Using continua to analyze qualitative data investigating epistemic beliefs about physics knowledge: Visualizing beliefs
Bibliographic record
Abstract
[This paper is part of the Focused Collection on Qualitative Methods in PER: A Critical Examination.] Epistemic beliefs about physics are most often investigated using quantitative instruments that reflect binary conceptualizations of those beliefs. This study reports from a qualitative study which used continua to represent the epistemic beliefs about physics knowledge of sixteen Western Canadian, high school physics teachers. Unlike other research, this study did not intend to compare epistemic beliefs to any specific epistemology of science. This article presents a novel, more nuanced means of analyzing interview data to construct profiles to describe epistemic beliefs. The epistemic belief profiles of the physics teachers in this study reflect each of four areas of a literature-derived theoretical framework regarding epistemic beliefs about physics knowledge. These four areas are individuals’ beliefs about the (a) source, (b) content, (c) certainty, and (d) structure of physics knowledge. The use of thematic analysis research methods and reasons for the placement of participants along continua are discussed. Potential classroom applications of this research include prompting discussions about student epistemic beliefs and collecting more nuanced representations of students’ epistemic beliefs to inform teaching. Published by the American Physical Society 2024
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.
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 | 0.048 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".