Opinions of the Physical Education Teachers about the Effect of Peer Education and Physical Activity Cards’ Usage on the Student Skills
Bibliographic record
Abstract
The purpose of this study was to determine the opinions of the physical education teachers about the effect of peer education and physical activity cards’ usage on the student skills. Physical education teachers used a questionnaire to follow the students through observation. One-hundred-twenty secondary school students in 2015-2016 semester in Elazığ participated to the study. The students in the sample group were divided into two 30-person groups and control group and experimental group were constituted. The formed groups were taught lessons by means of Physicak Activity Cards for 4 weeks. At the end of the four-week course period, opinions about peer education were obtained through individual and focus group interviews with the experimental and control group students. In addition, “Skill Learning Observation Forms” prepared by researchers and experts were also applied. In the analysis of the data, for qualitative dimension, descriptive analysis, for quantitative data, percentage, frequency, for repetitive measurements, t test and one-way variance analysis were used. The SPSS 22 package program was used for the analysis of the quantitative data. The quantitative data were explained through tabulation. Again, the data obtained from observation forms were presented in the form of tables with their frequency and percentage values. Themes were obtained by coding the qualitative data. When the influence of the physical activity cards on the students’ attitudes toward the class is considered, a significant difference was attained in the common effect of the experimental group. According to this result, the education received by the students in the experimental and control groups led to a significant difference in favor of the last-test at the pre- and post-program attitude levels. However, it was determined that being in different groups did not lead to a significant difference in students’ attitude scores. When we examined the student opinions about the cards, the most of the students expressed the opinions that teaching lessons through cards created difference in the classes and increased their participation in the class. It was also enhanced their exercise skills and self-confidence. They did not have difficulty while applying the cards, they led them to act in coordination, and they rendered the class more enjoyable.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".