The effects of integrated classroom based physical activity on on-task behavior for Aboriginal children in grades four and five
Bibliographic record
Abstract
There is a wide academic achievement gap between Aboriginal and non-Aboriginal youth. The rate of high school non-completion for Aboriginal Peoples is approximately 61% (Statistics Canada, 2006). The need to close this gap is great given the growth of the Aboriginal population in Canada. Research over the past decade has shown that physical activity improves the learning ability and academic performance of children (Tomporowski et al., 2011; CDC, 2010). Purpose: This study examined the effects of classroom based physical activity lessons that incorporated curricular content on the on-task behavior of grade four and five participants at an on-reserve elementary school. Methods: Time on task was assessed for thirteen participants (N=13) through direct observation before and after the intervention and before and after an inactive classroom lesson. A two way [time (beginning of lesson vs end of lesson) x period (active lesson vs non active lesson)] repeated measures ANOVA was conducted. Results: The intervention was effective in improving the on task behavior of the participants. On task behavior scores decreased from beginning of lesson to end of lesson lesson in the non active lesson period, while on task behavior scores increased from beginning of lesson to end of lesson lesson in the active lesson period. The two way repeated measures ANOVA revealed a significant time x period interaction [F(1, 12) = 36.067, p< .001]. Conclusion: This research illustrates that incorporating physically active lessons that reinforce curricular content into the classroom may be an effective way to improve the on-task behaviors of Aboriginal children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".