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CELL PHONES AND ACADEMIC ACHIEVEMENT 1
A Quantitative Study of Cell Phone Restrictions on
Academic Achievement in New Jersey High School Students
Danielle Nesci
Dissertation Submitted to the Doctoral Program
of the American College of Education
in partial fulfillment of the requirements for the degree of
Doctor of Education in Instructional Technology
December 2025
CELL PHONES AND ACADEMIC ACHIEVEMENT 2
A Quantitative Study of Cell Phone Restrictions on
Academic Achievement in New Jersey High School Students
Danielle Nesci
Approved by:
Dissertation Chair: Joshua Reichard, PhD
Committee Member: Cari Kennedy, EdD
CELL PHONES AND ACADEMIC ACHIEVEMENT 3
Copyright © 2025
Danielle T. Nesci
CELL PHONES AND ACADEMIC ACHIEVEMENT 4
Abstract
Cell phones have become essential for communication and information access. The problem is
that cell phone use may have detrimental effects on student learning, engagement, social
interactions, and academic achievement for secondary students in New Jersey high schools. Prior
research has examined the influence of cell phone policies on mental health and classroom
behaviors, but there have been limited studies exploring the differences in standardized test
scores. The purpose of this quantitative causal-comparative study was to examine the differences
in academic achievement on standardized tests among secondary students in New Jersey based
on cell phone use. Mayer’s cognitive theory of multimedia learning and Bandura’s social
learning theory provided the foundation for this study. The research questions and hypothesis
examined whether a difference exists in overall mean scores for Mathematics and English
Language Arts on standardized tests. Archival data obtained from the New Jersey Department of
Education’s website were used to compare the mean ELA and Math scores of 19 public
secondary schools in New Jersey that had instituted restrictive cell phone policies.
Nonparametric statistical analyses were conducted, and findings indicated that ELA mean scores
showed improvement, however, Math did not. After a post-hoc correction, neither was
statistically significant. The results of this study can assist school leaders and policymakers in
understanding how cell phone policies may influence academic performance. Recommendations
for future research include additional quasi-experimental and longitudinal studies.
Keywords: cell phones, academic achievement, standardized test scores, quantitative,
social learning theory, cognitive theory of multimedia learning
CELL PHONES AND ACADEMIC ACHIEVEMENT 5
Dedication
This dissertation is dedicated to my family.
To my husband, Joe, thank you for being my steady source of love and encouragement
through this long journey. You gave me the space and time I needed, even when it meant extra
work fell on your shoulders. You picked me up when I was tired, cheered me on when I doubted
myself, and reminded me why I needed to finish. I could not have done this without your
support.
To my daughter, Gabrielle, I hope this work shows you that you can do anything you set
your mind to. Even when things feel overwhelming, keep pushing forward, believe in yourself,
and never be afraid to chase your dreams. You inspire me every day, and I hope I’ve set an
example for you to follow your own path with confidence.
To my parents, Nick and Madeline, thank you for raising me to believe I could
accomplish whatever I set out to do. That belief carried me through the hardest moments. And
yes, you were right; I really could do anything, except play the flute, which was probably for the
best. That instrument and I were just never meant to be. I’m grateful you taught me resilience,
humor, and the value of hard work.
To my sister, Michele, thank you for showing me what it looks like to be a truly great
educator. Your passion, dedication, and integrity inspire me both personally and professionally,
and I am proud to follow the trail you blazed.
This accomplishment belongs to all of you as much as it does to me.
CELL PHONES AND ACADEMIC ACHIEVEMENT 6
Acknowledgements
I am deeply grateful to everyone who supported me throughout this dissertation journey.
To my chair, Dr. Joshua Reichard, thank you for your steady guidance and thoughtful
feedback. More than that, your calming presence kept me grounded when the stress and anxiety
felt overwhelming. Knowing you believed in me gave me the strength to keep going.
To my committee member, Dr. Cari Kennedy, thank you for your supportive feedback
and for always framing your suggestions in a way that built my confidence. You helped me grow
as both a writer and a researcher.
To all of the professors I had along the way, thank you for sharing your expertise, asking
the tough questions, and helping me find my voice. I especially want to acknowledge Dr. Lyndon
Godsall. Your enthusiasm for technology and lifelong learning, along with your infectious
positivity, left a lasting impression. Your energy made every interaction brighter, and your
passion will not be forgotten.
Finally, I want to recognize my colleagues and friends who traveled this road with me.
Lisa and Haim, I could not have made it through without you. Thank you for the late-night group
chats, the advice, the encouragement, and the laughter. Having you by my side made this process
so much more manageable and so much more meaningful.
This accomplishment is the result of not only my effort but also the incredible support
system that surrounded me every step of the way, which carried me through.
CELL PHONES AND ACADEMIC ACHIEVEMENT 7
Table of Contents
List of Tables ..................................................................................................................................11
Chapter 1: Introduction ................................................................................................................. 12
Background of the Problem .............................................................................................. 13
Statement of the Problem .................................................................................................. 14
Purpose of the Study ......................................................................................................... 15
Significance of the Study .................................................................................................. 16
Research Questions ........................................................................................................... 16
Hypotheses ........................................................................................................................ 17
Theoretical Framework ..................................................................................................... 18
Definition of Terms ........................................................................................................... 19
Assumptions ...................................................................................................................... 21
Scope and Delimitation ..................................................................................................... 21
Limitations ........................................................................................................................ 22
Chapter Summary ............................................................................................................. 23
Chapter 2: Literature Review ........................................................................................................ 25
Literature Search Strategy................................................................................................. 26
Theoretical Framework ..................................................................................................... 27
Cognitive Theory of Multimedia Learning ........................................................... 27
Social Learning Theory ......................................................................................... 29
CELL PHONES AND ACADEMIC ACHIEVEMENT 8
Research Literature Review .............................................................................................. 31
Benefits ................................................................................................................. 32
Motivation and Engagement ................................................................................. 32
Academic Achievement ........................................................................................ 34
Challenges ............................................................................................................. 37
Focus and Attention .............................................................................................. 38
Anxiety and Nomophobia ..................................................................................... 39
Social Norms and Problematic Smartphone Use .................................................. 41
Interventions and Proposed Solutions ................................................................... 42
Policies and Ambivalence ..................................................................................... 43
Strict Bans ............................................................................................................. 43
Balanced Policies .................................................................................................. 45
Research Topic Counterargument ......................................................................... 47
Chapter Summary ............................................................................................................. 48
Chapter 3: Methodology ............................................................................................................... 51
Research Methodology, Design, and Rationale ................................................................ 52
Methodology ......................................................................................................... 52
Design ................................................................................................................... 53
Role of the Researcher ...................................................................................................... 54
Research Procedures ......................................................................................................... 54
CELL PHONES AND ACADEMIC ACHIEVEMENT 9
Population and Sample Selection.......................................................................... 55
Recruitment ........................................................................................................... 55
Archival Data ........................................................................................................ 56
Data Instrument ..................................................................................................... 56
Archival Data ........................................................................................................ 57
Data Collection ..................................................................................................... 57
Data Preparation................................................................................................................ 58
Data Analysis .................................................................................................................... 59
Reliability and Validity ..................................................................................................... 60
Ethical Procedures ............................................................................................................ 61
Chapter Summary ............................................................................................................. 62
Chapter 4: Research Findings and Data Analysis Results ............................................................ 63
Data Collection ................................................................................................................. 64
Deviation From Data Collection Plan and Sample Size ....................................... 65
Baseline Descriptive and Demographic Characteristics of the Sample ................ 66
Data Cleaning.................................................................................................................... 67
Data Analysis and Results ................................................................................................. 68
Assumption Testing............................................................................................... 68
Results by Research Questions ............................................................................. 69
Post-Hoc Adjustment ............................................................................................ 71
CELL PHONES AND ACADEMIC ACHIEVEMENT 10
Reliability and Validity ..................................................................................................... 71
Internal Validity .................................................................................................... 72
External Validity ................................................................................................... 72
Reliability .............................................................................................................. 73
Chapter Summary ............................................................................................................. 73
Chapter 5: Discussion and Conclusions ........................................................................................ 75
Findings, Interpretations, and Conclusions ....................................................................... 76
Limitations ........................................................................................................................ 79
Recommendations for Future Research ............................................................................ 80
Implications for Leadership .............................................................................................. 82
Conclusion ........................................................................................................................ 83
References ..................................................................................................................................... 85
Appendix A Recruitment Letter .................................................................................................... 93
Appendix B Inclusion Questionnaire ............................................................................................ 94
Appendix C IRB Approval Letter ................................................................................................. 95
CELL PHONES AND ACADEMIC ACHIEVEMENT 11
List of Tables
Table
1: Baseline Characteristics of Participating Schools .....................................................................66
2: Descriptive Statistics for NJGPA Scores Before and After Policy Implementation ..................68
3: Mann-Whitney U Results ..........................................................................................................71
CELL PHONES AND ACADEMIC ACHIEVEMENT 12
Chapter 1: Introduction
Cell phones and smartphone technology have infiltrated our daily lives, affecting the
education, communication, and social interactions of more than 6 billion users globally (Farzana
et al., 2023; Mazzei, 2022). In the United States, 77% of schools report having a formal policy
related to cell phone use during the school day; however, the severity and enforcement of these
policies vary widely (National Center for Education Statistics, 2022). Countries such as France
and Spain have implemented national bans on mobile devices in schools to reduce distractions,
while others promote mobile phone use to enhance access to academic resources and narrow the
digital divide (Barfi et al., 2021; Böttger & Zierer, 2024). The wide range of policies and
enforcement practices suggests the answer may not be a one-size-fits-all approach, and points to
the need for further research on the impact of these policies on standardized test scores (Böttger
& Zierer, 2024). Further research may help determine whether strict bans to reduce distractions
are necessary or whether balanced policies promoting responsible cell phone use, reflecting
socially accepted norms outside the classroom, would be more advantageous (Dent et al., 2022;
Horn, 2023). Another benefit includes understanding how the results may inform teacher practice
and how technology can support, rather than hinder, learning (Schroeder et al., 2022). Findings
from this study may also contribute to discussions on mobile learning, increased access, and
educational equity for students from economically diverse backgrounds (Aldulaimi et al., 2021;
Barfi et al., 2021).
The background and statement of the problem, purpose, and significance will be
discussed in the introduction to give an understanding of the need for the study. Research
questions, hypotheses, and theoretical frameworks will provide the background to guide the
CELL PHONES AND ACADEMIC ACHIEVEMENT 13
research. To provide greater context for the study, definitions of key terms, assumptions, scope,
delimitations, and limitations will be outlined to clearly describe the design and focus.
Background of the Problem
The first cell phones were invented in the early 1970s but were not widely used until the
1990s. At that time, mobile phones could access the internet, but the modern smartphone, with
touchscreens and advanced computing capabilities, debuted in 2007 (Sadiq et al., 2022).
Although most secondary schools have instituted policies restricting the use of cell phones, 72%
of high school teachers still find cell phone distractions a significant problem (Roush, 2024).
While many parents initially supported schools’ policies addressing distractions, cheating, and
cyberbullying, there has been pushback against these policies (Selwyn & Aagaard, 2021).
Parents cite safety issues and advocate for immediate contact with their children, even though
cell phones may hinder communication during a crisis (Roush, 2024; Smale et al., 2021). Despite
the restrictive policies and potential challenges associated with cell phones, students have
perceived the benefits of using these devices as valuable learning tools to foster socialization and
collaboration since the early iterations of the devices (Digtyar et al., 2023). These students,
referred to as digital natives, have grown up with technology, enjoy active learning, and are
accustomed to switching between tasks quickly (Sadiq et al., 2022).
Cell phones have changed the way we learn, access information, and socialize with others
(Siyami et al., 2023). Students can be motivated to engage with educational materials and
resources to which they may not otherwise have access (Salhab & Daher, 2023). Mobile devices
also allow educators to personalize learning by providing scaffolding in lessons and to customize
feedback for learners (Aldulaimi et al., 2021; Schroeder et al., 2022). Students who are
experienced with technology are more likely to engage in discussions, watch educational videos,
CELL PHONES AND ACADEMIC ACHIEVEMENT 14
and access learning materials using their cell phones than they are to read a textbook or take
notes in class, increasing productivity (Asif et al., 2021; Zogheib & Daniela, 2022). Cell phones
also present challenges such as reduced focus and mental well-being (Joseph, 2024). Students
cannot imagine life without access to smartphones, even for brief periods, and they experience
special forms of anxiety known as nomophobia and the fear of missing out (Farzana et al., 2023;
Uher et al., 2023). Since the introduction of early smartphone models in 2007, administrators,
educators, and parents have struggled to balance the benefits and challenges of mobile devices in
the classroom, while researchers call for more balanced policies to help students foster healthier
mobile phone habits (Ochs & Sauer, 2022; Selwyn & Aagaard, 2021; Siyami et al., 2023).
Statement of the Problem
The problem is that cell phone use may have detrimental effects on student learning,
engagement, social interactions, and academic achievement for secondary students in New
Jersey high schools. With smartphones becoming a constant presence in students’ lives,
educators are increasingly concerned about how distractions, reduced face-to-face interactions,
and overuse of technology may influence classroom learning and behavior (Sadiq et al., 2022;
Silas & Mwila, 2024). These concerns have prompted the New Jersey Department of Education
(NJDOE) and the U.S. Department of Education to issue guidance for schools on developing or
refining cell phone policies to address these challenges (New Jersey Department of Education,
2024; U.S. Department of Education, 2024). As school leaders seek ways to support student
learning and well-being, examining the impact of these policies is both timely and necessary.
Studies have shown a correlation between problematic smartphone use and lower test
scores and academic performance (Böttger & Zierer, 2024). Social issues such as increased
anxiety and cyberbullying are exacerbated by mobile phones and are a source of distraction and
CELL PHONES AND ACADEMIC ACHIEVEMENT 15
decreased social engagement in the classroom (Siyami et al., 2023; Tadena et al., 2021). Cell
phones have positive implications by providing access to digital resources and allowing
educators to customize learning materials and feedback (Salhab & Daher, 2023). Existing
research does not adequately address the effects of cell phone policies on the diverse student
population of New Jersey’s secondary schools, which vary in socioeconomics and technological
resources (Böttger & Zierer, 2024).
Purpose of the Study
The purpose of this quantitative causal-comparative study was to examine the differences
in academic achievement on standardized tests among secondary students in New Jersey based
on cell phone use. Smartphone ownership among adolescents is as high as 95% in some regions
(Gajdics & Jagodics, 2022). The prevalence of these devices among secondary students is linked
to negative effects on academic achievement, social interaction, and mental health (Siyami et al.,
2023; Tadena et al., 2021). Numerous studies have been conducted both nationally and
internationally on the academic and psychological effects cell phone use has on adolescents;
however, limited research focuses specifically on high schools in New Jersey, where diversity
and socioeconomic factors may play a significant role (Sadiq et al., 2022; Selwyn & Aagaard,
2021; Zogheib & Daniela, 2022).
Quantitative methods provide meaningful and generalizable results by allowing
researchers to analyze the data through statistical tests (Johnson & Christensen, 2025). The
causal-comparative design enables the comparison of the dependent variable, standardized test
scores, before and after the implementation of the cell phone policy, the independent variable
(Creswell & Creswell, 2023). The target population consisted of the statewide standardized test
CELL PHONES AND ACADEMIC ACHIEVEMENT 16
scores of students in Grade 11 who attend a public high school in New Jersey that has
implemented a restrictive cell phone policy.
Significance of the Study
Research on the use of mobile phones in an educational setting may provide insight into
academic performance and strategies for the effective integration or regulation of smartphone use
(Salhab & Daher, 2023). The results may contribute to understanding the influence of cell phones
on attention, academic performance, and social behaviors (Sadiq et al., 2022; Schunk &
DiBenedetto, 2023). Students and educators can benefit from using mobile devices for
collaboration and personalized learning (Salhab & Daher, 2023). Policymakers can leverage the
findings to create balanced policies incorporating technological benefits to enhance educational
outcomes, while minimizing distractions and potential misuse (Böttger & Zierer, 2024; Grigic
Magnusson et al., 2023; Selwyn & Aagaard, 2021). Support for balanced policies encouraging
digital literacy education may help adolescents foster healthier relationships with technology and
reduce cyberbullying, aligning with societal norms outside of the classroom and promoting
positive social changes (Grigic Magnusson et al., 2023; Tadena et al., 2021).
