Elements of Self-Awareness Reflecting Teachers’ Emotional Intelligence
Bibliographic record
Abstract
Significantly, teachers of various educational settings play crucial roles in actualizing the purposes of education.In ensuring the effective attainment of the noble mission of learning and teaching, considerable emphasis mustbe given to teachers’ well-being notably their emotional intelligence (EI). In recent years there is growingempirical evidence that EI contributes significantly to performance and productivity. However, literature on therole of EI in enhancing teachers’ efficacy in Malaysia is scarce. This study attempts to fill this academic gap byexamining the potential of self-awareness, one of Goleman’s EI domains among teachers. Specifically, this studyaims to determine the elements of self-awareness that could enhance teachers’ efficacy from Malaysianperspective. Three focus group interviews were conducted with participants of both genders who comprise ofyoung and old teachers, inexperience and experienced teachers from three different schools. This study findsintent as important sub-domain of self-awareness in Malaysian context thus extended the earlier modeldeveloped by Goleman. Significantly this study may be beneficial to spread awareness on the vitality of EI toimprove psychological health and teaching success and, in turn, positive student outcomes. This study suggestsintegrated EI training that does not only increase teachers’ efficacy but also may decrease burnout syndromesand job dissatisfaction.
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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 | 0.001 |
| 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".