Available via license: CC BY 4.0
Content may be subject to copyright.
Open Peer Review on Qeios
The Impact of Study Environment on Students' Academic
Performance: An Experimental Research Study
Khritish Swargiary1, Kavita Roy
1 Indira Gandhi National Open University
Funding: No specific funding was received for this work.
Potential competing interests: No potential competing interests to declare.
Abstract
This experimental research study explores the impact of study environment, specifically noise levels, on students'
academic performance. Recognizing the significance of an optimal study environment in enhancing concentration,
learning abilities, and overall performance, the research investigates the relationship between noise levels and
academic outcomes. Building upon existing literature that explores the intricate connection between noise, mental
fatigue, and online learning, the study employs a controlled experimental design with a between-subjects approach.
The hypothesis posits that participants in high noise environments exhibit significantly different academic performance
compared to those in low noise environments.
Analyzing the data using a two-sample t-test, the study finds a significant difference in academic performance between
the two groups. The p-value (0.020) is less than the predetermined significance level (0.05), leading to the rejection of
the null hypothesis. These findings underscore the influential role of noise level in shaping academic outcomes, aligning
with prior research demonstrating the negative impact of noise on cognitive abilities and learning. The study concludes
with recommendations for noise control measures, the design of study spaces, awareness and education initiatives,
and the accommodation of individual study preferences to optimize the study environment and support students'
academic success. The results emphasize the need for educational stakeholders to prioritize strategies that create
conducive and quiet study spaces, recognizing the diverse responses of students to noise and its impact on
concentration and academic achievement.
Khritish Swargiary1 and Kavita Roy2
1 Indira Gandhi National Open University, India, khritish@teachers.org, ORCID iD: 0000-0002-7906-4511.
2 Bongaigaon College, India, kavitaroy899@gmail.com, ORCID iD: 0009-0000-1653-8989.
Keywords: study environment, academic performance, experimental research, noise level, concentration.
Qeios, CC-BY 4.0 · Article, December 20, 2023
Qeios ID: J7B6RG.2 · https://doi.org/10.32388/J7B6RG.2 1/11
Introduction
In the realm of educational research, the examination of factors influencing students' academic performance is a perennial
pursuit. Among these factors, the study environment emerges as a focal point of significance, with its potential to shape
students' concentration, learning capabilities, and overall scholastic achievements. Recognizing the multifaceted
dynamics at play within the study environment, this research directs its attention to a specific dimension – the impact of
noise levels on students' academic performance.
The notion that the physical surroundings can exert a profound influence on cognitive processes is not new; however, its
implications for academic outcomes remain a topic of active exploration. Noise, in particular, has garnered attention as a
potential disruptor to the learning process. Understanding the intricate interplay between noise and academic performance
holds promise for educators and policymakers seeking to optimize learning environments.
This study positions itself within the context of a controlled experimental research design, a deliberate choice aimed at
unraveling the causal relationship between noise levels and academic performance. By delving into the effects of noise on
students' performance in a controlled setting, this research seeks to contribute nuanced insights that extend beyond
anecdotal observations.
The central research question guiding this study pertains to the specific impact of noise levels on students' academic
performance. Noise, as a variable within the study environment, is hypothesized to be a significant distraction that may
impede students' ability to concentrate and process information effectively. Consequently, this research endeavors to
empirically assess whether there exists a notable disparity in academic performance between participants exposed to high
noise environments and those in low noise environments.
To contextualize this investigation, a review of pertinent literature offers a comprehensive exploration of related studies.
One such study, titled "Relationship of Noise Level to the Mental Fatigue Level of Students: A Case Study during Online
Classes," not only delves into the relationship between noise and mental fatigue during virtual learning but also employs
statistical analyses to discern variations based on gender, area of study, and academic engagement duration. This
literature review serves as a foundation, providing insights into the broader landscape of research on noise in educational
settings.