Research Questions
The use of cell phones in schools has had a polarizing effect on administrators, teachers,
and students. There is a division between those who find value in them and those who see them
as problematic. Standardized test scores, serving as the dependent variable, were compared using
statistical analysis to test scores before and after the implementation of a restrictive cell phone
policy, the independent variable. Using this design, the following research questions guided this
quantitative causal-comparative study:
CELL PHONES AND ACADEMIC ACHIEVEMENT 17
Research Question 1: What differences exist, if any, in state standardized exam mean
scores in ELA before and after restrictive cell phone policies are implemented among secondary
students in New Jersey?
Research Question 2: What differences exist, if any, in state standardized exam mean
scores in Mathematics before and after restrictive cell phone policies are implemented among
secondary students in New Jersey?
These research questions are aligned with the null and alternative hypotheses. Each
question examines whether a statistically significant difference exists in mean standardized test
scores before and after the implementation of restrictive cell phone policies. By focusing on ELA
and Mathematics, the questions aim to determine if limiting in-class cell phone use has a
measurable impact on academic achievement in New Jersey high schools.
Hypotheses
The hypotheses build upon the research questions and provide a framework for statistical
analysis. These hypotheses aim to test the potential causal relationships between the
implementation of restrictive cell phone policies, the independent variable, and student
performance on standardized tests in English Language Arts and Mathematics, the dependent
variable (Creswell & Creswell, 2023). The null and alternative hypotheses seek to measure the
differences in mean scores before and after policy changes. This allows a deeper understanding
of mobile phones' influence on academic outcomes (Selwyn & Aagaard, 2021).
H10: No statistically significant difference exists in ELA mean scores on state
standardized exams after banning cell phones in secondary schools in New Jersey.
H1a: A statistically significant difference exists in ELA mean scores on state standardized
exams after banning cell phones in secondary schools in New Jersey.
CELL PHONES AND ACADEMIC ACHIEVEMENT 18
H20: No statistically significant difference exists in Mathematics mean scores on state
standardized exams after banning cell phones in secondary schools in New Jersey.
H2a: A statistically significant difference exists in Mathematics mean scores on state
standardized exams after banning cell phones in secondary schools in New Jersey.
Theoretical Framework
The cognitive theory of multimedia learning (CTML) and social learning theory (SLT)
served as theoretical frameworks for studying the effects of cell phone use on New Jersey
Graduation Proficiency Assessment (NJGPA) test scores among secondary students in New
Jersey (Bandura, 1977; Mayer, 1997). According to CTML principles, presenting information in
multimedia format leads students to experience deeper learning and greater retention (Mayer,
2021). Based on SLT, when teachers and students model responsible academic use of their cell
phones, other students learn these behaviors by observing and imitating them (Bandura, 1977).
Studies have explored the impact of smartphones on academic performance and social well-
being, indicating that combining CTML and SLT allows a more thorough analysis of the
relationship between cell phone use and students’ educational outcomes on state tests (Böttger &
Zierer, 2024).
According to Mayer (1997), CMTL explains how students learn more effectively when
visual and verbal information is presented simultaneously rather than sequentially. The cognitive
theory of multimedia learning emphasizes that learning is improved when cognitive load is
managed and students are actively engaged in processing information (Kennedy & Romig,
2024). When mobile phones are purposefully integrated into instruction, they can serve as
powerful tools by providing access to videos, tutorials, educational apps, and other multimedia
resources. This supports CTML’s principle that combining words and visuals enhances
CELL PHONES AND ACADEMIC ACHIEVEMENT 19
understanding (Mayer, 2021). This multimodal delivery allows students to engage with the
material in ways that align with how they learn best (Immanuel & Hameed, 2023). Using CTML
principles, educators can create engaging, interactive lessons that promote personalized learning
by utilizing cell phones to present visual and audio options, thereby enhancing the learning
experience (Knoster & Goodboy, 2023).
Early human learning experiences involve observing others and emulating their actions
(Bandura, 1977). According to Bandura’s (1977) social learning theory, individuals learn through
social context by observing and imitating others. Watching how behaviors are rewarded or
punished helps students understand the consequences of their actions (Schunk & DiBenedetto,
2023). A useful lens to understand how students notice their peers using cell phones for learning
and how these behaviors are shaped by their social environment is provided by SLT (Barfi et al.,
2021). Educators play an important role by modeling responsible and purposeful use of
technology. When teachers demonstrate how to use cell phones for learning, collaboration, or
exchanging ideas, students are more likely to follow that example (Tadena et al., 2021).
Reinforcing these positive behaviors can help shift attitudes, making responsible phone use a
social norm that supports academic success (Barfi et al., 2021; Tadena et al., 2021).
Drawing on both CTML and SLT provides a framework for understanding the ways
mobile phone use might shape academic performance. Together, these perspectives helped frame
the study’s research questions, particularly in terms of how access to cell phones could affect
standardized test results. The literature review extends this foundation by examining in greater
detail how mobile device use intersects with student learning and achievement.
CELL PHONES AND ACADEMIC ACHIEVEMENT 20
Definition of Terms
It is crucial to clearly define the key terms used in order to understand and interpret them
as intended (Hyatt & Roberts, 2023). In cases where certain terms may have multiple meanings
or various interpretations, definitions help minimize ambiguity (Creswell &Creswell, 2023).
Providing definitions contributes to the overall rigor of a dissertation and facilitates evaluation
by the academic community (Hyatt & Roberts, 2023).
Ambivalence is defined as stakeholders’ simultaneous recognition of conflicting feelings
about the benefits and negative effects of mobile devices in the classroom environment (Dent et
al., 2022).
Cell phone policy is defined as the set of rules or guidelines provided by educational
institutions that govern the use or restriction of mobile phones throughout the school day (Smale
et al., 2021)
Nomophobia, a portmanteau of the words “no mobile phobia,” is defined as the feeling of
fear or anxiety experienced when individuals are unable to access their mobile phones due to
physical separation, connectivity problems, or insufficient charge (Uher et al., 2023).
Smartphone is defined as a handheld device with advanced computational features that
provide access to the internet, a wide variety of applications, and multimedia tools (Sadiq et al.,
2022). Smartphone can be used synonymously with the terms cell phone or mobile device.
Standardized test is defined as an assessment designed to evaluate students’ knowledge
and used to compare educational outcomes across populations, administered under consistent
conditions to ensure uniformity across test-takers (Creswell & Creswell, 2023).
CELL PHONES AND ACADEMIC ACHIEVEMENT 21
Assumptions
Beliefs or conditions assumed to be true without evidence are the basis for assumptions in
a study (Hyatt & Roberts, 2023). Acknowledging the existence of assumptions is essential to the
study’s design and validity (Creswell & Creswell, 2023). Assumptions in research are
unavoidable, especially in designs involving pre-existing data, but they can be articulated,
justified, and accounted for to ensure transparency and enhance credibility (Mertens, 2023).
Certain assumptions about the design and data of a study must be made to carry out the research
(Johnson & Christiansen, 2025).
It is accepted that the use of archival data collected by a reputable source, such as the
Department of Education, is reliable (Johnson & Christiansen, 2025). A key assumption made
was that the data obtained from the NJDOE website, used as the dependent variable, had been
collected and reported correctly. Second, pretest-posttest designs facilitate the measurement of
differences associated with an intervention (Creswell & Creswell, 2023). It was assumed that
implementing a restrictive cell phone policy could be isolated from other variables such as
teaching quality, socioeconomic factors, and school resources. Finally, it was assumed that
participants had understood and honestly answered the questions on the inclusion questionnaire.
Scope and Delimitations
The scope of a study refers to the explicit boundaries established to focus the research,
ensuring that the research questions are effectively addressed (Mertens, 2023). The study
comprised test scores from 11th grade students in public secondary schools in New Jersey, where
a restrictive cell phone policy has been implemented (New Jersey Department of Education,
2024a). New Jersey public schools were selected due to the diversity in the ethnic, cultural, and
socioeconomic backgrounds of their student bodies. The study focused on the year before and the
CELL PHONES AND ACADEMIC ACHIEVEMENT 22
year after the implementation of the restrictive cell phone policy to minimize the effects of
curriculum, teacher, demographic, or other changes that may occur over time (Mertens, 2023).
Delimitations are intentional exclusions to enhance the clarity and feasibility of a study
(Creswell & Creswell, 2023). To maintain focus on public schools, private and charter schools
were excluded from the study, as were magnet programs that require students to apply and
undergo a selection process. The study consisted of an examination of archival data to ensure
reliability and intentionally excluded behavioral effects due to their subjectiveness (Johnson &
Christensen, 2025). Narrowing the scope by excluding behavioral effects and non-public high
schools strengthens the internal validity of the study (Mertens, 2023).
Limitations
Limitations are inherent in research and are defined as factors beyond control that may
affect the validity and generalizability of a study (Creswell & Creswell, 2023; Mertens, 2023).
The design and method chosen, the type of data collected, the time frame, focus, biases, and
other factors could introduce limitations; therefore, it is critical to acknowledge and address them
to ensure transparency (Mertens, 2023). Causal-comparative research examines the differences
between groups but does not establish causality due to the lack of random assignment (Mertens,
2023).
A limitation of this study was the potential for selection bias, as schools with restrictive
policies may differ in other ways from those without such policies (Creswell & Creswell, 2023).
Socioeconomic factors, teacher quality, and whether the students had access to school-issued
mobile devices are confounding variables that may influence standardized test scores and
internal validity. Another limitation of the study that may have affected external validity is that
the study was conducted in public high schools in New Jersey. Findings may not be generalizable
CELL PHONES AND ACADEMIC ACHIEVEMENT 23
to other geographic locations, particularly private or charter schools, where demographic,
economic, or policy differences may exist (Creswell & Creswell, 2023). Finally, the pretest-
posttest model spans two academic years, limiting the ability to study long-term results (Mertens,
2023).
Biases may influence the interpretation of results when observing differences (Creswell
& Creswell, 2023; Mertens, 2023). This study employed statistical tests, such as t-tests or their
non-parametric equivalents, and the Bonferroni correction to ensure objectivity. To minimize the
risk of selection bias, efforts were made to incorporate participant schools that represent diversity
in demographics, including socioeconomics and location (Johnson & Christensen, 2025).
Chapter Summary
Mobile phones have become embedded in everyday life, reshaping how people connect,
learn, and share information; closer examination of their role in schools is unavoidable, yet not
without controversy (Olsen et al., 2022). Approaches to regulation differ: some school systems
attempt to eliminate devices to limit distraction, while others encourage their use as tools for
equitable access and instructional support (Böttger & Zierer, 2024; Selwyn & Aagaard, 2021). In
New Jersey, where schools serve students from varied cultural and socioeconomic backgrounds,
this tension is particularly pronounced. The issue at the heart of this study is the dual nature of
mobile phones in classrooms, where these devices can foster collaboration and provide learning
resources, but also create risks, such as diminished focus, engagement, and achievement.
The purpose of this quantitative causal-comparative study was to analyze differences in
standardized test performance in English Language Arts and Mathematics before and after
schools adopted restrictive cell phone policies. Using archival data from the New Jersey
Department of Education, the study focused on 11th grade scores to determine whether policy
CELL PHONES AND ACADEMIC ACHIEVEMENT 24
implementation produced statistically significant changes. The framework combined Mayer’s
cognitive theory of multimedia learning, which emphasizes the benefits of multimodal
instruction, and Bandura’s social learning theory, which highlights how students learn behaviors
by observing others (Bandura, 1977; Mayer, 1997). Together, these perspectives guided the
exploration of how cell phone use influences academic outcomes.
Several assumptions shaped the design, including the reliability of state data and the
ability to isolate policy effects from other variables. The scope was intentionally limited to public
high schools implementing restrictive policies, excluding private and charter schools, to maintain
consistency. Delimitations excluded behavioral outcomes to emphasize measurable test data.
Limitations included potential selection bias, socioeconomic differences across schools, and the
inability of causal-comparative methods to establish direct causality.
The study is significant because its findings may inform educators, policymakers, and
parents as they navigate the competing issues of safety, equity, and effective learning. By
addressing the complexities of cell phone policy and performance, the research contributes to
broader discussions on technology use in education. The next chapter reviews the literature on
mobile learning, policy trends, and their implications for student achievement.
CELL PHONES AND ACADEMIC ACHIEVEMENT 25
Chapter 2: Literature Review
Cellular phones have become an ever-present tool and a vital part of modern society
(Farzana et al., 2023). It is common to see people using cell phones while walking, eating, in
meetings, in stores, or even while using the restroom (Dent et al., 2022). It is rare to see someone
without a phone in their hand, pocket, or close to their person, and students are no different
(Holley & Park, 2020; Selwyn & Aagaard, 2021). The problem is that cell phone use may have
detrimental effects on student learning, engagement, social interactions, and academic
achievement for secondary students in New Jersey high schools. The purpose of this quantitative
causal-comparative study was to examine the differences in academic achievement on
standardized tests among secondary students in New Jersey based on cell phone use.
Within the field of educational technology, the use of instructional apps has contributed to
the transformation of cell phones into meaningful learning tools that support engagement and
increased student motivation (Salhab & Daher, 2023). The evidence of improved learning
outcomes from incorporating technology into education led many school districts to institute a
Bring Your Own Device (BYOD) policy, permitting the use of cell phones during the school day
(Fioravanti et al., 2024). Cell phones play a critical role as a tool for daily learning and
communication; students frequently use their smartphones for educational purposes (Fernandez
et al., 2024). However, the issue is multifaceted, and while there are academic benefits, student
use of cell phones also introduces concerns such as anxiety, bullying, depression, social isolation,
and reduced focus (Lemov, 2022). A growing number of schools have begun restricting the use
of cell phones, with some going so far as to require students to place them in locked pouches
upon entering the building (Dent et al., 2022). Nationwide, 77% of public schools have
restrictions on the non-academic use of cell phones or smartphones during the school day
CELL PHONES AND ACADEMIC ACHIEVEMENT 26
(National Center for Education Statistics, 2022). The challenge is balancing the potential
distractions against the educational opportunities these devices provide, while ensuring that
policies are communicated clearly and consistently (Smale et al., 2021). While there is
considerable research on the social and mental health issues cell phones have on secondary
students, the gap in the literature is the lack of studies that measure whether blanket cell phone
bans improve scores on standardized tests, such as the New Jersey Student Learning Assessment
(Selwyn & Aagaard, 2021).
The literature review explains the search strategy used to locate studies that were most
relevant to this topic. It also introduces the theoretical frameworks that shaped the research and
emphasizes both the potential benefits and the challenges tied to cell phone policies. In addition,
consideration is given to how these policies may affect academic achievement.
Counterarguments to restrictive policies are addressed, along with the need for continued
research on their influence, particularly in relation to standardized test scores.
Literature Search Strategy
The literature search was conducted using EBSCO, Google Scholar, and the American
College of Education (ACE) Library OneSearch feature. Results included articles and books
from the following databases: Academic Search Complete, Business Source Ultimate, CINAHL
Complete, Complementary Index, Education Resources Information Center (ERIC), Gale
Academic OneFile Select, Library, Information Science & Technology, Supplemental Index,
MEDLINE, and ProQuest. Key search terms that provided relevant results for this study included
cell phones, mobile phones, smartphones, mobile devices, mobile learning, m-learning,
secondary education, cell phone policies, cell phone bans, cell phone benefits, cell phone
challenges, interventions, solutions, attention, focus, distraction, mental health, anxiety,
CELL PHONES AND ACADEMIC ACHIEVEMENT 27
academic achievement, engagement, and motivation. Advanced searches were conducted, and
filters were applied to narrow down the results to seminal works and peer-reviewed articles
published within the last five years.
Theoretical Framework
Like the blueprints for building a house, the theoretical framework of a study is the
foundation on which the research is used to tie together the problem, purpose, and research
questions (Grant & Osanloo, 2014). The cognitive theory of multimedia learning (CTML) and
social learning theory (SLT) were used as theoretical frameworks to study the effects of cell
phones on NJGPA test scores in secondary students in New Jersey (Bandura, 1977; Mayer,
1997). Students experience deeper learning and greater retention when information is presented
in multimedia (Mayer, 1997). When teachers and students model responsible, academic use of
their cell phones for learning, SLT asserts that other students will learn those same behaviors by
observing and imitating others (Schunk & DiBenedetto, 2023). Combining CTML and SLT
allows a more thorough analysis of the relationship between cell phone use and students’
educational outcomes on state tests.
