Building mental health and social skills: The positive impact of jigsaw model in taekwondo course
Bibliographic record
Abstract
21st-century education emphasizes technology adaptation and high-level thinking skills, including the development of high-level thinking that puts high pressure on students' mental health and social skills. This study used a one-group pre-post design to test the effect of the jigsaw learning model on students' mental health and social skills in taekwondo lectures. The participants were 136 students (male = 109; female = 27) using a simple random sampling technique. Mental health data were collected using 35 items of the Mental Health Instrument with a 5-point Likert scale, and social skills data were collected using 45 items of the Social Skills Improvement System-Rating Scale with a 4-point Likert scale. Data were analyzed using descriptive statistics and paired sample tests with the help of the IBM SPSS version 29 program. The study's results proved that the mental health indicator that experienced the highest increase was "Harmony in mind," and the lowest was "Learning activities." In the social skills indicator, the highest increase was "Self-control," and the lowest was "Empathy". The jigsaw learning model has been proven to significantly improve students' mental health and social skills in taekwondo lectures. Thus, the jigsaw model can be used as an effective model to address students' mental health and social skills problems and support students' psycho-social development when facing various challenges and high academic demands at university. Keywords: Mental health, social skills, jigsaw model, cooperative learning
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".