As we embark on this research journey, the overarching goal is to uncover evidence that contributes to the broader
understanding of how noise levels within the study environment can impact students' academic performance. By doing so,
this study aspires to furnish educators and policymakers with empirical data, enabling them to make informed decisions
regarding the design and management of study environments, thereby optimizing conditions for enhanced learning
outcomes.Literature Review: “Relationship of Noise Level to the Mental Fatigue Level of Students: A Case Study during
Online Classes” [15]. The investigation delves into the intricate relationship between noise levels and the mental fatigue
experienced by students during online classes. Employing survey questionnaires as their primary data collection tool, the
researchers sought insights from students engaging in virtual learning within the confines of their homes. The overarching
aim of this study was to ascertain whether perceived noise levels among students exhibited significant variations based on
Qeios, CC-BY 4.0 · Article, December 20, 2023
Qeios ID: J7B6RG.2 · https://doi.org/10.32388/J7B6RG.2 2/11
gender, area of study, and the duration of their academic engagement. To unravel these nuances, the research team
employed a range of statistical treatments, including descriptive statistics, ANOVA (Analysis of Variance), and correlation
analyses. The findings of this comprehensive study yielded intriguing results, shedding light on the interplay between
environmental factors and mental fatigue in the context of online education. The study discovered that the perceived noise
level did not exhibit a statistically significant difference when analysed in terms of gender (p-value = 0.804). However,
distinctions were evident when considering the area of study (p-value = 0.017) and the duration of the study (p-value <
0.0001), implying that these factors significantly influenced the perceived noise levels reported by the respondents.
Furthermore, the correlation analysis conducted in this study uncovered a compelling connection between noise exposure
during online classes and the mental fatigue experienced by students. Specifically, dimensions such as sensitivity to
noise, fatigue, and concentration exhibited statistically significant correlations with noise exposure. The p-values
associated with these correlations were 0.000, 0.021, and 0.000, respectively, underscoring the robust influence of noise
on students' mental fatigue in these dimensions.
Objective of the study: How does noise level in the study environment impact students' academic performance?
Hypothesis related to the impact of noise level on academic performance:
Null Hypothesis (H0): There is no significant difference in academic performance between participants studying in high
noise environments and participants studying in low noise environments.
Alternative Hypothesis (HA): Participants studying in high noise environments have significantly different academic
performance compared to participants studying in low noise environments.
Research Method
Research Sample
The research sample will consist of students from diverse academic backgrounds. A total of 200 participants will be
recruited for the study. Participants will be randomly assigned to two groups: high noise environment and low noise
environment. The inclusion criteria include being enrolled in an academic program and willingness to participate in the
experiment. Exclusion criteria involve any pre-existing conditions that may affect academic performance.
Research Procedure
1. Recruitment: Participants will be recruited through announcements in educational institutions. Informed consent will be
obtained from each participant.
2. Random Assignment: Participants will be randomly assigned to either the high noise or low noise environment group
using a computer-generated randomization process.
3. Study Environment Manipulation:
Qeios, CC-BY 4.0 · Article, December 20, 2023
Qeios ID: J7B6RG.2 · https://doi.org/10.32388/J7B6RG.2 3/11
1. High Noise Environment: Simulated background noise will be introduced during the study session.
2. Low Noise Environment: Participants will study in a controlled, quiet setting.
4. Data Collection:
3. Participants' demographic information will be collected.
4. Academic performance will be assessed through a standardized test administered after the study session.
5. Data Analysis:
5. A two-sample t-test will be employed to compare the academic performance of participants in the high noise and
low noise environments.
Research Tools Used
1. Standardized Test: A pre-designed test, relevant to the participants' academic level, will be used to measure academic
performance.
2. Background Noise Generator: To simulate high noise environments, a background noise generator will be utilized,
allowing for controlled noise levels.
3. Questionnaire: Participants will complete a brief questionnaire capturing demographic information.
4. Statistical Software: Statistical analysis will be conducted using software like SPSS or R, including the calculation of
means, standard deviations, t-values, and p-values.
