Integrated Effects of Environmental and Classroom Noise on Attention and Learning in School-age Children

Abstract

Objectives:

This review investigated the effects of external and classroom noise on school-aged children’s cognitive performance. The analysis identified exposure patterns across studies; quantified effects on attention, memory, and reading processes; and developed a conceptual model connecting acoustic input, processing effort, and academic performance.

Methods:

A structured search was conducted to identify observational and experimental studies on noise exposure and cognitive outcomes in school environments. Eligible studies quantified noise using the equivalent continuous sound level, signal-to-noise ratio (SNR), and reverberation time (RT) and included outcomes in attention, memory, and reading tasks. Data were mapped across environmental noise and classroom acoustic conditions.

Results:

Classroom exposure levels commonly ranged between 65 and 77 dB(A), and external exposure from aircraft and road-traffic sources often exceeded 60 dB(A) during teaching periods. Lower SNRs and longer RTs reduced speech clarity and increased the processing effort needed to follow spoken information across the study design. Performance decreased in tasks involving attention, verbal memory, and reading accuracy. Children from lower socioeconomic backgrounds, bilingual learners, and pupils with weaker attention control exhibited larger performance reductions under identical acoustic conditions. Integrating these results produced a conceptual model in which external noise at school entry and classroom noise during instruction form a continuous exposure sequence that increases processing effort and reduces the learning capacity.

Conclusions:

Noise affects children by increasing processing load and reducing the cognitive resources available for learning. The model outlines this mechanism. The findings indicate acoustic improvements in classrooms and systematic noise monitoring in school environments.

KEY MESSAGES

• (1)External and classroom noise reduce attention, memory performance, and reading processes in school-age children.
• (2)Signal-to-noise ratio and reverberation time shape the processing demand during instructional tasks.
• (3)The exposure sequence identifies where acoustic conditions increase effort and limit the resources available for learning.

Introduction

Children spend most of their day in school environments where background noise is almost constant. Road traffic, aircraft, and urban activity contribute to the external acoustic load. Inside the classroom, speech overlap, movement, and reduced room acoustics cause noise. Unlike adults, children have a limited ability to ignore irrelevant sounds and to keep attention when several voices compete. Continuous exposure to moderate or high sound levels, even below those that cause hearing loss, decreases listening accuracy and comprehension and increases learning task effort. ^[1]

Studies conducted in the past decades have reported associations between environmental noise and school performance. Large surveys conducted near airports and main roads found that higher levels of aircraft or traffic noise correspond with lower reading comprehension and slower cognitive processing in children. These effects persist even after controlling for socio-economic status (SES). Chronic exposure to community noise affects core cognitive functions, such as attention and working memory, which are essential for learning in primary and secondary school children. ^[2]

Research has focused on the acoustic quality of classrooms beyond outdoor exposure. Field studies in occupied primary classrooms reported average noise levels around 70–77 dB(A) during active teaching periods, particularly during group work and movement. These values describe activity-related classroom noise, often accompanied by low signal-to-noise ratios (SNRs) and extended reverberation times (RTs). ^[3] Exposure to background noise, including road-traffic noise, reduces speech clarity and increases listening effort. Experimental studies have shown that such noise conditions impair verbal processing and are associated with increased errors in children’s memory and comprehension tasks. ^[4]

Socio-economic and individual factors influence how children respond to noise. Research in low-income schools reports higher exposure to environmental noise and reduced classroom acoustics, creating a combined risk. Schools in low-SES areas reported acoustic profiles with higher background levels and weaker insulation. Such differences point to noise as a hidden factor that widens educational gaps when it overlaps with other stressors. ^[5]

Research on noise and learning remains fragmented. Previous studies have addressed environmental noise and classroom acoustics as separate domains of exposure. Research on road traffic or aircraft noise typically relates outdoor equivalent continuous sound level (LAeq) measured at school locations to reading or memory outcomes, whereas classroom studies focus on SNR and RT during instructional tasks. This separation limits interpretation because external noise contributes to baseline cognitive load at school entry, while classroom acoustics determine processing demands during learning activities. Few studies have examined these exposures within a unified sequence that captures the cumulative daily load. As a result, exposure–response relationships remain incomplete, and the modifying role of socioeconomic background, language status, and attention control is difficult to interpret. ^[6]

This review integrates evidence on environmental and classroom noise into a conceptual framework. The model connects external exposure and in-class acoustic conditions through listening effort and cognitive resource use and shows how noise affects attention, cognition, and learning performance in school-age children. The framework supports the interpretation of vulnerability-related effects (VREs) and guides decisions in education and public health.

MATERIALS AND METHODS

Study Design

Study identification and selection were documented using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram to ensure transparent reporting of the literature screening process. This review was conducted as a narrative synthesis, and no systematic review protocol was registered. The objective was to summarize evidence on how environmental and classroom noise influence attention, memory, and learning performance in school-age children. This review combined findings from educational psychology, environmental acoustics, and public health to describe cognitive mechanisms and develop a conceptual model for school and policy use. Given the diversity of cognitive outcomes, task paradigms, exposure metrics, and reporting formats, quantitative synthesis was not feasible. Narrative synthesis was used to integrate evidence across heterogeneous study designs and outcome measures.

