Teaching Physical Fitness and Exercise Using Computer-Assisted Instruction
Bibliographic record
Abstract
Empirical evidence has demonstrated the benefits of physical activity in preventing chronic diseases and premature death. Unfortunately, there is a global trend of insufficient physical activity, which was aggravated by the recent global pandemic. Although physical education is often used to promote physical activity, the transition to online education made it difficult to teach fitness and exercise from a distance due to several limiting factors. This chapter aims to respond to these challenges by implementing a school-based public health intervention using a computer-assisted instruction (CAI) tool called VD-CAI. Through an experimental approach, it was found that VD-CAI as an instructional technology shows a performance advantage compared to other pedagogies. Specifically, students who used VD-CAI in their physical education courses received higher scores and exhibited a more positive attitude. This chapter contributes to the growing scientific evidence of the effectiveness of school-based health education programs as well as the expanding literature on CAI and physical education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".