Introduction
Think back to your school days. Were your desks arranged in neat rows and columns, or did you sit in groups? How did these seating arrangements affect your ability to focus and interact with your classmates? In our school, High School South in West Windsor-Plainsboro, classrooms have no walls, thus noise levels can be a significant challenge. This unique setup can really influence how students at our school concentrate and process information. In the contemporary educational landscape, the criss-crossing of neuroscience and pedagogy has garnered significant attention, particularly regarding how environmental factors impact students’ cognitive functions and learning outcomes. The classroom setting is not just a backdrop for learning; it’s a powerful space where elements such as seating arrangements, noise levels, and technological integration shape student experiences and influence how they process information.
The layout of seating arrangements can influence not only student interactions but also their engagement levels and the overall classroom atmosphere. According to Di Giacomo, Ranieri, and Lacasa (2017), strategic seating can enhance collaborative learning and improve cognitive outcomes. Meanwhile, ambient noise levels have also been identified as important determinants of concentration and cognitive processing, with implications for students’ ability to absorb and retain information. Excessive noise can disrupt attention and memory, leading to poorer academic performance (Di Giacomo et al., 2017). Creating quiet and conducive learning spaces helps maximize students’ cognitive function. Additionally, the infusion of educational technology fosters diverse learning means, offering tailored approaches that enhance cognitive engagement. Modern digital learning platforms, as explored by Di Giacomo et al. (2017), provide interactive and personalized learning experiences that can boost cognitive engagement and retention. However, the effectiveness of these technologies depends on their proper integration into the curriculum. Equally important is the influence of peer dynamics, where the social context can either stimulate or hinder academic motivation. Positive peer relationships can foster a supportive learning environment, while negative peer pressure can lead to stress and anxiety, adversely affecting cognitive performance. Understanding the dynamics of peer influence is crucial for developing strategies to support students’ mental and emotional well-being.
This is crucial for creating an optimal learning environment. While studying derivatives or knowing that Theodore Roosevelt was the 26th president can be interesting, the real key to learning is understanding how to learn. By promoting an environment that supports this meta-cognitive skill, we can enhance our ability to absorb and apply knowledge effectively.
The layout of seating arrangements can influence not only student interactions but also their engagement levels and the overall classroom atmosphere. According to Di Giacomo, Ranieri, and Lacasa (2017), strategic seating can enhance collaborative learning and improve cognitive outcomes. Meanwhile, ambient noise levels have also been identified as important determinants of concentration and cognitive processing, with implications for students’ ability to absorb and retain information. Excessive noise can disrupt attention and memory, leading to poorer academic performance (Di Giacomo et al., 2017). Creating quiet and conducive learning spaces helps maximize students’ cognitive function. Additionally, the infusion of educational technology fosters diverse learning means, offering tailored approaches that enhance cognitive engagement. Modern digital learning platforms, as explored by Di Giacomo et al. (2017), provide interactive and personalized learning experiences that can boost cognitive engagement and retention. However, the effectiveness of these technologies depends on their proper integration into the curriculum. Equally important is the influence of peer dynamics, where the social context can either stimulate or hinder academic motivation. Positive peer relationships can foster a supportive learning environment, while negative peer pressure can lead to stress and anxiety, adversely affecting cognitive performance. Understanding the dynamics of peer influence is crucial for developing strategies to support students’ mental and emotional well-being.
This is crucial for creating an optimal learning environment. While studying derivatives or knowing that Theodore Roosevelt was the 26th president can be interesting, the real key to learning is understanding how to learn. By promoting an environment that supports this meta-cognitive skill, we can enhance our ability to absorb and apply knowledge effectively.
Literature Review
Seating arrangement
Seating arrangement is a critical environmental factor that can profoundly influence students’ cognition and learning outcomes. Research by Cooper et al. (2000) found that flexible seating arrangements that encourage interaction among students lead to increased engagement and academic performance. They assert, "Classrooms designed for collaboration inherently promote student agency through enhanced interaction." This assertion highlights the importance of collaborative learning spaces in fostering cognitive engagement. Similarly, Mullet and Hobbs (2017) found that group seating arrangements facilitated discussions, which positively impacted higher-order thinking skills in students. Additionally, grouped or circular configurations promote collaborative learning and increase student interaction, enhancing communication skills and motivation to learn. For instance, a study by Johnson et al. (2014) found that students seated in clusters showed higher levels of engagement than those in traditional row seating, leading to improved retention of information and greater academic success.
Noise levels
Noise levels within a classroom environment can drastically affect students’ cognitive performance. High ambient noise has been linked to a decline in concentration, disrupting information processing and memory retention. For example, a study by Shield and Dudley (2012) demonstrated that elevated noise levels in classrooms significantly decreased students’ test performance compared to quieter settings. They conducted a review indicating that classrooms with lower ambient noise levels saw a remarkable improvement in students' attention and retention of lessons. They noted, "High levels of background noise negatively impact the cognitive processes involved in learning, particularly in tasks that require focused attention." This emphasizes the need for controlled acoustic environments in educational settings. In more recent research, Ransdell et al. (2017) investigated the effects of varying noise levels and concluded that controlled noise environments facilitate better focus and enhance learning efficiency, showing that students perform better academically in classrooms designed to minimize background noise distractions.
Educational technology
The integration of educational technology has transformed the development of cognitive skills in students. Research indicates that technology-enhanced learning environments allow for personalized educational experiences catering to diverse learning styles, thus improving cognitive engagement and academic performance. Schmid et al. (2014) stated, “The implementation of interactive technology creates opportunities for personalized learning, which caters to diverse student needs.” They reported that using interactive tools in classrooms improve students’ critical thinking skills and overall engagement with the material. However, excessive reliance on technology can lead to distractions; for instance, Rosen and Lim (2011) pointed out that students often struggle to maintain focus when technology becomes the primary means of instruction without sufficient guidance from educators. While technology can enhance motivation, it may also introduce distractions if not implemented thoughtfully. They found that "students often multitask with devices during lessons, which can lead to fragmented learning experiences." Thus, it becomes critical to strike a balance between utilizing technology and maintaining focus on learning objectives.
Peer pressure/friends
Think back to your school days. Perhaps you remember a time when friends influenced you to buy an extra piece of chocolate outside of class. But how about in class? Did you ever experience moments when your friends influenced your behavior, focus, and attention? These interactions can significantly impact how students engage with their studies and their cognitive development. Peer influence, especially the power of friendship groups, plays a huge role in shaping students’ motivations, cognitive engagement, as well as overall academic performance. Positive peer interactions encourage collaborative learning, leading to increased motivation and better academic results. For instance, supportive friendships within the academic setting positively correlate with higher achievement levels and greater engagement in school activities (Wentzel, 1998). However, detrimental peer pressure can lead to disengagement and distraction. Adverse peer dynamics can hinder academic focus, causing declines in cognitive performance (Brown & Larson, 2009). For example, if friends encourage off-task behavior during class, it can disrupt attention and reduce the ability to process and retain information. The increasing competitiveness in academic environments is a growing concern, particularly regarding its effects on students' mental health. Schools often emphasize high academic performance, leading to a culture where students strive to outdo one another. This drive for excellence can foster motivation and a strong work ethic but also lead to significant stress, anxiety, and other mental health issues. Research indicates that the pressure to succeed academically can result in negative psychological outcomes, such as depression, anxiety, and burnout. Adolescents and young adults, who are at critical developmental stages, are particularly vulnerable to these pressures. The constant need to compete with peers can create a sense of inadequacy and low self-esteem, contributing to a range of mental health challenges. Additionally, the fear of failure and high expectations set by parents, teachers, and the students themselves can exacerbate these issues. For instance, a study by Conley et al. (2014) found that high school students experiencing academic pressure reported higher levels of stress and anxiety, which negatively impacted their overall well-being. Another study by Luthar and Becker (2002) highlighted that students in high-achieving schools often face significant mental health challenges due to the intense pressure to perform.
