Correlation Between Thinking Styles and Teaching Styles of Prospective Mathematics Teachers
Bibliographic record
Abstract
Increasing the quality of education is based on changes thinking and teaching styles. Considering variance ofthinking styles and teaching styles person to person, identifying thinking styles and teaching styles of prospectivemathematics teachers is very important. So, the aim of this study is to determine the correlation between thinking andteaching styles of prospective mathematics teachers and to examine thinking styles and teaching styles of theprospective mathematics teachers by considering some demographic characteristics. The sample of the researchconsisted of 80 prospective mathematics teachers who studied at the Mathematics Education Department of AhmetKeleşoğlu Education Faculty at Necmettin Erbakan University. Relational screening model was used in analysis ofthe data. “Thinking Styles Scale” which was developed by Sternberg and Wagner (1992) and adapted to Turkish byBuluş (2006) and “Teaching Style Inventory” developed by Grasha (1994) and adapted to Turkish by Uredi (2006)were used as data collection tool in the research. According to the conclusion of the research, a positive moderatecorrelation was found between thinking styles and teaching styles of prospective mathematics teachers.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".