The Elements of Teachers’ Competency for Creative Teaching in Mathematics
Bibliographic record
Abstract
Creative teaching in mathematics based on teachers’ competency is important in helping to realize the objectives of the quality of education that is to be in the third position in international assessments such as Program for International Student Assessment (PISA) and Trends in Mathematics and Science Study (TIMSS). This study aims to explore and determine the elements of teachers’ competency for creative teaching in mathematics which will be used as the measured variables and the basis for the construction of instruments in the process of developing a model of creative teaching in mathematics based on teachers’ competencies. In order to identify the elements of teachers’ competency, a qualitative exploratory study was conducted in the form of document analysis, literature analysis and experts interviews. Document analysis and literature analysis were analyzed using systematic data analyses, while the findings from the interviews were analyzed using a frequency matrix table. The findings of the study showed that the Professional Knowledge, Functionality Skills and Creative Attitudes were identified as the critical elements of teachers’ competency for creative teaching in mathematics.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".