# The Impact of Noise on Learning in Children and Adolescents: A Meta-Analysis
## 3 Results
### Visualization of Meta-Analysis Results  
The following section presents the graphical tools used to visualize the results of the meta-analysis, specifically, the funnel plot (Figure 1). A forest plot is included in the Supplementary Materials (Figure S1). The effect size of \(-\)0.46 indicates an effect of a moderate magnitude according to [PERSON]'s guidelines. This suggests that the independent variable (in this case, noise) has a negative impact on the dependent variable (academic or cognitive performance). In other words, as noise increases, academic or cognitive performance decreases. The 95% confidence interval (\(-\)0.54 to \(-\)0.38) indicates that since the interval is below zero and does not include zero, we can affirm that the negative effect of noise is significant and not due to chance. Most effect sizes are located on the left side of the line, indicating a negative impact of noise on performance. The most substantial negative values correspond to \(-\)3.01 [\(-\)3.07, \(-\)2.95], reported by [17], with a small confidence interval; \(-\)2.98 [\(-\)3.50, \(-\)2.46] and \(-\)2.40 [\(-\)2.92, \(-\)1.88], reported [14]; \(-\)2.63 [\(-\)3.06, \(-\)2.20], reported by [21]; and \(-\)2.0 [\(-\)2.56, \(-\)1.44], reported by [27]. The study [17] reported a large effect size, demonstrating that there was a significant difference in perception scores between the high exposure and control groups in a large sample of 989 participants. Similarly, ref. [14] highlighted a significant reduction in accuracy with reduced bandwidth, noting higher accuracy at a 9 dB SNR compared to a 3 dB SNR in nonword repetition accuracy, although its sample size was limited to 17 subjects. In another study, ref. [21] discovered that noise negatively affects speech perception in children aged 6-8 years. Their comparison of silence to binaural conditions (LAeq = 63 dB, SNR-3 dB) revealed a significant negative effect, underscoring the detrimental impact of noise on young children's ability to understand speech. Moreover, ref. [27] demonstrated a significant negative impact of classroom noise and reverberation on comprehension performance in elementary-aged students. The study used a simulated classroom environment with varied reverberation times and background noise levels, showing that both reduced performance in comprehension tasks. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Estimate** \\ \hline \(\tau^{2}\) & 0.207 \\ T & 0.455 \\ I\({}^{2}\) (\%) & 96.943 \\ H\({}^{2}\) & 32.715 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Residual heterogeneity estimates. In contrast, the positive effects reported were small and not significant, as they included 0. Ref. [28] reported a small effect of irrelevant speech on reading comprehension, with LAeq = 66 dBA and background babble around 62 dBA and with the dominant frequency range between 500 and 1500 Hz, indicating that irrelevant speech slightly improves reading comprehension, although the effect size was small. Ref. [15] compared classroom noise in a control versus experimental group in the first evaluation of 8-year-old participants it conducted. The results showed a slight favoring of the control group, but this difference was not significant, and the authors could not explain this outcome. The funnel plot (Figure 2) showed a reasonably symmetrical distribution of points around the mean effect value, suggesting a partial absence of publication bias. Most studies clustered around the center, indicating that the estimated effect sizes are generally  
Figure 1: PRISMA flow diagram. consistent and reliable. However, some asymmetry was observed in the lower part of the plot, which may indicate the presence of small-study effects or potential publication bias. To further assess this, [PERSON]'s regression test for funnel plot asymmetry was conducted, revealing significant asymmetry (\(Z=-4.764\), \(p<0.001\)), suggesting the presence of publication bias.