Search Strategy and Data Sources

PubMed, Scopus, and Web of Science databases were searched for studies published between January 2000 and September 2025. In PubMed, searches were conducted in the Title and Abstract fields using a combination of free-text terms and Medical Subject Headings (MeSH). Free-text terms were combined with the MeSH terms “noise,” “cognition,” “attention,” “memory,” and “learning” using Boolean operators. In Scopus and Web of Science, searches were performed in the Title, Abstract, and Keywords fields using only free-text terms. The search string combined the following population, exposure, and outcome terms: (children OR students OR pupils) AND (noise OR sound OR acoustics) AND (attention OR cognition OR memory OR learning OR school performance). Filters included publication language (English), document type (peer-reviewed journal articles), and publication period. The searches returned PubMed (n = 92), Scopus (n = 81), and Web of Science (n = 74). After duplicate removal, 210 records were entered for title and abstract screening, as summarized in the PRISMA flow diagram [Figure 1]. All retrieved records were checked for duplicates before screening. Titles and abstracts were independently screened by two reviewers against the Population, Exposure, Comparator, and Outcome criteria. The screening ensured that each study included a defined population of school-age children, a measurable exposure to environmental or classroom noise, a comparator or reference condition, and at least one cognitive or educational outcome. Data extraction was conducted using a piloted Excel sheet that included the study design, population characteristics, noise metrics, exposure window, outcome measures, and adjusted effect estimates when available.

Figure 1.

PRISMA flow diagram of the study selection process.

Inclusion and Exclusion Criteria

The review included studies with school-age children (6–18 years) and exposure involved environmental noise (road traffic, aircraft, or urban sources) or classroom noise measured in decibels (dB) or described through acoustic indicators such as LAeq, SNR, or RT. Eligible studies assessed attention, working memory, reading, comprehension, or overall learning performance and were published in peer-reviewed journals in English.

Studies were excluded if they focused on infants, adults, or occupational populations, addressed hearing impairment only, were conference abstracts, were non-peer-reviewed, or were non-English. The restriction on English-language publications was defined a priori. The databases predominantly searched indexed peer-reviewed literature published in English in the relevant fields, and no eligible non-English full-text studies meeting the inclusion criteria were identified during screening. Additional references were retrieved from the bibliographies of relevant reviews and WHO reports. Grey literature sources, including International Organization for Standardization standards, dissertations, and non-peer-reviewed technical reports, were not systematically searched because the review focused on empirical evidence published in peer-reviewed journals. The full texts of potentially relevant studies were reviewed to confirm their eligibility. At full-text assessment, studies were excluded due to the wrong setting, irrelevant outcomes, or insufficient data for extraction, as documented in the PRISMA flow diagram.

Methodological Characteristics of the Included Studies

The included studies were examined through a methodological appraisal focusing on aspects of noise exposure and cognitive outcomes in school settings. The study design, noise measurement methods, reported acoustic indicators, measurement context and duration, cognitive outcomes assessed, and how contextual factors, such as SES, language background, age, and attention-related characteristics, were all considered in the appraisal.

Observational studies relied on field measurements or modeled estimates of environmental or classroom noise, with variation in measurement periods and spatial resolution. Experimental studies described controlled acoustic conditions but often involved small samples. Differences in outcome definitions and in the treatment of confounding and contextual variables. Table 1 presents the methodological characteristics of each included study for assessing strengths and limitations across the evidence base.

Table 1.

Summary of studies on noise exposure and cognitive performance in school-age children

Ref.	Study type	Noise context	Exposure metric	Cognitive domain	Contextual factors	Age/sex
Shield et al. [7]	Field study	Classroom and external	LAeq	Reading, memory	SES	Age: 4–11 years
Astolfi et al . [8]	Field study	Classroom	SNR, RT	Speech perception	Age	Age: 6–8 years; Boys and girls
Puglisi et al. [9]	Acoustic survey	Classroom	LAeq, RT	-	-	-
Minelli et al . [10]	Experimental	Classroom	SNR	Speech perception	-	-
Zhang et al . [11]	Experimental	Traffic / babble / music	LAeq	Reading, memory	-	Age: 9–13 years; Boys and girls
Shield et al . [12]	Experimental	Classroom	LAeq	Reading	Age	Age: 11–16 years; Boys and girls
Valente et al . [13]	Experimental	Simulated classroom	SNR, RT	Speech perception	-	Age: 8–12 years; Boys and girls
Caviola et al. [14]	Experimental	Classroom / traffic	LAeq	Reading / task performance	-	Age: 11–13 years; Boys and girls
van Kempen et al. [15]	Observational	Road / aircraft	LAeq	Reading, mathematics	SES	Age: 9–11 years; Boys and girls
Belojević et al. [16]	Observational	Traffic	LAeq	Executive function	Sex	Age: 7–11 years; Boys and girls
Seabi et al. [17]	Quasi-longitudinal	Aircraft	LAeq	Reading	SES, language	Age: 11–13 years; Boys and girls
Massonnié et al. [18]	Experimental	Classroom	LAeq	Creativity	Attention	Age: 5–11 years; Boys and girls
Carlie et al. [19]	Experimental	Background noise	SNR	Listening comprehension	SES, language	Age: 7–9 years; Boys and girls
Shield et al. [20]	Mixed	Classroom and external	LAeq	Reading, numeracy	SES	Age: 7–11 years; Boys and girls
Kapetanaki et al. [21]	Observational	Classroom and external	LAeq	Attention, memory	SES	Age: primary school age
Pujol et al. [22]	Observational	Road traffic	LAeq	Reading, mathematics	SES	Age: 8–9 years; Boys and girls
Stansfeld et al. [23]	Observational	Aircraft	LAeq	Reading, memory	SES	Age: 9–10 years; Boys and girls
Baek et al. [24]	Observational	Aircraft (military)	WECPNL	IQ / reasoning	-	Age: 10–11 years; Boys and girls
Haines et al. [25]	Observational	Aircraft	LAeq	Reading, mathematics	SES	Age: 11 years; Boys and girls
Bhang et al. [26]	Quasi-experimental	Traffic	LAeq	Attention	SES	Age: 10–12 years; Boys and girls

Note: IQ, intelligence quotient; LAeq, equivalent continuous sound level; RT, reverberation time; SES, socioeconomic status; SNR, signal-to-noise ratio; WECPNL, weighted equivalent continuous perceived noise level. The noise metrics are reported in dB(A) where applicable. Contextual factors indicate moderators or covariates considered in the original studies.

