Investigating and Overcoming Mathematics Anxiety in In-service Elementary School Teachers
Bibliographic record
Abstract
Mathematics anxiety impedes learning and success in mathematics and could be a hindrance for both teachers and students. This sequential mixed methods research project examined the nature of mathematics anxiety among elementary in-service teachers and how the anxiety differs in terms of various demographic factors. It also investigated mathematics teaching anxiety, its types, and relationship with mathematics anxiety. Data were collected through an online survey completed by 111 elementary in-service teachers and follow up face-to-face interviews with four of them. Findings from the survey indicated that: 17.1% of them had low level of mathematics anxiety, 64% experienced a moderate level, and 18.9% had a high level of mathematics anxiety; female participants experienced higher mathematics anxiety than males; and there were no significant differences in the mathematics anxiety for White and non-White participants, by participants’ mothers’ educational level, and socioeconomic status. Other findings showed that: there was positive correlation between mathematics anxiety and mathematics teaching anxiety; female participants had higher mathematics teaching anxiety due to subject knowledge and self- confidence than males; and beginning teachers had higher mathematics teaching anxiety than experienced teachers. In the interviews, participants attributed the causes of mathematics anxiety to their teachers’ teaching strategies, insensitive comments, and mean behaviors, and to their own lack of understanding of mathematics concepts. Lack of confidence in doing mathematics, lack of courage to express their feelings about mathematics to their teachers, negative attitude towards mathematics, and avoidance of mathematics courses were reported as aftermath effects of mathematics anxiety when participants were in school. Recommendations are made for joint efforts by the school boards, teacher educators, researchers, and parents to reduce mathematics anxiety among the teachers and break its re-occurring cycle and consequences.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".