Social media and technological advancements also play a role in exacerbating academic competitiveness. Platforms like Instagram, Facebook, and TikTok allow students to share their achievements and academic milestones, often creating a false sense of perfection and heightened peer comparison. This constant exposure to others' successes can intensify feelings of inadequacy and pressure to perform, further contributing to mental health challenges. The 24/7 nature of social media means that students are continually reminded of their academic standings, even outside school hours, making it difficult to escape the competitive environment. A quote from a student in a study by Twenge and Campbell (2018) encapsulates this issue: “Seeing my friends post about their grades and achievements makes me feel like I’m not doing enough, even when I’m trying my best”.
In many cultures, academic achievement is closely tied to future opportunities, social status, and family honor. These cultural expectations can place an immense burden on students, leading them to prioritize academic success over their well-being. Understanding these cultural dimensions is crucial in addressing the mental health impacts of academic competitiveness. By acknowledging the broader societal context, this research aims to provide a holistic view of the issue and offer culturally sensitive interventions to support students' mental health. While significant research has explored the impact of peer pressure, seating arrangements, noise levels, and educational technology on students' cognition, there remains a notable gap in understanding the interplay between these factors. Future inquiries could delve into how these elements affect neurodiverse students or how personalized learning environments could optimize cognitive outcomes. Longitudinal studies assessing the long-term impact of these environmental factors on academic achievement would provide richer data for creating effective classroom strategies. Such inquiry is essential for developing tailored educational methodologies that cater to diverse learning needs and enhance cognitive capacities.
The Gap
Much of the existing literature provides valuable insights into how seating arrangements, noise levels, educational technology, and peer interactions affect cognition. However, there is a notable gap in understanding the complexities of these factors on students with diverse learning profiles, especially those with neurodiverse conditions. While studies like Carretti et al. (2018) have shown the importance of individual cognitive and social differences in educational environments, research is lacking on how these differences interact with environmental factors over time. This raises the question: How do seating arrangements, noise levels, educational technology, and peer pressure impact the cognitive development of neurodiverse students compared to their neurotypical peers? Addressing this gap through longitudinal studies could lead to more personalized educational strategies that optimize learning for all students.
Seating arrangement is a critical environmental factor that can profoundly influence students’ cognition and learning outcomes. Research by Cooper et al. (2000) found that flexible seating arrangements that encourage interaction among students lead to increased engagement and academic performance. They assert, "Classrooms designed for collaboration inherently promote student agency through enhanced interaction." This assertion highlights the importance of collaborative learning spaces in fostering cognitive engagement. Similarly, Mullet and Hobbs (2017) found that group seating arrangements facilitated discussions, which positively impacted higher-order thinking skills in students. Additionally, grouped or circular configurations promote collaborative learning and increase student interaction, enhancing communication skills and motivation to learn. For instance, a study by Johnson et al. (2014) found that students seated in clusters showed higher levels of engagement than those in traditional row seating, leading to improved retention of information and greater academic success.
Noise levels
Noise levels within a classroom environment can drastically affect students’ cognitive performance. High ambient noise has been linked to a decline in concentration, disrupting information processing and memory retention. For example, a study by Shield and Dudley (2012) demonstrated that elevated noise levels in classrooms significantly decreased students’ test performance compared to quieter settings. They conducted a review indicating that classrooms with lower ambient noise levels saw a remarkable improvement in students' attention and retention of lessons. They noted, "High levels of background noise negatively impact the cognitive processes involved in learning, particularly in tasks that require focused attention." This emphasizes the need for controlled acoustic environments in educational settings. In more recent research, Ransdell et al. (2017) investigated the effects of varying noise levels and concluded that controlled noise environments facilitate better focus and enhance learning efficiency, showing that students perform better academically in classrooms designed to minimize background noise distractions.
Educational technology
The integration of educational technology has transformed the development of cognitive skills in students. Research indicates that technology-enhanced learning environments allow for personalized educational experiences catering to diverse learning styles, thus improving cognitive engagement and academic performance. Schmid et al. (2014) stated, “The implementation of interactive technology creates opportunities for personalized learning, which caters to diverse student needs.” They reported that using interactive tools in classrooms improve students’ critical thinking skills and overall engagement with the material. However, excessive reliance on technology can lead to distractions; for instance, Rosen and Lim (2011) pointed out that students often struggle to maintain focus when technology becomes the primary means of instruction without sufficient guidance from educators. While technology can enhance motivation, it may also introduce distractions if not implemented thoughtfully. They found that "students often multitask with devices during lessons, which can lead to fragmented learning experiences." Thus, it becomes critical to strike a balance between utilizing technology and maintaining focus on learning objectives.
Peer pressure/friends
Think back to your school days. Perhaps you remember a time when friends influenced you to buy an extra piece of chocolate outside of class. But how about in class? Did you ever experience moments when your friends influenced your behavior, focus, and attention? These interactions can significantly impact how students engage with their studies and their cognitive development. Peer influence, especially the power of friendship groups, plays a huge role in shaping students’ motivations, cognitive engagement, as well as overall academic performance. Positive peer interactions encourage collaborative learning, leading to increased motivation and better academic results. For instance, supportive friendships within the academic setting positively correlate with higher achievement levels and greater engagement in school activities (Wentzel, 1998). However, detrimental peer pressure can lead to disengagement and distraction. Adverse peer dynamics can hinder academic focus, causing declines in cognitive performance (Brown & Larson, 2009). For example, if friends encourage off-task behavior during class, it can disrupt attention and reduce the ability to process and retain information. The increasing competitiveness in academic environments is a growing concern, particularly regarding its effects on students' mental health. Schools often emphasize high academic performance, leading to a culture where students strive to outdo one another. This drive for excellence can foster motivation and a strong work ethic but also lead to significant stress, anxiety, and other mental health issues. Research indicates that the pressure to succeed academically can result in negative psychological outcomes, such as depression, anxiety, and burnout. Adolescents and young adults, who are at critical developmental stages, are particularly vulnerable to these pressures. The constant need to compete with peers can create a sense of inadequacy and low self-esteem, contributing to a range of mental health challenges. Additionally, the fear of failure and high expectations set by parents, teachers, and the students themselves can exacerbate these issues. For instance, a study by Conley et al. (2014) found that high school students experiencing academic pressure reported higher levels of stress and anxiety, which negatively impacted their overall well-being. Another study by Luthar and Becker (2002) highlighted that students in high-achieving schools often face significant mental health challenges due to the intense pressure to perform.
Social media and technological advancements also play a role in exacerbating academic competitiveness. Platforms like Instagram, Facebook, and TikTok allow students to share their achievements and academic milestones, often creating a false sense of perfection and heightened peer comparison. This constant exposure to others' successes can intensify feelings of inadequacy and pressure to perform, further contributing to mental health challenges. The 24/7 nature of social media means that students are continually reminded of their academic standings, even outside school hours, making it difficult to escape the competitive environment. A quote from a student in a study by Twenge and Campbell (2018) encapsulates this issue: “Seeing my friends post about their grades and achievements makes me feel like I’m not doing enough, even when I’m trying my best”.
In many cultures, academic achievement is closely tied to future opportunities, social status, and family honor. These cultural expectations can place an immense burden on students, leading them to prioritize academic success over their well-being. Understanding these cultural dimensions is crucial in addressing the mental health impacts of academic competitiveness. By acknowledging the broader societal context, this research aims to provide a holistic view of the issue and offer culturally sensitive interventions to support students' mental health. While significant research has explored the impact of peer pressure, seating arrangements, noise levels, and educational technology on students' cognition, there remains a notable gap in understanding the interplay between these factors. Future inquiries could delve into how these elements affect neurodiverse students or how personalized learning environments could optimize cognitive outcomes. Longitudinal studies assessing the long-term impact of these environmental factors on academic achievement would provide richer data for creating effective classroom strategies. Such inquiry is essential for developing tailored educational methodologies that cater to diverse learning needs and enhance cognitive capacities.