Data Extraction and Analysis

Each included study was thoroughly reviewed to extract information on the design, sample characteristics, type of noise exposure, acoustic parameters, and measured outcomes. Acoustic indicators (LAeq, SNR, and RT) were used to compare exposure characteristics across studies. The cognitive outcomes were grouped into attention, working memory, reading comprehension, and overall academic performance.

The analysis was structured around three domains: (a) external environmental noise from traffic or aircraft, (b) classroom acoustic conditions such as background speech and reverberation, and (c) socio-economic or individual vulnerability factors, including low SES, bilingual background, or attention difficulties.

<ST>Findings were synthesized narratively to identify patterns across study designs rather than to calculate pooled estimates. The comparative analysis focused on how specific acoustic parameters affected cognitive domains at different school levels. The integrated synthesis was then used to develop a conceptual model describing how external and classroom noise interact with neurocognitive processes and learning outcomes. The model summarizes cumulative exposure, listening effort, and vulnerability differences among children, offering a framework for interpreting evidence and guiding school and policy interventions.

The included studies reported diverse exposure metrics, observation periods, and outcome definitions, making a quantitative meta-analysis unfeasible. The analysis focused on identifying recurring patterns and comparing results across domains to describe how acoustic exposure affects attention, memory, and learning outcomes.

Multilayer Comparative Synthesis and Conceptual Modeling

Data analysis employed a multilayer comparative framework designed to account for heterogeneity in acoustic metrics, cognitive task formats, and study designs. The approach followed three analytical layers. The first layer characterized the acoustic exposure parameters reported in each study. When available, the extraction focused on the LAeq, maximum sound levels, SNR, RT, and spectral content. External noise sources (traffic, aircraft, and mixed urban exposure) were assessed separately from classroom conditions to document typical exposure ranges and identify values exceeding relevant recommendations. This layer established a standardized acoustic profile that supported the alignment between field measurements and controlled laboratory studies.

The second layer categorized cognitive outcomes into task families, including sustained attention, selective attention, switching, verbal working memory, reading accuracy, reading comprehension, and standardized academic assessments. Performance was compared across acoustic conditions using the quantitative indicators, such as error rates, reaction time differences, speech reception thresholds, and score decrements, reported by original studies. This allowed the mapping of variation in acoustic conditions onto specific cognitive processes.

The third layer examined contextual vulnerability. Studies were screened for SES, bilingual background, baseline attention characteristics, and prior achievement. When the effect modification was reported, the extracted data included subgroup magnitudes and direction. When subgroup data were only partially available, the values for first-language (L1) and second-language (L2) learners, SES categories, or attention-defined groups were retrieved where possible. This layer enabled the analysis of whether identical acoustic exposures resulted in differential cognitive outcomes across learner profiles.

A cross-layer synthesis aligned acoustic parameters from layer one with cognitive outcomes from layer two and vulnerability indicators from layer three. This produced a standardized exposure–response structure. The alignment identified recurring patterns across observational and experimental designs: conditions with higher LAeq, lower SNR, or longer RT increased the processing demand for speech perception, reducing the available cognitive resources for comprehension and memory encoding.

A cross-layer synthesis was used to structure a sequential representation of the exposure and processing load. The model represents exposure as a sequential process in which environmental noise increases baseline load before instruction, classroom noise adds further processing demand, and the observed effects are amplified or attenuated by vulnerability factors. The model summarizes how exposure unfolds across the school day.

RESULTS

Study Selection and Characteristics

Twenty studies that met the eligibility criteria were included in the narrative synthesis. ^[7‐26] Figure 1 summarizes each step of the selection process.

The dataset [Table 1] covered observational and experimental studies conducted with school-aged children. Sample sizes ranged from 30 to more than 2000 scholars. Environmental and classroom noise were measured using acoustic indicators, such as LAeq, SNR, and RT. The reported cognitive outcomes included attention, working memory, reading comprehension, and mathematical performance.

Table 1 summarizes the included studies by study design and noise exposure context. The table does not report the exposure duration, specific task types, or effect size estimates because these parameters were heterogeneous across studies and were not consistently reported in a comparable format. The duration of exposure ranged from short experimental tasks to long-term environmental exposure, and the structure and outcome measures of cognitive tasks varied. These elements are described in the reported results, which present the exposure characteristics and observed effects.

The evidence was organized into three thematic domains reflecting the main exposure contexts and mechanisms. The first domain focuses on environmental noise, including road traffic, aircraft, and urban exposure, and examines how chronic community noise affects attention and academic performance. The second domain examines classroom acoustic conditions, characterized by internal noise sources, such as speech overlap, low SNR, and long RT. The third domain explores educational vulnerability andanalyzing how SES, bilingual background, and individual attention profiles modify the cognitive effects of noise exposure.

Environmental Noise Exposure

Studies near transport routes have reported poorer reading-related outcomes and memory-related task performance in children exposed to aircraft or road-traffic noise, with associations persisting after adjustment for socio-economic variables. ^[23] High aircraft noise in school areas was related to lower standardized reading and mathematics scores, with attenuated effect magnitudes after adjustment for deprivation indicators. ^[25] Exposure to aircraft noise was associated with reduced reading comprehension, with measurable effects observed among pupils tested in a non-native language. ^[17] For road-traffic noise, 10 dB(A) increases in exposure corresponded to mean reductions of approximately 5.5 points in standardized reading and mathematics test scores. ^[15] Chronic military aircraft noise at a level of ≥80 weighted equivalent continuous perceived noise level (WECPNL) resulted in lower reasoning test scores compared with pupils in quieter school environments. ^[24] In controlled experimental settings, traffic-type noise produced slower response times and reduced accuracy in attention-based tasks relative to quiet conditions. ^[14,26] Cross-sectional studies in traffic-exposed schools reported higher error rates in executive-function tasks, specifically attention shifting and inhibitory control, among pupils from noisier classrooms. ^[15,16]

