Leveraging non-formal and informal learning in sport:An action research goal-setting intervention for high performing youth swimmers
Bibliographic record
Abstract
In recent years, growing attention has been paid to goal setting within sport as a method for enhancing performance among athletes. However, meta-analyses indicate that demonstrated effects vary. Interestingly, much of the focus within these studies has been on the effect of goal setting, with a notable gap involving attention to the learning process through which athletes learn to set and use goals. As such, we undertook an action research approach to explore this learning process over a 3 month period. This research process involved the collaborative creation and implementation of non-formal and informal learning initiatives into a 3-week goal setting program within a high performing youth swim club in Denmark. A total of 28 swimmers of both genders (aged 13 to 17 years) and their head coach were involved in the study. The non-formal initiatives included two workshops, group goal reflection activities, and the display of athletes’ goals (i.e., on their water bottles), while the informal involved frequent unscheduled conversations between athletes and the first author. Throughout the program, the first author conducted participant observation to observe the athletes’ engagement with the learning initiatives and to explore the athlete’s and coach’s experiences. In addition, based on a heterogeneous sampling strategy, eight post-program semi-structured interviews were conducted with athletes that displayed varying levels of engagement throughout the program. Through reflexive thematical analysis, we found that the informal initiatives were experienced as an additional benefit to the non-formal initiatives, as they allowed for discussions of specific topics that the athletes were having trouble with during practice. Our findings have the potential to inform future work by practitioners and researchers in relation to how they can best incorporate several learning strategies, including non-formal and informal approaches, within interventions using mental skills such as goal setting as a method for enhancing performance.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".