The Gap
Much of the existing literature provides valuable insights into how seating arrangements, noise levels, educational technology, and peer interactions affect cognition. However, there is a notable gap in understanding the complexities of these factors on students with diverse learning profiles, especially those with neurodiverse conditions. While studies like Carretti et al. (2018) have shown the importance of individual cognitive and social differences in educational environments, research is lacking on how these differences interact with environmental factors over time. This raises the question: How do seating arrangements, noise levels, educational technology, and peer pressure impact the cognitive development of neurodiverse students compared to their neurotypical peers? Addressing this gap through longitudinal studies could lead to more personalized educational strategies that optimize learning for all students.
Methods
Tobia et al.’s Study on Seating Arrangement
The study involved 77 primary school children (57.1% girls) aged between 8-11 years (mean age = 9.59 years, SD = 0.66) from Northern Italy. The sample was drawn from five classes, with parents providing consent for participation. One child with a cognitive disability was excluded from the analysis. The classrooms were set up in two different arrangements: 46 children were seated in rows, while the rest were arranged in clusters (couples) (Choi, Van Merriënboer, & Paas, 2014).
The study involved 77 primary school children (57.1% girls) aged between 8-11 years (mean age = 9.59 years, SD = 0.66) from Northern Italy. The sample was drawn from five classes, with parents providing consent for participation. One child with a cognitive disability was excluded from the analysis. The classrooms were set up in two different arrangements: 46 children were seated in rows, while the rest were arranged in clusters (couples) (Choi, Van Merriënboer, & Paas, 2014).
This study employed a two-condition within-participants design, manipulating classroom seating arrangements (clusters vs. rows and columns). The dependent variables were cognitive performance outcomes measured through logical reasoning, creativity, and Theory of Mind (ToM) tasks. This design was chosen over a between-participants design to control for individual differences in cognitive abilities, allowing each child to serve as their own control across different seating arrangements.
Initial independent t-tests were performed to explore differences in cognitive performance based on usual seating arrangements. The seating arrangement was experimentally manipulated during two sessions. In one session, children were seated in clusters (smaller interpersonal distance), while in the other, they were arranged in rows and columns (greater interpersonal distance). Each child's performance was measured in both seating conditions. This method was selected over others because it allowed for a direct comparison of cognitive performance across different seating configurations while minimizing confounding variables. A series of 2 (arrangement: clusters vs. rows-columns) x 2 (gender: girl vs. boy) ANOVAs were conducted to assess the impact of seating arrangements and gender on cognitive task performance. The MEMORE macro was employed to analyze mediation and moderation effects, considering moderators such as relational self-esteem, popularity, loneliness, and physiological reactions to proximity (Rose-Krasnor, 1997; McClure, 2000). The MEMORE macro was chosen for its ability to handle mediation and moderation analyses efficiently, providing detailed insights into the relationships between variables.
Shield and Dockrell’s Research on Noise Levels
This study utilized data collected from a performance assessment involving 158 eight-year-old children across state primary schools in three London boroughs, specifically chosen for their varying exposure to environmental noise. The design of this research aimed to ensure a representative sample with regard to socio-economic backgrounds, gauged through Free School Meals (FSM) scores, recognized as a reliable indicator of social disadvantage (Williamson & Byrne, 1977). The core objective was to quantify the impact of noise on children's cognitive performance, focusing on critical areas such as reading, spelling, and arithmetic skills. In order to achieve this, the study implemented a fundamentally robust approach by employing noise surveys as the primary method for collecting data on both internal and external noise levels at the participating schools. This method was favored since it provided direct, quantifiable measurements of noise exposure experienced by children in their learning environments. Such direct measurement is instrumental, as it captures real-world conditions rather than artificial laboratory simulations, which may fail to accurately reflect the everyday classroom experience (Shield & Dockrell, 2008).
External noise levels were meticulously recorded outside 142 state primary schools situated within three London boroughs. The researchers deliberately avoided regions dominated by aircraft noise to concentrate on road traffic, the primary source of ambient noise in urban settings. This focus was essential to ensure that findings remained relevant to commonplace urban noise sources and varied environmental conditions (Shield & Dockrell, 2008). Furthermore, internal noise surveys were conducted in 16 schools from boroughs A and B, selected to exemplify a wide range of external noise levels. These surveys encompassed measurements taken in 110 occupied classrooms and 30 empty classrooms, as well as corridors, foyers, and halls. This approach to internal noise assessment aimed to elucidate how different areas within school buildings not only contribute to noise levels but also how such noise penetrates classrooms, thereby influencing the learning environment (Shield & Dockrell, 2008). Demographic data—including percentages of children receiving free school meals (FSM), those for whom English was an additional language (EAL), and those with special educational needs (SEN)—were collected from government sources. This data was pivotal for controlling socio-economic factors in the analysis, facilitating a more precise evaluation of how noise impacted academic performance apart from variations attributable to socio-economic status (Shield & Dockrell, 2008).
Cognitive testing employed a set of verbal and non-verbal assessments specifically developed for the target age group, targeting key areas such as reading, spelling, speed of performance, and arithmetic. Tests were conducted under three distinct noise conditions designed to simulate varied classroom environments: a base or quiet condition (normal classroom atmosphere with no background noise), a babble condition (steady classroom noise at 65 dB(A) LAeq representing the average level measured during individual work), and a babble plus environmental noise condition (children's babble combined with intermittent noise events mimicking urban disturbances at 58 dB(A) LAmax). This multi-condition approach allowed for insightful comparisons regarding how different types and levels of noise affect children's cognitive performances in a realistic educational context (Shield & Dockrell, 2008).
Aljohani’s Research on Educational Technologies
Research on educational technologies has rapidly increased due to the emphasis on improving learning experiences through innovative solutions. A study conducted by Aljohani (2020) examined the effectiveness of various educational technologies in higher education settings, particularly focusing on the implementation of Massive Open Online Courses (MOOCs) and Learning Management Systems (LMS). The research employed a mixed-methods approach combining quantitative surveys and qualitative interviews to gather comprehensive data. Initially, a survey was distributed to over 500 students enrolled in various MOOCs at Rajamangala University of Technology Srivijaya, assessing their experiences, satisfaction, and perceived effectiveness of these technologies. The survey included Likert scale questions, which allowed respondents to rate their satisfaction on a scale from 1 to 5, with 1 being "very dissatisfied" and 5 being "very satisfied". Additionally, the study held structured interviews with 25 instructors who utilized these technologies. This method was chosen to complement the quantitative survey data, providing deeper insights into how instructors integrate these educational technologies into their teaching practices. The interviews allowed for open-ended responses, capturing instructors' personal experiences, challenges, and insights on the effectiveness of technologies in enhancing the learning experience. Data analysis involved statistical methods, including descriptive statistics to summarize survey responses, as well as thematic analysis for qualitative interview data. The combination of quantitative and qualitative data provided a well-rounded view of the educational technologies implemented and their impact on student learning.
Aashiq's Study on Emerging Educational Technologies
The findings revealed that emerging technologies have a significant and positive impact on cognitive development among higher education students, supporting Hypothesis 1 (H1). Specifically, the integration of emerging technologies, such as mobile devices, virtual reality, and online platforms, was associated with enhancements in cognitive processes, including attention, memory, problem-solving, and critical thinking skills (Graesser et al., 2022).
Additionally, the study confirmed Hypothesis 2 (H2), showing that digital social support significantly and positively impacts cognitive development among higher education students. Participants who reported higher levels of digital social support, facilitated through social media platforms, emails, and instant messaging, exhibited improved cognitive development outcomes. Furthermore, the results validated Hypothesis 3 (H3), indicating that digital social support mediates the relationship between emerging technologies and cognitive development. This suggests that the positive effects of emerging technologies on cognitive development are enhanced when students have access to robust digital social support networks (T1). The internal consistency of the scales used in the study was confirmed, with Cronbach's Alpha values of 0.657 for cognitive development and 0.801 for digital social support, indicating that the items within each scale were reliably measuring the intended constructs.
