A Child’s Family Relationship and Emotional Well-Being through Self-Regulated Learning in South Korea
Bibliographic record
Abstract
This paper focuses on the relationship between a child’s family relationship, emotional well-being, and self-regulated learning in Korea. The sample of this study consisted of 527 elementary school students who were selected from three different schools in South Korea. The data were collected using the family relationship scale, the self-regulated learning scale, and the emotional well-being scale. The results showed that the structural equation modeling analysis was a good model fit to indices. Family relationship such as cohesion or conflict had a direct and indirect effect on a student’s emotional well-being. Also, family cohesion and family conflict indirectly influenced a child’s emotional well-being through self-regulated learning. It was also evident that there were significant grade differences in the students' emotional well-being where 5th graders had higher positive emotions than 6th graders and 6th graders had higher family conflict than 5th graders. To sum up the findings, there was clear evidence that a positive family relationship predicted a student’s emotional well-being directly and anticipated a child’s emotional well-being indirectly through self-regulated 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".