Extending the Generality of the Qualities and Behaviors Constituting Effective Teaching
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
I surveyed 2 samples of Canadian undergraduates (N = 629) concerning their views of a “perfect instructor.” Students identified as many descriptors as they wished; I categorized them into 26 sets of qualities and behaviors. The top 10 categories included: (a) knowledgeable; (b) interesting and creative lectures; (c) approachable; (d) enthusiastic about teaching; (e) fair and realistic expectations; (f) humorous, happy, and positive; (g) effective communicator; (h) flexible and open-minded; (i) encourages student participation; and (j) encourages and cares for students. Of the 26 categories, 24 are akin to those found by Buskist, Sikorski, Buckley, and Saville (2002), reflecting an almost equal emphasis on teaching technique and the student–teacher relationship. These findings offer international support for their categories of effective teaching.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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