The Relationship between Emotional Intelligence and Educators’ Performance in Higher Education Sector
Bibliographic record
Abstract
The significance of emotions in the classroom has been thoroughly explored, but discussions on educators' abilities to recognize, regulate, and manage their emotions are still ongoing. This paper aims to look at the concept of emotional intelligence (EI) and how professors in higher education can use it to achieve better results in the form of emotional intelligence competencies (EIC). A total of 312 educators from 25 higher education institutes in the United Arab Emirates (UAE) participated in this study. In sampling the Emotional Intelligence Competencies for this study, we adopted Costa and Faria's (2015) EQ test, administered to the respondent. The Reuven Bar-On emotional intelligence scale was created and standardized to gather data. Using structural equation modeling, the validity and utility of a proposed model for EI-based teaching competencies and their relationship to critical strengths were evaluated (SEM). The findings show that EIC significantly impacts educator behavior, which in turn improves student success. In order to ensure successful instruction and remarkable performance, the study provides valuable recommendations to higher education institutes about the importance of recruiting new instructors with high skills in EI and providing training sessions for existing educators to improve their EI 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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".