A class where qualitative discussions, coming weeks before computationally complicated practice, helps students' problem solving abilities
Bibliographic record
Abstract
Psychologists have long known that an expert in a field not only knows significantly more individual facts/skills than a novice but also has these facts/skills organized into a mental hierarchy that links the individual facts (at the bottom of the hierarchy) together with larger more-encompassing ideas (at the top of the hierarchy). In the Spring quarter of 2012, UC Davis offered 4 sections (about 180 students each) of the first quarter of introductory physics, Physics 9A, covering Newtonian mechanics. One of these sections is a "treatment" group and had the entire 10-week quarter's set of ideas introduced, largely qualitatively, in the first 6 weeks followed by the 4 weeks where students learn to use those ideas to solve the algebraically complicated problems that physicists prize. The other three sections were organized as usual. The treatment group and one of the other sections were taught by the author and were identical (same homework, discussion, lecture, and lab) except for the organization of the content. After controlling for GPA as well as Force Concept Inventory pretest scores, the treatment group was found, with better than 99% confidence, to score higher on the final exam. Some curricular details of the treatment class are discussed in this paper.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.083 | 0.017 |
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".