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Record W3025050556 · doi:10.33137/jaste.v11i1.34249

Noise in Classrooms

2020· article· en· W3025050556 on OpenAlexaffvenue
Steven Lapp

Bibliographic record

VenueJournal for Activist Science and Technology Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNoise (video)PsychologyAction (physics)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Noise in the classroom has been found to have a negative impact on students. However, what can be done to lessen the impact of noise on student performance? How do students perceive noise in the classroom? How do students feel noise impacts on their ability to pay attention and learn in a classroom environment? My previous action project suggested that noise has a negative impact on student performance (Lapp, 2018). This action project was geared to determine whether cost effective baffolds found in Cawthra Park Secondary School’s library could lessen the noise levels. It was also geared to understand how students perceived noise and its effects in a classroom environment. The results suggested that the use of baffolds had a positive impact on lessening noise. It was also noteworthy that students tended to underestimate the noise levels in their classrooms. However, those that perceived the noise as being louder were more aware of its negative impacts than those who perceived the noise as being quieter.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.375
Teacher spread0.350 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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