GLOBAL TRENDS AND THEMES IN SOCIAL AND EMOTIONAL LEARNING (SEL) COMPETENCIES AMONG EDUCATORS: A BIBLIOMETRIC ANALYSIS
Bibliographic record
Abstract
Educators’ Social and Emotional Learning (SEL) competencies foster a comprehensive teaching and learning process. This bibliometric analysis investigates the research landscape of lecturers’ SEL competencies over the last 25 years. Data are collected from Scopus and were analysed using MS Excel, Harzing’s Publish or Perish, and VOSviewer. A total of 151 papers were selected for analysis using PRISMA. The results of this study highlighted publication trends, influential contributors, key research areas, and international collaborations in SEL. The number of publications related to SEL competencies rose sharply after 2020, peaking at 32 papers in 2024. The United States takes the lead in research contributions with 99 papers, followed by Canada and other countries across Europe and Asia. The concentration of SEL research in Social Sciences (128 papers) and Psychology (59 papers) illustrates a wide-ranging interdisciplinary relevance in SEL. SEL research indicates key themes include emotional intelligence, classroom management, student engagement, equity, and mental health. The widely cited works of Hellman & Milling (2020) and Mondi & Reynolds (2021) underscore the importance of SEL in education and early intervention studies. Co-authorship and co-citation analyses demonstrate active collaborative networks among scholars and institutions such as Arizona State University. Keyword co-occurrence analyses further revealed emerging trends like social justice, inclusion, and the integration of SEL into digital and culturally responsive educational practices. The study further suggests that it is essential to cultivate an SEL-supportive learning environment through educator SEL skills and techniques.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.037 | 0.024 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".