Empirical prediction of the effect of classroom design on verbal-communication quality
Bibliographic record
Abstract
This study used empirical prediction models to investigate how verbal-communication quality in 'small', 'medium' and 'large' classrooms varies with classroom design, and identified the optimal designs.Verbalcommunication quality was quantified by the room-average speech intelligibility.The design parameters studied were the occupancy, the unoccupied background-noise level, and whether or not the rooms were carpeted, had ceiling and/or wall absorption, or upholstered seats.The design parameters were varied, and the following quantities calculated: average classroom surface-absorption coefficient at 1 kHz, 1-kHz earlydecay time, A-weighted background-noise level, and A-weighted speech-signal to background-noise level difference.The conditions under which optimal verbal-communication quality occurred were identified.Quality did not vary with absorption or early-decay time in any systematic way.High background noise, combined with either high absorption or low early-decay time, can lead to very low verbal-communication quality.Quality was low for negative values o f signal-to-noise level, but increased quickly for higher values.In the 'small' and 'medium' classrooms, the optimal verbal-communication quality occurred with carpeting and absorption, and with un-upholstered seats.In the 'large' classroom, the optimal quality occurred with carpeting, absorption and upholstered seats.The most significant design factor in determining the verbalcommunication quality o f the rooms was the background noise.
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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 | 0.020 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".