Extending the Generality of the Qualities and Behaviors Constituting Effective Teaching
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.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".