Preservice Mathematics Teachers’ Metaphorical Perceptions towards Proof and Proving
Bibliographic record
Abstract
Since mathematical proof and proving are in the center of mathematics; preservice mathematics teachers’ perceptions against these concepts have a great importance. Therefore, the study aimed to determine preservice mathematics teachers’ perceptions towards proof and proving through metaphors. The participants consisted of 192 preservice mathematics teachers in a public university in Aegean Region. As a data collection tool, to reveal preservice mathematics teachers’ metaphorical perceptions towards the proof and proving, the form that includes the blanks in the sentences “Mathematical proof is like… Because…” and “Mathematical proving is like… Because…” Data was analyzed using content analysis method. Sixteen themes about the concept of proof and twenty themes about the concept of proving were composed. After taking a specialist opinion, Miles&Huberman’s reliability co-efficient was calculated .92. According to the research results; preservice mathematics teachers had positive perceptions for mathematical proof; but in the matter of proving, they had negative perceptions.As a recommendation, it should be conducted to present why preservice mathematics teachers’ perceptions about proof and proving are negative by using depth interviews.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".