The Spotlight Game Task
Participants in the study engaged in the Stoplight Game, designed to assess risk-taking behavior in different social contexts. The game consisted of navigating through intersections, where participants could choose to stop at yellow lights or proceed, risking a potential crash. Extensive pilot testing informed the following game parameters: intersection distances varied from 10 to 16 seconds, and vehicle braking duration was set at 500 milliseconds. The time between the stoplight turning yellow and reaching the intersection ranged from 2 to 4.5 seconds, affecting the perceived likelihood of successfully running a light. The wait time for the red light to cycle back to green was fixed at 3 seconds, and the crash penalty delay was set at 6 seconds. The probability of a crash following a risky decision was 40%. The first four intersections were programmed to allow safe passage, ensuring no negative outcomes, although participants were unaware of this setup. The remaining 16 intersections were randomized, with half leading to crashes after risky decisions.
Ethical Considerations
All data collected in the study investigating the cognitive effects of emerging educational technologies were anonymized to protect the identities of the respondents, and informed consent was obtained prior to participation in the survey. The research adhered to ethical guidelines outlined by the participating universities by including the right to withdraw from the study at any point without consequence. Additionally, the study was designed to minimize any potential distress or discomfort to participants by ensuring that survey questions were not intrusive and were relevant to the study's objectives. Data was stored securely and only accessible to the research team to maintain confidentiality.
In the fMRI study examining peer influence among adolescents, the ethical concerns were similarly prioritized. Researchers obtained informed consent from both the participants and their guardians, explicitly detailing the nature of the research, potential risks, and measures in place to ensure participant safety during fMRI scans. The research was designed to minimize discomfort and anxiety, with protocols established to provide immediate support should participants feel distressed while undergoing the procedure. The IRB approval for this study was crucial, as it ensured that the potential psychological effects stemming from peer evaluation during decision-making tasks were carefully assessed and mitigated.
Both studies incorporated a robust approach to protect participants from harm, emphasizing the importance of ethical transparency and accountability at all stages of the research process. These efforts helped cultivate a respectful environment for participants and ensured that the research design was methodologically sound and ethically responsible.
The Stoplight Game parameters, as configured in previous pilot testing, were integral to understanding decision-making behaviors. Key parameters included variations in distance between intersections, braking duration, and the timing of stoplight color changes. These variables were designed to simulate real-life decision-making pressures and potential risks. Specifically, the probability of a crash following a risky decision was set at 40%, reflecting the impact of unseen vehicles entering the intersection. Initial intersections were programmed to ensure no negative outcomes, allowing subjects to become accustomed to the game dynamics.
Results
Tobia et al.’s Research’s Results on Seating Arrangement
The descriptive statistics for individual characteristics and cognitive performance scores are summarized in Table 2.
The descriptive statistics for individual characteristics and cognitive performance scores are summarized in Table 2.
These statistics give us an idea of the participants' basic traits, such as gender distribution and loneliness scores, and set the stage for understanding the results. The correlations among these characteristics are shown in table 3, which highlights important relationships between variables like gender, relational self-esteem, and how well participants performed on cognitive tasks.
When we looked at the data analyzed by ANOVA (a statistical method that helps us see if there are differences between groups), we found a significant effect of seating arrangement on logical reasoning scores. Specifically, the results showed a statistic called F(1, 75) = 4.486 and a p-value of 0.037. This means there was a real difference in performance that wasn’t just due to chance, with children performing better in logical reasoning when seated in rows and columns compared to clusters. The η² (eta squared) value of 0.056 indicates the proportion of variance in logical reasoning scores that can be explained by the seating arrangement. So, basically, the rows and columns setup helped them focus better or get less distracted. You can see this effect in Figure 1.
For the Theory of Mind (ToM) tasks, one can notice a significant interaction between gender and seating arrangement. Here, the F statistic was 4.208, and the p-value was 0.044, meaning this was also a meaningful finding. Specifically, girls performed much better in rows and columns (p = 0.013), with a difference value (d) of 0.48, indicating a moderate effect size, which shows the strength of this difference. This suggests that this type of seating may help girls concentrate better on social cognition tasks, as depicted in Figure 2.
On the other hand, boys' performance stayed about the same no matter where they sat, meaning their understanding and processing during ToM tasks were not influenced by seating. It was also found that loneliness played a role in ToM tasks. The F statistic was 4.367, and the p-value was 0.040, showing that children who scored higher on loneliness tests didn't do as well when sitting in clusters. This tells us that being too close to others might not be helpful for kids who feel lonelier, as shown in Figure 3.
Additionally, researchers observed that kids who weren't as lonely did better on creativity tasks when they were in clusters, suggesting that being close to peers could spark more creative thinking. Overall, these findings highlight how different seating setups can affect how kids perform on various tasks and emphasize why teachers should think about these factors when arranging their classrooms.
Shield and Dockrell’s Results on Noise Levels
The results of the study reveal significant relationships between noise exposure and the academic performance of the children tested across various cognitive domains. The analysis focused on comparing performance under three distinct noise conditions: base (quiet), babble, and babble plus environmental noise. A total of 158 eight-year-old children from state primary schools participated in the assessment, with their performance measured in reading, spelling, speed of performance, and arithmetic.
Shield and Dockrell’s Results on Noise Levels
The results of the study reveal significant relationships between noise exposure and the academic performance of the children tested across various cognitive domains. The analysis focused on comparing performance under three distinct noise conditions: base (quiet), babble, and babble plus environmental noise. A total of 158 eight-year-old children from state primary schools participated in the assessment, with their performance measured in reading, spelling, speed of performance, and arithmetic.
Based on the cognitive testing conducted under the different noise conditions, the children demonstrated notable variations in their performance scores. This table shows the average scores (Mean) and how much the scores varied (Standard Deviation) for each cognitive test under different noise conditions. For reading assessments, children scored an average of 33.45 (SD = 11.62) in the base condition, while scores dropped to 27.59 (SD = 12.23) under the babble condition. Surprisingly, the babble plus environmental noise condition resulted in an average score of 39.48 (SD = 8.95), indicating a potential improvement in performance relative to the babble alone. For spelling tests, the results followed a similar trend, with children achieving a mean score of 9.55 (SD = 3.89) in the base condition, which decreased to 7.18 (SD = 4.59) in the babble condition. However, performance improved again in the babble plus environmental noise condition with a mean score of 11.68 (SD = 2.75). The arithmetic performance reflected trends consistent with the reading and spelling tests, showcasing how situational noise factors influenced cognitive performance. These results hint at an intriguing interaction where some noise conditions, particularly the babble combined with environmental noise, might create a more stimulating atmosphere that could benefit certain cognitive tasks. In the context of children with Special Educational Needs (SEN), the data revealed that these children were disproportionately affected by the noise conditions compared to their peers. For instance, under the babble condition, SEN children displayed a more significant decline in performance scores than typical children, underscoring the additional challenges faced by this group in noisy environments.
To illustrate the varied performance outcomes across the groups, table 5 presents mean performance scores of typical children and those with SEN in the three distinct noise conditions. Here, the differential impact of noise on academic performance is clearly laid out, with notable discrepancies visible in reading and spelling scores particularly influenced by the noise conditions. Among the external factors, noise levels were quantified using LAeq and LAmax, which gave context for the noise exposure encountered by the children in each borough. These metrics helped in understanding what environmental conditions had to do with the cognitive outcomes noted. For instance, table 4 provides a breakdown of external noise levels (LAeq and LAmax) for the schools surveyed, alongside corresponding socio-economic data (FSM, EAL, and SEN percentages).