Cognitive Theory of Multimedia Learning
In his cognitive theory of multimedia learning, Mayer (1997) proposed that students learn
more effectively from a combination of visual and verbal information than from verbal only. This
theory comprises three main principles: dual-channel processing, limited capacity, and active
processing. Dual-channel processing refers to the ability of students to learn by absorbing visual
and auditory information through separate channels (Mayer, 2021). Each channel has a limited
capacity but combining visual and auditory information can optimize the cognitive load. Once
the information has been received, it is synthesized through active processing from each channel.
CELL PHONES AND ACADEMIC ACHIEVEMENT 28
Multimedia content should be designed to follow three CTML principles: managing
essential processing, minimizing extraneous processing, and promoting generative processing.
Essential processing refers to the mental effort necessary to learn and understand new
information without overloading the learning channels (Mayer, 2021). To avoid extraneous
processing, Mayer (2021) recommends learning materials that exclude unnecessary or distracting
data, as learners may struggle to absorb additional knowledge. Generative processing is the idea
that multimedia content with a “social presence” creates a personal connection with students,
motivating and engaging them (Mayer, 2021).
According to Salhab & Daher (2023), cell phones serve as a tool for delivering
multimedia content. Mayer’s (1997) CTML framework serves as the lens through which cell
phones are viewed as a learning tool that may enhance academic achievement, as well as the
influence of restrictions on academic success. Mobile devices enable students to access
information when and where they need it, personalize their learning, and select instructional
materials tailored to their learning style (Mayer, 2021). When educators create or choose lessons
based on CTML principles, Mayer (2021) asserts that students are more engaged and learning is
more individualized. The impact of restricting access to cell phones and multimedia content, as
well as its effect on standardized test scores, can be examined through the lens of the CTML
framework (Mayer, 2021).
Other researchers have shown improvements in learning outcomes through enhancements
to online course design (Immanuel & Hameed, 2023; Knoster & Goodboy, 2023; Salhab &
Daher, 2023). Multimedia, which refers to instructional materials that combine words and
pictures to support learning, has been shown to support language acquisition and retention. For
example, when ESL students viewed content in their native language with English subtitles, they
CELL PHONES AND ACADEMIC ACHIEVEMENT 29
retained more vocabulary because the verbal and visual information worked together to form
stronger connections (Immanuel & Hameed, 2023). In a study applying CTML principles to
Zoom meetings, student engagement and comprehension increased, while cognitive load
decreased (Knoster & Goodboy, 2023). Salhab & Daher (2023) found that the use of mobile
learning (m-learning) influenced students’ perceptions of the social and motivational benefits of
mobile phones. These studies demonstrate that when multimedia content is designed according
to CTML principles, it can enhance learning and retention by leveraging verbal and visual
information.
Social Learning Theory
Social learning theory (SLT) was proposed by Bandura (1977), suggesting that
knowledge and behaviors can be acquired through observation and imitation of others. One
assumption is that role models play a key role in shaping learning (Bandura, 1977). The role
model is someone with influence whom a learner wants to emulate. When the behavior of those
role models is rewarded or punished, learners observe “vicarious reinforcement,” meaning they
do not have to experience the reward or punishment themselves (Bandura, 1986). Additionally,
Bandura (1977; 1986) identified four conditions that must be met for learning to occur: attention,
retention, reproduction, and motivation. A key principal of SLT suggests that individuals can be
shaped by their environment while also impacting their surroundings (Schunk & DiBenedetto,
2023).
As Schunk and DiBenedetto (2023) explain, SLT assumes that people learn behaviors by
observing others in social situations. The person being observed is more likely to be a role model
or someone they consider knowledgeable, which makes people pay close attention (Bandura,
1977). It is then the learner's responsibility to retain the information and store it for future use
CELL PHONES AND ACADEMIC ACHIEVEMENT 30
(Schunk & DiBenedetto, 2023). The observer must have the physical or mental ability to
reproduce the learned skill or behavior, which is not always possible due to personal limitations
(Bandura, 1986). For the learners to be motivated to adopt a behavior, they must have a sense of
self-efficacy or feel confident that they will succeed in their attempt (Schunk & DiBenedetto,
2023).
A central concept in SLT is “reciprocal determinism,” in which Bandura (1986) proposes
that the individual, their behavior, and the environment all influence one another. This concept
suggests that people’s behavior is shaped by their environment, but their behavior can also
change their environment (Bandura, 1986). For example, when a student misbehaves in class and
the teacher imposes a consequence, the student repeats the behavior because their need for
attention was met, even though it was negative. Another student observing this behavior will also
be influenced, as seeing others rewarded or punished for their actions affects whether they
choose to emulate that behavior.
According to Schunk and DiBenedetto (2023), SLT provides a framework for
understanding how students’ behaviors, such as cell phone use, are shaped through observation
and social interaction. The effects on academic achievement and engagement were investigated
by examining the use of cell phones through the lens of SLT, which suggests students model
responsible behaviors observed in their peers and teachers (Bandura, 1986). Positive
reinforcement of responsible technology use can shape students’ attitudes toward educational
technology, altering the social context to better support academic success (Barfi et al., 2021;
Tadena et al., 2021). Potential changes in behavior due to cell phone policy implementation may
be examined using the SLT framework as a method of explaining changes in academic outcomes
due to a reduction in distractions. Changes in test scores, if any, can also be explored through
CELL PHONES AND ACADEMIC ACHIEVEMENT 31
SLT and help answer the research questions by revealing how limiting cell phone use may
change the social aspect of learning.
According to Schunk and DiBenedetto (2023), social learning theory helps explain how,
for students in classroom and online settings, the adoption of behaviors and reinforcement of
social norms is influenced by observing peers and teachers. This was reinforced in a study by
Barfi et al. (2021), in which SLT was applied to understand ways in which high school students
interact with technology in their learning environments. Specifically, they showed that students
had positive perceptions of using social media for communicating and collaborating with each
other, affirming the influence of peers and teachers among students in virtual learning settings.
SLT was similarly explored in a study by Tadena et al. (2021), which explored how secondary
students observe and imitate the behaviors they see online. They found that in a virtual setting,
both positive outcomes, like increased empathy, and negative ones, such as cyberbullying,
occurred as a result of peer modeling. Taken together, these studies illustrate SLT in action,
demonstrating that technology use is a key channel through which social learning occurs.
Research Literature Review
The use of mobile devices in education remains a topic of active debate among both
researchers and practitioners (Selwyn & Aagaard, 2021). Studies highlight the benefits as well as
the difficulties associated with cell phones in the classroom (Dent et al., 2022; Fernandez et al.,
2024). Innovations in technology have altered the ways students find information, interact with
teachers and peers, and complete academic work (Siyami et al., 2023). Educational apps and
digital platforms have given students access to tutorials, videos, and other supplemental
resources that were previously less readily available (Salhab & Daher, 2023).
CELL PHONES AND ACADEMIC ACHIEVEMENT 32
A growing body of research has identified advantages of mobile learning. These
advantages include increased student motivation, engagement, and learning outcomes (Aldulaimi
et al., 2021; Asif et al., 2023; Digtyar et al., 2023; Horn, 2023; Schroeder et al., 2022; Zogheib &
Daniela, 2022). At the same time, concerns have been raised regarding the impact of mobile
phone use on students’ attention, social interactions, anxiety, and mental health (Farzana et al.,
2023; Gajdics & Jagodics, 2022; Selwyn & Aagaard, 2021; Siyami et al., 2023; Uher et al.,
2023). These competing findings have led to a wide range of approaches being taken by school
leaders, ranging from restrictive bans to more accepting attitudes that allow for individual
classroom teacher discretion (Asif et al., 2023; Digtyar et al., 2023; Farzana et al., 2023; Lemov,
2022; Tandon et al., 2020; Uher et al., 2023). These differing perspectives highlight the
importance of examining whether clear and consistently communicated cell phone policies in
secondary schools influence performance on state standardized tests.
Benefits
Although cell phones are often viewed with skepticism in school settings, researchers
emphasize that the devices themselves are not inherently disruptive, but rather mirror the cultural
and social norms that exist beyond the classroom (Farzana et al., 2022). Despite this, schools
frequently downplay or ignore the ways in which mobile apps can contribute positively to
learning (Horn, 2023). In fact, many researchers argue that when educators intentionally design
lessons around mobile learning, they are able to leverage this technology to build interactive and
dynamic environments that improve motivation, engagement, and academic achievement
(Digtyar et al., 2023; Horn, 2023). According to Horn (2023), school leaders must carefully
consider how these tools influence both learning outcomes and everyday teaching practices.
Motivation and Engagement
CELL PHONES AND ACADEMIC ACHIEVEMENT 33
Research on mobile learning indicates that smartphones can provide students with
interactive experiences that make learning more flexible and effective (Degter et al., 2023). In a
comparative study of foreign language learners, Digtar et al. (2023) found that mobile learning
and communication technologies enhanced accessibility while increasing motivation and
engagement. Their analysis confirmed that mobile phones can be integrated into traditional
classroom settings to support new knowledge acquisition, going beyond previous studies that
focused primarily on pedagogy or technology in isolation. According to Aldulami et al. (2021),
mobile learning strategies enable educators to personalize instruction, encourage students to
capture learning in real time, and promote their ownership of the learning process. Although
students often express skepticism that teachers will fully embrace the use of mobile phones in
teaching, this skepticism has not diminished students' enthusiasm for using these tools
themselves (Aldulaimi et al., 2021).
Engagement is also increased when mobile technology is applied to provide personalized
learning and customized feedback to students (Schroeder et al., 2022). Researchers monitored
307 students in two separate courses in a qualitative study by Schroeder et al. (2022) to observe
whether AI-generated courseware enhanced learning experiences and engagement. Comparable
to the benefits noted by Digtyar et al. (2023), who found that interactive learning facilitated by
mobile devices increased student motivation, Schroeder et al. (2022) found that AI-generated
courseware leveraged mobile devices to personalize learning and provide customized feedback
to students, enhancing engagement and comprehension. The AI powered adaptive technology on
their mobile devices also generated content to personalize formative practice for students
(Schroeder et al., 2022). Although mobile devices played a key role in providing customized
CELL PHONES AND ACADEMIC ACHIEVEMENT 34
content for learners, Schroeder et al. (2022) also emphasized the critical role of educators in the
implementation and feedback process.
In a mixed-methods study, Aldulaimi et al. (2021) employed a quantitative questionnaire
along with qualitative structured interviews to develop a learning model aimed at improving the
learning environment in Iraqi schools that faced cultural, economic, and infrastructure
challenges. The researchers noted the flexibility and accessibility that mobile learning provided,
enabling students to access educational materials however, whenever, and wherever they chose.
They also stressed the importance of creating meaningful integration with the curriculum
(Aldulaimi et al., 2021). Engagement and motivation were similarly noted in the analysis by
Digtyar et al.(2023), grounded in the premise that mobile technology enhances learning by
making educational experiences more accessible and adaptable. Aldulaimi et al.’s (2021) study
resulted in a detailed model for implementation that outlines specific steps to follow and
provides practical solutions that could be applied to any educational setting lacking technological
infrastructure or funding.
When implementing a plan for mobile technology, stakeholders can capitalize on the
flexibility and accessibility that mobile devices offer, allowing students to engage with
educational materials and course content at any time and from anywhere they choose (Aldulaimi
et al., 2021). Mobile learning, specifically through cell phones, has significantly improved
student motivation and engagement due to the increased flexibility and convenience in time and
location (Zogheib & Daniela, 2022). Where traditional classroom environments rely on rigid
schedules and structured classroom methods, mobile learning is customizable, personalized, and
convenient (Aldulaimi et al., 2021; Digtyar et al., 2023; Schoeder et al., 2022).
Academic Achievement
CELL PHONES AND ACADEMIC ACHIEVEMENT 35
One central aim of this study is to clarify how variations in cell phone use correspond
with differences in academic performance. Students often view their phones as valuable learning
tools, which encourage them to interact with digital resources, such as apps, video lectures, and
online discussions, more readily than with traditional methods like reading a textbook or taking
handwritten notes (Asif et al., 2023; Zogheib & Daniela, 2022). Evidence from a quantitative
survey of 320 medical students illustrates this shift: participants identified Android phones as
useful for accessing course content, engaging in discussions, watching lectures, organizing notes,
and managing schedules (Asif et al., 2021). However, the same study revealed that over half of
respondents reported little to no textbook use, and nearly half reported taking notes occassionally
or not at all in class.
In contrast, Asif et al. (2021) reported that 56% of students frequently or very frequently
viewed video lectures, and 89% shared learning materials at least occasionally. Their descriptive
analysis identified several phone-based activities that students considered most beneficial to
academic success, including participation in group discussions, managing schedules, accessing
course content, using specialized apps, and submitting assignments. Both Asif et al. (2021) and
Zogheib and Daniela (2022) emphasized that mobile phones were not simply sources of
distraction but tools that could be used to enhance productivity and learning outcomes. Although
Asif et al.’s (2021) work centered on medical students using Android devices, the findings
suggest broader applicability to students in other disciplines and across different types of mobile
technology.
In another quantitative study of 209 students, comprising 103 from a public school in
Europe and 106 from a private school in the Middle East, Zogheib & Daniela (2022) employed a
quantitative questionnaire with a Likert scale to assess students’ perceptions of the impact of
CELL PHONES AND ACADEMIC ACHIEVEMENT 36
mobile phone use on their academic achievement. The questionnaire measured students’ attitudes
toward the perceived ease of use, usefulness, mobility, and enjoyment of cell phone use, as well
as the effects on their intention to use cell phones for academic purposes (Zogheib & Daniela,
2022). Similar to the findings of Asif et al. (2021), where the vast majority of students used cell
phones to watch video lectures and share learning content, students in Zogheib & Daniela’s
(2022) study viewed cell phones as having a positive impact on their academic performance,
citing quick access to information, communication with peers, and the ability to stay organized.
Most importantly, Zogheib & Daniela (2022) concluded that the increase in cell phone use did
not negatively impact academic performance and supports the use of cell phones as mobile
learning devices.
A meta-analysis of 70 articles on the impact of mobile phones on high school students,
which included studies from countries in Africa, Asia, Australia, Europe, and North and South
America, highlighted the effects of mobile devices on academic outcomes (Sadiq et al., 2022).
According to Sadiq et al. (2022), perceptions of cell phones have changed over time, particularly
after the COVID-19 pandemic, which necessitated a transition to remote learning for teachers
and students. Sadiq et al. (2022) noted that mobile devices have also transitioned from being a
tool for entertainment and social connections to a learning tool that satisfies educational needs.
The researchers found that when mobile phones were used intentionally and structured for
academic purposes, they positively affected academic achievement (Asif et al., 2021; Sadiq et
al., 2022; Zogheib & Daniela, 2022). However, when mobile phones were used primarily for
entertainment and social media, research showed a negative impact on academics, behavior, and
mental health (Sadiq et al., 2022; Zogheib & Daniela, 2022).
CELL PHONES AND ACADEMIC ACHIEVEMENT 37
When students perceive that their mobile phone is easy to use, portable, useful, and
enjoyable, it impacts their inclination to use it for academic purposes, resulting in increased
productivity (Zogheib & Daniela, 2022). The impact of the COVID-19 pandemic on education
and remote learning cannot be ignored, and Sadiq et al. (2022) attribute the increased acceptance
of cell phones for mobile learning to this shift. Both Zogheib & Daniela (2022) and Sadiq et al.
(2022) agree that location and culture have minimal influence on the perceived usefulness and
academic advantages of mobile devices.
Challenges
The use of smartphone technology in schools has sparked a heated debate among
educators, policymakers, parents, and students (Böttger & Zierer, 2024). Researchers point to
extensive evidence of lower concentration, sustained attention, and recall (Lemov, 2022; Siyami
et al., 2023). There is growing concern for students who are experiencing social and emotional
issues such as stress, anxiety, depression, cyberbullying, and addiction (Gajdics & Jagodics,
2022; Selwyn & Aagaard, 2021). Adolescent brains are constantly in low attention and task-
switching behavior, switching to a new task every 19 seconds, which Lemov (2022) asserts is
exacerbated by the use of cell phones, helping to create a culture of social isolation and
disconnection. Researchers also denote other issues impacting teens’ cell phone use related to
mental health, such as nomophobia (NMP), an anxiety disorder caused by fear of being
disconnected from a mobile phone or signal, which is correlated with excessive cell phone use
(Uher et al., 2023). To address these challenges, researchers have proposed physical and
behavioral interventions to reduce the negative effects on students' mental well-being and
attentiveness and to promote mindful smartphone practices (Ochs & Sauer, 2022; Olson et al.,
CELL PHONES AND ACADEMIC ACHIEVEMENT 38
2023). These skeptics note concerns about decreases in attention, social interaction, and mental
health (Gajdics & Jagodics, 2022; Selwyn & Aagaard, 2021).
