Is There a Relationship between Creativity and Mathematical Creativity?
Bibliographic record
Abstract
The aim of this study is to examine the mathematical creativity of individuals who think they have creative thinking skills. Forty-four teachers who work in private schools participated in this study and they have their pedagogical formation training from a public university in order to be a public teacher. Also participants have at least one year of experience. Mixed method research is defined as combining qualitative and quantitative methods, approaches and concepts in successive studies (Creswell, 2003). This study was determined as a mixed method research since data first analyzed by content analysis (qualitative) and then relationship and comparison analysis (quantitative). In order to analyse problem situation of the study, two testing instruments one of which is ready to use were utilized. Problem solving and problem posing test as two items was developed by the researcher. On item was given to participants as a geometry problem in the testing instrument and they were asked to solve this problem by using as many different methods as they can. The other item was given to participants as a semi-structured geometry situation. They were asked to pose as many problems as they can by using this situation. Data obtained from solving draft were divided into categories in terms of flexibility, fluency and originality according to content analysis from qualitative data analyses for each participants.As a result of the study, according to the teachers’ creativity that they stated, we can talk about their mathematical creativity only if they can pose an authentic problem.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".