Additionally, correlations emerged between external noise levels and children's academic performance, as represented in Figure 3. This graph depicts the relationship between external noise parameters—specifically LAeq and LAmaxA—and average Key Stage 1 (KS1) literacy test scores. The X-axis represents different external noise levels measured in decibels (dB), while the Y-axis indicates average scores achieved by children on the literacy tests. In analyzing this graph, a discernible negative relationship is apparent: as external noise levels increase, students' literacy scores tend to decline. This trend reinforces the premise that heightened noise exposure adversely affects academic performance. The trend line depicted in the graph illustrates a downward slope, effectively communicating the inverse relationship between increased external noise levels and literacy achievement among primary school children.
Aljohani’s Results on Educational Technologies
The results from Aljohani's study indicated compelling data regarding the effectiveness of educational technologies in improving student learning outcomes. The survey revealed that 78% of students reported a satisfaction level of 4 or 5, indicating they were either satisfied or very satisfied with the MOOCs and LMS utilized in their courses. Notably, the average satisfaction rating across all respondents was 4.2 out of 5. (see Appendix A for student satisfaction survey results)Furthermore, qualitative interviews with instructors revealed common themes regarding the challenges faced while implementing educational technologies. Instructors expressed difficulties in engaging students in an online environment, particularly due to technological barriers and varied levels of digital literacy among students. Despite these challenges, 86% of instructors acknowledged that educational technologies streamlined course management and enhanced communication with students.
Aashiq's Results on Emerging Educational Technologies
In this study, a total of 500 participants were recruited, comprising an equal distribution of 250 males and 250 females, aged between 18 and 30 years. The participants were drawn from a diverse range of academic disciplines across various Chinese universities. The data collection was facilitated through a self-directed survey, which was divided into four sections: personal demographics, current technology usage, digital social support, and cognitive development. The survey was made available in both English and Chinese to ensure accessibility for all participants, and participation was entirely voluntary, with assurances of confidentiality and anonymity provided to all respondents (Aashiq et al., 2023).
The survey utilized a multi-point Likert scale to measure the constructs of interest. For emerging technology, eight statements were adapted from Sosa et al. (2022), employing a seven-point scale ranging from "Almost Never" to "Almost Always." The Cronbach’s Alpha value for this scale was found to be 0.657, indicating acceptable internal consistency. Cognitive development was assessed using three statements adapted from Shokoohi-Yekta et al. (2013), measured on a five-point scale from "strongly disagree" to "strongly agree," with a Cronbach’s Alpha value of 0.657, also reflecting acceptable reliability. Lastly, digital social support was evaluated through statements adapted from Sharma and Devkota (2019), using a five-point scale, and this scale demonstrated a higher internal consistency with a Cronbach’s Alpha value of 0.801 (Aashiq et al., 2023).
The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 3 software. This method allowed for the examination of both direct and indirect relationships among the variables of emerging technology, digital social support, and cognitive development. The findings indicated that emerging technology has a positive and significant relationship with cognitive development, and that digital social support serves as a significant mediator in this relationship among higher education students (Aashiq et al., 2023).
The Stoplight Game Task’s Results on Peer Influence
The behavioral results indicated that participants' average total driving times were nearly identical across different age groups and social contexts, suggesting no significant impact of age or peer presence on driving times. As shown in table 6, adolescents had average times of 329 seconds when alone and 327 seconds with peers. Young adults averaged 328 seconds alone and 327 seconds with peers, while adults averaged 326 seconds in both conditions. No significant effects were found for age (F(2,38) = 0.20, p = 0.82) or social context (F(1,38) = 0.21, p = 0.65), and no interactions were significant (F(2,38) = 0.025, p = 0.98). Despite significant behavioral differences in adolescents' risk-taking between peer and alone conditions, their driving times did not differ significantly (t(13) = 0.42, p = 0.68) (Chein et al., 2010).
Aljohani’s Results on Educational Technologies
The results from Aljohani's study indicated compelling data regarding the effectiveness of educational technologies in improving student learning outcomes. The survey revealed that 78% of students reported a satisfaction level of 4 or 5, indicating they were either satisfied or very satisfied with the MOOCs and LMS utilized in their courses. Notably, the average satisfaction rating across all respondents was 4.2 out of 5. (see Appendix A for student satisfaction survey results)Furthermore, qualitative interviews with instructors revealed common themes regarding the challenges faced while implementing educational technologies. Instructors expressed difficulties in engaging students in an online environment, particularly due to technological barriers and varied levels of digital literacy among students. Despite these challenges, 86% of instructors acknowledged that educational technologies streamlined course management and enhanced communication with students.
Aashiq's Results on Emerging Educational Technologies
In this study, a total of 500 participants were recruited, comprising an equal distribution of 250 males and 250 females, aged between 18 and 30 years. The participants were drawn from a diverse range of academic disciplines across various Chinese universities. The data collection was facilitated through a self-directed survey, which was divided into four sections: personal demographics, current technology usage, digital social support, and cognitive development. The survey was made available in both English and Chinese to ensure accessibility for all participants, and participation was entirely voluntary, with assurances of confidentiality and anonymity provided to all respondents (Aashiq et al., 2023).
The survey utilized a multi-point Likert scale to measure the constructs of interest. For emerging technology, eight statements were adapted from Sosa et al. (2022), employing a seven-point scale ranging from "Almost Never" to "Almost Always." The Cronbach’s Alpha value for this scale was found to be 0.657, indicating acceptable internal consistency. Cognitive development was assessed using three statements adapted from Shokoohi-Yekta et al. (2013), measured on a five-point scale from "strongly disagree" to "strongly agree," with a Cronbach’s Alpha value of 0.657, also reflecting acceptable reliability. Lastly, digital social support was evaluated through statements adapted from Sharma and Devkota (2019), using a five-point scale, and this scale demonstrated a higher internal consistency with a Cronbach’s Alpha value of 0.801 (Aashiq et al., 2023).
The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 3 software. This method allowed for the examination of both direct and indirect relationships among the variables of emerging technology, digital social support, and cognitive development. The findings indicated that emerging technology has a positive and significant relationship with cognitive development, and that digital social support serves as a significant mediator in this relationship among higher education students (Aashiq et al., 2023).
The Stoplight Game Task’s Results on Peer Influence
The behavioral results indicated that participants' average total driving times were nearly identical across different age groups and social contexts, suggesting no significant impact of age or peer presence on driving times. As shown in table 6, adolescents had average times of 329 seconds when alone and 327 seconds with peers. Young adults averaged 328 seconds alone and 327 seconds with peers, while adults averaged 326 seconds in both conditions. No significant effects were found for age (F(2,38) = 0.20, p = 0.82) or social context (F(1,38) = 0.21, p = 0.65), and no interactions were significant (F(2,38) = 0.025, p = 0.98). Despite significant behavioral differences in adolescents' risk-taking between peer and alone conditions, their driving times did not differ significantly (t(13) = 0.42, p = 0.68) (Chein et al., 2010).
Neuroimaging analyses during the decision-making phase revealed that activity in the lateral prefrontal cortex (specifically the dorsolateral prefrontal cortex) was stronger for older participants. This region is associated with cognitive control and was treated as a region of interest (ROI). The neural peer effect, measured as the difference in neural activation between peer and alone conditions (βpeer - βalone), was further examined across prefrontal (PFC), ventral striatum (VS), and orbitofrontal (OFC) regions. Figure 4 illustrates these findings, showing the relationship between age and the neural peer effect across these ROIs.
During the outcome phase, analysis revealed no significant interaction between age and social context affecting brain activity related to reward outcomes. Regions showing significant effects of age during crashes included the right middle temporal gyrus (BA 21), left posterior cingulate (BA 23), left parahippocampal area, right superior frontal gyrus (BA 9), and right superior temporal gyrus. For successful trials, the right superior frontal gyrus (BA 10) and left middle frontal gyrus (BA 46/10) were more active in adolescents compared to adults. However, no significant interactions between age and social context were found, supporting the idea that peer presence primarily affects anticipatory decision-making processes rather than outcome processing. These findings are summarized in table 7.