Focus and Attention
Research on mobile phone use has approached the issue of attention and learning from
different angles. Siyami et al. (2023), using a quantitative design with high school students,
found that greater cell phone use was associated with significantly lower levels of attention,
leading the authors to recommend reducing classroom distractions to support focus. By contrast,
Selwyn and Aagaard (2021) adopted a qualitative perspective, questioning the idea that
problematic phone use should be framed as an addiction. Instead, they argued it is better
understood as a habit or dependency. Despite these differing methods and conclusions, both
studies emphasize the importance of promoting healthier patterns of phone use among students.
In a quantitative study of 120 teenagers with a mean age of 17, Siyami et al. (2023)
purposively sampled groups of normal, moderate, and extreme cell phone users by administering
the Jafarzadeh mobile phone addiction questionnaire, which utilizes a 5-point Likert scale. To
investigate the extent to which mobile phones impact attention and academic performance,
Siyami et al. (2023) used a computerized Stroop test. The Stroop test measures attention and
focus and consists of two levels. The first level requires participants to name a desired color from
a group of colors, and the second level asks participants to identify the color associated with a
word, even if it differs from the word's meaning (Siyami et al., 2023). The results of Siyami et al.
(2023) showed that even participants who used mobile phones normally had significantly lower
attention spans (0.55 ± 1.28) as measured by the Stroop test, and this effect was even more
pronounced (1.60 ± 2.69) in extreme users. Interestingly, the moderate user group scored the
lowest (0.50 ± 2.18). Siyami et al. (2023) asserted that attention supports cognitive functioning
CELL PHONES AND ACADEMIC ACHIEVEMENT 39
and impacts academic success, making it essential to improve focus and minimize distractions in
the learning environment. The importance of sustained attention in learning is underscored in a
study by Farzana et al. (2023), which included an attention check, despite not being primarily
interested in studying distractions. Participants were asked to select a specific answer, regardless
of its accuracy, as part of their survey, which resulted in some participants being removed from
the study for providing an incorrect response (Farzana et al., 2023).
Despite using different methodologies, Siyami et al. (2023) and Selwyn and Aagaard
(2021) both focused on how mobile phone use affects attention and learning. A key theme in
Selwyn and Aagaard’s (2021) research was the assertion that problematic cell phone use had not
been proven to be an addiction, is not included in the DSM-5, and cannot be considered a
medical diagnosis. Claiming mobile phone use is more of a bad habit or a functional dependence,
Selwyn and Aagaard (2021) acknowledged that some unhealthy habits, or vices, can be difficult
to change. While Zogheib and Daniela’s (2022) study shows that digital devices allow students
to multitask, Selwyn and Aagaard (2021) state that digital distraction diverts students’ attention
to non-educational tasks, rather than simply dividing it. According to Selwyn and Aagaard
(2021), multitasking can be academically beneficial to students, such as when a student takes
notes during instruction. However, digital distractions and students’ use of mobile phones for off-
task behavior are problematic and impair academic performance. Additional research is needed
in the areas of problematic overuse, distraction, and their impact on learning. Studies indicate
that students should be educated and encouraged to develop healthy habits regarding cell phone
use in schools (Selwyn & Aagaard, 2021; Siyami et al., 2023).
Anxiety and Nomophobia
CELL PHONES AND ACADEMIC ACHIEVEMENT 40
Mobile phones have become a valuable tool for many people, especially secondary
school students, who can form strong attachments to these devices (Gajdics & Jagodics, 2021).
Studies by Gajdics and Jagodics (2021) and Uher et al. (2023) investigated the relationship
between mobile phone use and anxiety in secondary students. In a quantitative study of 324
secondary students by Gajdics and Jagodics (2021), the anxiety levels of high school students on
a normal school day were compared to an experimental “mobile free day” where participants did
not have access to their phones during classes. Additionally, Uher et al. (2023) conducted a
mixed-methods study of 373 secondary students to examine cell phone dependence, attachment,
and the separation anxiety experienced when objects that provide a sense of safety and security,
such as mobile phones, are removed.
Gajdics and Jagodics (2021) measured students’ anxiety and engagement on two
consecutive Fridays. On the first Friday, which was a normal day, students completed surveys to
measure their usage and attachment to mobile devices, anxiety, and engagement. By the second
Friday, the “mobile free day,” Gajdics and Jagodics (2021) reported that 27% of the students had
dropped out of the study. The remaining participants were asked to lock their mobile devices in
the teachers’ room for the entire school day and complete the anxiety and engagement
inventories again. Similarly, Uher et al. (2023) administered an anonymous survey, comprised of
20 questions, to investigate the degree of mobile phone addiction experienced by students from
11 schools, ranging from 13 to 16 years of age. The questions were separated into four distinct
categories where the user may or may not have possession of a mobile device or, for some
reason, lost access to data or the ability to communicate (Uher et al., 2023).
The results of these studies showed that the anxiety levels in Gajdics and Jagodics’
(2021) study were higher overall on a mobile free day than on a normal day, and students who
CELL PHONES AND ACADEMIC ACHIEVEMENT 41
access social networks more frequently with their mobile devices experienced higher anxiety
levels than the mean. Uher et al. (2023) found that 99.5% of respondents experienced at least a
very mild form of anxiety, and 10% exhibited signs consistent with behavioral addiction. This
type of anxiety is known as nomophobia (NMP), a portmanteau of the words “no mobile
phobia,” and results in stress, comparable to how a child feels separation anxiety when apart
from their parent (Gajdics & Jagodics, 2021; Uher et al., 2023). There is also a gender imbalance
in the degree of NMP experienced, where girls are experiencing a greater degree than boys,
especially when they are unable to communicate with friends and family immediately (Uher et
al., 2023).
Social Norms and Problematic Smartphone Use
Perceived norms can significantly influence the formation of our attitudes and behaviors
(Farzana et al., 2023; Gajdics & Jagodics, 2021). In their quantitative study of 200 participants
aged 18 to 82 years, Farzana et al. (2023) investigated whether social norms play a role in
problematic smartphone use (PSU). Several assessments were administered to participants to
identify perceived norms, reassurance-seeking behavior, depression, impulsivity, and primary
uses of smartphones (Farzana et al., 2023). Both Gajdics and Jagodics (2021) and Farzana et al.
(2023) acknowledge that social pressure plays a significant role in shaping perceptions of normal
smartphone use, as well as in fostering increased checking habits among adolescents that
reinforce attention-seeking behaviors. Younger generations are more likely to overuse
smartphones than older ones, and Farzana et al. (2023) suggest that this is due to the
phenomenon known as “fear of missing out” or FOMO. The generational differences reported by
Farzana et al. (2023) advise that adolescents are more likely to use smartphones primarily for
social media and experience higher levels of PSU. However, the negative correlation between
CELL PHONES AND ACADEMIC ACHIEVEMENT 42
age and PSU indicates that as people age, their impulsivity control improves, thereby reducing
problematic behaviors (Farzana et al., 2023). These findings suggest that the psychological and
mental health of young people may be negatively affected when access to cell phones is
restricted, warranting efforts in public health and education to promote healthier smartphone
habits and shift social dynamics (Gajdics & Jagodics, 2021; Farzana et al., 2023).
Interventions and Proposed Solutions
To address PSU and other negative effects of smartphones on students, researchers have
examined physical and behavioral interventions and possible solutions (Ochs & Sauer, 2022;
Olson et al., 2023). Ochs and Sauer’s (2022) quantitative study divided 97 university students,
aged 18 to 29, into intervention-based groups to examine the effectiveness of switching
frequently used apps to a different screen. In contrast, Olsen et al. (2023) conducted two separate
studies with 121 participants aged 18 to 33, one with and one without a control group, that used a
“nudge-based intervention” comprised of 10 potential strategies. Participants were allowed to
select from the list and determine their level of commitment to the interventions. These
interventions included both physical and behavioral options, including leaving their phone at
home or in another room, disabling notifications and other haptics, turning off the phone when
going to bed, hiding social media apps, and using a computer for email, web search, and
shopping (Olsen et al., 2023).
Researchers contend that using technological tools as a potential solution to a technology
problem may create a conflict of interest, as phone and app producers prioritize maximizing
screen time rather than reducing it (Olsen et al., 2023). Despite this argument, both studies
reported that interventions reduced the occurrence of PSU (Ochs & Sauer, 2022; Olson et al.,
2023). Both groups of participants in Ochs and Sauer’s (2022) study, those who used grayscale
CELL PHONES AND ACADEMIC ACHIEVEMENT 43
and those who relocated frequently used apps, experienced decreased screen time and increased
awareness of their usage. Olson et al. (2023) also reported that participants showed a significant
reduction in their PSU scores and screen time, as well as an improvement in working memory.
However, interviews indicated that despite the positive effects, participants reported a “fear of
missing out” leading to an increase in anxiety (Olson et al., 2023).
Policies and Ambivalence
Cellular ambivalence refers to the recognition of the advantages and disadvantages of
mobile phones in an educational setting, causing educators and administrators to choose between
banning them or embracing them as educational technology (Dent et al., 2022). Researchers
agree that a clear and consistent policy is needed and that students and parents should be
educated on the role of mobile phones in shaping students' social and cultural interactions and
communication (Selwyn & Aagaard, 2021; Tandon et al., 2020). Lemov (2022) suggests that
schools should offer alternate ways for students to communicate and collaborate. Others also
asserted that students should be educated about the consequences of mobile phone use and
supported in developing healthy and balanced habits (Böttger & Zierer, 2024; Dent et al., 2022;
Horn, 2023).
Strict Bans
To reduce distractions and address behavioral issues, strict bans on cell phones have been
implemented in schools across the globe, most notably in New York City public schools, but also
in entire countries such as France, Spain, and Switzerland (Böttger & Zierer, 2024). In a
qualitative analysis of existing literature, Selwyn and Aagaard (2021) studied the impact of strict
cell phone policies on problematic overuse, distraction, and cyberbullying. They acknowledged
that although cell phone bans addressed immediate distractions, students adapted and found
CELL PHONES AND ACADEMIC ACHIEVEMENT 44
alternative methods of diversion (Selwyn & Aagaard, 2021). Even the effort to reduce
cyberbullying only temporarily postponed it and did not address the root causes of bullying,
which Selwyn and Aagaard (2021) asserted continued through other means and outside of
school.
Many studies cite the reason for removing cell phones from the classroom is to remove
the distraction caused by their presence when students use them for social media or other non-
academic purposes (Farzana et al., 2023; Horn, 2023; Selwyn & Aagaard, 2021; Siyami et al.,
2023; Zogheib & Daniela, 2022). A logical conclusion would be that removing a distraction
would reduce off-task behavior. However, rather than improving student engagement, Grigic
Magnusson et al. (2023) revealed that additional challenges arose, including student resistance,
which impacted teacher-student relations. Their qualitative, longitudinal study followed eight
teachers and approximately 50 students who agreed to completely restrict mobile phone use for
an entire school year (Grigic Magnusson et al., 2023). Like Gajdics and Jagodics (2021), who
also reported that mobile-free days did not result in a significant increase in class engagement
scores, Grigic Magnusson et al. (2023) found that students were instead distracted by using their
laptops, negatively impacting teachers’ trust in students who refused to comply.
Although a vast majority of secondary schools in the United States have cell phone
policies in place, there is inconsistency in their enforcement, especially during non-instructional
time (Tandon et al., 2020). In the first nationwide quantitative survey of 210 secondary schools
in the US, Tandon et al. (2020) found that 97% of middle schools and 91% of high schools
restrict the use of cell phones during class time. Even when teachers and students initially
supported the mobile phone ban due to their belief that it would provide positive outcomes in
classroom culture and academic outcomes, Grigic Magnusson et al. (2023) reported that it soon
CELL PHONES AND ACADEMIC ACHIEVEMENT 45
began to create some challenges. Teachers quickly began to make exceptions to the cell phone
ban, realizing that mobile phones could be used as a pedagogical tool for learning. Mobile
phones were more convenient and natural for students to use than laptops for tasks such as using
a calculator, taking a picture of their work, listening to or recording a voice note, or looking up
the definition of a word (Grigic Magnusson et al., 2023).
Balanced Policies
Strict bans on mobile devices may have certain benefits; however, enforcement poses
significant challenges, as students often circumvent the rules, such as by submitting an empty
case or claiming they did not bring the device, and teachers may experience fatigue or
forgetfulness (Grigic Magnusson et al., 2023). Issues with enforcement and non-compliance
reduce effectiveness and undermine results, as Böttger & Zierer (2024) state that 29% of students
reported using their cell phones despite the ban. Several studies support the argument that
schools need balanced policies that provide teachers with autonomy to regulate cell phone use in
their classrooms, alignment with societal views on cell phone use, and education on ways to
promote healthy and responsible use habits (Böttger & Zierer, 2024; Holley & Park, 2020; Horn,
2023).
In a 2-year mixed-methods case study of a high school where teachers were given
autonomy to create and implement their own cell phone policies, researchers found that the 71
teachers who participated had a positive view of the latitude they were given (Holley & Park,
2020). This supports the idea that allowing teacher discretion provides the opportunity to tailor
usage explicitly for the needs of their students, and Holley and Park (2020) described how it
allowed educators to develop strategies for keeping students on task. With the constantly
expanding availability of educational apps, Horn (2023) points out that blanket cell phone bans
CELL PHONES AND ACADEMIC ACHIEVEMENT 46
ignore the potential benefits that educational apps have, such as enhanced instruction and
increased engagement. Teachers need to use their discretion and be granted the freedom to
integrate cell phones when appropriate (Horn, 2023).
Dent et al. (2022) explored cellular ambivalence and the relationship between societal
norms and school cell phone policies in an anthropological ethnography. They emphasized that
mobile phones are a pervasive element of modern life, used for communication, learning, and
entertainment, and reflect broader societal attitudes outside the classroom. While strict bans
attempt to reduce distractions, the researchers suggested such measures can be out of step with
students’ everyday experiences beyond school, where mobile phone use is integral and culturally
accepted.
In a qualitative analysis of blanket cell phone bans, Horn (2023) supported balanced
policies that align with modern technology usage in everyday life. Such approaches foster
learning, encourage engagement, and expand access to information. Both Dent et al. (2022) and
Horn (2023) emphasized that school policies are more effective when they mirror societal norms.
Aligning classroom expectations with students’ lived experiences can reduce resistance and
covert use, while also supporting positive relationships, social well-being, and educational
integration.
There is a growing consensus underscoring the need for school programs that address
mobile phone use as a habit rather than an addiction, promoting healthy and responsible cell
phone use (Böttger & Zierer, 2024; Holley & Park, 2020; Horn, 2023; Selwyn & Aagaard, 2021).
In their meta-analysis of five quantitative studies on academic performance and social behavior,
Böttger and Zierer (2024) advocated for increasing media literacy by teaching the responsible
use of technology, promoting its potential for learning, and preventing misuse and negative
CELL PHONES AND ACADEMIC ACHIEVEMENT 47
impacts on social well-being. When teachers were empowered to create lessons incorporating
structured, mindful phone use, Holley and Park (2020) asserted that students developed healthier
phone habits, improving their self-regulation and enhancing school climate. Horn (2023) also
supports the idea of allowing cell phone use in controlled circumstances to teach students how
they can be used as a learning tool, promoting student autonomy and responsible behavior,
arguing that failure to do so results in a missed opportunity for students to develop lifelong
digital literacy skills.
Research Topic Counterargument
While some studies suggest that restricting the use of cell phones in the classroom may
improve academic performance and attention, others argue that this assumption may be overly
simplistic in an educational environment such as a classroom (Böttger & Zierer, 2024; Sadiq et
al., 2022). Studies like Gajdics and Jagodics (2021) note that the relationship between a cell
phone ban and academic achievement is not straightforward and point to other variables that may
influence the outcomes, such as teacher quality, student motivation, and classroom environment.
These factors can also influence students’ learning and test performance, highlighting the concept
that banning cell phones may contribute to the outcomes but is not solely responsible for them
(Horn, 2023).
Overly restrictive policies can result in additional anxiety and disrupt social norms,
putting students' mental health and social well-being at risk (Farzana et al., 2023; Gajdics &
Jagodics, 2021). While Gajdics and Jagodics (2021) assumed that cell phone bans aimed at
minimizing distractions would reduce anxiety, they found the opposite to be true, and “mobile-
free” days resulted in higher anxiety, especially among students who reported stronger
attachments to their cell phones. Farzana et al. (2023) also reported that students experienced
CELL PHONES AND ACADEMIC ACHIEVEMENT 48
higher anxiety levels that potentially affected their focus and attention in the classroom. The
findings were attributed to smartphone use for social interactions and social media, which
contributed to FOMO when cell phone use was restricted. (Farzana et al., 2023). Sadiq et al.
