The Development of Teachers’ Epistemic Beliefs about Science: A Review of the Literature
Bibliographic record
Abstract
Empirical research studies investigating teachers’ epistemic beliefs about science are scattered across the science education literature. This research summarizes and describes the literature investigating the development of teachers’ epistemic beliefs about science over the last 25 years. The focus of this summary is on how teachers’ epistemic beliefs about science have been investigated and conceptualized as well as the themes arising from empirical studies considering this construct. Using a systematic literature review (PRISMA protocol), empirical research studies investigating the development of teachers’ epistemic beliefs about science were identified. Thematic analysis was used to analyze, summarize, and interpret data. Findings indicate that teachers’ epistemic belief development is commonly studied using interventions to instruct teachers about and engage them with the epistemic underpinnings of science. The nature of science was the most frequently used framework to conceptualize teachers’ epistemic beliefs about science, with few studies using approaches more common in the general epistemic belief or epistemic belief development literature. Reflections on and interpretations of the summary of this systematic literature are provided, including potential considerations for future directions of research investigating the development of teachers’ epistemic beliefs about science.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".