The Relationship Between Teacher Candidates’ Self-Efficacy and Attitudes of Sports History
Bibliographic record
Abstract
The objective of the present study is to analyze the relationship between physical education and sports teacher candidates’ academic self-efficacy and attitudes towards the course of sports history. The population of the present study consists of 240 junior and senior teacher candidates studying at School of Physical Education and Sports at Yozgat Bozok University and Faculty of Sports Sciences at Erciyes University and Mehmet Akif Ersoy University. The sample group consists of 138 participants randomly selected from the population. A Personal Information Form developed by the researcher, academic self-efficacy scale developed by Jerusalem and Schwarzer (1981) and adapted to Turkish context by Yılmaz, Gürçay, and Ekici (2007), and the attitude scale towards the course of sports history developed by Yılmaz, Namlı, and Kan (2013) were used for data collection. The data obtained in the study were analyzed using a personal computer. Numbers, percentages, mean values and standard deviations were used as descriptive statistical methods in the data analysis. Pearson correlation analysis was applied to the continuous variables of the study. As a result, a positive correlation was found between physical education teacher candidates’ self-efficacy and attitudes towards the course of sports history. It can be suggested that this tendency stems from teacher candidates’ academic development during their education as a university student, which is a transition to a professional life, and their belief in the importance of transferring their academic development to younger generations during their professional career.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".