“I feel like a pack a day smoker”: Teacher and student voices from noisy classrooms
Bibliographic record
Abstract
Concerns about the effects of poor classroom acoustics on student learning (particularly on students who are deaf or hard of hearing) have been expressed since the 1950s, yet research continues to indicate that poor classroom acoustics are both common, and related to poorer student outcomes in academic achievement, attention, behavior, and most recently, mental health. Teachers are not immune to the detrimental effects of noise; for them, research indicates a higher incidence of voice problems, absenteeism and job stress. However, little is reported about what teachers and students themselves have to say about their experiences attempting to learn under adverse listening conditions, and what changes for them when acoustical conditions improve. Their voices are largely missing from the research literature but they illuminate the problem in ways that quantitative data does not always capture. This presentation by an educational audiologist with over 35 years of experience in classrooms will provide an overview of the research on learning under adverse listening conditions, with a focus on representing teacher and student voices through qualitative research and anecdotal reports. The title is a quote from a teacher describing her voice problems and fatigue after a day of teaching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".