Exposure duration and higher LAeq levels were associated with altered cognitive outcomes. Pupils attending schools located under flight paths demonstrated slower annual gains in standardized reading scores compared with children attending quieter schools. ^[17] Longer exposure duration was associated with higher teacher-reported distractibility and lower sustained-attention ratings in noise-exposed cohorts relative to matched controls from quieter areas. ^[26] In schools situated 150–300 m from major roadways, higher LAeq levels were associated with reduced short-term memory span and lower accuracy in information-recall tasks during classroom assessments. ^[16] In laboratory-based tasks simulating transport noise at 65–70 dB(A), children showed lower correct-response rates in vigilance and target-detection tasks compared with quiet conditions, indicating reduced task efficiency under continuous environmental noise exposure. ^[14]

Study-specific quantitative results indicated dose–response relationships between noise exposure and cognitive performance, including measurable score reductions in standardized academic tests and lower accuracy in attention-related tasks under transport-related noise conditions. Among observational school-based studies reporting classroom LAeq, the mean noise levels exceeded the recommended indoor limits, with reported values typically exceeding 55 dB(A).

Table 2 summarizes the key quantitative findings reported for environmental noise exposure, including effect estimates and confidence intervals where available.

Table 2.

Key quantitative findings for environmental noise exposure

Exposure contrast	Outcome	Effect estimate (as reported)	95% CI
Road traffic noise at school (per 10 dB(A) increase) [15]	Executive functioning (SAT arrow errors)	Regression coefficient (B) = +0.27 errors	0.08–0.46
Aircraft noise exposure (high-noise and low-noise schools) moderated by the home language [17]	Reading comprehension	Interaction effect	-
Aircraft noise at school (per 20 dB increase) [23]	Reading comprehension	Estimated standardized decrement of approximately −0.125 to −0.20 SD (based on country-specific effects)	-
Military aircraft noise (≥80 WECPNL and control schools) [24]	Reasoning score	Regression coefficient (β) = −3.41 points	−6.32 to −0.51
Chronic aircraft noise exposure at school (noise contour bands) [25]	Reading and mathematics performance (SATs)	Dose–response trend; association attenuated after SES adjustment	-
Experimental traffic noise [60.8–62.8 dB(A)] and background noise [43.5–46.1 dB(A)] [26]	Full-scale IQ	Mean difference = −6.50 IQ points	-

Note: dB(A), A-weighted decibels; EF, executive functioning; IQ, intelligence quotient; SAT, switching attention test; SD, standard deviation; WECPNL, weighted equivalent continuous perceived noise level. The noise exposure contrasts and effect estimates are reported as in the original studies.

Classroom Acoustic Conditions

Field surveys in primary classrooms measured sound levels between 70 and 77 dB(A) during teaching activities, exceeding the recommended 35 dB(A) for learning environments. The background noise levels during lessons reached up to 72 dB(A), with an average difference of about 20 dB(A) between quiet and active periods. ^[7] Other measurements reported LAeq values close to 77 dB(A) and shorter RT after acoustic treatment. ^[9]

Experimental studies reported reduced speech intelligibility and understanding when the SNR fell below 10 dB(A) or when RT exceeded the recommended thresholds. Under simulated classroom conditions characterized by low SNR or increased RT, children showed reduced accuracy in speech intelligibility tasks, reflected by fewer correct responses. ^[13] Acoustic interventions increased SNR by 8–15 dB(A) and improved the speech reception thresholds, especially in rooms treated with acoustic panels. ^[10]

Laboratory tasks revealed distinct patterns on the basis of noise source and performance effects. Multitalker babble produced larger performance declines than other background noise types, while shorter RT mitigated performance losses during task execution. ^[11,12] Additional experimental studies involving children aged 5–11 years reported greater performance reductions during noisy tasks among pupils with weaker baseline attention control. ^[18]

Controlled trials conducted in real classrooms reported a 10–20% reduction in correct-response rates during speech-in-noise tasks when RT remained above 0.7 seconds. ^[10] Studies that combined acoustic measurements with classroom assessments note that pupils working in rooms with an SNR below 6 dB(A) had lower phonological-processing scores and reduced accuracy in sentence-repetition tasks compared with classmates in classrooms with higher SNR levels. ^[9]

Table 3 summarizes the key quantitative findings reported for classroom acoustic conditions, with effect estimates and confidence intervals where available.

Table 3.

Key quantitative findings for classroom acoustic conditions

Acoustic condition / contrast	Outcome	Effect estimate (as reported)
Classroom noise during teaching 70–77 dB(A) [7]	Acoustic exposure during lessons	Difference in noise level: ≈20–40 dB(A), above recommended classroom levels
Quiet and active classroom periods [7]	Background acoustic exposure	Mean difference in noise level ≈20 dB(A)
Classroom LAeq before and after acoustic treatment [9]	Reverberation characteristics of classrooms	Reduction in RT after treatment
Acoustic intervention (panels) [10]	Signal-to-noise conditions	Improvement in speech reception threshold (ΔSRT): ≈8–15 dB SNR
Multitalker babble and other types of noise [11,12]	Task performance	Performance decrement under multitalker and environmental noise
Low SNR (<10 dB(A)) and high SNR [13]	Speech intelligibility	Reduced sentence recognition scores under lower SNR conditions
RT above the recommended thresholds [13]	Speech understanding	Reduced accuracy
Noisy and quiet tasks (as a baseline attention control modifier) [18]	Task accuracy	Greater reduction in task accuracy under noise in children with weaker selective attention

Note: dB(A), A-weighted decibels; LAeq, equivalent continuous sound level; RT, reverberation time; SNR, signal-to-noise ratio. Acoustic conditions refer to classroom or simulated classroom environments defined in the original studies. The effect estimates are reported in the source publications.

