Student Teachers’ Self-perception of their Mathematical Skills and their Conceptions about Teaching Mathematics in Primary Schools
Bibliographic record
Abstract
Research has shown that more often than not beliefs rather than knowledge are major determinants in pedagogical decisions that teachers make in the classroom (Doudin, Pons, Martin & Lafortune, 2003). Self-perceptions appear to be strongly influenced by students’ relationship with mathematical contents and students’ perceptions about their own ability to master these contents. The importance of self-perceptions, beliefs and pre-conceptions in decision making leads to this question: does student teachers’ self-perception of their mathematical competence influence their conceptions about mathematics teaching? To answer this question we compared the results obtained from two questionnaires administered to student teachers. The first questionnaire focused on conceptions about teaching mathematics while the second focused on self-perceptions in mathematics. This paper analyzes in the context of Boyer's Dialectical reconstruction of knowledge model (Boyer & Mailloux, 2012) the links between student teachers’ conceptions about mathematics education and their perceptions of their own mathematical skills.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".