Rating, ranking, and understanding acoustical quality in university classrooms
Bibliographic record
Abstract
Nonoptimal classroom acoustical conditions directly affect speech perception and, thus, learning by students. Moreover, they may lead to voice problems for the instructor, who is forced to raise his/her voice when lecturing to compensate for poor acoustical conditions. The project applied previously developed simplified methods to predict speech intelligibility in occupied classrooms from measurements in unoccupied and occupied university classrooms. The methods were used to predict the speech intelligibility at various positions in 279 University of British Columbia (UBC) classrooms, when 70% occupied, and for four instructor voice levels. Classrooms were classified and rank ordered by acoustical quality, as determined by the room-average speech intelligibility. This information was used by UBC to prioritize classrooms for renovation. Here, the statistical results are reported to illustrate the range of acoustical qualities found at a typical university. Moreover, the variations of quality with relevant classroom acoustical parameters were studied to better understand the results. In particular, the factors leading to the best and worst conditions were studied. It was found that 81% of the 279 classrooms have "good," "very good," or "excellent" acoustical quality with a "typical" (average-male) instructor. However, 50 (18%) of the classrooms had "fair" or "poor" quality, and two had "bad" quality, due to high ventilation-noise levels. Most rooms were "very good" or "excellent" at the front, and "good" or "very good" at the back. Speech quality varied strongly with the instructor voice level. In the worst case considered, with a quiet female instructor, most of the classrooms were "bad" or "poor." Quality also varies with occupancy, with decreased occupancy resulting in decreased quality. The research showed that a new classroom acoustical design and renovation should focus on limiting background noise. They should promote high instructor speech levels at the back of the classrooms. This involves, in part, limiting the amount of sound absorption that is introduced into classrooms to control reverberation. Speech quality is not very sensitive to changes in reverberation, so controlling it for its own sake should not be a design priority.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".