During the outcome phase, analysis revealed no significant interaction between age and social context affecting brain activity related to reward outcomes. Regions showing significant effects of age during crashes included the right middle temporal gyrus (BA 21), left posterior cingulate (BA 23), left parahippocampal area, right superior frontal gyrus (BA 9), and right superior temporal gyrus. For successful trials, the right superior frontal gyrus (BA 10) and left middle frontal gyrus (BA 46/10) were more active in adolescents compared to adults. However, no significant interactions between age and social context were found, supporting the idea that peer presence primarily affects anticipatory decision-making processes rather than outcome processing. These findings are summarized in table 7.
Discussion
Seating Arrangement
The results indicate that classroom seating arrangements can influence students' thinking and learning. When students sat in rows and columns, they performed better on logical reasoning tasks, likely because this setup helps them focus and reduces distractions (Choi, Van Merriënboer, & Paas, 2014). The data in Table 2 supports this, showing higher average scores in logical reasoning for students seated in rows and columns. Girls, in particular, benefited from this arrangement in Theory of Mind tasks, which involve understanding others' thoughts and feelings. This setup might help them concentrate better on these tasks (Rose-Krasnor, 1997; McClure, 2000). Figure 2 illustrates this difference, showing that girls' scores improved significantly in this seating arrangement. Boys, however, didn't show the same sensitivity to seating arrangements for Theory of Mind tasks, suggesting they might not be as affected by classroom setup (Choi, Van Merriënboer, & Paas, 2014). This is evident in Figure 2, where boys' performance remained consistent across different seating arrangements. Loneliness also impacted performance. Students who felt lonelier did worse in clusters, possibly because being too close to others was uncomfortable or distracting (Rose-Krasnor, 1997). Figure 3 shows this pattern, indicating lower scores for lonelier children in clustered seating. Conversely, less lonely students performed better on creativity tasks in clusters, indicating that proximity to peers can inspire creativity (McClure, 2000). These findings suggest that teachers should tailor seating arrangements to the task and individual student needs. Rows and columns are beneficial for tasks requiring focus and logical thinking, especially for girls, while clusters can enhance creativity if students are comfortable with close proximity to classmates. The data in Table 3 provides further evidence of the relationships between seating arrangements, loneliness, and cognitive performance, reinforcing these conclusions.
Noise Levels
The impact of noise levels on how well students perform in the classroom is pretty well-known, with big implications for their learning. Research shows that high noise levels can mess with students’ concentration, information processing, and memory (Shield & Dudley, 2012; Ransdell et al., 2017). In our analysis, based on Shield and Dockrell’s study, it was found that noise conditions really affected performance in reading, spelling, and arithmetic.
Students did best in a quiet environment (base condition), with average reading scores of 33.45 (SD = 11.62). These scores dropped to 27.59 (SD = 12.23) under the babble condition. Interestingly, performance improved in the babble plus environmental noise condition (mean = 39.48, SD = 8.95). This suggests that while noise generally hurts cognitive functions, some types of noise might actually help create a more stimulating environment (Shield & Dockrell, 2008). This aligns with Ransdell et al. (2017), who found that controlled noise environments can boost learning efficiency, though the exact reasons are still a bit unclear.
They also found that students with Special Educational Needs (SEN) had bigger drops in performance under noisy conditions, showing they are more vulnerable to distractions (Shield & Dockrell, 2008). This matches other research that says noise affects students with additional learning needs more (Dockrell & Shield, 2006). Our correlation analysis showed that higher noise levels were linked to lower literacy scores (see Figure 3).
Educational Technology
The integration of educational technology has markedly transformed the landscape of cognitive development in academic settings. Research consistently highlights the benefits of technology-enhanced learning environments, which cater to diverse learning styles and enhance cognitive engagement (Schmid et al., 2014). The study by Aljohani (2020) provides empirical support for these benefits, revealing high levels of student satisfaction with educational technologies such as MOOCs and LMS. Specifically, 78% of students reported satisfaction levels of 4 or 5, indicating a positive reception of these tools in their learning experiences. This finding aligns with Schmid et al. (2014), who noted that interactive technologies foster personalized learning, thus improving critical thinking skills and engagement.
However, the implementation of educational technology is not without challenges. Rosen and Lim (2011) highlighted the potential for technology to become a distraction if not managed properly, leading to fragmented learning experiences. This concern was echoed in Aljohani’s interviews with instructors, who noted difficulties in maintaining student focus and engagement due to technological barriers and varying levels of digital literacy among students. Despite these challenges, 86% of instructors acknowledged that educational technologies effectively streamlined course management and enhanced communication, illustrating that thoughtful integration can mitigate some of these issues.
Aashiq’s study further underscores the positive impact of emerging technologies on cognitive development, particularly through the mediation of digital social support. The results demonstrated that higher levels of digital social support, facilitated by platforms such as social media and instant messaging, significantly enhance cognitive development. This finding is consistent with the literature suggesting that robust support networks can amplify the benefits of technology on learning outcomes (Graesser et al., 2022).
The analysis of the data from both Aljohani and Aashiq's studies, utilizing methods like Partial Least Squares Structural Equation Modeling (PLS-SEM), highlights a complex interplay between technology, cognitive development, and social support. Figures and graphs from Aashiq's study indicate a positive correlation between the use of emerging technologies and cognitive skills, moderated by digital social support. These results suggest that while technology can drive cognitive enhancement, its efficacy is significantly bolstered by the presence of strong support networks. While educational technologies offer significant advantages in fostering cognitive skills and improving learning experiences, their successful integration requires careful consideration of potential distractions and the need for adequate support structures. Balancing these factors is crucial for optimizing the benefits of educational technology and ensuring its effective use in enhancing student learning outcomes.
Peer Influence
We looked at how peer influence affects cognitive and behavioral outcomes using the Stoplight Game, which measures risk-taking behavior in different social settings. The results showed that having peers around didn’t change driving times much, but it did affect risk-taking, especially in adolescents. For example, adolescents had average driving times of 329 seconds when alone and 327 seconds with peers, showing that peer presence didn’t really change overall driving times (Chein et al., 2010). However, neuroimaging data gave us more details on how peer influence affects thinking. During decision-making, older participants showed stronger activation in the dorsolateral prefrontal cortex (DLPFC), a brain area linked to cognitive control (see Figure 4). This suggests that older people might use more cognitive control when making decisions with peers around. There were no significant interactions between age and social context on outcome processing, meaning peer influence mainly affects decision-making rather than evaluating outcomes. The neuroimaging results, shown in Table 7, also revealed age-related differences in brain activity during crash outcomes. Adolescents had higher activation in the right superior frontal gyrus (BA 10) and left middle frontal gyrus (BA 46/10) during successful trials compared to adults. This might mean adolescents put in more cognitive effort during decision-making. On the other hand, adults showed significant activation in areas like the right middle temporal gyrus (BA 21) and left posterior cingulate (BA 23) during crashes, indicating a more developed response to negative outcomes.
These results show that peer presence affects decision-making processes but doesn’t significantly impact overall performance times. The neuroimaging data suggest that while peers influence anticipatory processes, the outcomes of these decisions are processed differently across age groups. Adolescents’ increased brain activity during successful trials suggests they are more engaged in risk-taking, while adults’ neural responses to crashes show a more stable reaction to decision outcomes. These findings match previous research on peer influence and cognitive development, highlighting the need to understand how peer presence affects decision-making. Chein et al. (2010) found that peer influence can change risk-taking behavior through cognitive and neural processes rather than direct performance changes. The difference between anticipatory and outcome processing supports this, suggesting that educational strategies should consider how peer influence shapes decision-making and cognitive engagement.
