Study on Emotional Intelligence Levels of Physical Education Teachers in Terms of Various Variables
Bibliographic record
Abstract
For teaching profession, one of the important types of intelligence is emotional intelligence. Emotional intelligence levels of especially physical education teachers are expected to be high. The aim of this study is to examine emotional intelligence levels of physical education and sports teachers based on various variables. In this research, single screening model which is one of the General Survey models has been adopted. Population of the study consists of 320 Physical Education Teachers working in Ağrı in 2018-2019 academic years and its sample group consists of a total of 106 physical education teachers, of which 23 are female and 83 are male and who were selected from this population by random sampling method. “Personal Information Form” developed by researchers has been used in order to determine demographic characteristics of Physical Education Teachers, “Schutte Emotional Intelligence Scale” developed by Schutte et al. (1998) has been used in order to measure their emotional intelligence levels. According to findings, it has been seen that emotional intelligence levels of physical education teachers differentiate significantly in favor of physical education and sports teachers do not have administrative function in sub-dimension of utilizations of emotions according to administrative function variable. It has been determined that there is no significant difference between them according to gender, age and professional experience, education grade which is worked in, educational level, marital status, status of doing sports and smoking variables. Consequently, it is seen that emotional intelligence levels of physical education teachers are above medium level and there is no difference between them in terms of other variables except variable of having administrative function.
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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 | 0.000 | 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) | 0.002 | 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".