Subjective assessment of listening environments in university classrooms: Perceptions of students
Bibliographic record
Abstract
A questionnaire is developed to evaluate perception of the listening environment by university students. The objectives were to develop a questionnaire-based measurement tool, derive a measure of perceived classroom-listening quality, use the questionnaire to investigate factors that enhance, impair, or do not affect perceived listening quality, and consider the implications for classroom design. The questionnaire was administered to over 5700 students in 30 classrooms at one university. Physical and acoustical measurements were also performed in each classroom. The questionnaire included items that recorded aspects of student perception, as well as individual, course-, and instructor-specific factors. Responses to 19 perception items generated a perception of listening ease (PLE) score for each student and a classroom-average score. Decreased PLE was associated with women, English-second-larguage students, those with hearing impairment, students not interested in the course material, and those who found the material difficult. Increased PLE was associated with higher speech transmission index, acceptable lighting, temperature and seating, better instructor voice, increased visual-aid use, and easier course material. Results indicate that PLE is a useful measure of student perception of the classroom-listening environment, and that optimal classroom acoustical design must take into consideration "in-use" conditions, as well as classroom physical characteristics.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".