Educational Vulnerability Factors

Studies examining socio-economic and individual vulnerability factors reported larger performance losses under noisy conditions among children with reduced educational resources. Pupils assessed in a non-native language showed lower reading comprehension scores under aircraft noise exposure than peers tested in their primary language. ^[17] External and classroom noise were associated with lower standardized reading and mathematics scores, even after adjustment for SES indicators. ^[20]

Classrooms in low-SES areas recorded indoor noise levels around 70 dB(A) and outdoor levels of approximately 57 dB(A), exceeding the recommended limits for learning environments. Classrooms facing busy transport routes exhibited lower SNR and less favorable acoustic conditions than classrooms located in quieter areas. ^[21] In environments with higher road-traffic noise exposure, pupils from lower-SES households showed steeper declines in standardized reading and mathematics scores, even after adjustment for parental education. ^[22]

Experimental studies further demonstrated that educational vulnerability modified the effects of noise on task performance. Children from low-SES households or bilingual backgrounds showed reduced accuracy in narrative comprehension tasks under multitalker background noise compared with peers with higher educational resource profiles. Additional task-level data indicated that pupils from low-SES or bilingual backgrounds required higher SNR values to achieve comparable accuracy levels, resulting in larger performance gaps at SNR levels below 10 dB(A). ^[19]

Table 4 summarizes the key quantitative findings reported for educational vulnerability factors, with effect estimates and confidence intervals where available.

Table 4.

Key quantitative findings for factors of educational vulnerability

Vulnerability factor/ contrast	Outcome	Effect estimate (as reported)
Aircraft noise exposure, non-native and native language assessment [17]	Reading comprehension	Interaction effect between aircraft noise exposure and home language on reading comprehension; lower mean reading comprehension scores in non-native language pupils
Multitalker background noise, low-SES or bilingual, and peers [19]	Accuracy of narrative comprehension	Lower narrative listening comprehension accuracy in multitalker babble noise, with larger performance differences between low-SES/bilingual and non-vulnerable pupils
External and classroom noise (adjusted for SES) [20]	Reading and mathematics scores	Negative correlation coefficients (r) between LAeq/LAmax and standardized reading and mathematics scores after SES adjustment
Low-SES school areas [21]	Indoor and outdoor noise levels	Indoor noise ≈70 dB(A); outdoor noise ≈57 dB(A)
Classrooms near busy transportation routes and quieter areas [21]	Acoustic conditions	Lower SNR and poorer acoustic quality in classrooms near traffic
Road-traffic noise exposure, stratified by SES [22]	Reading and mathematics scores	Negative regression coefficients between school LAeq, day, and standardized reading and mathematics scores, with larger score reductions observed in pupils from low-SES backgrounds

Note: dB(A), A-weighted decibels; LAeq, equivalent continuous sound level; LAmax, maximum sound level; SES, socioeconomic status; SNR, signal-to-noise ratio. Effect estimates are reported as in the original studies and describe the statistical measures used, such as interaction effects, correlation coefficients, or regression coefficients, as well as measured noise levels, reflecting differences between vulnerability groups under varying noise exposure conditions.

Across the included studies, some noise-outcome combinations showed null or non-significant associations after adjustment or in longitudinal analyses, whereas no positive associations were reported between noise exposure and cognitive performance. Adjustment for SES and vulnerability indicators was associated with reduced effect estimates for several noise-outcome relationships. In some cross-sectional analyses, associations remained significant after adjustment, whereas some outcomes became non-significant in longitudinal models. This pattern was not consistent across studies or cognitive domains and varied with exposure context and adjustment approach.

Integrative Synthesis and Conceptual Model

The comparative analysis identified convergent evidence across the acoustic, cognitive, and contextual domains. Studies reported wide variation in exposure levels; however, several acoustic patterns were observed across designs. Classrooms frequently exceeded the recommended values for the LAeq, and many laboratory studies reproduced similar conditions using a controlled SNR and RT. External exposure to traffic and aircraft noise frequently reached levels associated with decreased reading and attention performance. These patterns formed a discernible exposure profile.

Measured exposure ranges overlapped across studies, and classroom sound levels clustered in a narrow band that can be reproduced in laboratory simulations, while external aircraft or traffic noise frequently exceeded levels associated with reduced reading and attention performance. ^[15,21,22] This consistency across independent datasets supports the comparability of exposure conditions used in laboratory simulations.

Across studies, pupils presented lower task efficiency when acoustic conditions worsened. Pattern held across attention, memory, and reading tasks. Several studies have also reported longer reaction times and higher error rates under noisier conditions. Despite differences in measurement protocols, these findings were reported in both observational and experimental work. ^[7,18] Contextual factors modified these outcomes. Children from lower socioeconomic backgrounds, bilingual learners, and pupils with weaker attention control experienced larger performance reductions under identical acoustic conditions. Subgroup analyses indicated that the same noise level could produce different magnitudes of decline depending on vulnerability profiles. ^[18,19] The cross-layer synthesis mapped these elements into a sequential exposure–response pattern. Greater listening effort redirects resources away from comprehension and memory. Listening effort is expressed as a conceptual formulation illustrating the combined influence of key acoustic parameters:

E = f(LA_eq, 1/SNR, RT) where, f denotes a functional relationship that summarizes how acoustic parameters jointly influence listening effort. This expression is a conceptual approximation rather than an empirical model and reflects the combined influence of LAeq, SNR, and RT described across the included studies. Higher LAeq, lower SNR and RT increase the effort required to follow speech during instruction.

This shift limits the performance of tasks that require sustained processing. When the external noise at the school entrance coincided with the classroom noise during instruction, the combined load reduced performance across multiple cognitive domains.

