Comparison of Emotional Intelligence Levels and Problem Solving Skills of Prospective Teachers According to Different Variables
Bibliographic record
Abstract
IQ is considered as a true criterion of intelligence while emotional intelligence is considered as a decisive in order tobe happy and successful in life. It is of interest to the educational system that emotional intelligence can bedeveloped at the same time. Emotional intelligence gained in the family will help to improve the school life,overcome the obstacles that people will encounter in their lives and solve the problems. In this study, emotionalintelligence levels and problem solving skills of the prospective teachers were examined according to differentvariables. In this study, the cross sectional survey design was used to investigate the research questions with 1033prospective teachers, 813 of whom were women and 220 were men, who agreed to participate in the study. The studygroup was chosen from the students of education faculty of the public university located near the black see region ofTurkey. As a means of collecting data, the Bar-On Emotional Intelligence Scale, the Problem Solving Scale, and thePersonal Information Form were used to obtain data from the participants. As a result of the study, the problemsolving skills of prospective teachers don’t differ according to gender and the class level; It was also found thatemotional intelligence did not differ according to gender and the class level, but it had a significant differenceaccording to age and department variables. In line with these results, in order to educate teachers with high level ofemotional intelligence and problem solving skills, attention should be paid to the emotional characteristics of theteacher candidates. The change of emotional intelligence with different factors should be examined in follow upstudies.
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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 | 0.000 |
| 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) | 0.002 | 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".