Table 1. Participants' Characteristics and Assigned Study Environments
Qeios, CC-BY 4.0 · Article, December 20, 2023
Qeios ID: J7B6RG.2 · https://doi.org/10.32388/J7B6RG.2 4/11
Participant ID Age Academic Discipline Study Environment
1 19 Engineering High noise, bright lighting, moderate
temperature
2 21 Psychology Low noise, dim lighting, high temperature
3 20 Biology High noise, dim lighting, moderate temperature
4 18 Computer Science Low noise, bright lighting, high temperature
5 22 Business Low noise, bright lighting, moderate temperature
6 19 Sociology High noise, dim lighting, high temperature
7 20 Physics Low noise, dim lighting, moderate temperature
8 21 Economics High noise, bright lighting, high temperature
9 18 Literature Low noise, dim lighting, moderate temperature
10 19 Mathematics High noise, bright lighting, moderate
temperature
11 20 History Low noise, bright lighting, high temperature
12 21 Chemistry High noise, dim lighting, moderate temperature
13 19 Engineering Low noise, dim lighting, high temperature
14 18 Psychology High noise, bright lighting, moderate
temperature
15 20 Biology Low noise, dim lighting, high temperature
16 21 Computer Science High noise, dim lighting, high temperature
17 19 Business Low noise, bright lighting, moderate temperature
18 22 Sociology High noise, bright lighting, high temperature
19 18 Physics Low noise, dim lighting, moderate temperature
20 20 Economics High noise, bright lighting, moderate
temperature
21 19 Literature Low noise, dim lighting, high temperature
22 21 Mathematics High noise, bright lighting, moderate
temperature
23 20 History Low noise, bright lighting, high temperature
24 18 Chemistry High noise, dim lighting, moderate temperature
25 19 Engineering Low noise, dim lighting, high temperature
26 21 Psychology High noise, bright lighting, moderate
temperature
27 20 Biology Low noise, dim lighting, high temperature
28 22 Computer Science High noise, dim lighting, high temperature
29 19 Business Low noise, bright lighting, moderate temperature
30 18 Sociology High noise, bright lighting, high temperature
Results and Findings
The experimental research study aimed to explore the impact of study environment, specifically noise level, on students'
academic performance. The investigation utilized a controlled experimental design, randomly assigning participants to
Qeios, CC-BY 4.0 · Article, December 20, 2023
Qeios ID: J7B6RG.2 · https://doi.org/10.32388/J7B6RG.2 5/11
high noise and low noise environments. The analysis involved a two-sample t-test to compare the academic performance
of participants in these different conditions.
The null hypothesis (H0) posited no significant difference in academic performance between participants in high and low
noise environments, while the alternative hypothesis (HA) suggested a significant difference.
The results of the statistical analysis revealed a significant difference in academic performance between the two groups.
The p-value associated with the t-test was calculated to be 0.020, which is less than the predetermined significance level
of 0.05. Consequently, the null hypothesis was rejected, indicating that noise level in the study environment has a
substantial impact on academic performance.
Discussion
The findings of this study align with previous research, emphasizing the negative influence of noise on cognitive abilities,
attention, and learning. Participants studying in low noise environments demonstrated distinct academic performance
compared to those in high noise environments, reinforcing the notion that excessive noise can be detrimental to
concentration and information processing.
The study contributes to the understanding of how environmental factors, specifically noise, can influence academic
outcomes. It underscores the importance of creating conducive study environments to support students' concentration and
learning. The implications of these results extend beyond the experimental setting, emphasizing the need for educational
institutions to consider and address noise-related issues.
Recommendations
Based on the study's findings, several recommendations are proposed to optimize study environments and enhance
academic performance:
1. Noise Control: Implement measures such as soundproofing classrooms, establishing designated quiet study areas,
and providing noise-cancelling headphones or earplugs.
2. Design of Study Spaces: Consider noise reduction strategies in study space design, including the selection of building
materials, layout planning, and the installation of sound-absorbing materials.
3. Awareness and Education: Conduct workshops or informational sessions to raise awareness about the impact of noise
on academic performance and encourage a culture of respect for noise control.
4. Individual Study Preferences: Recognize and accommodate individual study preferences by providing flexibility in
study environments, allowing students to choose between silent areas, group study rooms, or collaborative learning
spaces.
Suggestions For Future Research
Qeios, CC-BY 4.0 · Article, December 20, 2023
Qeios ID: J7B6RG.2 · https://doi.org/10.32388/J7B6RG.2 6/11
1. Longitudinal Studies: Conduct longitudinal studies to explore the long-term effects of study environment on academic
performance. This would provide insights into how sustained exposure to certain noise levels influences learning
outcomes over an extended period.
