Effects of University Students' Educational Satisfaction on Convergence and Creative Competencies
Bibliographic record
Abstract
The purpose of this study is to understand the effect of individual and educational factors on convergence and creative competencies of university students. In order to achieve the research purpose, educational satisfaction and creativity and convergence capabilities were measured among 1,379 students in 2021, and multiple regression analysis was conducted using the optimization scale method. The findings showed that if educational satisfaction which is an educational factor that can be managed by a university is high, then the influence of individual factors on the convergence and creative competencies can be reduced. In addition, it was found that satisfaction with extra-curriculum did not affect convergence and creative competencies, but satisfaction with liberal arts and major curricula had an effect on convergence and creative competencies. In particular, it was found that satisfaction with the major curriculum had greater influence on convergence competency, and satisfaction with the liberal arts curriculum had greater influence on creative competency. Based on the research results, it is recommended that universities open various liberal arts and major subjects, and maintain a high level of quality according to the students’ level. Moreover, liberal arts subjects must be directed towards improving creativity, and major subjects must be focused on improving job performance for employment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".