Fundamental Motor Skills Assessments Predicting Academic Performance: A Systematic Review
Bibliographic record
Abstract
Background: Motor skills are crucial predictors of academic achievement in preschool children; effective motor skill interventions require assessment tools to evaluate motor performance and intervention efficacy. This study aimed to evaluate motor skill assessment tools in terms of their domains and psychometric properties to determine and understand the effect of motor ability on academic performance. Methods: A comprehensive electronic search was performed in PubMed, Scopus, ProQuest databases, and Google Scholar motor engine between January 2013 and May 2025 for all accessible articles involving the application of standardized, psychometrically sound motor proficiency skill tools. Results: A total of eight motor proficiency assessment tools were identified. The MABC-2 and BOT-2 were the most commonly used for predicting academic performance. The psychometric properties and applications of all tools were appraised and compared. Conclusion: Applying these standardized and psychometrically sound tools provides crucial insights into the link between motor competence and students' academic achievement, which has important implications for early identification, intervention, and educational practices.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".