Analysis of Factors Influencing Creative Personality of Elementary School Students
Bibliographic record
Abstract
This quantitative research examined factors that affect elementary students’ creativity and how those factors correlate. Aiming to identify significant factors that affect creativity and to clarify the relationship between these factors by path analysis, this research was designed to be a stepping stone for creativity enhancement studies. Data were gathered from 208 students in 3 fifth-grade classes and 3 sixth-grade classes in 5 different schools located in Seoul, Korea. Survey questions, asked through five-score Likert-scale items, focused on attentiveness in science class, creativity and scientific attitude, which has been shown by the literature to have positive influences on one another. The findings include that their scientific attitude, attentiveness, and creativity correlated with significance, where gender did not have an effect on the relationship. Gender and age of the students have shown no significant effect on their scientific attitude, attentiveness or creativity. Scientific attitude, attentiveness and creativity have demonstrated positive effects to each other, the effect being stronger from scientific attitude to creativity (0.659) than the other two, attentiveness & scientific attitude (0.32) and attentiveness & creativity (0.368). Scientific attitude affects creativity most directly (0.659), and attentiveness would affect creativity more as a cofactor next to the scientific attitude (0.213) rather than when it’s by itself (0.154). That is, if a teacher devises a certain way to enhance attentiveness of students during their science class, their scientific attitude and attentiveness would increase, giving them a solid chance to enhance their creativity consequently.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".