Promoting Reflective Physics Teaching Through the Use of Collaborative Learning Annotation System
Bibliographic record
Abstract
Effective physics teaching requires extensive knowledge of physics, relevant pedagogies, and modern educational technologies that can support student learning. Acquiring this knowledge is a challenging task, considering how fast modern technologies and expectations of student learning outcomes and of teaching practices are changing Therefore 21st-century physics teachers should be supported in developing a different way of thinking about technology-enhanced physics teaching and learning. We call it Deliberate Pedagogical Thinking with Technology, and base it on the original Pedagogical Content Knowledge and Technological Pedagogical Content Knowledge frameworks. However, unlike the two aforementioned frameworks, the Deliberate Pedagogical Thinking with Technology emphasizes not only teachers’ knowledge, but also their attitudes and dispositions about using digital tools in order to support student learning. This paper examines how an online system that allows an ongoing discussion of videos uploaded on it by the students can support reflection in physics teacher education. Examples of using such a system in physics teacher education and teacher-candidates’ feedback on their experiences with it are also discussed.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".