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Record W2022159962 · doi:10.1121/1.4809284

Impact of classroom noise on reading and vocabulary skills in elementary school-aged children

2004· article· en· W2022159962 on OpenAlexaff
Prudence Allen, Nashlea Brogan, Chris Allan

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2004
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsWestern University
Fundersnot available
KeywordsQUIETVocabularyReading (process)Noise (video)AudiologyPsychologyPeabody Picture Vocabulary TestTest (biology)Developmental psychologyComputer scienceMedicineLinguisticsArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Classroom noise levels often exceed recommendations and, in large scale retrospective studies, it has been suggested that higher noise levels often correlate significantly with poorer academic performance [e.g., Shield., et al. (2002)]. However, experimental data on the performance of individual children are limited. This study therefore examined the effect of noise on the performance of children in grades 3–4 and 7–8 on standardized tests of oral reading, silent reading, and vocabulary (the Gray Oral Reading Test, the Gray Silent Reading Test, and the Peabody Picture Vocabulary Test). Each child completed parallel forms of a test in quiet and in classroom noise presented at 60 dB SPL. Required speech was presented at +10 S/N. Results from grouped data showed significantly reduced performance in noise only on the silent reading task and only for the older group of children. However, across tasks, when the effects of noise were evaluated as a function of childrens quiet performance levels, the noise effect was shown to be significant for the children performing at above average levels in quiet. These findings suggest that the effect of classroom noise may vary significantly across tasks and children. [Work supported by CLLRNet.]

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.335
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicNoise Effects and ManagementFrench-language works237,207