Beyond Representations: How Teachers’ Epistemologies of Models Shapes Students’ Engagement with Scientific Modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modeling is an important part of science education, with an “epistemology of models” underpinning modeling practices. While teacher professional development may emphasize the representational aspects of models, it may also overlook teachers’ epistemological understandings of models and its impact on teaching modeling as a practice. This study investigated how teachers’ epistemologies influence students’ development of models. We examined three science teachers’ epistemological perspectives on models, focusing on dimensions such as their understanding of the nature and purpose of the models, model multiplicity, evaluation, and changeability. Data from teachers’ interviews and classroom observations of teachers were analyzed using cross-case analysis. The results revealed that teachers’ epistemologies of models appeared to be manifested through two apparent paradigmatic lenses, primarily positivism and constructivism. Teachers operating with mainly constructivist perspectives encouraged students to build, evaluate, and revise models based on empirical evidence and scientific consensus. Conversely, those with mainly positivistic perspectives engaged students in model creation, primarily for verification or confirmation. Our findings indicate that two teachers sought evidence to support models, explored multiple models, and encouraged interpretive explanations, whereas one converged on existing “single-form” models. This study contributes to current understandings of the role of teachers’ epistemologies as part of their professional knowledge, highlighting its importance in shaping effective science education and model-based teaching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it