Use of parkour in primary school physical education to develop motor creativity, divergent thinking, movement competence, and perceived motor competence
Bibliographic record
Abstract
Purpose As an extension of adventure education in school physical education, parkour has the potential to develop problem-solving and creative thinking along with the development of curriculum linked motor competencies and self-competence in a fun and risk-taking environment. This study aimed to investigate whether an adventure education model-based parkour intervention is more effective than the regular physical education in development of divergent thinking, motor creativity, movement competence and perceived motor competence of fourth-grade students.Methods A quasi-experimental, between group design was used. The participants were 55 fourth-grade students (Intervention: 28 from 1 school, Comparison: 27 from 2 schools) in rural regions. An eight-week parkour intervention (16 sessions) was designed and conducted for the intervention group, while the comparison group continued the regular physical education curriculum. One-way ANCOVA was used for the data analysis (p < .01). Measurement tools included Play Creativity, Divergent Thinking: Realistic Presented Problems, Körperkoordinationstest für Kinder: KTK and Perceived Motor Competence Questionnaire in Childhood (PMC-C).Findings Significant differences in motor creativity [F(1,52) = 9.76, p = .003, partial η2 = .158], divergent thinking skills [fluency F(1,52) = 33.14, p < .001, partial η2 = .389; originality F(1,52) = 7.39, p = .009, partial η2 = .124], movement competence [F(1,52) = 34.45, p < .001, partial η2 = .398], and perceived motor competence [F(1,52) = 7.44, p = .009, partial η2 = .125] were observed when compared to the comparison group. The intervention integrity was assessed to be 90%, and the attendance rate of the participants in the intervention was 87.2%.Conclusions This adventure education model-based parkour unit was effective in developing general and movement related creativity, as well as movement competence and perceived motor competence of primary school students. This study has implications for deploying movement exploratory approaches such as parkour as a means to achieve quality physical education characteristics which could involve professional development on the adventure education framework, parkour specific training, as well as non-linear and physical literacy enriched pedagogical 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".