Student Perceptions of Teachers’ Nonverbal and Verbal Communication: A Comparison of Best and Worst Professors across Six Cultures
Bibliographic record
Abstract
Students from six countries—Australia, Japan, Mexico, Sweden, Taiwan, and the United States—recalled the extent to which their best or worst professors used various forms of communication that have been associated with effective teaching. Across cultures, best professors were perceived to employ more nonverbal expressiveness, relaxed movement, in-class conversation, and out-of-class communication than worst professors. Relative to Japanese and Taiwanese students, Australian and U.S. students perceived their professors to use more nonverbal expressiveness. Students from Australia, Sweden, and the U.S. also perceived their best professors to use more in-class conversation than students from Japan or Taiwan perceived their best professors to use. However, Australian and U.S. students also perceived their best professors to use less out-of-class communication than did students from the other four countries. There were also differences in the forms of communication that discriminated between best and worst professors in each culture. For example, nonverbal expressiveness and in-class conversation were the best discriminators for Australian and U.S. students, whereas out-of-class communication and relaxed movement were the best discriminators for Japanese and Taiwanese students.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".