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Article
Exploring Factors
That Influence
Technology-Based
Distractions in Bring
Your Own Device
Classrooms
Robin Kay
1
, Daniel Benzimra
1
, and Jia Li
1
Abstract
Previous research on distractions and the use of mobile devices (personal digital
assistants, tablet personal computers, or laptops) have been conducted almost exclu-
sively in higher education. The purpose of the current study was to examine the
frequency and influence of distracting behaviors in Bring Your Own Device secondary
school classrooms. Quantitative and qualitative data were collected from 181 sec-
ondary school students (55 female and 126 male) enrolled in three schools across
Canada. Almost 80% of the students reported being on task regularly when using
mobile devices in class. However, students also engaged in at least one of five dis-
tracting activities occasionally or regularly with their mobile devices including emailing
(64%), surfing the web (65%), using social media (52%), instant messaging (32%), and
playing games (30%). Female students engaged with social media significantly more
than male students, whereas male students played games significantly more than
female students. Students were rarely distracted by peer use of mobile technology
devices. Students were more distracted by their own use of mobile devices when
engaged in independent or group work, and less distracted with lectures and student
presentations. Students claimed that teacher and school restrictions were probably
the most effective method to limit distracting behavior while learning.
Journal of Educational Computing
Research
0(0) 1–22
!The Author(s) 2017
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DOI: 10.1177/0735633117690004
journals.sagepub.com/home/jec
1
University of Ontario Institute of Technology, Oshawa, ON, Canada
Corresponding Author:
Robin Kay, University of Ontario Institute of Technology, 11 Simcoe Street North, Oshawa, ON,
Canada, L1H 7L7.
Email: robin.kay@uoit.ca
Keywords
laptop, tablet, mobile device, distraction, secondary school, gender, pedagogy,
survey
Overview
A mobile device allows a student to engage in educational activities any place,
anytime (Kukulska-Hulme, 2005; Naismith, Lonsdale, Vavoula, & Sharples,
2004). The most common mobile technologies used by students are smart-
phones, tablets, notebooks, laptops, and hybrid devices (Chen, Seilhamer,
Bennett, & Bauer, 2015; Poll, 2015). Ubiquitous access to mobile computer
devices is becoming more prevalent in primary, secondary, and tertiary school
systems and represents a potentially valuable educational opportunity (Hwang,
Tsai, & Yang, 2008; Kay, 2008). Extensive research on student use of mobile
devices in the classroom has been conducted in higher education (Fang, 2009;
Gaudreau, Miranda, & Gareau, 2014; Junco, 2012a, 2012b). Primary advan-
tages of using mobile devices are increased student performance, better commu-
nication among students and teachers, and improved learning experiences
(Aguilar-Roca, Williams, & O’Dowd, 2012; Awwad & Ayesh, 2013; Barak,
Lipson, & Lerman, 2006; Kay & Lauricella, 2011a; Ragan, Jennings, Massey,
& Doolittle, 2014). Key challenges of using mobile devices in class are limiting
distractions and developing effective implementation and management strategies
(Bowman, Levine, Waite, & Gendron, 2010; Junco, 2012a, 2012b; Rosen,
Carrier, & Cheeva, 2013). Some authors have argued that the cognitive cost
associated with engaging in distracting behaviors can have a significant negative
impact on academic performance (Burak, 2012; Fried, 2008; Junco, 2012a,
2012b; Kraushaar & Novak, 2010; Ravizza, Hambrick, & Fenn, 2014). To
date, the majority of research on mobile device use has concentrated
on higher education, perhaps because of the prevalence of these devices in
colleges and universities (Dahlstrom, Walker, & Morgan, 2013). However,
access to mobile devices in secondary schools has grown significantly, as more
schools are moving toward Bring Your Own Device (BYOD) programs (Hwang
et al., 2008).
A number of gaps exist in the research on investigating the use of mobile
devices in the classroom. First, although comprehensive research on the advan-
tages and challenges of using these devices in the classroom has been examined
in higher education (Fried, 2008; Gaudreau et al., 2014; Kay & Lauricella,
2011a, 2014; Kraushaar & Novak, 2010; Lindroth & Bergquist, 2010), secondary
school students’ perspectives have not been explored. Second, limited research
has concentrated on specific activities that secondary school students
engage in while using mobile devices in class. Third, while specific tasks that
higher education students pursue with their devices has been researched
2Journal of Educational Computing Research 0(0)
(Gaudreau et al., 2014; Judd & Kennedy, 2011; Junco, 2012a, 2012b; Ragan
et al., 2014), the factors that lead to distracting behavior have not been
examined.
The purpose of the current study was twofold. First, we explored the pre-
valence of specific distracting behaviors that secondary school students engage in
while participating in a BYOD program. Second, we examined factors that
might influence distracting behaviors including gender, peer distraction, and
instructional method.
