The effects of teachers' beliefs on elementary students' beliefs, motivation, and achievement in mathematics
Bibliographic record
Abstract
Introduction According to Pajares (1992), teachers’ beliefs can be deeply personal, unaffected by persuasion, and either implicitly or explicitly expressed in daily routines. Their beliefs can be formed by chance, an intense experience or a succession of events, and may include beliefs about different facets of teaching and learning. Teachers hold beliefs about students, learning, teachers and teaching, the nature of knowledge and knowing, the roles of schools in society, and the curriculum, to name a few (Levitt, 2001). Whatever their origin or object, research has shown that beliefs influence a wide variety of cognitive processes including memory, comprehension, deduction and induction, problem representation, and problem solution (Pintrich, 1990). Importantly, the study of teachers’ beliefs provides a valuable means of analyzing and understanding the complex relationship between beliefs and student outcomes (Hofer and Pintrich, 2002; Pajares, 1992; Schraw and Olafson, 2002). In his review of research on teachers’ beliefs, Pajares (1992) reported that teachers’ beliefs about teaching and learning, including beliefs about students, significantly influence teachers’ classroom practices. Moreover, he found that teachers’ beliefs are more likely to influence the types of instructional strategies they implement in the classroom than their knowledge about a particular content area or instructional strategies. As Peterman (1993) and Tobin (1993) observed, the primary way in which teachers’ educational beliefs are given meaning is through their expression in the classroom.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".