(2022) cautioned that policies should adopt a balanced perspective, considering both the
potential for unintended benefits and unintended consequences.
Studies that advocate balanced cell phone policies, which grant teachers autonomy, also
present challenges, such as inconsistent enforcement and varied student experiences (Grigic
Magnusson et al., 2023; Holley & Park, 2020). Grigic Magnusson et al. (2023) observed that
teacher-initiated policies were not consistently enforced, leading some students to resist, which
strained teacher-student relationships. Similarly, when teachers were given discretion in
implementing cell phone policies, it resulted in students having different experiences and caused
confusion in other teachers' classrooms (Holley & Park, 2020).
Chapter Summary
Mobile phones are ubiquitous in modern society, and adolescents are no exception,
frequently using them for communication and social media to avoid feelings of FOMO (Dent et
al., 2022; Farzana et al., 2023). Numerous studies have been conducted on the advantages and
disadvantages of using mobile phones in an educational environment (Böttger & Zierer, 2024;
Farzana et al., 2023; Gajdics & Jagodics, 2021; Schroeder et al., 2022; Selwyn & Aagaard,
2021). Using cell phones can benefit academic achievement, engagement, and motivation
(Digtyar et al., 2023; Horn, 2023). The enjoyment of using mobile devices for learning increases
student motivation, and the ability of teachers to create and personalize instruction and feedback
increases self-efficacy and engagement (Aldulaimi et al., 2021; Digtyar et al., 2023). Students
are also more likely to engage with digital content than they are with paper-based materials (Asif
CELL PHONES AND ACADEMIC ACHIEVEMENT 49
et al., 2023; Zogheib & Daniela, 2022). While students perceive mobile phones to have a positive
influence on their academic achievement, the results showed both positive and negative
outcomes depending on the primary use attributed to mobile phones (Asif et al., 2021; Sadiq et
al., 2022; Zogheib & Daniela, 2022). Reduced focus and attention, as well as social and
emotional issues, are among the challenges acknowledged by researchers when cell phone use is
permitted in educational settings (Gajdics & Jagodics, 2022; Lemov, 2022; Selwyn & Aagaard,
2021; Siyami et al., 2023). Although many educators and administrators call for total bans on cell
phones in the classroom, there is increasing evidence that balanced policies giving teachers
discretion to focus on deliberate integration of mobile devices may lead to responsible use while
also acknowledging social norms (Böttger & Zierer, 2024; Holley & Park, 2020; Horn, 2023;
Selwyn & Aagaard, 2021).
This research aimed to investigate whether there was a difference in standardized test
scores among secondary students in New Jersey high schools that have implemented restrictive
cell phone policies, which limit classroom use. While prior studies have investigated social,
behavioral, and motivational dimensions of cell phone use, few have measured its impact on
state-mandated assessments such as the New Jersey Graduation Proficiency Assessment (Böttger
& Zierer, 2024; Farzana et al., 2023; Gajdics & Jagodics, 2021; Selwyn & Aagaard, 2021). Two
theoretical frameworks provided a basis for understanding how cell phones influence cognitive
processes and how policy shapes the social environment (Bandura, 1977; Mayer, 1997).
Combining the theories of CTML and SLT guided the examination of the research questions and
hypotheses, providing a foundation for understanding the differences in academic achievement
when cell phones are removed. By examining whether differences in standardized test scores
CELL PHONES AND ACADEMIC ACHIEVEMENT 50
emerge between schools with restrictive and permissive policies, this study extends the
knowledge base in a way that can inform both policy decisions and instructional practices.
A review of the existing literature underscores the need for nuanced, context-specific
policies that acknowledge both the risks and benefits of student cell phone use. The findings also
highlight the role of instructional design and social modeling in shaping outcomes. Building on
these insights, the methodological approach employed to examine the relationship between
restrictive cell phone policies and standardized test performance among secondary students in
New Jersey is outlined next.
CELL PHONES AND ACADEMIC ACHIEVEMENT 51
Chapter 3: Methodology
The need to balance students' reported reliance on their cell phones for study with the
potential for distraction is an ongoing challenge (Chen et al., 2023; Smale et al., 2021). The
problem is that cell phone use may have detrimental effects on student learning, engagement,
social interactions, and academic achievement for secondary students in New Jersey high
schools. The purpose of this quantitative causal-comparative study was to examine the
differences in academic achievement on standardized tests among secondary students in New
Jersey based on cell phone use. A comparison of test scores, the dependent variable, from before
and after the enactment of a restrictive cell phone policy, the independent variable, was
conducted after the policy's implementation using statistical analysis. Using this design, the
following research questions were answered:
Research Question 1: What differences exist, if any, in state standardized exam mean
scores in ELA before and after restrictive cell phone policies are implemented among secondary
students in New Jersey?
Research Question 2: What differences exist, if any, in state standardized exam mean
scores in Mathematics before and after restrictive cell phone policies are implemented among
secondary students in New Jersey?
The research questions are aligned with the null and alternative hypotheses by assessing
the impact of cell phones on the mean scores in ELA and Mathematics.
H1ο: No statistically significant difference exists in ELA mean scores on state
standardized exams after banning cell phones in secondary schools in New Jersey.
H1a: A statistically significant difference exists in ELA mean scores on state standardized
exams after banning cell phones in secondary schools in New Jersey.
CELL PHONES AND ACADEMIC ACHIEVEMENT 52
H2ο: No statistically significant difference exists in Mathematics mean scores on state
standardized exams after banning cell phones in secondary schools in New Jersey.
H2a: A statistically significant difference exists in Mathematics mean scores on state
standardized exams after banning cell phones in secondary schools in New Jersey.
The research methodology, design, and rationale are aligned with the problem, purpose,
and research questions. They provided a foundation for the role of the researcher and outlined the
research procedures, data instruments, and processes for data cleaning and analysis. These
elements served as a guide for maintaining validity and reliability in the study. Ethical measures
are also addressed, ensuring that the research was conducted in a responsible manner.
Research Methodology, Design, and Rationale
Quantitative methods enable researchers to test hypotheses, measure variables, and
conduct statistical analyses (Creswell & Creswell, 2023). This method produces results that can
be generalized to a larger population (Creswell & Creswell, 2023). A causal-comparative design
should be used when participants cannot be randomly assigned to separate groups or when a
condition on which two groups will be compared already exists (Campbell & Stanley, 1966). A
pre-test and post-test examination of data allows researchers to evaluate the differences before
and after an intervention, measuring the change in the dependent variable (Campbell & Stanley,
1966; Creswell & Creswell, 2023).
Methodology
The purpose of this quantitative causal-comparative study was to examine the differences
in academic achievement on standardized tests among secondary students in New Jersey based
on cell phone use. Quantitative methods allow researchers to measure and analyze data using
statistical techniques that provide objective and generalizable results (Johnson & Christensen,
CELL PHONES AND ACADEMIC ACHIEVEMENT 53
2025). A quantitative methodology was employed to investigate the impact of cell phone use on
differences in academic achievement on state-standardized exams among secondary students in
New Jersey. Test scores from before the enactment of restrictive cell phone policies to after the
policy implementation were compared using statistical analysis.
Design
A quantitative causal-comparative design was employed to compare mean scores before
and after implementing a restrictive cell phone policy. The causal-comparative design enables the
researcher to investigate whether the independent variable influences the dependent variable
(Campbell & Stanley, 1966). In this case, the cell phone policy was the independent variable, and
its effect on standardized test scores, the dependent variable, was measured. This design has no
time or resource constraints because the data has already been collected and reported by the state.
A pretest-posttest model is an effective method for analyzing the difference between before and
after the implementation of an intervention (Mertens, 2023). Schools selected for participation
had the cell phone policies in effect for at least one full school year. Because the data spanned
more than one school year, the samples were not equal or related in nature. Although this study
utilized independent rather than dependent groups, a quasi-experimental design would not be
appropriate because random assignment and manipulation of the independent variable was not
possible (Creswell & Creswell, 2023).
To compare the differences between two independent groups, an independent t-test can be
applied to data that is normally distributed. If the data is not normally distributed, the non-
parametric equivalent, the Mann–Whitney U-test, is used (Seigel, 1956). In this study, each
research question can be addressed by conducting the appropriate test to determine whether
CELL PHONES AND ACADEMIC ACHIEVEMENT 54
statistically significant differences exist in the data before and after implementation of a cell
phone policy.
Role of the Researcher
To minimize their influence on data, quantitative researchers must strive to be objective
and reduce their biases (Johnson & Christensen, 2025). Researchers need to collect and analyze
data in a way that provides transparency in their methods and when presenting their findings
(Salkind, 2017). No personal or professional contact was made with any of the participants in
this study. Publicly available archival data prepared for purposes other than this study were used.
Permission was not required, and no incentives were provided for its use. Efforts were made to
control variables that might undermine the reliability and validity of the study, such as using data
that have already been anonymized and contain no identifying information (Department of
Education, 2024). The reliability and validity of the data, as well as the analysis and
interpretation, were maintained to ensure the results were objective and generalizable (Johnson
& Christensen, 2025).
Research Procedures
Causal comparative designs in quantitative research are employed to compare means
before and after the implementation of an intervention (Campbell & Stanley, 1966). The causal-
comparative design enables researchers to investigate whether the independent variable
influences the dependent variable (Creswell & Creswell, 2023). Since the cell phone policy has
already been implemented, a pretest-posttest model is an effective method for analyzing the
difference in scores before and after the intervention.
CELL PHONES AND ACADEMIC ACHIEVEMENT 55
Population and Sample Selection
The target population for the study included multiple public secondary schools in New
Jersey. Nationwide, 77% of public schools have restrictions on the non-academic use of cell
phones or smartphones during the school day (National Center for Education Statistics, 2022).
Therefore, of the 612 public high schools in the state, about 471 schools are estimated to have
cell phone policies against classroom use or employ signal-blocking pouches. A sample size
calculator was used to generate the appropriate number of participants for a before-and-after
study (Kohn, 2024). Standard estimates for the probability of rejecting the null hypothesis, the
probability of failing to reject the null hypothesis, effect size, and standard deviation were used
to determine the sample size (Salkind, 2017). Using 20% for power and probability of a Type II
error, 0.5 for effect size, and a standard deviation of 1.0, a sample size of 19 participants was
calculated. Consideration was given to representing geographic and socioeconomic diversity in
the selection process. To qualify for inclusion, high schools in New Jersey that have
implemented a policy to restrict the use of cell phones during the school day were deliberately
selected using a purposive sampling method. Purposive sampling is a non-probability sampling
strategy used to select participants based on whether their characteristics align with the study’s
objectives (Eshenaur Spolarich, 2023). Schools with no formal cell phone policy or minimal
restrictions on cell phone use during the school day were excluded from participation.
Recruitment
Site permission was not required to conduct this study, as all data and information have
been collected and posted publicly online. To determine which high schools meet the selection
criteria, the New Jersey Principals and Supervisors Association (NJPSA) email group was used
to send a recruitment letter to all members via email (see Appendix A). The recruitment letter
CELL PHONES AND ACADEMIC ACHIEVEMENT 56
contained a link to an electronic Inclusion Criteria Questionnaire (see Appendix B). The
questionnaire requested the name of the school district and school for matching purposes, as well
as whether the school has a policy that restricts cell phone use during classroom instruction. If a
restrictive policy existed, respondents were asked which academic school year the policy was
implemented. When an insufficient number of responses were received, follow-up phone calls
were placed to the high school’s administrative offices, and the inclusion questionnaire was
conducted verbally. Informed consent was not required since the data is not traceable back to any
individual, and a waiver was not requested from the Institutional Review Board (IRB).
Archival Data
Archival data from students who completed state standardized exams in the 11th grade
were used in the study. Standardized test scores are posted annually on the NJDOE's website
(Department of Education, 2024). This information is available to anyone with internet access
and can be viewed or downloaded in MS Excel format. Site approval is not required for
deidentified information obtained from the NJDOE for research purposes or when used to make
decisions informed by data (New Jersey Statewide Data System, 2023).
Data Instrument
The appropriate selection of a data collection instrument is imperative to the validity and
reliability of the study's findings (Johnson & Christensen, 2025). Archival data have already been
collected for other purposes, saving time in the data collection process and providing a useful
means for examining trends over time (McGregor, 2018). In this study, archival data from
standardized tests were used to determine whether limiting cell phone use is associated with
changes in academic achievement.
CELL PHONES AND ACADEMIC ACHIEVEMENT 57
Archival Data
Academic performance was measured by student achievement on state standardized tests
administered to all 11th grade students. The test measures the extent to which the student is
considered ready for graduation in ELA and Mathematics. This systematic collection promotes
the reliability and validity of the findings (Wagner & Gillespie, 2019). Standardized test scores
are posted annually on the NJDOE's website, where anyone with internet access can view and
download the information (New Jersey Department of Education, 2024). Once downloaded, the
quantitative data obtained from the website can then be examined through statistical analysis.
Standardized test scores provide information for comparing academic achievement before
and after the cell phone policy intervention, which is necessary for examining the research
questions. Data analysis involves examining test scores at different points in time, which can
reveal any changes caused by an intervention (McGregor, 2018). The quantitative data from the
exams were used to assess whether the cell phone policies in various high schools created a
measurable change in students’ performance in ELA and Mathematics before and after the
policy’s implementation. This process ensures that the findings are based on objective evidence
and that valid and reliable conclusions are reported (Wagner & Gillespie, 2019).
Data Collection
Test scores from students who completed the state standardized tests for ELA and
Mathematics in the 11th grade to assess their readiness for graduation are posted annually on the
NJDOE Statewide Assessment Report website for all schools that administered exams (New
Jersey Department of Education, 2024). Files are sorted by county and can be viewed or
downloaded in MS Excel format by anyone with internet access. Statistical information about
mean scores is available for each high school. Data from years before the restrictions on cell
CELL PHONES AND ACADEMIC ACHIEVEMENT 58
phones can be compared to data from years after the implementation of these restrictions to
determine any differences. Data files also contain demographic information, such as the number
of students who are male or female, economically disadvantaged, English language learners,
Hispanic, African American, or those who require testing accommodations. This dataset was
collected by the state as reported by the school and verified by each student on the day the exam
was administered, but it has not been used for analysis.
Data Preparation
To prepare the data for analysis, the Excel files were filtered to narrow down the results
and extract information only for the participating schools. All other schools and districts were
removed from the data. Columns of data that contain information about demographic categories
not being studied were also removed. Only data relevant to this research were included in the file
used for statistical analysis. Since the dataset was collected by and used for statistical purposes
by the state, several cleaning techniques have already been applied to the dataset to ensure
standardization, deal with missing data, and handle outliers (New Jersey Department of
Education, 2023b). The use of various Excel features facilitated the preparation and cleaning of
data. Spell check, find and replace, data validation, and special functions, such as trim, were used
to remove extra spaces. Numerical fields containing non-numeric data were edited to ensure
calculations could be performed. Data missing from any of the schools, and that could not be
obtained, resulted in the school’s exclusion from the study. For Research Questions 1 and 2, rows
of data with missing student scores for ELA or Math in either the pre-test or the post-test year
were eliminated before data analysis.
CELL PHONES AND ACADEMIC ACHIEVEMENT 59
Data Analysis
Statistical analysis was conducted using Microsoft Excel due to its familiarity, ease of
use, and extensive support. Assumption testing was conducted to determine whether the dataset
met the necessary conditions for statistical testing (Johnson & Christensen, 2025). Normality was
assessed using the Shapiro-Wilk test, with a p-value of 0.05. Excel is practical for initial data
cleaning, basic descriptive statistics, visualizations, and advanced features necessary for more in-
depth statistical analyses (Ford & Scandura, 2023). The data were thoroughly cleaned to identify
and rectify any missing values, duplicates, or inconsistencies.
After data cleaning and assumption testing, descriptive statistics were calculated to
summarize the central tendencies and dispersion of the data, providing a basic understanding of
the data. Next, inferential statistics, such as independent t-tests or the nonparametric Mann-
Whitney U test, were conducted to determine if there were significant relationships in the data
before and after implementing the cell phone policy. Then, a Levene's test was performed to
check for homogeneity of variances, followed by a Bonferroni correction for post hoc testing to
reduce the chance of obtaining a false positive result, thereby minimizing the risk of a Type I
error.