Repeated exposure can reinforce differences in daily performance. Figure 2 illustrates a sequential pathway from noise exposure to learning outcomes. Environmental noise from traffic or aircraft appears as an initial source that increases mental load at the start of the school day and contributes to classroom sound conditions characterized by high background noise, low signal-to-noise ratio, and long reverberation time. These acoustic conditions increase the effort required to understand speech and reduce the cognitive resources available for learning tasks. Reduced attention, weaker verbal memory, and higher error rates are immediate learning consequences, followed by lower school performance. The magnitude of these effects varies according to SES and attention control, with a higher risk of educational gaps over time among pupils with fewer resources.

Figure 2.

Conceptual model illustrating how environmental noise and classroom acoustics, including low signal-to-noise ratio (SNR), increase listening effort and reduce cognitive resources for learning. Effects are more pronounced in pupils with low socioeconomic status (SES) or weak attention control.

The external noise typically ranged between 55 and 77 LAeq dB(A) for road-traffic and aircraft sources. Indoor classroom conditions usually had low signal-to-noise ratios, often ranging from 0 to 10 dB(A), and reverberation times exceeding the recommended limits, suggesting a continuous acoustic burden during teaching. Under these combined acoustic conditions, the studies reported lower accuracy in reading and listening tasks, longer reaction times in attention and switching tasks, and reduced performance on standardized assessments. The performance losses were unevenly distributed across the learner profiles. The scenario reflects the exposure conditions most frequently documented in the reviewed evidence and summarizes the cumulative cognitive and educational effects observed across studies

DISCUSSION

Acoustic conditions in the external environment and the classroom influence attention, memory, and reading processes. The LAeq, SNR, and RT dictate the effort required to discern the speech signal. Children rely on additional reconstruction processes when speech clarity decreases, which reduces the resources available for understanding, memory encoding, and task execution. Temporal and spectral degradation increase cognitive effort during speech processing. These patterns appear in observational and experimental designs and reflect how acoustic parameters interact with learning-related cognitive mechanisms. ^[16,20]

Traffic noise, aircraft noise, and classroom chatter affect cognitive performance through various mechanisms. Traffic noise is continuous and predictable, increasing the auditory load and attentional demands. Its effects are mainly observed in tasks requiring prolonged concentration, such as reading and standardized academic testing, where dose–response relationships are reported. Aircraft noise consists of high-intensity, intermittent events with abrupt onset. These characteristics disrupt attention during tasks. This mechanism is consistent with the effects observed on reading understanding and learning progression, particularly in pupils assessed in a non-native language, where linguistic processing demands are higher. Classroom chatter differs from environmental transport noise because it spectrally and temporally overlaps with speech. This overlap results in informational masking, which interferes with speech perception and phonological processing. The reported effects include reduced speech intelligibility, lower accuracy in speech-in-noise tasks, and performance declines when SNR and RT exceed the recommended limits. Compared with transport noise, classroom chatter affects moment-to-moment language processing, whereas cumulative academic outcomes are less affected. These findings indicate that cognitive effects depend not only on sound level but also on temporal structure, predictability, and informational content.

The synthesized results refine existing theoretical views on noise effects in educational settings by specifying when and under what conditions noise-related cognitive costs emerge. The observed associations between adverse acoustic conditions and reduced attention, speech processing, and task accuracy are compatible with models that treat listening effort as a consequence of increased perceptual demand under limited cognitive capacity. The attenuation or loss of statistical significance after adjustment for SES, language background, or attention control indicates that noise does not act as an isolated determinant of learning performance. These findings place limits on exposure–response interpretations that assume stable effects across contexts. The presence of null findings in longitudinal or fully adjusted analyses points to alternative explanations, including differences in instructional context, task sensitivity to noise, and adaptation to repeated exposure in chronically noisy environments. The variability in adjustment strategies and outcome measures across studies further constrains cross-study comparison. The evidence suggests that noise-related effects arise from interacting constraints on cognitive resources, rather than from a single dominant pathway.

Previous studies measured noise exposures in separate domains. Research on external noise quantified road-traffic or aircraft LAeq at the school perimeter and examined associations with reading and memory outcomes, without considering in-class acoustic parameters such as SNR or RT. ^[23] While laboratory and classroom experiments have investigated SNR and RT during listening tasks, their protocols did not incorporate the external LAeq variations that pupils experience before instruction begins. ^[13] This separation creates a methodological gap because external noise establishes the initial cognitive load, whereas classroom acoustics determine the processing demands during tasks. The present synthesis evaluates both components within the same analytical structure and uses standardized acoustic metrics to describe their combined impact. The model integrates external exposure and classroom acoustics into a single processing sequence, an element that has been absent from previous studies that treated these components separately.

The model formalizes the transition from perceptual decoding to working-memory engagement as acoustic conditions degrade. Vulnerability factors shape this transition. Analyses stratified by socioeconomic background, language exposure, and attention control expose distinct performance patterns across groups exposed to the same acoustic conditions. Differences in linguistic load, baseline attention, and daily classroom environments shaped how strongly children responded to the same acoustic conditions. ^[19] Children with weaker selective-attention skills show earlier declines in performance when exposed to multitalker noise, leading to less idea generation and lower response precision under the same acoustic conditions that do not affect peers with a higher degree of attention control. ^[18] These findings indicate that acoustic exposure interacts with specific cognitive and educational characteristics, producing different performance outcomes even when measured noise levels are identical. These subgroup differences can be explained by models of limited cognitive capacity and listening effort, which describe how degraded acoustic input increases the resources required for perceptual decoding, leaving fewer resources available for higher-order processing. Under similar LAeq, SNR, and RT values, children with higher linguistic processing demands, reduced attentional regulation, or greater baseline cognitive load reach this capacity limit earlier. This mechanism provides a theoretical basis for the observed VRE effects.

