Low-Cost and Scalable Classroom Equipment to Promote Physical Activity and Improve Education
Bibliographic record
Abstract
BACKGROUND: We tested a low-cost and scalable set of classroom equipment, called Active Classroom Equipment, which was designed to promote physical activity while children learn. We hypothesized the Active Classroom Equipment would be associated with increased physical activity without impairing learning. METHODS: Fourteen first-grade students in a public elementary school (7 females, 7 males, aged 6.9 ± (SD) 0.4 years, 24 ± 5.4 kg, BMI 15.8 ± 2.6 kg/m2) used the Active Classroom Equipment for 30 minutes each day throughout the school year. Five-day physical activity was measured using validated triaxial accelerometers at baseline (before the intervention began) and on 4 sequential occasions during the 9-month intervention. RESULTS: For the baseline period, 5-day physical activity averaged 157 ± 65 AU/min. When the 14 children accessed the Active Classroom Equipment, their mean 5-day physical activity was 229 ± 103 Acceleration Units (AU)/ min (P < .0001). There were sequential increases in physical activity over the 9-month intervention (Quarter 1: 163 ± 94 AU/min, Quarter 2: 227 ± 108 AU/min, Quarter 3: 278 ± 61 AU/min, Quarter 4: 305 ± 65 AU/min). Students' Dynamic Indicators of Basic Early Literacy Skills (DIBELS) scores improved. CONCLUSION: Active Classroom Equipment may be one approach to increase 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".