Redefining Mathematical Jitters: Pioneering A Scale For Children's Anxiety In Numerical Contexts
Bibliographic record
Abstract
Mathematical Anxiety is commonly defined as feelings of tension and anxiety that interfere with the manipulation of numbers and the solving of mathematical problems in academic situations and even in daily life. It negatively impacts the academic achievements of children. In this study, a Mathematical Anxiety Scale was developed to evaluate mathematical anxiety among primary school students aged between 8 to 10 years. The scale contains twenty-four items that measure mathematical anxiety in six domains: anxiety in doing mathematical calculations, performing mathematical activities, mathematical thinking, mathematical evaluation and doing everyday mathematical tasks. The validation of the scale was done using the data of 250 girls and 237 boys participants. The frequency analysis using SPSS shows the Mean=41.37, Standard Deviation ( ) =7.01, and Median=40.00 as obtained. The criteria of scores developed are: A score above 54 shows extremely high math anxiety, a score between 46–54 shows high math anxiety while scores 37–45, 29–36, and less than 29 shows moderate math anxiety, low math anxiety, and extremely low math anxiety respectively. The Cronbach Alpha coefficient calculated for the evaluation of the internal consistency was determined as 0.728 and this value shows that the scale had good reliability. With the results of this study, the scale’s content validity was evaluated and it was shown to have acceptable valid features in all.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".