The positioning of these vulnerability factors inside the acoustic-cognitive processing chain is a second methodological contribution. Previous research investigated socioeconomic background, linguistic load, and attention capacity as factors influencing noise effects; however, these characteristics were primarily analyzed through post-hoc stratifications. ^[18,21] Under degraded acoustic conditions, the model indicates where processing load increases. The structure clarifies how identical LAeq, SNR, and RT values can generate different processing demands across children, enabling a more precise interpretation of subgroup differences.

The model identifies how different stages of the school day contribute to processing load and illustrates where transitions from decoding to using working memory occur. The model treats processing load as a mediating state that connects acoustic parameters to attention and memory demands. ^[13] Educational vulnerability modifies these processes, as the same acoustic conditions create different processing requirements depending on linguistic exposure, socioeconomic context, and selective-attention capacity. ^[19] Functional acoustic parameters such as LAeq, SNR, and RT provide a basis for planning and mitigation at the school level. ^[10,13] These components illustrate how daily exposure to environmental and classroom noise can accumulate into measurable variation in academic performance.

The formal representation of listening effort as a state variable that associates acoustic metrics with working memory load is a third technical element. This step is absent in previous studies that report associations but do not describe the intermediate processing stage in functional terms. The introduction of the listening effort as an intermediate step shows how changes in LAeq, SNR, or RT redistribute processing resources across decoding, storage, and response execution.

The model can be applied in three ways. Acoustic assessments gain a structured sequence that combines external exposure, classroom parameters, and learner characteristics. School-based noise studies obtain a clear set of elements that must be measured to quantify listening effort. The identification of points where instructional load or acoustic conditions should be adjusted benefits educational planning.

The model structure can be transferred to occupational safety systems that evaluate cognitive load under degraded acoustic conditions. The same sequence of external exposure, task-specific acoustic parameters, and individual processing characteristics applies to work environments with communication demands. LAeq, SNR, and RT influence the processing load during task execution in these environments. Using these parameters in workplace assessments helps identify tasks in which acoustic degradation increases listening effort and reduces operational accuracy. This approach supports risk evaluations in speech communication-related activities, monitoring tasks, or rapid decision-making.

Operational use of the model in school environments requires technical competence in acoustic measurement and interpretation. Personnel must be able to record and interpret LAeq, octave-band levels, SNR, and RT using standardised protocols. They also need to recognise how these parameters modify listening effort and where shifts from perceptual decoding to working-memory engagement occur during instructional tasks. Applying the model in situ requires the ability to relate measured values to speech perception threshold ranges, processing load, and task efficiency. To support consistent measurement and interpretation, short technical modules can be included in school-level acoustic audits.

These results inform school design, educational planning, and environmental policy decisions by specifying which acoustic conditions require intervention. Acoustic parameters with the strongest effect on cognitive performance can be modified through architectural measures that increase the SNR, RT, and limit external noise intrusion. ^[10] These adjustments are important in schools serving lower-SES communities, where indoor and outdoor levels often exceed the recommended limits and pupils already operate with higher baseline processing demands. ^[21] Routine acoustic assessments can support planning in these settings, and noise reduction can be prioritized in vulnerable urban areas. ^[21] External noise management is also relevant, given the documented associations between LAeq and academic performance around school sites. ^[23] The framework can support cross-national harmonization of school acoustic guidelines by providing a common basis for defining functional thresholds related to learning outcomes. The model developed in this review provides a structure for identifying pupils at greater risk of performance loss and for refining exposure–response analyses and intervention strategies.

The findings have implications for school infrastructure, classroom design, and educational policy. Acoustic parameters that influence cognitive performance can be modified through architectural and organizational measures. Field surveys have reported that many classrooms operate above the recommended acoustic limits. ^[7] These conditions are more frequent in dense urban areas and in socioeconomically disadvantaged neighborhoods, where dedicated surveys have documented similar patterns. ^[21] Increasing the SNR through acoustic panels, installing sound-absorbing materials, and limiting avoidable activity noise reduces the processing load during speech perception. These measures align with guidance issued by the World Health Organization (WHO) on indoor air quality (AQ3) and with the ANSI/ASA S12.60 standard for classroom acoustics, developed by the Acoustical Society of America (ASA) and approved by the American National Standards Institute (ANSI), which specifies a maximum reverberation time of 0.6 seconds and a minimum signal-to-noise ratio of +15 dB(A) for effective speech perception. These results operate at micro and macro levels, from task-specific processing effects in classrooms to structural disparities across school systems, and align with international acoustic guidelines used in educational planning.

Schools in socioeconomically disadvantaged areas often present weaker acoustic profiles. Surveys in these environments report higher background levels, insufficient insulation, and longer reverberation times, which are conditions that increase the processing load during learning tasks. ^[21] Improving classroom acoustics is a priority in high-exposure schools. External noise should also be considered in infrastructure planning, since elevated LAeq around school buildings is associated with lower academic performance in urban environments. ^[23] Oral assessments and reading tasks are sensitive to acoustic conditions, and better acoustic control during testing reduces noise-related variation in performance.

Integration of these exposure characteristics with documented subgroup differences is a further contribution. Treating socioeconomic status, linguistic load, and attention capacity as functional modifiers within the acoustic–cognitive sequence, it becomes possible to specify where processing efficiency differs between groups, even when LAeq, SNR, and RT are identical. This placement strengthens the interpretation of stratified outcomes and supports more precise estimation of exposure–response functions. The effect is most evident in low-SNR environments, where bilingual pupils and those with reduced attentional regulation show early performance decline. ^[18,19] Mechanistically, these modifiers determine how rapidly perceptual demands approach available cognitive capacity under degraded acoustic conditions. Socio-economic context is associated with differences in baseline cognitive load and exposure history, linguistic load increases decoding demands during speech processing, and competing auditory information regulation is constrained by attentional capacity. As a result, identical acoustic parameters can impose different processing loads across groups, leading to earlier performance decline in vulnerable subpopulations.

