Physics Exam Problems Reconsidered: <i>Using Logger Pro to Evaluate Student Understanding of Physics</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it