Restructuring physics labs to cultivate sense of student agency
Bibliographic record
Abstract
Instructional physics labs offer students unique opportunities to develop an understanding of experimentation. By transforming labs to be more open ended and experimentation focused, instructors can better support student agency and choice. In this study, we examine students' overall sense of and perceptions about agency in two experimentation-focused labs: one course primarily taken by physics majors and another course primarily taken by engineering majors. We compare the sense of and perceptions about agency between the different courses and between men and women in each course. Between the start and the end of the semester, we found a positive shift in students' sense of agency in the lab activities in both courses, with no difference between men's and women's shifts. Additionally, we found empirical evidence that the majority of the students preferred the final, most open-ended Project lab. Our qualitative analysis revealed that most of the students perceived the opportunities for agency positively, citing "freedom" as their reason for preferring the Project lab. Both women and men in the course for engineering majors showed similar patterns. Fewer women in the physics majors course, however, chose the final project lab as their favorite and less often attributed their preference to freedom. We discuss possible interpretations of these results and implications for instruction.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".