Los programas de educación emocional happy 8-12 and happy 12-16. Evaluación de su impacto en las emociones y el bienestar//The emotional education programs happy 8-12 and happy 12-16. Evaluation of their impact in emotions and well-being
Bibliographic record
Abstract
RESUMENDurante los últimos años se ha visto confirmada la importancia de las variables emocionales y de bienestar para un correcto desarrollo académico y personal del alumnado. Por este motivo ha existido un auge en las intervenciones y programas que trabajan estas variables. El objetivo de la presente investigación es conocer la diferencia existente entre las variables emocionales y de bienestar en alumnado de primaria y de secundaria después de haber participado en los programas de Educación Emocional Happy 8-12 y Happy 12-16 durante un curso escolar. Para el desarrollo del estudio se contó con la participación de un total de 574 alumnos de educación primaria y otros 903 de educación secundaria. El diseño de la investigación fue cuasiexperimental, pretest, postest con grupo control. Los instrumentos utilizados fueron los siguientes: CDE, CDE-SEC, STAIC, STAI y la media de las notas académicas. En cuanto a los resultados, se realizó un análisis de la varianza (ANOVA) con un factor intersujetos y un factor intrasujetos para cada una de las muestras. Se detectó una mejora en las escalas de competencias emocionales y ansiedad en las dos muestras, especialmente en primaria. También existió una importante mejoría en el rendimiento académico. Así pues, los resultados del presente estudio demuestran que el entrenamiento de las competencias emocionales en educación primaria mejora las competencias emocionales, disminuye la ansiedad y potencia el rendimiento académico, resultados que no se manifiestan de manera tan significativa en educación secundaria, aunque sí que muestran una tendencia muy parecida.ABSTRACTDuring the last years it has been confirmed that emotional and well-being variables are of the uppermost importance for a proper academic and personal development of the students. Thus, there has been an important growth of interventions and programs that train this variables. The aim of the present research is to explore the differences among emotional and well-being variables in primary and secondary education students after undergoing the Happy 8-12 and the Happy 12-16 programs during an academic course. For the development of the study a total of 574 primary education students and 903 of secondary education participated in the study. The investigation followed a pre-post quasi-experimental design with a control group. The instruments that were used for the assessment included: CDE, CDE-SEC, STAIC, STAI and the average of the academic marks. Regarding the results, an analysis of covariance (ANOVA) with an intersubject factor and intrasubject factor was carried out for each one of the samples. An improvement in emotional scales and anxiety was found for the two samples, especially in the primary school sample. There was also an important improvement in the academic achievement. All in all results of the present study showed that the training of the emotional competences in primary education improves the emotional competences, reduces anxiety and enhances academic achievement. These results are not that prominent in secondary education, but they show a similar pattern.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".