Literature Review
We conducted a comprehensive review of the literature on the use of mobile
devices with a focus on distracting behaviors. For the purpose of this article,
distractions are operationally defined as activities that are not directly related
to achieving designated learning outcomes in a class (Gerow, Galluch, &
Thatcher, 2010; Taneja, Fiore, & Fischer, 2015). The majority of the studies on
distracting behavior focus on laptop devices as opposed to tablets or mobile
phones. We will discuss two main themes in detail: distracting behavior (i.e.,
communication, searching the web, and entertainment) and factors that influence
distracting behavior (i.e., gender, peer behavior, and instructional method).
Distracting Behaviors
Researchers have reported that higher education students engage in a variety of
distracting behaviors on their laptops during class. In at least three studies, 50%
to 65% of students engaged in distracting laptop-based activities during class
(Jacobsen & Forste, 2011; Ragan et al., 2014). The literature review revealed
three main categories of distraction: communication, searching for information,
and entertainment (Aguilar-Roca et al., 2012; Awwad, Ayesh, & Awwad, 2013;
Barak et al., 2006; Fried, 2008; Kay & Lauricella, 2014; Ragan et al., 2014;
Turkle, 2011).
A number of researchers found that students engaged in distracting commu-
nication-based activities such as following social media feeds and emailing while
in class (Aguilar-Roca et al., 2012; Awwad et al., 2013; Barak et al., 2006; Fried,
2008; Kay & Lauricella, 2014). Awwad et al. (2013) surveyed female engineering,
science, and information technology university students’ laptop activities during
class and observed that the majority were frequently using their devices for
social media and email. Other studies have also reported university students
used social media (Aguilar-Roca et al., 2012; Barak et al., 2006; Kay &
Lauricella, 2014) or sent instant messages (Fried, 2008) during class. Turkle
(2011) argued that the idea of sending out an email or texting during class is
so commonplace, that students no longer feel the need to conceal this distracting
behavior.
Kay et al. 3
Researchers have also observed higher education students searching for var-
ious personal information during class (Fried, 2008; Kay & Lauricella, 2014;
Ragan et al., 2014). Fried (2008) and Kay and Lauricella (2014) both reported
that students would surf the web during class time. Ragan et al. (2014) added
that undergraduate students surfing the web for personal reasons accounted for
almost 40% of all distracting activities.
Several researchers reported that higher education students used their mobile
devices for entertainment purposes during class, such as playing games or watch-
ing videos (Awwad et al., 2013; Barak et al., 2006; Fried, 2008; Kay &
Lauricella, 2014; Tallvid, Lundin, Svensson, & Lindstro
¨m, 2015). Kay and
Lauricella (2014) revealed that higher education students would play games
on their laptop during class, albeit infrequently. Awwad et al. (2013), Barak
et al. (2006), and Fried (2008) noted similar results for higher education students
playing games on their laptops while in class. Tallvid et al. (2015) indicated that
30% to 50% of junior high school students (Grades 7–9) reported playing games
on occasion or daily with their mobile devices during class.
Factors That Influence Distracting Behaviors
A limited number of studies have looked at factors that might lead students
away from on-task learning activities to distracting behavior with their mobile
devices (Carrier, Cheever, Rosen, Benitez, & Chang, 2009; Judd & Kennedy,
2011; Junco, 2012b; Spink, 2013). Four possible influences have been noted
including gender, peer behaviors, the instructional method used, and restrictions
implemented.
Gender. A number of researchers have explored male and female attitudes, beha-
viors, and use regarding technology (Barker & Aspray, 2006; Kay, 2008; Kay &
Lauricella, 2011b; Rideout, Foehr, & Roberts, 2010; Sanders, 2006). Kay (2008),
in an extensive review of the literature on gender and technology use, reported
that there were small or no differences between male and female students with
respect to attitude, behaviors, and use of technology in elementary, secondary,
and tertiary school environments.
For mobile device use, one of the main differences between male and female
students regarding distracting activities is the type of activity chosen. For
example, male students appear more likely to play video games on their
laptop devices than female students (Barker & Aspray, 2006; Kay, 2008;
Rideout et al., 2010; Sanders, 2006; Tallvid et al., 2015). On the other hand,
female students may be more likely to use the computer for communication
(e.g., email and social networking) than male students (Rideout et al., 2010).
Kay and Lauricella (2011b) also observed that female undergraduate students
were more likely to engage in on-task laptop behaviors compared to male
undergraduate students.
4Journal of Educational Computing Research 0(0)
Peer behavior. Some researchers have observed that peer behaviors with mobile
devices can have a significant impact on surrounding students in class. Aguilar-
Roca et al. (2012), in their study of undergraduate students, explained that a
major distraction was the sound produced from keyboard keys tapping. Fried
(2008) added the clicking of keys and light emitted from the screen could lead to
decreased efficiency and academic performance. Fang (2009) noted that phones
were distracting in class because of the sounds from ringtones and vibrations.