A 95% confidence interval was used to estimate the range in which the true mean is likely
to fall (Creswell & Creswell, 2023). To determine whether there is a statistically significant
difference in mean scores, a p-value of 0.05 was used. For example, if the p-value for the
difference in mean scores in ELA is 0.03, there is a significant difference; a p-value greater than
0.05 suggests no statistically significant difference (McGregor, 2018).
CELL PHONES AND ACADEMIC ACHIEVEMENT 60
Reliability and Validity
The validity and reliability of a study's findings depend on the selection of an appropriate
data collection instrument (Johnson & Christensen, 2025). Validity is how well the assessment
measures what it is designed to measure, and reliability refers to its ability to deliver consistent
results (Creswell & Creswell, 2023). The state reports that the assessment is valid, as it is aligned
with the New Jersey Core Curriculum Content Standards (NJCCCS), ensuring that the results
accurately reflect student learning and are reliable due to rigorous field testing and
standardization of testing administration across all locations (New Jersey Department of
Education, 2023c).
Internal validity refers to whether any observed differences result from manipulating the
independent variable and not from other outside factors (Salkind, 2017). Potential threats to
internal validity could be inconsistencies with the testing instrument or administration
procedures. This was addressed by ensuring that all participants were selected from test
administrations where no irregularities were reported. Whether the study's findings can be
generalized to people or settings outside of the study is a key consideration in evaluating external
validity (Salkind, 2017).
To improve the generalizability of this study, an effort was made to select participants
from diverse demographics, including location, setting, and socioeconomic status. Ensuring that
research findings are based on evidence rather than the personal views or biases of the researcher
is the basis of objectivity (Johnson & Christensen, 2025). To maximize objectivity and reduce
the influence of personal biases and other external factors, the data did not contain individual
student scores. Pseudonyms were applied to the participating schools, all participants were
treated equally, valid and reliable data were used, and findings were reported transparently.
CELL PHONES AND ACADEMIC ACHIEVEMENT 61
Ethical Procedures
Research involving human subjects must adhere to strict ethical guidelines to ensure that
participants' rights, confidentiality, and privacy are protected (National Commission for the
Protection of Human Subjects of Biomedical and Behavioral Research, 1979). Archival data
made publicly available and anonymized, so that information cannot be traced back to
individuals, does not require informed consent (American Psychological Association, 2020).
Participation by principals interested in responding to the online survey was strictly voluntary,
and they had the right to withdraw from the study at any time (American Psychological
Association, 2020). The online inclusion questionnaire did not collect personally identifiable
information; data was coded and stored to ensure confidentiality. The goal of research is to
design strict guidelines that protect participants from harm and ensure the research contributes
positively to the field of education (National Commission for the Protection of Human Subjects
of Biomedical and Behavioral Research, 1979).
To ensure equity, an impartial selection process was implemented (National Commission
for the Protection of Human Subjects of Biomedical and Behavioral Research, 1979).
Purposeful, stratified sampling ensured adequate representation of different regions throughout
the state. In addition, care was taken to ensure participants were not inconvenienced and that the
potential benefits were available. Potential participants received a recruitment letter with a link to
the inclusion questionnaire. Participation was voluntary, and participants could have withdrawn
at any time. There were no workplace considerations or conflicts of interest present. Data was
downloaded after approval was received from the American College of Education’s Institutional
Review Board and was stored in a secure cloud drive that was password-protected with multi-
CELL PHONES AND ACADEMIC ACHIEVEMENT 62
factor authentication (see Appendix C). After three years, the data will be permanently deleted
from the cloud drive.
Chapter Summary
Cell phones are an integral part of students’ lives, yet there is a growing movement in
education that seeks to restrict their use during classroom instruction. The issue is complex
because cell phones offer benefits such as access to educational learning apps and multimedia
resources, but also pose challenges like distractions and mental health issues. An examination
was conducted to determine whether the implementation of stringent cell phone policies makes a
difference in the standardized test scores of high school students in New Jersey. A quantitative
causal-comparative design examined standardized test scores obtained from the NJDOE website.
An inclusion questionnaire was used to identify and select schools that have implemented a
policy restricting the use of cell phones during classroom instruction. The dataset was
downloaded, cleaned, and analyzed using MS Excel. Descriptive and inferential statistics, along
with post hoc testing, were conducted to compare mean scores before and after the
implementation of the policy. Reliability and validity issues have been considered, and efforts
have been made to mitigate threats to objectivity and internal and external validity. These efforts
include the use of data from strictly controlled test administration procedures, the selection of
participants from diverse demographics, and the coding of participants to ensure anonymity.
Ethical guidelines were observed by ensuring the rights of the participants and protecting them
from harm while contributing positively to the field of education.
CELL PHONES AND ACADEMIC ACHIEVEMENT 63
Chapter 4: Research Findings and Data Analysis Results
The widespread use of mobile phones in the classroom has raised concerns among
educators, parents, and administrators due to their potential to distract students and hinder
educational performance (Siyami et al., 2023). Despite the evidence that mobile devices can
support learning when intentionally integrated into pedagogy in ways that enhance usefulness
and student engagement, many schools have implemented restrictive phone policies aimed at
improving focus and academic outcomes (Selwyn & Aagaard, 2021; Zogheib & Daniela, 2022).
Due to the limited evidence available, the debate surrounding the effectiveness of such policies
remains (Grigic Magnusson et al., 2023). To address this, a quantitative causal-comparative
design using archival data was conducted to examine differences in standardized test scores in
New Jersey public high schools before and after policy implementation.
The problem is that cell phone use may have detrimental effects on student learning,
engagement, social interactions, and academic achievement for secondary students in New
Jersey high schools. The purpose of this quantitative causal-comparative study is to examine the
differences in achievement on standardized tests among secondary students in New Jersey based
on cell phone use. According to Selwyn and Aagaard (2021), many school districts have
implemented policies restricting mobile phone use during instructional time, but few have
examined whether such restrictions result in a measurable difference in academic achievement.
As schools continue to adopt policies to address student distractions and improve focus, leaders
require data-driven insights to determine whether restricting phone use is an effective strategy
(Selwyn & Aagaard, 2021).
In the following sections, the findings and analysis of the collected data will be presented.
An overview of the data collection process, including participant response rates and any
CELL PHONES AND ACADEMIC ACHIEVEMENT 64
deviations from the proposed plan, is discussed. The data cleaning procedures used to prepare the
dataset for analysis are described, followed by the results of the statistical tests that were
conducted to answer the research questions. Reliability and validity procedures are reported to
support the credibility of the findings. A summary of the chapter will provide brief answers to the
research questions and transition to the last chapter.
Data Collection
The data were collected from 18 public high schools in New Jersey, following
Institutional Review Board (IRB) approval, and were collected between March and April 2025,
in accordance with the plan detailed in Chapter 3. Of the approximately 600 public high schools
in the state, only 65 eventually provided responses through an electronic inclusion questionnaire,
which was distributed to identify schools with restrictive mobile phone policies and the school
year in which these policies were implemented. All data were collected virtually, and each school
completed the inclusion questionnaire once. Once eligibility was determined, archival test score
data were retrieved from the New Jersey Department of Education (NJDOE) website for the
applicable school years. No personal or student-level identifiable information was collected.
A recruitment letter with a link to the inclusion questionnaire was distributed through a
Leadership Connect bulletin board hosted by the New Jersey Principals and Supervisors
Association (NJPSA), which has approximately 6,700 members. The initial post resulted in 21
responses, and due to the low response rate, additional measures were needed to expand the
results. Additional efforts included sending direct emails to principals, posting on professional
social media platforms, and making phone calls to school and district offices. These efforts
resulted in 65 responses.
CELL PHONES AND ACADEMIC ACHIEVEMENT 65
Schools needed to meet two criteria for eligibility, including confirmation of a phone-free
instruction policy and availability of standardized test scores for English Language Arts (ELA)
and Mathematics (Math) on the New Jersey Graduation Proficiency Assessment (NJGPA) for
both the school year before and the school year of the policy’s implementation. Many schools
were excluded due to a lack of restrictive policies, test scores were unavailable for one or both
years because of the COVID-19 pandemic, or the NJDOE suppressed results due to small
subgroup sizes. Of the 65 complete responses, only 18 schools met the criteria for inclusion in
the final sample.
Deviation From Data Collection Plan and Sample Size
Data were collected by sharing a recruitment letter explaining the research, an inclusion
questionnaire, and archival standardized test scores as initially proposed. While there were no
major deviations from the IRB-approved data collection plan, a challenge arose from low
response rates, necessitating the use of expanded recruitment methods. Additional recruitment
efforts were conducted in accordance with ethical and IRB guidelines, including social media
posts, personalized emails, and telephone follow-ups.
The initial sample size was anticipated to be at least 19 schools based on the power
analysis included in the proposal. Only 18 schools met the eligibility criteria out of 65 responses,
slightly below the initial target. Due to the time constraints of completing the study, further
outreach was discontinued. This minor variation was due to exclusions based on incomplete
score reporting, data suppression, or lack of a qualifying policy. Despite this, the final sample
size of 18 provided sufficient data for statistical analysis.
CELL PHONES AND ACADEMIC ACHIEVEMENT 66
Baseline Descriptive and Demographic Characteristics of the Sample
Demographic information was collected and recorded for the 18 schools in the final
sample. The inclusion criteria incorporated New Jersey public high schools that implemented a
restrictive cell phone policy during instructional time in school years for which data were
available. Schools represented the three geographic regions of North, Central, and South Jersey,
including urban, suburban, and rural areas. A variety of socioeconomic populations are also
included in the sample (see Table 1).
Table 1
Baseline Characteristics of Participating Schools
School Code
Geographic Region
School Setting
SES Status
School 1
Central
Suburban
Mid
School 2
South
Suburban
High
School 3
South
Suburban
Mid
School 4
North
Suburban
Mid
School 5
Central
Suburban
High
School 6
North
Urban
Low
School 7
South
Suburban
Mid
School 8
Central
Suburban
High
School 9
Central
Suburban
High
School 10
Central
Urban
Low
School 11
South
Rural
Mid
School 12
South
Rural
Low
School 13
South
Suburban
Low
School 14
Central
Urban
Low
School 15
Central
Suburban
Mid
School 16
Central
Suburban
Mid
School 17
Central
Suburban
Mid
School 18
South
Suburban
Low
Note. Geographic region was determined based on New Jersey’s general regional divisions:
North, Central, and South. School setting designations are based on district typology: urban,
suburban, and rural (New Jersey Department of Education, 2024c). Socioeconomic status (SES)
CELL PHONES AND ACADEMIC ACHIEVEMENT 67
was estimated by the percentage of students eligible for free or reduced-price lunch (FRPL),
where >60% FRPL was categorized as low.
Data Cleaning
Once data collection was completed, data cleaning procedures were conducted to ensure
accuracy, consistency, and readiness for analysis. First, responses to the inclusion questionnaire
were reviewed to determine eligibility. Only schools that responded “yes” to implementing a
mobile phone policy restricting use during instructional time and had test scores available for all
four components of the NJGPA were included. Many schools were excluded due to missing or
suppressed test scores, reporting non-restrictive policies, or implementing policies during school
years when the pre- or post-test year coincided with non-administration of the exams, resulting
from the COVID-19 pandemic. This process resulted in a total of 18 schools that were retained
out of the original 65 respondents.
Archival test scores were downloaded from the NJDOE website in Excel. CSV file
format. Test scores for the year before and during the policy implementation for ELA and Math
were transferred to the spreadsheet created from the inclusion questionnaire. Checks for
consistency were performed to ensure schools had complete test scores for both academic years
and test subjects. Data were organized and sorted by school, year, and subject. Any schools that
did not meet the inclusion criteria or were missing any required test scores were flagged and
filtered out.
The cleaned dataset was reviewed to confirm that appropriate formatting was applied for
statistical analysis. Test score columns were checked to verify that scores were formatted as
numbers and there were no missing values. Categorical and demographic information, including
geographic region, school setting, and socioeconomic status, was also reviewed to ensure
CELL PHONES AND ACADEMIC ACHIEVEMENT 68
consistency, completeness, and accuracy. No further corrections or exclusions were made during
this process. A reliable foundation for statistical analysis, where descriptive statistics and
independent t-tests could be used to examine differences in test scores before and after the
mobile phone policy implementation, was made possible using the final Excel workbook.
Data Analysis and Results
Descriptive statistics and Mann-Whitney U tests were conducted to determine whether a
statistically significant difference existed in standardized test scores before and after the
implementation of restrictive cell phone policies (see Table 2). Scores on the NJGPA exam in
ELA and Math were collected for 18 New Jersey public high schools for the year before and
after the policy implementation. The data were considered independent, as they represented
different cohorts of 11th grade students from one year to the next. They were analyzed using
Mann-Whitney U tests with an alpha level of 0.05. Standard procedures, involving ranking the
combined pre-policy and post-policy scores and comparing the distributions between groups,
were followed (Field, 2024).
Table 2
Descriptive Statistics for NJGPA Scores Before and After Policy Implementation
Test Area
Pre-policy
Post-policy
Mean
SD
Skewness
Mean
SD
Skewness
ELA
741.0
22.9
-0.099
757.8
15.14
-0.203
Math
729.6
14.5
0.492
726.0
13.5
0.355
Note. NJGPA = New Jersey Graduation Proficiency Assessment. SD = standard deviation.
Assumption Testing
The primary assumptions for Mann-Whitney U tests include the independence of
observations and ordinal or continuous data without requiring normal distribution (Field, 2024).
CELL PHONES AND ACADEMIC ACHIEVEMENT 69
Students differed from one year to the next, meeting the criteria for independence by design. To
preliminarily assess normality, skewness statistics were calculated for each test score category
(see Table 2). Skewness values fell within the ±1.0 range, with a low of -0.203 for the posttest
ELA and a high of 0.492 for the pretest Math, which is considered acceptable for small samples
when assessing normality (Field, 2024). Histograms of pre-policy and post-policy NJGPA scores
were also reviewed to examine the distributions of scores. Although visual inspections suggested
approximately symmetric distributions, the small sample size (n = 18) limited the precision of
these assessments. Given the sample size and potential inaccuracy of the normality assessment,
the nonparametric Mann-Whitney U test was considered the most appropriate analysis method.
This decision ensured that results were based on conservative assumptions, reducing the
potential for Type I and Type II errors that can occur when parametric assumptions are applied in
small datasets (Field, 2024).
Results by Research Questions
Mann-Whitney U tests were conducted to evaluate differences in corresponding groups to
answer each research question. All tests were performed using a two-tailed alpha level of 0.05. A
Bonferroni correction was applied to control for Type I error due to multiple comparisons,
adjusting the significance threshold to 0.025. The two research questions that guided the
statistical analysis were:
Research Question 1: What differences exist, if any, in state standardized exam mean
scores in ELA before and after restrictive cell phone policies are implemented among secondary
students in New Jersey?
CELL PHONES AND ACADEMIC ACHIEVEMENT 70
Research Question 2: What differences exist, if any, in state standardized exam mean
scores in Mathematics before and after restrictive cell phone policies are implemented among
secondary students in New Jersey?
The analysis revealed that ELA scores improved following the implementation of
restrictive cell phone policies. These results were supported by the Mann-Whitney U test, which
showed a statistical significance at the conventional alpha level of 0.05. When a Bonferroni
correction was applied to control for a Type I error, it did not meet the stricter significance
threshold at an alpha level of 0.025 (see Table 3). As a result, the null hypothesis could not be
rejected. This limited the ability to conclusively determine if the policy made a difference in
ELA scores. However, the direction of the change and a moderate z-score suggest a potentially
meaningful relationship worthy of further investigation with larger sample sizes in the future.
The Mann-Whitney U test revealed that there was no statistically significant difference in
Math scores between the pre-policy and post-policy cohorts. These results suggest that
implementing a restrictive cell phone policy did not have a measurable difference in Math scores
as measured by the NJGPA (see Table 3). The absence of a measurable difference may indicate
that other variables had a greater influence on Math performance during the study period. The
null hypothesis could not be rejected. It is also possible that cell phone use has a greater impact
on language and literacy tasks than on quantitative reasoning.
This analysis did not use confidence intervals since they are generally associated with
parametric procedures that assume normality. Exact values for the Mann-Whitney U statistic,
standardized z-scores, and two-tailed p-values were reported using a 95% confidence level. This
approach maintained statistical precision and rigor while aligning with the assumptions and
framework of nonparametric interpretation.
CELL PHONES AND ACADEMIC ACHIEVEMENT 71
Table 3
Mann-Whitney U Results
Test Area
U
z
p-value
ELA
93.5
-2.17
.030
Math
141
-0.66
.506
Note. U = Mann-Whitney U statistic; p = two-tailed significance level.
