Attitudes towards mathematics: exploring beliefs shared by elementary students
Bibliographic record
Abstract
Mathematics education has long dealt with negative attitudes which may be based on or can create misconceptions, myths, phobias, and anxiety. A cultural norm of negative dispositions has developed to include beliefs that genetics and gender influence learning. Attention to the affective domain is essential to gauge current dispositions. The intent of this study is to explore and compare the beliefs held by a sample of adults and students in School District #28 (Quesnel). The survey queried thoughts on self-efficacy as math learners, an enjoyment of doing math, and a willingness to engage mathematics. Results show that student self-efficacy is high. Despite this, the negative and neutral attitudes towards learning mathematics continue to be powerful. Most participants continue to experience frustration and too many suffer embarrassment. Analysis revealed interesting trends such as the stronger positive attitudes held by girls in this sample or the general reluctance of all students regarding mathematics in high school and in the workplace. Descriptive statistics show that the only significant difference can be found on seven items between genders. Of interest are the strong student results that virtually dismiss the existence of the misconceptions of gender and genetic influences on ability that are still held by many adults. Recommendations include: developing awareness of learning styles specific to mathematics learning, increasing teacher awareness regarding the continuum of math skills, challenging educators to make teaching and learning math more fun, and creating materials that address the unknowns of mathematics beyond the elementary years. --P.ii.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".