Jassawalla, Sashittal, and Malshe (2009) reported that undergraduate students
were distracted when they could see other students in the classroom engage in
cyber-slacking. Gerow et al. (2010) explained that other students might find it
difficult to focus on their academics as the distracting activities of their peers are
in competition with the lesson of the instructor.
Instructional method. The instructional method used to integrate mobile technol-
ogy may influence the frequency of distracting behaviors. Fried (2008) explained
that distractions caused by laptops might occur more in an unstructured, lecture-
based environment, where students are not given explicit and technology-based
learning tasks to complete. She also argued that students need to be informed by
instructors of the possible distractions posed by using laptops in class in order to
control or avoid them. Ragan et al. (2014) claimed that free use of laptops by
students during class offered unlimited opportunity for distraction. Kay and
Lauricella (2011c) similarly argued that the absence of purposeful laptop inte-
gration during class can result in students engaging in distracting online
activities.
Csikszentmihalyi (1990) explained that if a student were in a state of flow
during the lesson, they might fall out of flow if the task became too challenging.
Certain active instructional methods may increase flow (collaboration) while
other passive methods (lecture and video presentations) might interrupt flow
(Beard, 2015). Lindroth and Bergquist (2010) argued for a more concerted
effort on the part of instructors to integrate mobile technologies into their
class to prevent distracting behavior. Kay and Lauricella (2011a) echoed this
sentiment claiming that thoughtful and meaningful integration of laptops into
the lesson should reduce distracting behavior and engage students in more posi-
tive learning experiences.
Restrictions. The implementation of restrictions on mobile device use in the class-
room can have an impact on both on-task and distracting behaviors associated
with mobile device integration. Ragan et al. (2014) argued that allowing for
unfettered access to laptops affords students the opportunity for an infinite
source of distractions. Some teachers ban the use of mobile devices in their
classroom to eliminate potentially distracting behavior (Fang, 2009; Skiba,
2011). For example, a law school banned laptops from the classroom to
reduce device-related distractions and increase student engagement in the
Kay et al. 5
actual lesson (Fang, 2009). However, a complete ban on the use of laptops in the
classroom would appear to be counterproductive given the research that
espouses the advantages of laptop use (Fang, 2009; Skiba, 2011). Grimes and
Warschauer (2008) offer a less drastic alternative to banning mobile devices
where student input is sought on maximizing benefits and minimizing
distractions.
Research Questions
Limited research has been conducted on the specific distracting behaviors that
secondary school students engage in while using mobile devices in class.
Furthermore, factors that could influence these distracting activities have not
been examined in high school settings. Therefore, we addressed two research
questions in this study:
1. What distracting activities do secondary school students engage in during
class in a BYOD program?
2. What factors (i.e., gender, peers, and instructional method) influence second-
ary school students’ participation in distracting activities in a BYOD
program?
Method
Context
The study was conducted within three Canadian Accredited Independent
Schools (CAIS) that had implemented BYOD programs for at least 7 years.
School A, located in a suburban area in the province of British Columbia
(population of 76,000) is a coeducational private school with about 500 JK to
Grade 12 students. School B, located in a rural area in British Columbia
(population of 8,000), is a coeducational private school with about 480 Grade
8 to 12 students. Finally, School C, located in a suburban area in the province of
Quebec (population of 20,000), is a boys-only school with about 570 JK to
Grade 11 students.
Participants
One hundred eighty-one secondary school students (126 male, 55 female) parti-
cipated in this study. Students were 13 years old (n¼5, 3%), 14 years old
(n¼17, 9%), 15 years old (n¼46, 25%), 16 years old (n¼53, 29%), 17 years
old (n¼40, 22%), or 18 years old (n¼15, 8%). With respect to grade, 22%
(n¼40) were enrolled in Grade 9, 30% (n¼66) in Grade 10, 30% (n¼55) in
Grade 11, and 14% (n¼26) in Grade 12.
6Journal of Educational Computing Research 0(0)
Research Design
This study used a convergent model of quantitative and qualitative data analysis
(Creswell, 2014). Quantitative data (Appendix A, Items 1–5), collected from
questions based on a 9-point semantic differential scale, provided an overall
understanding of the frequency of various distracting activities displayed in
BYOD classrooms. Qualitative data from an open-ended question (Appendix
A, Item 6) helped to expand on the quantitative results with a thematic content
analysis (Weber, 1990; Zhang & Wildemuth, 2009).
