Do Prospective Teachers have Anxieties about Teaching Mathematics?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study is to analyse the level of prospective classroom and mathematics teachers’ anxieties about teaching mathematics. Freshman and junior prospective teachers from educational faculties of two different universities participated in this study. “Anxieties About Teaching Mathematics Scale” which was developed by Peker (2006) and “Personal Information Form” which was developed by the researchers were adapted to determine prospective teachers’ anxiety levels about teaching mathematics. After collecting data, correlations between anxiety levels of prospective teachers and different variables such as their majors at university, their grade levels, the type of high school that they were graduated, gender and attitudes of their family were examined. Average scores, t-test and variance analyses were used in data analyses and scheffe test was employed when necessary.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it