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Record W4400477137 · doi:10.46392/kjge.2024.18.3.199

Analyzing the Effectiveness of Creativity Courses on University Students' Perceptions and Creative Mindset Changes : An Application of Integrated Research Methods

2024· article· en· W4400477137 on OpenAlexaff
Minhee Yoo

Bibliographic record

VenueThe Korean Association of General Education · 2024
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMindsetCreativityPerceptionPsychologyCreative thinkingCreativity techniqueMathematics educationPedagogySocial psychologyComputer science

Abstract

fetched live from OpenAlex

The purpose of this study is to examine the changes in the perception of creativity and creative mindset among university students before and after attending a creativity-focused general education program. The study was conducted using a mixed-methods approach, targeting 55 students from freshmen to seniors who expressed their willingness to participate and were enrolled in a general education courses at a university in the Chungnam region. The findings indicate that there was a change in the overall perception of creativity from the perspectives of creative thinking, creative outcomes, creative characteristics, and creative stimuli. Additionally, there was a positive change in the creative mindset of the students. This suggests that creativity-focused general education had a positive effect on the development of students' creative competencies. Through this study, we aim to provide useful information for the design and implementation of education programs that enhance creativity, by broadly understanding the impact on university students' perception of creativity and their creative mindset.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.060
GPT teacher head0.494
Teacher spread0.433 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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