Differences in Creative Personality and Attitude, Creative Problem Solving, and Convergence Thinking of College Students According to Self-Regulation and Cognitive Flexibility Training VR Program Participation
Bibliographic record
Abstract
This study used a cognitive flexibility training VR program that can view and solve problems from various perspectives, self-regulation, and self-regulation with the super cognitive ability required for creativity to improve college students' creative personality and attitude, creative problem solving, and convergence thinking ability. In addition, differences in creative personality and attitude, creative problem solving, and convergence thinking ability were analyzed before and after participation in the program. In this study, 55 students were recruited voluntarily among four-year E University students in Gyeonggi-do. The experimental tool is VR content developed by FNI Co., Ltd. The main contents are mainly composed of improving self-control ability, strengthening positivity, and enhancing interpersonal relationships. The IBM SPSS Statistics 25 program was used to analyze the collected data to test the research question. First, frequency analysis and descriptive statistical analysis were performed to examine the mean and standard deviation of the sociodemographic factors and measurement variables of the subjects. Moreover, to determine how consistently the measuring instrument was used, the reliability was validated by calculating the Cronbach's coefficient value. Furthermore, factor analysis was utilized to minimize the number of items in the developed measurement tool by removing variables unrelated to the component to be studied. Factor analysis has the purpose of reducing variables, removing unnecessary variables, identifying variable characteristics, evaluating the validity of measurement items, and creating variables using factor scores. In addition, to confirm the statistical significance of the average values of creative personality and attitude, creative problem solving, and convergence thinking before and after participating in the VR program, a matched-sample t-test was performed. As a result of the study, it was found that participation in the self-regulation and cognitive flexibility VR program had a positive effect after participation compared to before participation in creative personality and attitude, creative problem solving, and convergence thinking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".