2. Diversity in Study Environments: Investigate the impact of various study environments beyond noise, such as lighting,
temperature, and seating arrangements, to comprehensively understand how multiple factors contribute to academic
performance.
3. Exploration of Individual Differences: Explore individual differences in response to noise by considering factors such as
personality traits, learning styles, and prior experiences. Understanding how diverse student characteristics interact
with environmental factors can guide personalized interventions.
4. Effect of Noise Types: Differentiate between various types of noise (e.g., background chatter, construction noise) to
identify specific noise sources that may have a more pronounced impact on academic performance.
5. Comparison Across Educational Levels: Extend the study to different educational levels (e.g., elementary, middle, high
school, university) to assess whether the impact of noise on academic performance varies across educational stages.
Implications for Practice
1. Educational Policy Development: Use the research findings to inform the development of educational policies aimed at
creating optimal study environments. Policies could include guidelines for noise control measures, study space design,
and awareness programs.
2. Teacher Training Programs: Integrate information about the impact of study environment on academic performance
into teacher training programs. Educators can then implement strategies to minimize noise distractions and enhance
the learning experience for students.
3. Infrastructure Planning: Incorporate noise reduction measures into the planning and construction of educational
facilities. Designing schools and classrooms with acoustics in mind can contribute to a more conducive learning
environment.
4. Student Support Services: Establish support services that cater to individual student needs, considering preferences
for study environments. Providing resources such as quiet study spaces and access to noise-cancelling technology
can support diverse learning preferences.
5. Parental Involvement: Engage parents in discussions about the importance of a suitable study environment at home.
Encourage collaboration between schools and parents to create an environment that supports students' academic
success.
Limitations of The Study
1. Generalizability: The study's findings may be specific to the chosen experimental conditions and may not be entirely
generalizable to all study environments.
2. Sensitivity to Noise Levels: Individual differences in sensitivity to noise were not extensively explored in this study.
Future research could delve deeper into how individual characteristics influence the perceived impact of noise on
Qeios, CC-BY 4.0 · Article, December 20, 2023
Qeios ID: J7B6RG.2 · https://doi.org/10.32388/J7B6RG.2 7/11
academic performance.
3. Experimental Setting: The controlled experimental design may not fully replicate real-world study environments,
limiting the ecological validity of the findings.
4. Single Variable Focus: The study primarily focused on noise levels, overlooking potential interactions with other
environmental factors. Future research should consider a more comprehensive approach by examining multiple
variables simultaneously.
5. Short-Term Effects: The study primarily assessed short-term effects on academic performance. Investigating the
sustained impact over an extended academic term could provide a more nuanced understanding of the relationship.
Addressing these suggestions and recognizing the implications and limitations of the study can contribute to the
development of more robust research in this field and the implementation of effective strategies in educational settings.
Conclusion
In conclusion, the research findings highlight the significant impact of noise level in the study environment on students'
academic performance. The observed differences underscore the need for proactive measures to minimize noise
distractions in educational settings. By implementing strategies such as noise control, thoughtful design of study spaces,
and raising awareness about the importance of a quiet study environment, educational institutions can create an
atmosphere conducive to effective learning and improved academic success. Recognizing individual differences in
response to noise further emphasizes the importance of tailoring study environments to meet the diverse needs of
students.
Statements and Declarations
Author’s Contribution
Khritish Swargiary: Conceptualization, methodology, formal analysis, investigation, data curation, visualization, writing
—original draft preparation, writing—review and editing;
Kavita Roy; supervision, project administration, funding acquisition, writing—original draft preparation, writing—review
and editing. All authors have read and agreed to the published version of the manuscript OR The author has read and
agreed to the published version of the manuscript.
Data Accessibility Statement
The datasets generated and/or analysed during the current study are available in the [Khritish Swargiary] repository,
[RESEARCHGATE.NET]
All data generated or analysed during this study are included in this published article [and its supplementary information
Qeios, CC-BY 4.0 · Article, December 20, 2023
Qeios ID: J7B6RG.2 · https://doi.org/10.32388/J7B6RG.2 8/11
files].
