Artificial intelligence-enhanced assessment of fundamental motor skills: validity and reliability of the FUS test for jumping rope performance
Bibliographic record
Abstract
Introduction: Fundamental motor skills (FMS) are foundational for lifelong physical activity and talent development. However, their development is often overlooked in favor of sport-specific outcomes in physical education (PE). This study aimed to evaluate FMS proficiency among students enrolled in traditional and school-based sport PE programs and explore implications for early specialization and motor competence. Methods: = 2,238) using the validated Fundamental Motor Skills in Sport (FUS) test. Participants were grouped based on enrollment in traditional PE or school-based sport PE programs. Proficiency was classified into four levels based on mastery across six motor tasks. Results: The majority of students in both groups failed to meet the basic FMS proficiency threshold. Specifically, 72% of boys and 77% of girls in sport PE programs were below elementary proficiency, compared to 90% of boys and 92% of girls in traditional PE. While sport PE students outperformed their peers, significant deficits remained. Gender differences showed boys had advantages in object control skills, while girls performed better in coordination-oriented tasks. Discussion: Both traditional and sport PE programs fall short of supporting adequate FMS development, potentially due to overemphasis on early specialization and lack of instructional support for motor competence. These findings underscore the need for curricular reforms and targeted teacher training to prioritize broad motor skill development and promote long-term participation in physical activity.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".