Examination of Lifelong Learning Trends of Physical Education and Sports Teachers on Different Variables
Bibliographic record
Abstract
Objective: The aim of this study is to examine the lifelong learning trends of physical education and sports teachers with regard to various variables and to compare them with the literature. Method: 113 physical education teachers working in secondary schools and high schools in Tokat province and its districts during the 2019-2020 academic year participated in the study. In order to obtain the research data, the “Lifelong Learning Scale” adapted to Turkish by Engin, Kör and Erbay (2016) and the demographic information questionnaire created by the researchers were used. This research is a descriptive study in scanning model. The research data were subjected to normality test and the research data were analyzed according to the results. In the analysis of the data, 0.05 significance level was taken as the criterion. In order to determine the level of lifelong learning competence of physical education and sports teachers, the variables with 2 level were analyzed by using t test statistics, and the variables with 3 or more levels were analyzed by the ANOVA F test statistic. Conclusion: According to the results of this study which was conducted on different variables, physical education teachers’ lifelong learning tendency scores are high. At the same time; gender, professional seniority and and the lvel of the institution (primary school, high school etc.) are not effective factors on lifelong learning motivations og physical education and sports teachers.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".