Can multimedia enhance tactical teaching-learning-training in soccer? The case of Sphero™
Bibliographic record
Abstract
Purpose The study aimed to compare the players’ and coach’s individual perception of tactical competence before and after an intervention based on imagery techniques and a multimedia resource (SpheroTM). Methods SpheroTM, a teleguided miniature ball rolling in any direction, was applied to teach tactics before and during training sessions and games over a 5-week specific training program. The ball was used to represent a game ball, and cones were placed on a miniature soccer pitch to simulate the play and explain the coach’s directives. The execution and the understanding of pre-game directives were respectively evaluated by the coach and the players themselves, before and after the training program. An empirical pre-post treatment was used to compare 245 rates provided by 14 players of an under-14 amateur soccer team in Canada. Descriptive analysis was performed and the t-test or Wilcoxon test (z) were used for pairedcomparison (pre- vs. post-training). The statistical significance of the results was set at p < 0.05. Results After 5 weeks of training with the use of SpheroTM, results showed that the players appreciated their ability to play in accordance with directives, and that the coach was able to observe his directives through how they played, although the assessment scores of understanding remained the same. Conclusions A teaching setup involving SpheroTM allowed a good understanding but training was necessary to reach better assessment scores for the actual application of the directives.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".