Effect of Personal and Social Responsibility-Based Social-Emotional Learning Program on Emotional Intelligence
Bibliographic record
Abstract
Programs for social-emotional learning implemented in schools are important in terms of improving students’ emotional and social skills. In the study, a 16-week program was prepared using the Social-Emotional Learning (SEL) model based on the Personal and Social Responsibility Model (TPSR). The program was implemented in three different school types and the effect of the TPSR-SEL program on the emotional intelligence levels of the students was examined according to the school types. The research group was composed of 162 students in total who study at Anatolian High School (n=55), Vocational Technical Anatolian High School (n=51) and Sports High School (n=56) in Afyon province. In research, pattern with experimental pre-test final-test control group was used. Experiment and control groups were formed in all three-school types. The 16-week TPSR-SEL program was applied to the students in the experimental groups and the current curriculum was applied to the students in the control groups. The Schutte Emotional Intelligence Scale (SEI-S) was used as a data tool. Standard deviation—mean and Two-Way Covariance Analysis (Two-Way ANCOVA) was used for data analysis. As a result of the descriptive statistics, it was determined that there were significant differences between the SEI-S total scores of the experiment and control groups, that there was a significant increase in the final-test total scores of the students in experiment group in all three types of schools, and that the total scores of the control group students remained at the same level. In addition, it was found that the joint effects of participation in the TPSR-SEL program and school type on emotional intelligence scores were significant.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".