Rating, ranking, and understanding acoustical quality in university classrooms
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».