Data Collection
The dependent variables in this study, distractions, were assessed using two
survey questions (Appendix A, Items 1 and 2) focusing on (a) the extent to
which students engaged in on-task activities and (b) the frequency in which
students were involved in specific distracting activities (i.e., email, instant messa-
ging, social media, playing games, and surfing the web).
The independent variables, factors that could influence participation in
distracting activities, were assessed with questions about gender, peer laptop
behavior, and instructional method (Appendix A, Items 3–5). A follow-up,
open-ended question was used to further explore students’ beliefs about how
laptops could be made less distracting and more beneficial (Appendix A, Item 6).
Data Analysis
We conducted a frequency analysis to provide an overview of how often students
engaged in distracting activities when using their laptops. Next, we conducted a
multivariate analysis of variance to assess gender differences in participating in
distracting activities. Finally, we used content analysis to analyze student com-
ments from the open-ended question. We attempted to understand each parti-
cipant’s ‘‘social reality in a subjective but scientific manner’’ (Zhang &
Wildemuth, 2009, p. 308). The first step of the content analysis procedure was
to read all of the responses to gather a general impression. Next, we reviewed
responses again to identify preliminary themes. Finally, we examined all ratings
one more time to check for accuracy and overlapping themes.
Procedure
We contacted 22 CAIS school across Canada to ask whether they might be
willing to participate in the study. Four CAIS schools (14% response rate)
agreed to participate. When a student and their parent(s) consented to partici-
pate, they were sent a link to an online survey (Appendix A). Over a period of 11
weeks, 181 participants filled in the survey. Each survey took approximately
10 minutes to complete.
Kay et al. 7
Results
Type and Frequency of Distractions
Students (n¼149) responded to how often they engaged in on-task behaviors on
their laptops during class. On a 9-point scale, the mean score was 6.6 (SD ¼1.9)
indicating that students were frequently on-task when using their laptops.
Almost 80% (n¼114) of the students reported that they often, frequently,
almost always, or always engaged in on-task laptop behaviors during class.
Less than 10% of students (n¼13) reported that they never, almost never, or
rarely engaged in on-task behaviors.
Students rated how frequently they engaged in five distracting activities
during class. Emailing and surfing the web were the most frequent distracting
activities with about two thirds of students engaging in this behavior on
occasion or regularly. Just over half the students participated in social
media activities on occasion or regularly. Finally, one third of the students
sent instant messages or played games on occasion or regularly when in class
(see Table 1).
Factors That Influence Distracting Behaviors
Gender. An independent ttest revealed that female students (M¼7.1, SD ¼1.5)
claimed they engaged in on-task behaviors significantly more often than male
students (M¼6.3, SD ¼2.1; t¼2.3, df ¼122, p<.05). A multivariate analysis of
variance comparing female and male students with respect to distracting activ-
ities was significant (Hotelling’s T, F¼4.28, p<.005). Female students used
social media (Cohen’s d¼0.47, p<.05) significantly more than male students,
whereas male students played games significantly more than female students
during class (Cohen’s d¼0.57, p<.01). According to Cohen (1988, 1992), the
Table 1. Frequency of Distracting Activities Engaged in During Class (n¼146).
Item Mean
a
SD Never
b
(%) On occasion
c
(%) Regularly
d
(%)
Email 5.1 2.1 36 23 41
Surf the web 4.7 2.4 36 25 40
Social media 3.9 2.3 48 27 25
Instant message 3.1 2.0 68 17 15
Play games 2.7 1.9 70 19 11
a
Note. 9-point Likert Scale (1 ¼never to 9 ¼always).
b
Includes never,almost never, and rarely.
c
Includes on occasion and sometimes.
d
Includes often,frequently,almost always, and always.
8Journal of Educational Computing Research 0(0)
effect sizes for mean differences between female and male students in social
media use and playing games are moderate. There were no significant gender
differences for sending emails, instant messaging, or surfing the web (see Table
2).
Peer distraction. On a 9-point scale, the mean score for how often a student was
distracted by another student’s laptop was 3.7 (SD ¼1.8) indicating that this
happened on occasion. Over 50% of the students (n¼94) noted that they were
never, almost never, or rarely distracted by a peer’s laptop. One third of the
students (n¼60) were distracted by a peer’s laptop on occasion or sometimes.
Finally, 15% (n¼27) were distracted often, frequently, almost always, or always
by another student’s device.
Instructional method. Students were asked how often they were distracted and
started using their laptop when certain instructional methods were used in
class (Appendix A, Item 5). As indicated in Table 3, over 70% of students
Table 2. Gender Differences in Frequency of Distracting Activities during Class (n¼141).
Measure
Male students Female students
Cohen’s dFMSDMSD
Email 4.9 2.1 5.5 2.1 0.28 2.2
Instant messaging 3.0 2.1 3.4 1.9 0.20 1.6
Social media 3.5 2.3 4.6 2.2 0.47 6.5*
Playing games 3.0 2.0 2.1 1.4 0.57 8.7**
Surfing the web 4.8 2.4 4.6 2.3 0.09 0.3
Note. *p<.05. **p<.01.
