (Knowles, Holton III, & Swanson, 2012). The purpose of the current study was to explore how coaches describe approaches with their MAs to discover how they align with andragogical principles. Eleven coaches were interviewed regarding their approaches in working with Masters swimmers. Data were thematically analyzed according to the six APM principles. The results revealed the bidirectional pattern of communication between the coaches and MAs, the coaches’ awareness of the athletes’ matured self-concept and prior experiences, the personalized goal oriented approach, the various approaches coaches used to motivate, and strategies that the coaches used to prepare MAs for training. The findings suggest that coaches who reported approaches in keeping with andragogical principles more effectively accommodated their MAs’ interests. When their approaches countered the principles, there appeared to be a disconnect between the coaches’ approaches and the MAs’ preferences. Together, these results provide evidence of the importance of coaches’ understanding of adult learning principles when coaching MAs.
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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 | 0.005 | 0.009 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".