The Effect of Various Local Dances on Prospective Physical Education Teachers’ Attitudes towards the Folk Dance Course
Bibliographic record
Abstract
In this study, the effect of various local dances on prospective physical education teachers' attitudes towards the folk dance course was examined. The research was conducted by using an experimental design with pre-test/post-test control group. A total of 46 female students, which includes an experimental group consisting of 23 students attended the folk dance class and a control group consisting of 23 students did not attend the folk dance class, who are studying at Niğde Ömer Halisdemir University, School of Physical Education and Sports in the Department of Teaching in the spring term of the 2017-2018 academic year and who are aged 20 years, voluntarily participated in the study. In this research, the Folk Dance Course Attitude Scale for Prospective Physical Education Teachers developed by Turan (2015) was used. In the experimental group, the folk dances training program, which continues for 14 weeks with the program of 2 days of a week for 2 hours at each day, was conducted. The data were analyzed by using SPSS 22 (Statistical Package for the Social Sciences) package program. In the analysis of data, dependent sampling t-test was used for examining the differences between experimental and control groups. As a result of the research, it was observed that there was a significant difference in favor of students who took the folk dance training. This result shows that various local dances are effective in developing a positive attitude towards this course.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".