A technical element in the model is how transition points in the processing load are defined. The model aligns the external LAeq with the in-class acoustic parameters along a single exposure axis. This alignment illustrates where perceptual demands shift toward the use of working memory. Earlier studies described these processes separately, and combining them clarifies how degraded acoustic cues change the distribution of processing resources and why performance varies in tasks that require sustained verbal processing. ^[13]

Positioning listening effort as an intermediate state variable improves the interpretability of findings across heterogeneous study designs. Studies using different tasks-reading, narrative comprehension, switching tasks, or inhibition measures-converge on the same processing bottleneck when acoustic degradation exceeds a threshold defined jointly by LAeq, SNR, and RT. ^[13] The integration of these data provides a functional description of how acoustic constraints propagate through attentional and memory systems during instructional activities.

Integrating the exposure components clarifies which acoustic conditions intensify differences in task performance across scholar groups. Variability in cognitive control and interference suppression further shapes how children respond to competing auditory information during instructional activities. ^[27] Schools in high-exposure areas experience increased baseline processing load, and pupils with lower cognitive reserves reach performance thresholds earlier under noisy conditions. ^[15,17] Daily exposure patterns can accumulate over time and shape academic trajectories.

Previous reviews have examined environmental noise and its effects on children’s learning or have focused on classroom acoustics and cognitive performance. ^[6,20] Similar integrative approaches have been proposed in occupational safety and health research, where heterogeneous exposure data and individual vulnerability were structured within a single decision framework to support risk management for sensitive groups. ^[28] These studies did not integrate external exposure, classroom parameters, and learner vulnerability within the same processing sequence. This review combines these components into a single framework that traces how acoustic conditions influence listening effort and performance outcomes.

The limitations of this review stem from the heterogeneity of study designs, acoustic measurements, and outcome instruments used. Noise exposure was quantified through various metrics and composite indices such as WECPNL, which reduces comparability across studies. Measurement intervals differed, with some studies using brief snapshots and others using full-day monitoring. Cognitive outcomes lacked standardization; tasks ranged from brief attention tests to comprehensive reading assessments, and psychometric validation varied across studies. Most of the included studies were observational. Without longitudinal follow-up, causal inferences remain limited, and cumulative exposure cannot be quantified. Sample representativeness was variable, as many studies relied on school-based or convenience samples that may not reflect the broader population of school-age children. Socioeconomic indicators were inconsistently measured, often aggregated at the school level rather than the individual level, which may undervalue observed disparities. Only a few studies simultaneously measured external and internal noise, despite this reflecting real-world exposure patterns. As a result, the proposed model should be interpreted as a descriptive representation of processing pathways rather than a predictive or empirically calibrated framework. The evidence base is of moderate uniformity, with strong alignment in direction, yet variability in measurement precision. Classroom occupancy and behavioral noise patterns also differed across studies, which may have influenced measured LAeq and SNR values and further reduced comparability.

An additional limitation relates to geographic coverage. Most of the included studies were conducted in Europe, North America, and East Asia, while data from Latin America, Africa, and rural settings are rare. The educational environments in these underrepresented regions may differ in terms of building characteristics, classroom density, dominant noise sources, and access to acoustic mitigation measures, which limits the generalizability of the findings.

Future research needs to establish standardized acoustic protocols and spectral characteristics in school-based studies. Using standardized metrics would enable precise exposure–response modeling and facilitate meta-analytic synthesis. Studies should combine external and classroom measurements within the same time window to capture daily exposure. Longitudinal studies enable the measurement of learning loss across academic periods, addressing current knowledge gaps regarding long-term effects. SNR and RT should be manipulated in laboratory experiments across a wider range of controlled conditions to refine functional thresholds for speech intelligibility and cognitive load. Neurocognitive measures can refine the analysis of effort-related processes. Pupillometry and electroencephalography provide continuous indicators of processing load under controlled SNR and masking conditions, and identify the stages where acoustic degradation modifies resource allocation. ^[29] These approaches complement behavioral findings from classroom and laboratory studies, where reduced response precision and earlier performance decline occur under low SNR or multitalker noise. Applying these methods to children would permit direct quantification of listening effort and facilitate understanding of how acoustic limitations influence task performance during learning.

CONCLUSION

This review identifies consistent patterns between adverse acoustic conditions and reduced cognitive efficiency. Rather than a unified exposure sequence, the proposed framework represents a conceptual organization of external and classroom sound exposures along a common processing pathway. The framework describes how baseline load at school entry and in-class acoustic demands may act in sequence to increase listening effort as acoustic conditions degrade.

Given that only a limited number of studies simultaneously measured external and internal noise, the framework does not constitute an empirically integrated exposure model. Instead, it provides a descriptive structure that combines evidence derived from partially overlapping exposure domains. Learner-level differences are incorporated to indicate where children diverge in processing efficiency under comparable LAeq, SNR, and RT conditions, without implying uniform exposure–response relationships. Listening effort is treated as an intermediate state variable that connects acoustic parameters to working-memory use and task performance. The framework provides a conceptual basis for organizing future empirical work, particularly studies designed to jointly assess external and classroom noise, refine exposure sequencing, and examine effort-related processes across diverse learner groups.

Availability of Data and Materials

All data generated or analyzed during this study are included in this published article.

Author Contributions

Conceptualization, D.O.B., D.C.D., and L.I.C.; methodology, D.O.B., D.C.D., and L.I.C.; validation, D.C.D., L.I.C., and T.V.C.; formal analysis, D.O.B. and C.C.; data curation, D.O.B.; writing-original draft preparation, D.O.B.; writing-review and editing, D.O.B., D.C.D., L.I.C., and T.V.C.; visualization, D.C.D., L.I.C., T.V.C., and C.C; supervision, L.I.C. All authors have read and agreed to the published version of the manuscript.

Ethics Approval and Consent to Participate

Not applicable.

Conflicts of Interest

There are no conflicts of interest.

Funding Statement

This research received no external funding.