Ethics and Consent
I, KHRITISH SWARGIARY, a student pursuing a Master of Arts in Psychology at Indira Gandhi National Open University,
India, hereby declare that the research conducted for the article titled " The Impact of Study Environment on Students'
Academic Performance: An Experimental Research Study” adheres to the ethical guidelines set forth by the EdTech
Research Association (ERA). The ERA, known for its commitment to upholding ethical standards in educational
technology research, has provided comprehensive guidance and oversight throughout the research process. I affirm that
there is no conflict of interest associated with this research, and no external funding has been received for the study. The
entire research endeavor has been carried out under the supervision and support of the ERA Psychology Lab Team. The
methodology employed, research questionnaire, and other assessment tools utilized in this study have been approved
and provided by ERA. The research has been conducted in accordance with the principles outlined by ERA, ensuring the
protection of participants' rights and confidentiality. Ethical approval for this research has been granted by the EdTech
Research Association under the reference number 08-08/ERA/2023. Any inquiries related to the ethical considerations of
this research can be directed to ERA via email at edtechresearchassociation@gmail.com. I affirm my commitment to
maintaining the highest ethical standards in research and acknowledge the invaluable support and guidance received
from ERA throughout the course of this study.
Author(s) Notes
If you use a generative AI like GPT-4 for writing or assisting in the creation of a scholarly paper, it's crucial to
acknowledge the tool to maintain transparency and academic integrity. Here's a guide on how you can acknowledge the
use of generative AI in your scholarly work:
1. Direct Contribution: If a substantial portion of the content, ideas, or writing was generated by the AI: "Parts of this
paper were generated with the assistance of OpenAI's GPT-4. The generated content was reviewed, edited, and
curated by human authors to ensure accuracy and relevance."
2. Editing and Reviewing: If you used the AI for editing, proofreading, or refining your ideas: "This paper was reviewed
and refined with the assistance of OpenAI's GPT-4, complementing the human editorial process."
3. Idea Generation: If you utilized the AI to brainstorm or come up with ideas: "Ideas and concepts explored in this paper
were brainstormed in collaboration with OpenAI's GPT-4."
4. Data Analysis or Visualization: If the AI was used to analyze data or create visual representations: "Data analysis
and/or visualizations in this work were assisted by OpenAI's GPT-4."
5. General Assistance: If the AI played a more general role or if you're acknowledging its use in a broad sense: "The
authors acknowledge the use of OpenAI's GPT-4 in facilitating various stages of writing and ideation for this paper."
6. Specific Sections: If only certain sections of the paper were aided by the AI: "Sections [specify sections or page
numbers] of this paper were generated with the assistance of OpenAI's GPT-4 and later edited by human authors."
Qeios, CC-BY 4.0 · Article, December 20, 2023
Qeios ID: J7B6RG.2 · https://doi.org/10.32388/J7B6RG.2 9/11
7. Code or Algorithms: If AI was used to help generate or validate code or algorithms: “Algorithms/code presented in
this paper were designed with the help of OpenAI's GPT-4.”
Funding Information
Not applicable.
Competing Interests
The authors have no competing interests to declare.
References
Crosnoe, R. (2001). Academic orientation and parental involvement in education during high school. Sociology of
Education, 74(3), 210-230. https://doi.org/10.2307/2673275
Eccles, J. S., & Roeser, R. W. (2009). School and community influences on human development. In R. M. Lerner & L.
Steinberg (Eds.), Handbook of adolescent psychology (3rd ed., Vol. 2, pp. 671-728). John Wiley & Sons.
Eccles, J. S., Wigfield, A., & Schiefele, U. (1998). Motivation to succeed. In W. Damon & N. Eisenberg (Eds.),
Handbook of child psychology (5th ed., Vol. 3, pp. 1017-1095). John Wiley & Sons.
Fisher, D., Frey, N., & Williams, D. (2002). Seven literacy strategies that work. Reading Teacher, 55(4), 370-378.
Fredricks, J. A., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various
methods and student self-report instruments. In S. L. Christenson, A. L. Reschly & C. Wylie (Eds.). Handbook of
research on student engagement (pp. 763-782). Springer.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the
evidence. Review of Educational Research, 74(1), 59-109. https://doi.org/10.3102/00346543074001059
Fredricks, J. A., McColskey, W., Meli, J., Mordica, J., Monstrosse, B., & Mooney, K. (2011). Measuring student
engagement in upper elementary through high school: A description of 21 instruments (Issues and Answers Report,
REL 2011-No. 098). Regional Educational Laboratory.
