Bibliographic record
Abstract
At a number of U.S. universities, some physicists are focusing their research effort on physics education research (PER). This paper examines this development in terms of the knowledge of teaching and learning, curriculum projects and practices it has produced. First, a selective review of research and curriculum development projects provides an introduction to PER for readers unfamiliar with it. Studies based on surveys and interviews are emphasized, as well as curriculum projects that make use of microcomputer-based laboratory tools (MBL). Other efforts are mentioned more briefly, but illustrate the breath of research and development activity. Following the review, I examine the evidence for the effectiveness of some of the curricula discussed, and identify three areas in which greater interaction between the PER and educational researchers working in other fields should be fostered: (a) statistical data analysis, (b) micro-analysis of learning situations, and (c) ways in which subject matter knowledge in physics can contribute to school-based projects and educational research. The concluding section of the paper argues for multi-disciplinary graduate programs in physics education, which are intended to provide a solid base in physics as well as research and innovation in education. PACS Nos.: 01.40.Fk, 01.50.Ht
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.074 | 0.032 |
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".