Investigation of the Relationship Between Class Teachers’ Levels of Mathematical Thinking and Mathematics Teaching Anxiety in Terms of Different Variables
Bibliographic record
Abstract
The current study aimed to investigate the relationship between class teachers’ level of mathematical thinking and level of anxiety about mathematics teaching in terms of different variables. To this end, the correlational and causal comparative method, one of the qualitative research methods, was used in the study. The study group of the current research is comprised of 509 class teachers working in state primary schools in the city of İstanbul in the 2019-2020 school year. As the data collection tools, the “Class Teachers’ Mathematical Thinking Scale” and the “Mathematics Teaching Anxiety Scale” were used. In the analysis of the data obtained from the scales, descriptive and parametric analyses (t-test and ANOVA) and Pearson Product-Moment Correlation were used. A low and negative correlation was found between the class teachers’ levels of mathematical thinking and mathematics teaching anxiety. Moreover, the class teachers’ levels of mathematical thinking and mathematics teaching anxiety were found to be varying significantly depending on gender. In addition, the class teachers’ levels of mathematical thinking and mathematics teaching anxiety were also found to be varying depending on the type of high school graduated and the length of service in the profession.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".