Changing the Metacognitive Orientation of a Classroom Environment to Stimulate Metacognitive Reflection Regarding the Nature of Physics Learning
Bibliographic record
Abstract
Problems persist with physics learning in relation to students' understanding and use of representations for making sense of physics concepts. Further, students' views of physics learning and their physics learning processes have been predominantly found to reflect a ‘surface’ approach to learning that focuses on mathematical aspects of physics learning that are often passed on via textbooks and lecture-style teaching. This paper reports on a teacher's effort to stimulate students' metacognitive reflection regarding their views of physics learning and their physics learning processes via a pedagogical change that incorporated the use of a representational framework and metaphors. As a consequence of the teacher's pedagogical change, students metacognitively reflected on their views of physics and their learning processes and some reported changes in their views of what it meant to understand physics and how they might learn and understand physics concepts. The findings provide a basis for further explicit teaching of representational frameworks to students in physics education as a potential means of addressing issues with their physics learning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".