In-Service Mathematics Teachers’ Beliefs About Teaching, Learning and Nature of Mathematics and Their Mathematics Teaching Practices
Bibliographic record
Abstract
The aim of this study is four fold: (a) to investigate the beliefs of elementary (grades 1-3) and middle school (4-6 grades) math teachers about teaching, learning and nature of mathematics; (b) to explore their teaching practices of mathematics; (c) to study the impact of their educational qualifications, years of experience, major on their beliefs toward teaching, learning and nature of mathematics, and; (d) to explore the relationship between their beliefs about teaching learning and nature of mathematics and their teaching practices. Data were collected using two questionnaires: the Math Teacher Beliefs Scale and the Mathematics Teaching Practices Scale. The study sample consisted of 101 teachers who teach in 11 private schools located in Amman, Jordan. The result of this study showed that teachers’ beliefs towards teaching and learning mathematics are more inclined towards being constructive or mixed in between. It was also concluded that the teaching practices lean towards constructivism. There were no significant differences attributed to years of experience, academic level, major, or at what stage they teach, whether it revolves around the their beliefs towards teaching and learning mathematics or towards teaching practices (from teachers’ perspective). The study results revealed a statistically significant correlation between what the teachers believe and what teaching practices they put into use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".