Post-Hoc Adjustment
All statistics were conducted using a two-tailed alpha level of 0.05. A Bonferroni
correction was applied to reduce the likelihood of a Type I error due to multiple comparisons.
The adjusted alpha level for two comparisons was set at 0.025. Only p-values below this
corrected threshold were considered statistically significant. The Bonferroni method was selected
due to its conservativeness and appropriateness for studies involving smaller sample sizes. Using
Bonferroni corrections reduced the potential for Type I errors but also increased the risk of Type
II errors (Field, 2024). There is the possibility of masking true effects in studies with smaller
sample sizes or modest effects. This is the case with ELA scores, which were not statistically
significant after the Bonferroni adjustment.
Reliability and Validity
Reliability and validity ensure the accuracy and credibility of quantitative research
findings (Creswell & Creswell, 2023). Internal validity refers to the degree to which a study can
confidently establish a causal relationship between variables (Mellinger & Hanson, 2021).
External validity refers to the extent to which a study’s findings can be generalized to other
settings, populations, or time periods (Field, 2024). Internal and external validity were addressed
through a rigorous design, selection of data sources, and attention to potential threats. The
CELL PHONES AND ACADEMIC ACHIEVEMENT 72
measurement instrument's reliability, or consistency, and its ability to produce comparable results
through repeated applications were addressed using state-administered standardized tests.
Internal Validity
This causal-comparative study supported internal validity by clearly defined inclusion
criteria, consistent data collection procedures, and standardized test scores obtained from the
NJDOE. State-administered standardized tests such as the NJGPA are developed and
administered using rigorous psychometric procedures to ensure reliability (American
Educational Research Association et al., 2014). Random assignment was not feasible due to the
archival nature of the data; however, the use of schools with diverse geographic and
socioeconomic settings, along with varied policy implementation periods, helped to reduce
selection bias. Other issues may have influenced test scores, such as disruptions to learning and
testing environments due to the COVID-19 pandemic, district-issued devices serving as a new
source of distraction, differences in teacher quality across schools or academic years, and
variations in student learning differences. These factors may have affected student achievement,
limited the ability to isolate the effect of restricting mobile phone use, and influenced the
observed differences in test scores, presenting limitations to internal validity.
External Validity
The extent to which a study’s findings can be generalized beyond the sample is known as
external validity (Field, 2024). This study included 18 diverse public high schools with
restrictive cell phone policies across North, Central, and South Jersey. Sample schools
encompassed a range of settings, including rural, suburban, and urban areas, with diverse socio-
economic backgrounds. Although the sample reflects the broad geographic and demographic
variation within New Jersey, generalizability to other states or regions should be approached with
CELL PHONES AND ACADEMIC ACHIEVEMENT 73
caution due to potential differences in policy enforcement, testing procedures, and population
characteristics. The study’s generalizability may be limited by self-selection bias due to reliance
on schools that voluntarily responded to the inclusion questionnaire.
Reliability
The ability to repeat the measurement and receive consistent results over time is a key
factor in determining the reliability of an instrument (Creswell & Creswell, 2023). The reliability
of the data used in this study was supported by the use of standardized tests administered by the
NJDOE and developed using psychometric procedures to ensure fairness and consistency. A
search of publicly available documentation revealed that reliability coefficients for the NJGPA
were not published at the time of this study (New Jersey Department of Education, 2024b;
Pearson, 2024). Systematic data entry and cleaning procedures were conducted to ensure the
accuracy, consistency, and completeness of the information. The use of publicly available and
verified data further contributed to the study's reliability.
Chapter Summary
Cell phones in schools are a controversial topic among students, parents, teachers, and
administrators (Tandon et al., 2020). Although they have the potential to be a learning tool, they
can also be a nuisance and a distraction (Zogheib & Daniela, 2022). An analysis of NJGPA
scores from 18 New Jersey public high schools that have adopted policies that limit the use of
cellular devices during academic instruction was conducted to evaluate the difference in
academic performance. Results from the Mann-Whitney U tests revealed that ELA scores
showed a statistically significant improvement following policy implementation at an alpha level
of 0.05. After a Bonferroni adjustment was applied, the findings showed that ELA scores did not
CELL PHONES AND ACADEMIC ACHIEVEMENT 74
reach significance. Math scores showed no statistically significant difference between the pre-
policy and post-policy results, indicating no measurable effect on mathematics achievement.
The findings suggest that restrictions on mobile devices during classroom instruction may
have a greater impact on literacy outcomes than on mathematical performance. Due to the small
sample size and conservative post-hoc adjustment, these results should be interpreted carefully.
The outcomes provide a foundation for practical discussion and serve as the basis for a more in-
depth analysis of the results, an examination of the limitations, and recommendations for future
research, which will be discussed in the final chapter.
CELL PHONES AND ACADEMIC ACHIEVEMENT 75
Chapter 5: Discussion and Conclusions
Schools are competing with technology for students’ attention, and to win the fight, many
have implemented restrictions on cell phone use during academic instruction. The problem is that
cell phone use may have detrimental effects on student learning, engagement, social interactions,
and academic achievement for secondary students in New Jersey high schools. The purpose of
this quantitative causal-comparative study was to examine the differences in academic
achievement on standardized tests among secondary students in New Jersey based on cell phone
use. Test scores from before and after the implementation of policies limiting student phone use
during instructional time were compared to measure any differences.
Analysis revealed that ELA mean scores improved significantly following the
implementation of a restrictive policy. However, there was no significant improvement in Math
mean scores. After a post-hoc correction, ELA scores approached significance, while differences
in Math scores were not statistically significant. These results suggest a potential relationship
between reduced phone access and literacy achievement. Although the evidence was not strong
enough to indicate a relationship between restricting phone use and Math scores, it warrants
further study to draw firm conclusions.
Mayer’s (2021) cognitive theory of multimedia learning and Bandura’s (1977) social
learning theory are employed to interpret the findings regarding the relationship between
distraction, attention, and modeled behavior and academic performance. Conclusions are drawn
based on the statistical findings and their alignment with the theoretical framework. The study’s
limitations are considered in light of several factors and outline the implications for educational
policymakers and future research.
CELL PHONES AND ACADEMIC ACHIEVEMENT 76
In the following sections, the data are interpreted, and their relevance to prior research is
examined. For those interested in the role of mobile technology in learning, the next steps for
educators and researchers are identified. The sections are organized around two research
questions.
Findings, Interpretations, and Conclusions
The study's results are interpreted through the existing literature and theoretical
frameworks. From this, conclusions are drawn that remain within the scope of the data. Analysis
reflects how the findings support, extend, or differ from prior research and considers the
implications for academic performance and educational policy. The analysis is organized into
four parts: a comparison with previous studies, an interpretation through theoretical frameworks,
an examination of study limitations, and final conclusions.
The results of this study contribute to current discussions of the academic impact of
mobile device use in secondary schools and are discussed in detail in Chapter 4. Initial findings
indicated that ELA mean scores improved significantly, with no significant difference in Math
scores following the implementation of restrictive cell phone policies. Research Question 1
asked whether there were differences in ELA mean scores before and after the implementation of
restrictive cell phone policies. Schools with restrictions on cell phones experienced a significant
increase in ELA scores prior to a Bonferroni correction. After the correction was applied to
control for Type I errors, the results did not meet the threshold for significance. The result was
that the null hypothesis for Research Question 1 could not be rejected. The direction and scale of
the improvement suggested by these findings indicate a potential relationship for future research
to explore.
CELL PHONES AND ACADEMIC ACHIEVEMENT 77
Math mean scores were examined to determine whether a difference existed before and
after restrictive policies were implemented in Research Question 2. Math scores showed no
improvement, and the results were not statistically significant even without a correction. As a
result, the null hypothesis for Research Question 2 could also not be rejected. Although the
results were not statistically significant, the positive trend in test scores suggests that restricting
cell phone use could improve academic outcomes, particularly in literacy. Due to the limitations
of the study, the results should be interpreted with caution and considered exploratory.
These findings confirm the adverse effects of cell phone use during instructional time
reported in existing literature. This study found that ELA scores improved after the
implementation of restrictive cell phone policies. However, after the Bonferroni correction was
applied ELA scores did not meet the stricter threshold for significance. Math scores remained
consistent despite the policy implementation.
The view that cell phone use served as a cognitive and behavioral distraction in a learning
environment is supported by several studies (Ochs & Sauer, 2022; Selwyn & Aagaard, 2021).
Frequent phone use was associated with a decline in attention and academic performance
(Siyami et al., 2023). The improvement in ELA scores is consistent with research suggesting that
reducing distractions improves results in reading-based tasks (Rettie et al., 2025). The lack of
significant findings in Math is reflective of previous studies that found phone restriction had
mixed or minimal effects on quantitative achievement (Gajdics & Jagodics, 2022; Sadiq et al.,
2022). The results of this study confirm the impact of cell phone policies reported in some prior
research findings and extend the knowledge base by distinguishing differences by subject area.
A central principle of Mayer’s (2021) cognitive theory of multimedia learning is that
humans have a limited capacity for working memory. When the channels for learning receive
CELL PHONES AND ACADEMIC ACHIEVEMENT 78
information from multiple sources, primarily when sustained attention is required, cognitive
overload occurs. When mobile phones are removed from the learning environment, students can
dedicate more cognitive resources to instructional content, resulting in greater academic
achievement, particularly in verbal processing tasks required in ELA classrooms.
Learning through observation and imitation, key elements in Bandura’s (1977) social
learning theory, provided a meaningful lens for interpreting the study's findings. School policies
that restrict the use of phones provide a foundation for modeling behavioral expectations. When
students observe peers not using their phones and teachers and administrators consistently
enforce the policy, they are more likely to comply with and emulate this behavior, thereby
remaining engaged in learning. Reinforcement of behavioral norms may have contributed to
increased focus and self-regulation, which, in turn, may help explain the improved academic
performance.
The study's results address the potential effects of restricting cell phone use during
instructional time on academic outcomes. ELA scores showed improvement in schools that had
restrictive cell phone policies. Scores for ELA were initially significant; however, after a post-
hoc correction, neither ELA nor Math showed enough improvement to be statistically significant.
The study did not provide enough evidence to reject the null hypothesis for either research
question. The conclusions were made cautiously and remained grounded in the data. The
reported findings were consistent with the limitations of a non-experimental design.
The most important conclusion is that restrictive cell phone policies may contribute to
moderate academic improvement, especially in assessments with reading-based tasks. These
results align with theories that suggest reducing digital distractions enhances cognitive focus and
engagement (Rettie et al., 2025). However, the lack of statistically significant results and the
CELL PHONES AND ACADEMIC ACHIEVEMENT 79
inability to control for external variables make these findings unique to the analyzed population.
Findings should not be generalized beyond the context of the schools and testing years studied.
No claims of causation were made, and no conclusions were drawn beyond what the data
supported. Individual opinions and assumptions about technology use in schools were
intentionally excluded from analysis. The study contributes to the growing body of research on
mobile device use in public schools and its impact on academic achievement. Additional research
should be conducted using experimental or longitudinal methods to validate and expand upon
these results.
Limitations
All research designs involve limitations that may affect the interpretation, validity, and
generalizability of the findings (Creswell & Creswell, 2023). Several factors may have
influenced the internal and external validity of this study. One element that may have influenced
the interpretations was the use of a causal-comparative design. Another factor was the
differences in student characteristics and demographics. In the years examined by the study,
students may have still been experiencing the effects of remote learning due to the COVID-19
pandemic. Finally, test scores may not be an effective measure of academic achievement.
Causal-comparative research is a non-experimental quantitative design used to analyze
differences and explore possible cause-and-effect relationships between naturally occurring
groups without manipulating the independent variable (Creswell & Creswell, 2023). Differences
in standardized test scores were analyzed for the same school before and after the
implementation of restrictive cell phone policies. Without a control group or random assignment,
it is not possible to rule out alternative explanations for the observed changes in scores.
Differences in instructional practices or teacher quality may have also contributed to the results,
CELL PHONES AND ACADEMIC ACHIEVEMENT 80
limiting the ability to determine causality. Additionally, student characteristics may have factored
into the differences in students’ scores. Student motivation, prior achievement, and access to
academic support services could have influenced test outcomes independently of phone policies.
Demographics such as socioeconomic status, gender, and race are often linked to academic
success (Rakesh et al., 2025). These student characteristics were not considered in this study.
In the broader context of education, during the pretest and posttest periods, students may
still have been recovering from the academic and psychological effects of the COVID-19
pandemic. It is possible that several factors unrelated to cell phone policy implementation,
including returning from remote learning, mental health issues, and varying degrees of
technology access, contributed to the increased performance. During the pandemic, many
schools issued mobile devices to all students, and most continue the one-to-one model post-
pandemic. This model presented another limitation of the study. The effects of restricting
personal mobile devices were examined; however, school-issued devices may have served as an
alternative source of distraction.
Publicly available archival data were used to compare test scores before and after policy
implementation. The use of archival data limited how data were collected and reported for this
study. This raised potential concerns regarding the reliability and consistency of the reported test
scores. Differences in testing environments, administration procedures, and reporting practices
across years may have introduced measurement error. Due to these limitations, the findings may
not be generalizable to other schools, grade levels, or standardized testing systems.
Recommendations for Future Research
Based on the findings and limitations of this study, several areas for further exploration
are recommended to strengthen the evidence surrounding instructional cell phone policies and
CELL PHONES AND ACADEMIC ACHIEVEMENT 81
their potential academic effects. These recommendations are grounded in the findings and
limitations and are intended to guide future research in meaningful and practical ways. Future
studies should investigate causal relationships using quasi-experimental or longitudinal designs
to better understand the underlying causes. Researchers should examine the academic effects of
school-issued devices as potential sources of distraction. In addition, the effects of phone
restrictions should be explored by subject area.
Since a non-experimental, causal-comparative pretest-posttest design was employed in
this study, it was not possible to determine whether the implementation of the cell phone policy
directly caused the observed changes in test scores. Future research should employ designs such
as interrupted time-series analysis, matched comparison groups, or multi-year longitudinal
tracking to isolate the impact of cell phone restrictions more effectively. These methods would
allow researchers to control more variables and draw stronger conclusions about causality
(Privitera, 2024).
Although the focus of this study was on personal mobile phones, students continued to
use district-issued devices, such as Chromebooks, tablets, and laptops. Even though these tools
are essential for instruction, they can also be misused by students and become sources of
distraction (Selwyn & Aagaard, 2021). Research has shown that multitasking on school devices
can reduce concentration and retention, even when the technology is intended for educational
purposes (Zogheib & Daniela, 2022). Future studies should investigate how school-issued
technology influences on-task and off-task behavior, and whether feature restrictions or digital
literacy training can mitigate these effects.
A greater increase was observed in ELA scores compared to Mathematics following
policy implementation. However, the results were not statistically significant. Mayer’s (2021)
CELL PHONES AND ACADEMIC ACHIEVEMENT 82
cognitive theory of multimedia learning suggests that reading and comprehension tasks are
particularly vulnerable to cognitive overload resulting from distractions. Further research should
be conducted to explore the effects of cell phone restrictions on specific subject areas,
particularly those that require sustained attention to text.
The observed improvement in assessment scores suggests that cell phone restrictions
during instructional time may support increased student focus. Practitioners should consider
implementing or expanding policies that limit phone use during instructional time, especially in
subjects that require reading comprehension and analysis of text-based materials. Professional
development to help educators manage classroom technology should be provided in conjunction
with the implementation of these policies. Policymakers should also support schools by
providing clear, flexible guidance for in-class device use, while allowing for local variations.
Effective technology policy should strike a balance between access and boundaries that support
attention, self-regulation, and equitable access (Selwyn & Aagaard, 2021).
Implications for Leadership
The findings have several important implications for educational leaders who manage the
use of mobile devices in schools. The modest improvement in test scores suggests that restricting
cell phone use during instruction may enhance student focus and performance, especially in
reading-intensive areas. Leaders at the school and district level should consider how consistent
enforcement, professional development in technology management, and the broader effects on
student well-being can positively influence the learning environment.
The importance of consistency is a key takeaway for school and district administrators, as
well as others in leadership roles. Uneven enforcement of school policies can lead to confusion
and reduce the effectiveness of policies (Grigic Magnusson et al., 2023). Leaders should
CELL PHONES AND ACADEMIC ACHIEVEMENT 83
recognize that outcomes related to cell phone restrictions depend not only on the policy but on
how it is implemented. School and district administrators should work collaboratively with staff
to develop shared expectations and buy-in, support consistent enforcement, and provide channels
for open communication from stakeholders. When policies are well communicated, students are
more likely to recognize and understand boundaries, and teachers are better equipped to maintain
classroom focus (Ochs & Sauer, 2022).
