Comparing the Self-regulation of Grade 9th Students with Different Personalities and Studying in Schools of Different Sizes
Bibliographic record
Abstract
The objective of this research is to study and compare the self-regulation of grade 9th students with different personality types and who study in schools of different sizes. The sample group used in this study consisted of 860 students from Sisaket province, Thailand, who were randomly selected through a multi-stage random sampling method. Of these, 185 students had an extroverted personality type, while 675 had an introverted personality type, and they were studying in special large schools (216 students), large schools (160 students), medium-sized schools (302 students), and small schools (182 students). The research tool used was a self-regulation assessment questionnaire, which is a 5-point Likert scale questionnaire with 9 questions, having an internal consistency (IOC) ranging from 0.60 to 1.00, item total correlation ranging from 0.62 to 0.82, and reliability of 0.93. The data was analyzed using statistical techniques such as mean, standard deviation (S.D.), Two-way ANOVA, and Bonferroni. The research findings revealed that: 1) there was no interaction between personality type and school size in relation to self-regulation, 2) grade 9th students with different personality types (extroverted vs. introverted) showed statistically significant differences in self-regulation, with introverted students having higher levels of self-regulation than extroverted students, and 3) grade 9th students who studied in schools of different sizes (special large, large, medium, and small) showed significant differences in self-regulation.
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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 | 0.001 | 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".