Emotional Intelligence of Instructors and the Quality of Their Instructional Performance
Bibliographic record
Abstract
<p class="apa">Quality of instructional performance is the key skill needed for every teaching faculty for achieving the level of pedigree in the present educational scenario. However, the truth is that there are minimal studies to analyze the competency linking emotional intelligence to quality of instructional performance. Therefore the present attempt is to study the role of E.I. among University Instructors and analyze their quality of instructional performance. A sample of 110 Professors participated in the survey from different Universities in the Emirates of Abu Dhabi and Al-Ain. Emotional Quotient Inventory scale was developed to measure the EQ of Instructors. Statistical technique like ‘t’ test and ANOVA was used to find the test mean difference between two groups and more than two groups. The result revealed that emotional intelligence and self-efficacy had very significant relationship towards their work attitude followed by their performance.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".