Table 3. Frequency of Distraction as a Function Instructional Method.
Item Mean
a
SD Never
b
(%) On occasion
c
(%) Regularly
d
(%)
Independent work 4.8 2.2 27 36 36
Group work 4.0 2.2 47 30 23
Lecture 3.8 2.4 52 23 25
Student presentation 3.0 2.1 69 17 13
a
Note.9-point Likert Scale (1 ¼never to 9 ¼always).
b
Includes never,almost never, and rarely.
c
Includes on occasion and sometimes.
d
Includes often,frequently,almost always, and always.
Kay et al. 9
reported that they were distracted on occasion or regularly and used their laptops
when they were doing independent work. About half of the students responded
that they were distracted on occasion or regularly when engaged in group work
or watching a lecture. Almost one third of students responded that they were
distracted on occasion or regularly during student presentations.
Making Mobile Devices Less Distracting or More Beneficial
We asked students how they could make laptops less distracting or more ben-
eficial in the classroom (Appendix A, Item 6). As indicated in Table 4, five
themes emerged from the 217 comments offered: restrictions, teacher impact,
no change needed, self-control, and better software. The most frequent sugges-
tion (97 comments) was that teachers needed to implement more restrictions
including stricter supervision during class, blocking certain websites, only
using laptops when needed, and limiting Wi-Fi access.
Sample suggestions from this theme included the following:
Really bare down on laptops and make sure no one is using them [inappropriately]
and if they are take them away, unless they are doing something productive.
Online restrictions to some websites would be efficient in a theoretical work ethic
sense, but the students would be irritated by these limitations.
Being supervised when using them and the teacher supervising your screen.
Table 4. Comments How to Make Laptops More Beneficial or Less Distracting (n¼217).
Theme Subtheme label n%
Restrictions
(n¼97, 45%)
Stricter supervision in class 34 16
Block websites 33 15
Only use when needed 21 10
Limit Wi-Fi access 9 4
Teacher impact
(n¼52, 24%)
Improve laptop integration 43 20
Better teacher education on laptop use 9 6
No change needed
(n¼38, 12%)
Fine as they are already or no problem 38 18
Self-control
(n¼18, 8%)
Self-control, restraint, freedom to choose 18 8
Better software or apps
(n¼12, 6%)
Access to specific apps 9 4
E-copies of the textbook 3 1
10 Journal of Educational Computing Research 0(0)
I think not making Wi-Fi readily available to students while in class would defi-
nitely make everyone more productive.
The teacher having the students completely closing their devices during lessons,
otherwise, there really is no way to stop it.
A number of students (27 comments) thought that teacher impact was germane and
better training and integration were required. Specific suggestions were:
Technology could be more useful in class if teachers understood how to use it
properly. Along with this, digital device use could be more beneficial if teachers
knew what the opportunities for distraction are and could avoid them.
Have specific instructions, including where to go and what to do, to keep students
more on task.
Have a mandatory limit on what needs to be done by the end of the class to ensure
people are pushed to work to get the minimum done.
Have more engaging activities that can actually keep students focused instead of
making them so bored that they want to shop or play games or surf online.
If the activities/assignments done in class were more interactive within the laptop, it
would be harder to get off topic surfing the web since you have to be paying
attention to what is happening on your screen.
On the other hand, 20% of the comments suggested that students were fine
with laptop use way it was. Sample responses were as follows:
I’m not sure. They’re pretty beneficial.
I do not really think you can, because they are already quite beneficial;
If you are using them a lot in the class they are already being used to the full
potential.
I don’t know if they can be more beneficial. It’s just using them at the appropriate
times.
I like them the way they are, they force the student to be responsible and smart
about his workload.
About 10% of the comments indicated that more self-control was needed to
limit distractions and maximize benefits. Representative responses included
Kay et al. 11
the following:
The students need to make the choice: do I stay on topic, or do I go on Facebook?
And then they can pay the price for their actions. If they are constantly distracted
and cannot multi-task they will see it reflected in their effort grades as well as class
grade. I believe it is up to the student to have the moral strength to know when and
when not to be on Facebook or watching videos.
Personal willpower to only open tabs needed or documents in use.
Stop messing around and stay on task.
Laptops are already beneficial. The challenge is if a person decided to use the
laptop for its benefits instead of a distraction.
If a student does not want to work then you cannot force them to work. It’s like the
expression: you can bring a horse to water but you can’t make him drink.
[We need to] control ourselves.
Finally, just over 5% of the comments referred to the need for better software
tools. Sample responses included the following:
I think if we could turn questions that are asked during class into a poll which can
be voted instantaneously and the results shown in front of all the students.
