Research development of teachers' emotional intelligence in the 21st century: A bibliometric analysis
Bibliographic record
Abstract
Emotional intelligence (EI) has been recognized as a critical factor in shaping teachers' teaching effectiveness, classroom management, professional well-being, and mental health. As a key psychological construct in the teaching profession, teacher emotional intelligence (TEI) has received increasing scholarly attention in recent years. This study employs bibliometric analysis to quantitatively and visually analyze 258 academic publications on TEI from the WOS core databases in the 21st century. The study reports some bibliometric characteristics of these publications, in which it found that the number of annual publications over the years went through two distinct phases: a nascent and early exploratory period (2000-2018) and a rapid growth period (2019-2023). Spain and China emerged as leading contributors, while Canada's studies demonstrated the highest citation impact (average citations per article). The study also shows other valuable information such as the primary sources of publications, the most prolific authors and institutions, and the most cited publications, etc. Moreover, the relationships among items of publications and keywords were identified and analyzed to present the research status of this field. "Burnout", "job satisfaction" and "self-efficacy" are the high-frequency and core keywords. It can be found that research on TEI has constantly evolved into more diverse themes in the 21st century, with "EI", "higher education" and "engagement" being consistent themes across different development periods. Notably, topics like "support", "mindfulness" and "intention" are emerging, expected to continue gaining intention. Based on data analysis and literature reading, this article provides several possible directions for future research in TEI.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.148 | 0.209 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".