Leaders also have a responsibility to collect data to monitor the academic and social
effects of implemented policies. Monitoring academic performance, student behavior, and social
interactions before and after policy changes can help determine whether those interventions are
producing the intended outcomes (Selwyn & Aagaard, 2021). This reflective approach enables
district and school leaders to refine their strategies and make adjustments in response to the
needs of staff and students.
At the district level, leadership can contribute to positive social change focused on device
use. Students can be supported in developing self-regulation skills and stronger attention, which
will benefit them both academically and in lifelong learning habits (Mayer, 2021). Clear and
developmentally appropriate policies, supported by data-informed practices and inclusive
decision-making, can also help families manage technology use outside of school and create a
cohesive message regarding appropriate technology use (Rakesh et al., 2025).
Conclusion
Standardized test scores from 18 New Jersey public high schools were examined to
determine whether restricting cell phone use during instructional time influenced academic
performance on ELA and Math assessments. Significant improvements were made in ELA test
scores, but not in Math. Neither subject was statistically significant after post-hoc adjustments.
CELL PHONES AND ACADEMIC ACHIEVEMENT 84
Still, the results suggest a connection between limiting phone access and improved academic
performance in assessments.
When interpreted through the lens of cognitive and social learning theories, this study
adds to the growing body of research on mobile device use in schools and subject-specific
findings. Alignment with Mayer’s (2021) cognitive theory of multimedia learning and Bandura’s
(1977) social learning theory helped to explain how reduced distractions and behavioral
modeling may support learning. While the data did not support causal conclusions, the increase
in scores suggests the need for further experimental or longitudinal research.
The importance of consistent policy enforcement, provision of professional development
for educators, and routine evaluation of data are key implications for teachers, administrators,
and policymakers. The focus should be on policies that can be adjusted and adapted to meet the
needs of both students and educators in response to outcomes. The findings suggest that the use
of technology in schools should be intentional and based on evidence. Understanding how
technology influences learning underscores the importance of thoughtful, data-informed
leadership in shaping policies that support academic success, thereby contributing to the broader
knowledge base.
CELL PHONES AND ACADEMIC ACHIEVEMENT 85
References
Aldulaimi, M., Kadhim, T., Kamil, I., & Zahra, M. (2021). A mobile learning model to improve
the learning environment in Iraqi secondary schools. Webology, 18(2), 972–981.
https://doi.org/10.14704/WEB/V18I2/WEB18367
American Educational Research Association, American Psychological Association, National
Council on Measurement in Education, & Joint Committee on Standards for Educational
and Psychological Testing. (2014). Standards for educational and psychological testing.
https://www.testingstandards.net/uploads/7/6/6/4/76643089/9780935302356.pdf
American Psychological Association. (2020). Publication manual of the American Psychological
Association (7th ed.). American Psychological Association. https://doi.org/10.1037/
0000165-000
Asif, M., Ghazanfar, M., Shahzad, K., & Siddique, R. (2021). Use of Android phone for study
purposes among students of Nishtar Medical University, Multan. Library Philosophy &
Practice, 1–24.
Bandura, A. (1977). Social learning theory. Prentice Hall.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice
Hall.
Barfi, K., Brandford, B., & Valentina, A. (2021). Integration of social media for smart pedagogy:
Initial perceptions of senior high school students in Ghana. Education and Information
Technologies, 26(3), 3033–3055. https://doi.org/10.1007/s10639-020-10405-y
Böttger, T., & Zierer, K. (2024). To ban or not to ban? A rapid review on the impact of
smartphone bans in schools on social well-being and academic performance. Education
Sciences, 14(8), 906. https://doi.org/10.3390/educsci14080906
CELL PHONES AND ACADEMIC ACHIEVEMENT 86
Campbell, D., & Stanley, J. (1966). Experimental and quasi-experimental designs for research.
R. McNally.
Chen, B., Denoyelles, A., Brown, T., & Seilhamer, R. (2023, January 25). The evolving
landscape of students' mobile learning practices in higher education. EDUCAUSE
Review. https://er.educause.edu/articles/2023/1/the-evolving-landscape-of-students-
mobile-learning-practices-in-higher-education
Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed
methods approaches (6th ed.). SAGE Publications.
Dent, A., Bell, J., & Kuipers, J. (2022). Cellular ambivalence in a digital age. Anthropology
Today, 38(1), 18–20. https://doi.org/10.1111/1467-8322.12698
Department of Education. (2024). Statewide assessment reports. State of New Jersey.
https://www.nj.gov/education/assessment/results/reports/index.shtml
Digtyar, O., Kuvshinova, E., Shirokikh, A., & Kameneva, N (2023). Modern methods of
teaching foreign languages. Amazonia Investiga, 12(72), 112–122. https://doi.org/
10.34069/AI/2023.72.12.10
Eshenaur Spolarich, A. (2023). Sampling methods: A guide for researchers. Journal of Dental
Hygiene, 97(4), 73–77. https://jdh.adha.org/content/97/4/73
Farzana, T., Gilliland, S., & LaCour, M. (2023). The overlooked role of social norms in
problematic smartphone usage. Journal of Social Media in Society, 12(2), 22–43.
Fernandez, M., Dimla, A., Ocampo, L., Cabrera, T., Mamangun, L., Dela Cruz, J., Dela Rosa, R.,
& Dominado, N. (2024). The mobile phone use of senior high school learners.
International Journal of Multidisciplinary: Applied Business & Education Research,
5(7), 2835–2860. https://doi.org/10.11594/ijmaber. 05.07.34
CELL PHONES AND ACADEMIC ACHIEVEMENT 87
Field, A. (2024). Discovering statistics using IBM SPSS statistics (6th ed.). SAGE Publications.
Fioravanti, R., Ozimek, D., Rodriguez, J., Zulkosky, K., Costea, D., Good, L., Minchhoff, D.,
Crothers, J., & Dielmann, K. (2024). Bring Your Own Device (BYOD) Student
experiences and policy considerations for nontraditional colleges. International Journal
of Adult, Community & Professional Learning, 31(2), 21–41.
https://doi.org/10.18848/2328-6318/CGP/v31i02/21-41
Ford, L. & Scandura, T. (2023). The handbook of survey development. Survey development and
application. SAGE Publications.
Gajdics, J., & Jagodics, B. (2022). Mobile phones in schools: With or without you? Comparison
of students’ anxiety level and class engagement after regular and mobile-free school days.
Technology, Knowledge & Learning, 27(4), 1095–1113. https://doi.org/10.1007/s10758-
021-09539-w
Grant, C., & Osanloo, A. (2014). Understanding, selecting, and integrating a theoretical
framework in dissertation research: Creating the blueprint for your
“House.” Administrative Issues Journal: Connecting Education, Practice, and
Research, 4(2), 12–26.
Grigic Magnusson, A., Ott, T., Hård af Segerstad, Y., & Sofkova Hashemi, S. (2023).
Complexities of managing a mobile phone ban in the digitalized schools’
classroom. Computers in the Schools, 40(3), 303–323. https://doi.org/10.1080/07380569.
2023.2211062
Holley, D., & Park, S. (2020). Cell phone policy in a public high school. Journal of Management
Policy & Practice, 21(4), 82–87. https://doi.org/10.33423/jmpp.v21i4.3244
CELL PHONES AND ACADEMIC ACHIEVEMENT 88
Horn, M. (2023). Ban the cellphone ban: Blanket policies ignore the potential of app-powered
learning. Education Next, 23(1), 76–77.
Hyatt, L., & Roberts, C. (2023). The dissertation journey: A practical and comprehensive guide
to planning, writing, and defending your dissertation. Corwin Publishers.
Immanuel, J., & Hameed, M. (2023). Cognitive theory of multimedia learning powered code-
switching interface for vocabulary acquisition of ESL students. Journal of Language
Teaching & Research, 14(6), 1439–1448. https://doi.org/10.17507/jltr.1406.01
Johnson, R., & Christensen, L. (2025). Educational research: Quantitative, qualitative, and
mixed approaches (8th ed.). SAGE Publications.
Joseph, S. (2024). Why Australia’s social media ban for kids may breach its constitution.
Verfassungsblog, 2366–7044. https://doi.org/10.59704/b485174a4bd7eb6e
Kennedy, M.., & Romig, J. (2024). Cognitive load theory: An applied reintroduction for special
and general educators. Teaching Exceptional Children, 56(6), 440–451.
https://doi.org/10.1177/00400599211048214
Knoster, K., & Goodboy, A. (2023). A Zoom teaching experiment using CTML principles of
multimedia design. Communication Quarterly, 71(4), 367–389. https://doi.org/10.1080
/01463373.2023.2203829
Kohn, J. (2024). Sample size for before after study (paired t-test): Sample size calculators.
https://sample-size.net/sample-size-study-paired-t-test/
Lemov, D. (2022). Take away their cellphones ... So we can rewire schools for belonging and
achievement. Education Next, 22(4), 8–16.
Mayer, R. (1997, January 1). Multimedia learning: Are we asking the right questions?
Educational Psychologist, 32(1), 1–20. https://doi.org/10.1207/s15326985ep3201_1
CELL PHONES AND ACADEMIC ACHIEVEMENT 89
Mayer, R. (2021). Multimedia learning (3rd ed.). Cambridge University Press.
https://doi.org/10.1017/9781316941355
McGregor, S. (2018). Understanding and evaluating research. (Vols. 1–0). SAGE Publications.
https://doi.org/10.4135/9781071802656
Mellinger, C., & Hanson, T. (2021). Methodological considerations for survey research: Validity,
reliability, and quantitative analysis. Linguistica Antverpiensia, New Series–Themes in
Translation Studies, 19. https://doi.org/10.52034/lanstts.v19i0.549
Mertens, D. (2023). Research and evaluation in education and psychology: Integrating diversity
with quantitative, qualitative, and mixed methods (6th ed.). SAGE Publications.
National Center for Education Statistics. (2022). Percentage of public schools with various safety
and security measures: Selected years, 1999–2000 through 2019–20. https://nces.ed.gov/
programs/digest/d22/tables/dt22_233.50.asp
National Center for Education Statistics. (2023). COE–Concentration of public school students
eligible for free or reduced-price lunch. https://nces.ed.gov/programs/coe/indicator
/clb/free-or-reduced-price-lunch
National Commission for the Protection of Human Subjects of Biomedical and Behavioral
Research. (1979). The Belmont Report: Ethical principles and guidelines for the
protection of human subjects of research. US Government Printing Office.
New Jersey Department of Education. (2024a). Assessment resource materials. Official Site of
the State of New Jersey. https://www.nj.gov/education/assessment/resources/
New Jersey Department of Education. (2024b). Considerations for the development of policies to
address cell phone usage in schools. Official Site of the State of New Jersey.
CELL PHONES AND ACADEMIC ACHIEVEMENT 90
https://www.nj.gov/education/broadcasts/2024/aug/28/ConsiderationsfortheDevelopment
ofPoliciestoAddressCellPhoneUsageinSchools.pdf
New Jersey Department of Education. (2024c). Data and reports portal. Official Site of the State
of New Jersey. https://www.nj.gov/education/doedata/
New Jersey Statewide Data System (2023). Data access and use policy. State of New Jersey.
[Policy]. https://njsds.nj.gov/wp-content/uploads/Acceptable_Use_Guidelines_
Revised_2023.pdf
Ochs, C., & Sauer, J. (2022). Curtailing smartphone use: A field experiment evaluating two
interventions. Behaviour & Information Technology, 41(16), 3598–3616. https://doi.org/
10.1080/0144929X.2021.2007284
Olson, J., Sandra, D., Chmoulevitch, D., Raz, A., Veissière, S. (2023). A nudge-based
intervention to reduce problematic smartphone use: Randomised controlled
trial. International Journal of Mental Health and Addiction, 21, 3842–3864 .
https://doi.org/10.1007/s11469-022-00826-w
Pearson. (2024). New Jersey Assessments: Test administration manuals and resources.
https://nj.mypearsonsupport.com/
Privitera, G. (2024). Research methods for the behavioral sciences. SAGE Publications.
Rakesh, D., Sadikova, E., & McLaughlin, K. (2025). Associations among socioeconomic
disadvantage, longitudinal changes in within-network connectivity, and academic
outcomes in the ABCD study. Developmental Cognitive Neuroscience.
https://doi.org/10.1016/j.dcn.2025.101587
Rettie, L., Marsh, J. E., Liversedge, S. P., Wang, M., & Degno, F. (2025). Auditory distraction
during reading: Investigating the effects of background sounds on parafoveal
CELL PHONES AND ACADEMIC ACHIEVEMENT 91
processing. Quarterly Journal of Experimental Psychology (2006), 78(6), 1017–1040.
https://doi.org/10.1177/17470218241269327
Roush, K. (2024). States enact policies restricting cell phone use in schools. AJN American
Journal of Nursing, 124(12), 10. https://doi.org/10.1097/01.NAJ.0001095168.58523.e6
Sadiq, S., Anasse, K., & Slimani, N. (2022). The impact of mobile phones on high school
students: Connecting the research dots. Technium Social Sciences Journal, 30, 252–270.
https://doi.org/10.47577/tssj.v30i1.6299
Salhab, R., & Daher, W. (2023). The impact of mobile learning on students’ attitudes towards
learning in an educational technology course. Multimodal Technologies and Interaction,
7(7), 74. https://doi.org/10.3390/mti7070074
Salkind, N. (2017). Exploring research (9th ed.). Pearson.
Schroeder, K., Hubertz, M., Van Campenhout, R., & Johnson, B. (2022). Teaching and learning
with AI-generated courseware: Lessons from the classroom. Online Learning, 26(3), 73–
86. https://doi.org/10.24059/olj.v26i3.3370
Schunk, D., & DiBenedetto, M. (2023). Albert Bandura’s legacy in education. Theory Into
Practice, 62(3), 205–206. https://doi.org/10.1080/00405841.2023.2226560
Selwyn, N., & Aagaard, J. (2021). Banning mobile phones from classrooms: An opportunity to
advance understandings of technology addiction, distraction and cyberbullying. British
Journal of Educational Technology, 52(1), 8–19. https://doi.org/10.1111/bjet.12943
Siegel, S. (1956). Nonparametric statistics for the behavioral sciences. McGraw–Hill.
Siyami, M., Moghadam, M., & Avaz, K. (2023). Investigating the effect of mobile phone use on
students’ attention span and academic performance. Journal of Fundamentals of Mental
Health, 25(4), 279–285.
CELL PHONES AND ACADEMIC ACHIEVEMENT 92
Smale, W., Hutcheson, R., & Russo, C. (2021). Cell phones, student rights, and school safety:
Finding the right balance. Canadian Journal of Educational Administration and Policy,
195, 49–64. https://doi.org/10.7202/1075672ar
Tadena, S., Kim, S., & Lee, L. (2021). Empathy, cyberbullying, and cybervictimization among
Filipino adolescents. Child Health Nursing Research, 27(1), 65–74. https://doi.org/10.
4094/chnr.2021.27.1.65
Tandon P., Zhou C., Hogan C., Christakis D. (2020). Cell phone use policies in US middle and
high schools. JAMA Network Open, 3(5), e205183. https://doi.org/10.1001/jamanetwork
open.2020.5183`
Uher, I., Kachlík, P., Schubertová, A., Yip, C., Ruszkiewicz, K., & Kimaková, T. (2023). Mobile
phone use and its threat to dependence among secondary school students -
An explanatory study. Annals of Agricultural and Environmental Medicine:
AAEM, 30(2), 306–314. https://doi.org/10.26444/aaem/162402
U.S. Department of Education. (2024). Planning together: A playbook for student personal
device policies. https://tech.ed.gov/files/2024/12/Planning-Together-A-Playbook-for-
Student-Personal-Device-Policies_v25r4-FINAL.pdf
Wagner, W., & Gillespie, B. (2019). Using and interpreting statistics in the social, behavioral,
and health sciences. SAGE Publications. https://doi.org/10.4135/9781071814284
Zogheib, B., & Daniela, L. (2022). Students’ perception of cell phones effect on their academic
performance: A Latvian and a Middle Eastern university cases. Technology, Knowledge &
Learning, 27(4), 1115–1131. https://doi.org/10.1007/s10758-021-09515-4
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Appendix B
Inclusion Questionnaire
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Appendix C
IRB Approval Letter
