Investigation of Prospective Teachers' Attitudes towards Game and Physical Activities Course
Bibliographic record
Abstract
The purpose of this research is to determine the attitudes of prospective teachers towards game and physical activities subject. In the first part of the study, it is aimed to describe the attitudes of prospective teachers towards game and physical activities course. In the second part of the study, it was analyzed whether the variables of gender and department studied significantly differed on the attitudes of prospective teachers towards game and physical activities course. Screening method was used in this research. Candidates studying in the physical education and sports teaching, classroom teaching and preschool teaching departments of Kırşehir Ahi Evran University participated in the study on a voluntary basis. "Personal Information Form" and "Attitude Scale for Game and Physical Activities Course" were used in the data collection process. When testing the research data, 0.05 significance level was taken. In the analysis of the data, ANOVA technique was used. The prospective teachers' attitudes towards the course of game and physical activities course are generally high (X̅ = 4.01). The attitudes of teacher candidates towards the course of game and physical activities course differ significantly according to their genders (p <.05) and departments (F = 8,278, p <.05). Research results show that prospective teachers' attitudes towards the course of game and physical activities course are generally at a high level. However, the mean of male candidates is higher than the mean of female candidates. The attitudes of prospective teachers towards physical education and sports teaching are positive and at high level.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".