Make use of extra programs such as Photoshop for projects, iMovies, etc.
[We need] more online tools that would allow the class to collaborate online.
Have the textbooks on them [laptops] so that we cannot lose them and they are less
heavy in our bags.
Discussion
The purpose of this study was to examine the type, frequency, and potential
influences of distracting activities that secondary school students engage in while
using mobile devices in BYOD programs. Before discussing distractions,
though, it is important to note that 8 out of 10 students reported that they
were on-task often or regularly while using their mobile device in class. While
secondary students participate in a number of distracting behaviors, their pri-
mary focus is to use mobile devices to achieve the learning goals or tasks set out
12 Journal of Educational Computing Research 0(0)
in class. These students appear to be more on-task when using mobile devices
than their higher education peers. Although Kay and Lauricella (2011c) noted
that university students reported being on task 80% to 90% of the time, two
other studies reported on-task rates at about 50% for higher education students
(Awwad et al., 2013; Ragan et al., 2014). There are at least two plausible reasons
why this difference might exist. First, secondary schools classes are typically
much smaller compared with those in colleges or universities, and therefore
distracting activity is more easily detected by the instructor. Second, a more
structured set of external school rules that govern younger secondary school
students are generally not applicable or regimented for higher education
students.
Type and Frequency of Distractions
While claiming to be on task most of the time, one third to two thirds of
secondary school students reported that they engaged in a wide range of dis-
tracting activities while on their mobile devices during class. The shifting of
attention between on-task and distracting activities, even if it occurs occasion-
ally, can lead to decrease in performance due to the brain having to switch
tasks, establish priorities, and refocus and reengage in learning (Bowman
et al., 2010; Judd & Kennedy, 2011; Kraushaar & Novak, 2010). Therefore,
the absolute time that a student is on- or off-task may not be as critical as the
act and cognitive cost of shifting attention from learning tasks to distracting
behavior.
Based on a thorough review of the literature, five distractions were
examined including email, surfing the web, social media, instant messaging,
and playing games. Email was the most cited distraction, with two thirds
of the students reporting that they used it on occasion or regularly. This
result is inconsistent with previous results in higher education. Junco (2012b)
and Kay and Lauricella (2014) reported the frequency of using email during
class at 10% and 35%, respectively. Students in this study, though, did not
articulate why emailing in class was prevalent. Turkle (2011) proposed that
email has become so commonplace that students do not see it as a distraction
or prohibited activity. It is also possible that email was the most frequently
reported distraction, because it can be used for both academic and nonacademic
purposes, consequently most schools do not restrict its use through security
firewalls.
Surfing the web was the second most frequently reported distraction. Nearly
two thirds of students reported surfing the web during class on occasion or
regularly. This result is somewhat inconsistent with research in higher education
where surfing the web was not as frequent (Kay & Lauricella, 2014; Ragan et al.,
2014). Some students explained that searching the web was initially used for
academic reasons, but if they were bored, they would surf for personal reasons.
Kay et al. 13
Because searching the web is used by students for both learning and recreation,
there is the potential for increased risk of distraction.
Use of social media was the third most frequent distraction pursued by sec-
ondary school students during class—about half engaged in this activity on
accession or regularly. It is somewhat surprising, given the teenage demographic,
that nearly half of the students reported never or rarely using social media during
class. Social media use might be limited by school firewalls and small-class
supervision at the secondary school level, thereby curbing student use during
class. One would anticipate, then, more frequent use of social media in higher
education classes given that restrictions and blocking are atypical. Social media
distractions during class, though, are reported less often in higher education.
Ragan et al. (2014) reported social media use at 20%, Junco (2012b) at 28%,
Aguilar-Roca et al. (2012) at 40%, and Kay and Lauricella (2014) between 40%
and 50%. More detailed research, perhaps in the form of interviews or focus
groups, is needed to understand the dynamics of social media use in classroom
settings.
Instant messaging was a distraction for one third of secondary school stu-
dents on occasion or regularly. The frequency of instant messaging during class is
similar to that reported by Junco (2012b) but far less than the 60% noted by
Kraushaar and Novak (2010). Lower rates of instant messaging during class
may reflect a relatively recent societal shift toward using social media tools and
texting.
Finally, playing games was a distracting activity for only 3 out of 10 students
on occasion or regularly. This result is somewhat consistent with previous studies
in higher education. For example, Kay and Lauricella (2014) reported 80% of
higher education students never or rarely played games during class. Ragan et al.
(2014) added that students played games only about 8% of the time during class.
On the other hand, Fried (2008) reported that students played games 25% of the
time, and Tallvid et al. (2015) observed game playing by students 23% to 45% of
the time. The infrequent use of playing games in secondary school is most likely
explained by the use of the web filters to block access. In addition, smaller class
sizes in secondary schools may facilitate supervision and subsequent restriction
of game playing activity.
