The Teaching Practices Inventory: A New Tool for Characterizing College and University Teaching in Mathematics and Science
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
We have created an inventory to characterize the teaching practices used in science and mathematics courses. This inventory can aid instructors and departments in reflecting on their teaching. It has been tested with several hundred university instructors and courses from mathematics and four science disciplines. Most instructors complete the inventory in 10 min or less, and the results allow meaningful comparisons of the teaching used for the different courses and instructors within a department and across different departments. We also show how the inventory results can be used to gauge the extent of use of research-based teaching practices, and we illustrate this with the inventory results for five departments. These results show the high degree of discrimination provided by the inventory, as well as its effectiveness in tracking the increase in the use of research-based teaching practices.
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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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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