Reviewing Emotional Intelligence Levels and Time Management Skills among Students of School of Physical Education and Sports
Bibliographic record
Abstract
This study aimed at exploring emotional intelligence levels and time management skills of students of school of physical education and sports (SPES) and assessing their emotional intelligence levels and time management skills in terms of some variables. 309 students who studied at SPES of Erciyes University during the 2017-2018 academic year participated in the study that was designed in screening model. In order to determine participant students’ emotional intelligence levels and time management skills; “Schutte Emotional Intelligence Scale”—Turkish adaptation of which was performed by Tatar, Tok, & Saltukoglu (2011) and Time Management Inventory—Turkish adaptation of which was performed by Alay & Kocak (2002) were used as data collection tools. In the study; Mann-Whitney U Test and Independent-Samples T Test were employed for paired comparisons while One Way Anova and Kruskal Wallis Test were used for multiple comparisons. For measurement of correlations, Pearson Correlation Analysis technique was used. According to study results, emotional intelligence levels and time management skills of students of school of physical education and sports were at “moderate level”. Students’ emotional intelligence levels differed significantly in terms of sex, academic grades and academic departments but not in terms of type of high school, sportive branch and age. Students’ time management skills did not change considerably in terms of age, sex, sportive branch, type of high school, academic grades and academic departments. Moreover, a positive and significant correlation existed between students’ emotional intelligence levels and time management skills.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".