Factors That Influence Distracting Behaviors
Gender. Gender appears to have a moderate impact on participation in distract-
ing behavior. Female students reported engaging in social media or networking
more frequently than male students, and male students engaged more frequently
in gaming. These results are consistent with previous research in higher educa-
tion (Barker & Aspray, 2006; Kay, 2008; Rideout et al., 2010; Sanders, 2006;
Tallvid et al., 2015). On the other hand, no significant differences between male
and female students were found for email, surfing the web, or instant messaging.
14 Journal of Educational Computing Research 0(0)
Differences in female students’ social media use and male students’ gaming
behaviors have also been reported for college and university students (Barker
& Aspray, 2006; Kay, 2008; Rideout et al., 2010).
Peer distractions. In the current study, over 50% of secondary school students
were never or rarely distracted by a peer’s device. Furthermore, no student
mentioned this type of distraction as being a problem in the open-ended ques-
tion responses. However, previous research in higher education has reported
that mobile device activity in class can have a significant and negative impact
on surrounding students (Aguilar-Roca et al., 2012; Fang, 2009; Fried, 2008;
Gerow et al., 2010; Jassawalla et al., 2009). It is possible, as stated earlier, that
the small-class, highly supervised culture in secondary schools might discourage
students from engaging in highly distracting activities, the kind that might cap-
ture the attention of their peers.
Instructional method. In this study, instructional method had an effect on the
frequency of distracting behaviors. Four standard teaching approaches were
assessed including independent work, group activities, lectures, and student
presentations. Independent work assigned to students so they can actively con-
struct meaning and apply knowledge has been touted as a highly effective way to
learn (Hattie, 2012; Petty, 2009; Prensky, 2010). Somewhat surprisingly, stu-
dents were most distracted when they were asked to complete independent
work, with almost 85% reporting that they engaged in distracting activities on
occasion or regularly. Students commented that minimal teacher supervision
occurred during independent activities, so they were free to surf the web,
check email, or use social media for personal reasons. This finding is consistent
with results reported by previous studies on structured and unstructured class
environments with laptop use (Kay & Lauricella, 2011c; Ragan et al., 2014). For
example, Ragan et al. (2014) argued that unrestricted use of laptops can provide
students with an unlimited source of distraction. Likewise, Kay and Lauricella
(2011c) noted that unstructured integration of laptops could lead to students
toward self-distracting activities. In summary, a constructivist approach to
learning involving independent work may not be effective if it is not thoughtfully
constructed, organized, and challenging.
Considerable evidence suggests that collaboration is an effective approach to
teaching and learning (Hattie, 2012; Petty, 2009; Prensky, 2010). One would
expect, then, that secondary school students would be engaged and focussed
when learning with mobile devices in a group work scenario. However, over
half of the students reported being distracted on occasion or always during group
work. Some students explained that they would socialize while working with
their peers. Other students noted that they would finish the assigned work in less
time and then surf the web or use social media. It is possible that the appropriate
structure, timing, and scaffolding required for effective collaborative learning
Kay et al. 15
was not robust enough to keep this age-group on task, especially when the
temptation of distracting Internet activities was readily available.
Passive learning through lectures has been widely cited as a less effective
teaching approach in secondary schools (Hattie, 2012; Petty, 2009; Prensky,
2010). One might expect student attention to shift toward distracting activ-
ities during a long lecture. While some students engaged in distracting beha-
viors during lectures, over 50% reported never or rarely being distracted by
their mobile devices. The small class culture of feeling supervised may have
played a role. Unlike independent or group work, the teacher’s focus would
most likely be directly on the students. Additionally, students explained that
during lectures they used their mobile devices to take notes. It may have
been difficult to simultaneously take notes and engage in distracting
activities.
Finally, 70% of students reported never or rarely being distracted during
student presentations. Student presentations are similar to lectures in terms of
teaching philosophy—a relatively passive communication of information.
However, a few students explained that they would not use their laptops
during student presentations out of respect for their peers. If they were present-
ing to the class, they certainly would not want to see other students distracted by
their laptops.
Making Mobile Devices Less Distracting or More Beneficial
Secondary students offered five suggestions about reducing distractions and
maximizing the benefit of using mobile devices: restrictions, teacher impact,
no change needed, more self-control, and better software. Almost half the stu-
dents recommended that tighter external restrictions were necessary to reduce
distracting behaviors, whereas only 8% suggested that more self-control was the
answer. Perhaps secondary students recognize that, on average, they are unable
to control self-distracting activities and need strictly enforced regulations and
policies. However, if strict controls are enforced at the secondary school level,
distractions could be a more serious problem when students participate in less-
controlled higher education settings.
