Physics Exam Problems Reconsidered: <i>Using Logger Pro to Evaluate Student Understanding of Physics</i>
Bibliographic record
Abstract
In the past few decades, the physics teaching community has witnessed a surge in creative and often effective ways of using technology to improve physics instruction.1–3 Most of these findings suggest how technology can help instructors create interactive learning environments and how interactivity influences the effectiveness of physics learning.4 However, every physics teacher knows that in order for any teaching method to be effective, the exams have to test the skills and concepts addressed by the teacher. Exam content and style sends the clearest message to students about what skills and content are valued by instructors. The mismatch between what we intend to teach and what we effectively test in exams is of great concern to many science teachers. These were our motives for creating data-rich questions to be used in the exams in a large undergraduate first-year physics course at the University of British Columbia. These data-rich questions were developed to support the use of an innovative teaching pedagogy called Interactive Lecture Experiments.3
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".