A Learning Environment
Creating an effective classroom isn’t just about focusing on one element at a time; it’s about understanding how these elements work together. For instance, seating arrangements can influence noise levels. When students are seated in rows and columns, it can help reduce chatter and distractions, making it easier to maintain a quieter environment. This setup can be particularly beneficial in open-layout schools like WWP High School South, in which there are no walls and managing noise is crucial for maintaining focus and cognitive performance (Shield & Dockrell, 2008; Ransdell et al., 2017). Noise levels, in turn, can affect how well educational technology is used. In a noisy classroom, students might find it harder to concentrate on digital lessons or interactive activities. However, with controlled noise levels, students can better engage with educational technologies like MOOCs and LMS, which have been shown to enhance learning experiences and satisfaction (Aljohani, 2020). But if noise isn’t managed, students might get distracted and end up playing games on Cool Math Games instead of focusing on their work (Rosen & Lim, 2011). Additionally, Friends can impact whether students stay focused or get distracted, which ties back to seating arrangements. If friends are seated together, they might chat more, increasing noise levels and making it harder for everyone to concentrate. On the other hand, positive peer interactions can boost engagement and motivation, especially when using educational technology. For example, working on group projects with peers can make learning more interactive and enjoyable, but it requires a balance to ensure it doesn’t become too noisy or distracting. Combining these elements, an ideal learning environment at WWP High School South would involve strategic seating arrangements to minimize distractions and manage noise levels effectively. This would create a conducive atmosphere for using educational technologies, enhancing cognitive engagement and learning outcomes. Additionally, fostering positive peer interactions and support systems can further optimize the learning environment, ensuring that students are motivated and focused. By balancing these factors—seating, noise, technology, and peer influence—we can create a more effective and engaging learning environment that maximizes student performance and well-being.
The results indicate that classroom seating arrangements can influence students' thinking and learning. When students sat in rows and columns, they performed better on logical reasoning tasks, likely because this setup helps them focus and reduces distractions (Choi, Van Merriënboer, & Paas, 2014). The data in Table 2 supports this, showing higher average scores in logical reasoning for students seated in rows and columns. Girls, in particular, benefited from this arrangement in Theory of Mind tasks, which involve understanding others' thoughts and feelings. This setup might help them concentrate better on these tasks (Rose-Krasnor, 1997; McClure, 2000). Figure 2 illustrates this difference, showing that girls' scores improved significantly in this seating arrangement. Boys, however, didn't show the same sensitivity to seating arrangements for Theory of Mind tasks, suggesting they might not be as affected by classroom setup (Choi, Van Merriënboer, & Paas, 2014). This is evident in Figure 2, where boys' performance remained consistent across different seating arrangements. Loneliness also impacted performance. Students who felt lonelier did worse in clusters, possibly because being too close to others was uncomfortable or distracting (Rose-Krasnor, 1997). Figure 3 shows this pattern, indicating lower scores for lonelier children in clustered seating. Conversely, less lonely students performed better on creativity tasks in clusters, indicating that proximity to peers can inspire creativity (McClure, 2000). These findings suggest that teachers should tailor seating arrangements to the task and individual student needs. Rows and columns are beneficial for tasks requiring focus and logical thinking, especially for girls, while clusters can enhance creativity if students are comfortable with close proximity to classmates. The data in Table 3 provides further evidence of the relationships between seating arrangements, loneliness, and cognitive performance, reinforcing these conclusions.
Noise Levels
The impact of noise levels on how well students perform in the classroom is pretty well-known, with big implications for their learning. Research shows that high noise levels can mess with students’ concentration, information processing, and memory (Shield & Dudley, 2012; Ransdell et al., 2017). In our analysis, based on Shield and Dockrell’s study, it was found that noise conditions really affected performance in reading, spelling, and arithmetic.
Students did best in a quiet environment (base condition), with average reading scores of 33.45 (SD = 11.62). These scores dropped to 27.59 (SD = 12.23) under the babble condition. Interestingly, performance improved in the babble plus environmental noise condition (mean = 39.48, SD = 8.95). This suggests that while noise generally hurts cognitive functions, some types of noise might actually help create a more stimulating environment (Shield & Dockrell, 2008). This aligns with Ransdell et al. (2017), who found that controlled noise environments can boost learning efficiency, though the exact reasons are still a bit unclear.
They also found that students with Special Educational Needs (SEN) had bigger drops in performance under noisy conditions, showing they are more vulnerable to distractions (Shield & Dockrell, 2008). This matches other research that says noise affects students with additional learning needs more (Dockrell & Shield, 2006). Our correlation analysis showed that higher noise levels were linked to lower literacy scores (see Figure 3).
Educational Technology
The integration of educational technology has markedly transformed the landscape of cognitive development in academic settings. Research consistently highlights the benefits of technology-enhanced learning environments, which cater to diverse learning styles and enhance cognitive engagement (Schmid et al., 2014). The study by Aljohani (2020) provides empirical support for these benefits, revealing high levels of student satisfaction with educational technologies such as MOOCs and LMS. Specifically, 78% of students reported satisfaction levels of 4 or 5, indicating a positive reception of these tools in their learning experiences. This finding aligns with Schmid et al. (2014), who noted that interactive technologies foster personalized learning, thus improving critical thinking skills and engagement.
However, the implementation of educational technology is not without challenges. Rosen and Lim (2011) highlighted the potential for technology to become a distraction if not managed properly, leading to fragmented learning experiences. This concern was echoed in Aljohani’s interviews with instructors, who noted difficulties in maintaining student focus and engagement due to technological barriers and varying levels of digital literacy among students. Despite these challenges, 86% of instructors acknowledged that educational technologies effectively streamlined course management and enhanced communication, illustrating that thoughtful integration can mitigate some of these issues.
Aashiq’s study further underscores the positive impact of emerging technologies on cognitive development, particularly through the mediation of digital social support. The results demonstrated that higher levels of digital social support, facilitated by platforms such as social media and instant messaging, significantly enhance cognitive development. This finding is consistent with the literature suggesting that robust support networks can amplify the benefits of technology on learning outcomes (Graesser et al., 2022).
The analysis of the data from both Aljohani and Aashiq's studies, utilizing methods like Partial Least Squares Structural Equation Modeling (PLS-SEM), highlights a complex interplay between technology, cognitive development, and social support. Figures and graphs from Aashiq's study indicate a positive correlation between the use of emerging technologies and cognitive skills, moderated by digital social support. These results suggest that while technology can drive cognitive enhancement, its efficacy is significantly bolstered by the presence of strong support networks. While educational technologies offer significant advantages in fostering cognitive skills and improving learning experiences, their successful integration requires careful consideration of potential distractions and the need for adequate support structures. Balancing these factors is crucial for optimizing the benefits of educational technology and ensuring its effective use in enhancing student learning outcomes.
Peer Influence
We looked at how peer influence affects cognitive and behavioral outcomes using the Stoplight Game, which measures risk-taking behavior in different social settings. The results showed that having peers around didn’t change driving times much, but it did affect risk-taking, especially in adolescents. For example, adolescents had average driving times of 329 seconds when alone and 327 seconds with peers, showing that peer presence didn’t really change overall driving times (Chein et al., 2010). However, neuroimaging data gave us more details on how peer influence affects thinking. During decision-making, older participants showed stronger activation in the dorsolateral prefrontal cortex (DLPFC), a brain area linked to cognitive control (see Figure 4). This suggests that older people might use more cognitive control when making decisions with peers around. There were no significant interactions between age and social context on outcome processing, meaning peer influence mainly affects decision-making rather than evaluating outcomes. The neuroimaging results, shown in Table 7, also revealed age-related differences in brain activity during crash outcomes. Adolescents had higher activation in the right superior frontal gyrus (BA 10) and left middle frontal gyrus (BA 46/10) during successful trials compared to adults. This might mean adolescents put in more cognitive effort during decision-making. On the other hand, adults showed significant activation in areas like the right middle temporal gyrus (BA 21) and left posterior cingulate (BA 23) during crashes, indicating a more developed response to negative outcomes.