Interestingly enough, the second most frequent response to reducing distrac-
tions and increasing benefits was to improve the quality, structure, and mean-
ingfulness of mobile device integration. This finding is consistent with student
feedback on the impact of instructional method, and their asking for improve-
ments in planning, organization, clarity of directions, and engagement. As Kay
and Lauricella (2014) noted, meaningful, relevant, and engaging use of mobile
devices to attain learning outcomes should naturally limit distractions.
Improving the quality of technological integration may also address self-control
challenges as students will be less likely to stray from interesting, authentic, well-
planned lessons.
16 Journal of Educational Computing Research 0(0)
Educational Implications
There are several educational implications worth considering based on the
results of this study. First, while students may be on task 80% to 90% of the
time, they will engage in distracting behaviors if lessons are not well planned,
organized, challenging, and engaging. Mobile devices have considerable poten-
tial to help improve learning, but they also provide readily available opportu-
nities for self-distraction and entertainment. Second, while self-control is may be
the optimal solution to moderating distracting behaviors, the majority of sec-
ondary students claim that restrictions from the school and teachers are needed
to limit distracting activities. Teachers need to actively monitor student behavior
when using mobile devices in class in order to maximize learning. Finally, gender
appears to play a role in the type but not the frequency of distracting behaviors
engaged in during class.
Limitations and Future Research
Based on the findings from this study, there are several opportunities for guiding
future research. First, while a survey method provides a preliminary understand-
ing of distracting activities with mobile devices in secondary school classroom,
future studies should use qualitative methodology in the form of interviews and
focus groups to understand the dynamics and catalysts of distracting behaviors.
Second, a more detailed description and analysis of instructional activities need
to be conducted and analyzed to understand how to better implement technol-
ogy and limit distractions. For example, it is worthwhile to link individual lesson
plan activities to distracting technology-based behaviors. Third, a more
balanced scale assessing both positive learning experiences and distractions
would help provide a more comprehensive understanding of when learning is
minimized or maximized. Fourth, instructors need to be part of the overall
analysis of distractions. Teachers can provide insights about context and peda-
gogy that would inform data analysis and interpretation of the results. Finally,
monitoring actual activity on a mobile device with tracking programs like
RescueTimeß, rather than relying on students recall and perceptions would
provide a more precise measure of beneficial and distracting activity.
Appendix A—Student Survey
Distractions
1. How often do you engage in on-task activities during class?
2. The following questions ask about your activities on the laptop during class.
a. Email.
b. Instant message.
Kay et al. 17
c. Social media or networking.
d. Play games (online or offline).
e. Surf the web.
Factors That Could Influence Distractions
3. Gender?
4. In a given class, how often are you distracted by another student’s laptop
activity? (never,almost never,rarely,on occasion,sometimes,often,frequently,
almost always,always).
5. In a given class, how often are you distracted and start using your laptop
when the following instructional methods are used? (never,almost never,
rarely,on occasion,sometimes,often,frequently,almost always,always).
a. Lecture.
b. Independent work.
c. Group work.
d. Student presentation.
Open-Ended Follow-Up Question
6. How could you make laptops less distracting or more beneficial in class?
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The authors received no financial support for the research, authorship, and/or publication
of this article.
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Author Biographies
Robin Kay has published over 120 articles, chapters and conference papers in the
area of computers in education, is a reviewer for five prominent computer
education journals, and has taught computers, mathematics, and technology
for over 20 years at the high school, college and university level. Current projects
include research on laptop use in teacher education, learning objects, classroom
response systems, e-learning, video podcasts, gender differences in computer
related behaviour, emotions and the use of computers, and factors that influence
how students learn with technology. He completed his Ph.D. in Cognitive
Science (Educational Psychology) at the University of Toronto, where he also
earned his masters degree in Computer Applications in Education. He is cur-
rently a Professor in the Faculty of Education and the Director of Graduate
Studies at the University of Ontario Institute of Technology in Oshawa, Canada.
Jia Li is an assistant professor, the Faculty of Education at the University of
Ontario Institute of Technology, Oshawa, Canada. She was a Canada-U.S.
Fulbright Scholar at the Harvard Graduate School of Education, Cambridge,
Massachusetts, US. and also received an international postdoctoral fellowship
award at Queen’s University, Kingston, Ontario, Canada. She completed her
masters and doctoral studies at Ontario Institute for Studies of Education,
University of Toronto, Ontario, Canada. Her research has focused on data-
driven instructional design and innovative interventions for diverse learners, in
particular, technology-enhanced language and literacy instruction. She has pub-
lished broadly in the areas of emerging technologies and education, computers
assisted language and literacy education.
Daniel Benzimra completed his M.A. at the University of Ontario Institute of
Technology in Digital Technologies in 2016. He is currently an Instructional
Designer at Top Hat.
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