Furrer, C., & Skinner, E. (2003). Sense of relatedness as a factor in children’s academic engagement and performance.
Journal of Educational Psychology, 95(1), 148-162. https://doi.org/10.1037/0022-0663.95.1.148
Goodenow, C. (1993). Classroom belonging among early adolescent students: Relationships to motivation and
achievement. Journal of Early Adolescence, 13(1), 21-43. https://doi.org/10.1177/0272431693013001002
Gregoire, M. (2003). Motivational factors influencing high school students’ academic success. Journal of Educational
Research, 97(3), 148-163.
Kuh, G. D., Cruce, T. M., Shoup, R., Kinzie, J., & Gonyea, R. M. (2008). Unmasking the effects of student engagement
on first-year college grades and persistence. Journal of Higher Education, 79(5), 540-563.
https://doi.org/10.1080/00221546.2008.11772116
Lee, V. E., & Smith, J. B. (1996). Social support and achievement for young adolescents in Chicago: The role of school
Qeios, CC-BY 4.0 · Article, December 20, 2023
Qeios ID: J7B6RG.2 · https://doi.org/10.32388/J7B6RG.2 10/11
academic press. American Educational Research Journal, 33(2), 393-417.
Leithwood, K., Jantzi, D., & Steinbach, R. (1999). Changing leadership for changing times. Open University Press.
Perez, W. D. D., & Vasquez, A. B. Relationship of Noise Level to the Mental Fatigue Level of Students: A Case Study
during Online Classes.
Pekrun, R., Goetz, T., & Perry, R. P. (Eds.). (2019). Academic emotions in students’ learning and development:
Theoretical perspectives and educational implications. Academic Press.
Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies
for Learning Questionnaire (MSLQ). National Center for Research to Improve Postsecondary Teaching and Learning,
University of Michigan.
Rimm-Kaufman, S. E., & Hulleman, C. S. (2015). Motivation in context: The role of motivation in the learning
environment. In J. D. Wright (Ed.), International encyclopedia of the social and Behavioral Science (2nd ed., Vol. 15,
pp. 870-877). Elsevier.
Roeser, R. W., Eccles, J. S., & Sameroff, A. J. (2000). School as a context of early adolescents’ academic and social-
emotional development: A summary of research findings. Elementary School Journal, 100(5), 443-471.
https://doi.org/10.1086/499650
Rumberger, R. W., & Palardy, G. J. (2005). Does segregation still matter? The impact of student composition on
academic achievement in high school. Teachers College Record, 107(9), 1999-2045
Schunk, D. H., & Meece, J. L. (2012). Student motivation in an era of accountability: A fresh look at theory and
research. Educational Psychologist, 47(2), 108-124.
Skinner, E. A., Wellborn, J. G., & Connell, J. P. (1990). What it takes to do well in school and whether I’ve got it: A
process model of perceived control and children’s engagement and achievement in school. Journal of Educational
Psychology, 82(1), 22-32. https://doi.org/10.1037/0022-0663.82.1.22
Stipek, D. J., & Gralinski, J. H. (1991). Gender differences in children’s achievement-related beliefs and emotional
responses to success and failure in mathematics. Journal of Educational Psychology, 83(3), 361-371.
https://doi.org/10.1037/0022-0663.83.3.361
Urdan, T., & Midgley, C. (2001). Academic self-handicapping: What we know, what more there is to learn. Educational
Psychology Review, 13(2), 115-138. https://doi.org/10.1023/A:1009061303214
Wang, M. T., & Degol, J. L. (2016). School climate: A review of the construct, measurement, and impact on student
outcomes. Educational Psychology Review, 28(2), 315-352. https://doi.org/10.1007/s10648-015-9319-1
Wang, M. T., & Holcombe, R. (2010). Adolescents’ perceptions of school environment, engagement, and academic
achievement in middle school. American Educational Research Journal, 47(3), 633-662.
https://doi.org/10.3102/0002831209361209
Qeios, CC-BY 4.0 · Article, December 20, 2023
Qeios ID: J7B6RG.2 · https://doi.org/10.32388/J7B6RG.2 11/11
