An Educational Review of Social and Emotional Learning Research in Turkey in the 21st Century
Bibliographic record
Abstract
This research aims to examine research on Social and Emotional Learning (SEL) in Turkey in the 21st century. A qualitative approach was used in the sample of 43 studies conducted in Turkey between January/2001-January/2023, including research articles and theses in SEL. The data was collected through document analysis from the databases of the Turkey National Thesis Center of the Council of Higher Education and Ankara Social Sciences University Library, and the Documentation Department. The data was analyzed by descriptive analysis. Findings proved that SEL measurement tools were developed and adapted as self-reports from primary school to higher education. SEL is correlated with desirable/positive and undesirable/negative traits in children and youth. There are short-term interventions developed and adapted. These programs are effective in improving SEL. As a result, it can be declared that the importance of SEL in raising healthy, successful, and happy generations is accepted in Turkey. However, there is a need for more systematic studies on SEL, both in theory and in practice. First of all, SEL standards should be determined at the national level, considering the cultural sensitivity of the SEL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".