These results show that peer presence affects decision-making processes but doesn’t significantly impact overall performance times. The neuroimaging data suggest that while peers influence anticipatory processes, the outcomes of these decisions are processed differently across age groups. Adolescents’ increased brain activity during successful trials suggests they are more engaged in risk-taking, while adults’ neural responses to crashes show a more stable reaction to decision outcomes. These findings match previous research on peer influence and cognitive development, highlighting the need to understand how peer presence affects decision-making. Chein et al. (2010) found that peer influence can change risk-taking behavior through cognitive and neural processes rather than direct performance changes. The difference between anticipatory and outcome processing supports this, suggesting that educational strategies should consider how peer influence shapes decision-making and cognitive engagement.
A Learning Environment
Creating an effective classroom isn’t just about focusing on one element at a time; it’s about understanding how these elements work together. For instance, seating arrangements can influence noise levels. When students are seated in rows and columns, it can help reduce chatter and distractions, making it easier to maintain a quieter environment. This setup can be particularly beneficial in open-layout schools like WWP High School South, in which there are no walls and managing noise is crucial for maintaining focus and cognitive performance (Shield & Dockrell, 2008; Ransdell et al., 2017). Noise levels, in turn, can affect how well educational technology is used. In a noisy classroom, students might find it harder to concentrate on digital lessons or interactive activities. However, with controlled noise levels, students can better engage with educational technologies like MOOCs and LMS, which have been shown to enhance learning experiences and satisfaction (Aljohani, 2020). But if noise isn’t managed, students might get distracted and end up playing games on Cool Math Games instead of focusing on their work (Rosen & Lim, 2011). Additionally, Friends can impact whether students stay focused or get distracted, which ties back to seating arrangements. If friends are seated together, they might chat more, increasing noise levels and making it harder for everyone to concentrate. On the other hand, positive peer interactions can boost engagement and motivation, especially when using educational technology. For example, working on group projects with peers can make learning more interactive and enjoyable, but it requires a balance to ensure it doesn’t become too noisy or distracting. Combining these elements, an ideal learning environment at WWP High School South would involve strategic seating arrangements to minimize distractions and manage noise levels effectively. This would create a conducive atmosphere for using educational technologies, enhancing cognitive engagement and learning outcomes. Additionally, fostering positive peer interactions and support systems can further optimize the learning environment, ensuring that students are motivated and focused. By balancing these factors—seating, noise, technology, and peer influence—we can create a more effective and engaging learning environment that maximizes student performance and well-being.
Limitations and Future Direction
This analysis has several limitations that affect how broadly the findings can be applied. Firstly, the noise levels at WWP High School South, with its open-layout design, introduce variability that’s not usually present in more controlled research environments. The lack of walls leads to fluctuating noise conditions that could impact cognitive performance in unpredictable ways. Future research should measure actual noise levels in similar open-space environments and analyze how these variations affect learning outcomes. Secondly, relying on self-reported data about educational technology usage might not fully capture the real impact of these tools. Students’ subjective reports can be biased and inaccurate, which could distort our understanding of how technology influences cognitive engagement. To overcome this, future studies should use objective metrics like software usage logs and direct observations to get a clearer and more precise measure of technology’s impact on learning. The Stoplight Game used to assess peer influence might not fully reflect the actual issues of real-life peer interactions. The game’s structured scenarios may oversimplify the dynamic nature of peer influence in actual classroom settings. Future research should use real-life observations or advanced simulations that better mimic the multifaceted interactions among peers to get a more accurate picture of how peer influence affects decision-making and cognitive performance. Additionally, the study’s sample size and demographic homogeneity limit how well the findings can be generalized. With only 40 participants, the results might not represent broader student populations. Expanding the sample size and including a more diverse group of students from various schools and backgrounds would make the findings more applicable to different educational contexts. Lastly, individual differences in how students respond to peer influence weren’t examined. Personality traits, social status, and prior experiences can drastically influence how peer interactions impact cognitive performance. Future research should explore these individual differences and consider additional factors like family background and extracurricular involvement to provide a more complete understanding of how peer influence affects students.
Implications
Firstly, understanding how these factors interact can help educators design more effective learning environments. For example, arranging seats in rows and columns can reduce noise and distractions, which is particularly beneficial in open-layout schools like WWP High School South. This setup can enhance focus and cognitive performance, especially for tasks that require concentration. However, managing the variability in noise levels in such open spaces is crucial to maintaining an optimal learning environment (Shield & Dockrell, 2008; Ransdell et al., 2017). While technology can enhance learning experiences and engagement, it can also become a distraction if not managed properly. Educators should consider using objective metrics to monitor technology usage and ensure it is being used effectively to support learning. This approach can help maximize the benefits of educational technology while minimizing potential distractions (Aljohani, 2020; Rosen & Lim, 2011). Positive peer interactions can boost engagement and motivation, but it’s important to balance these interactions to prevent increased noise and distractions. Educators should foster a supportive peer environment that encourages collaboration without compromising focus and productivity (Chein et al., 2010). Moreover, the analysis underscores the need for future research to address the limitations identified. Conducting studies with larger and more diverse samples, measuring actual noise levels in open-space environments, and using real-life observations to assess peer influence can provide a more comprehensive understanding of these factors. Additionally, exploring individual differences such as personality traits, socio-economic status, and psychological factors can help tailor educational strategies to meet the diverse needs of students (Shield & Dockrell, 2008; Ransdell et al., 2017).
Conclusion
Reflecting on the analysis of seating arrangements, noise levels, educational technology, and peer influence, it’s clear that these factors significantly shape the learning environment and cognitive performance of students. Many of us have experienced firsthand how the setup of our classrooms, the noise around us, the technology we use, and the friends we interact with can impact our ability to focus and learn. For instance, as mentioned earlier, arranging seats in rows and columns can help reduce distractions and maintain a quieter environment, which is particularly beneficial in open-layout schools like WWP High School South. This setup can enhance focus and cognitive performance, especially for tasks that require concentration (Shield & Dockrell, 2008; Ransdell et al., 2017). However, managing noise levels in such open spaces remains a challenge. We’ve all been in situations where a noisy classroom makes it hard to concentrate, whether it’s due to friends chatting or the general hustle and bustle of school life. Educational technology, when used in the right manner, can greatly enhance learning experiences. Tools like MOOCs and LMS have shown to improve engagement and satisfaction among students (Aljohani, 2020). But we’ve also seen how easily technology can become a distraction. Who hasn’t seen classmates playing games on Cool Math Games instead of focusing on their work? It’s crucial for educators to find a balance, using technology to support learning while minimizing distractions (Rosen & Lim, 2011). Positive interactions with friends can boost motivation and engagement, but they can also lead to increased noise and distractions if not managed well. We’ve all experienced how friends can either help us stay focused or lead us off track, especially during group projects or study sessions (Chein et al., 2010). Looking ahead, future research should address the limitations identified in this analysis. Larger and more diverse samples, real-life observations, and objective measures of technology usage can provide a more comprehensive understanding of these factors. Additionally, exploring individual differences such as personality traits, socio-economic status, and psychological factors can help tailor educational strategies to meet the diverse needs of students (Shield & Dockrell, 2008; Ransdell et al., 2017).
Citations
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Tobia, V., Sacchi, S., Cerina, V., Manca, S., & Fornara, F. (2022). The influence of classroom seating arrangement on children’s cognitive processes in primary school: The role of individual variables. Current Psychology (New Brunswick, N.J.). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7602767/
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Appendix A
Aljohani's Data on Educational Technology
Student Satisfaction Survey Results
Student Satisfaction Survey Results
- Sample Size: 500 students
- Satisfaction Ratings:
- Rating 5 (Very Satisfied): 34%
- Rating 4 (Satisfied): 44%
- Rating 3 (Neutral): 15%
- Rating 2 (Dissatisfied): 5%
- Rating 1 (Very Dissatisfied): 2%
- Instructor Interview Insights
- Sample Size: 25 instructors
- 56% reported difficulty engaging students online.
- 32% noted varying levels of digital literacy causing disparities in student participation.
- 86% noted improvements in communication and course management efficiency.