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Reducing In-Class Smartphone Usage Through Extrinsic Rewards: Reducing In-Class Smartphone Usage Through Extrinsic Rewards:
Learning Outcomes and Student Perceptions Learning Outcomes and Student Perceptions
Farhad Mohammad Afzali
College of IS&T
, farhadafzali@fulbrightmail.org
Kevin Lumbard
Creighton University
, kevinlumbard@creighton.edu
Follow this and additional works at: https://aisel.aisnet.org/amcis2025
Recommended Citation Recommended Citation
Afzali, Farhad Mohammad and Lumbard, Kevin, "Reducing In-Class Smartphone Usage Through Extrinsic
Rewards: Learning Outcomes and Student Perceptions" (2025).
AMCIS 2025 Proceedings
. 24.
https://aisel.aisnet.org/amcis2025/sig_hci/sig_hci/24
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Reducing In-Class Smartphone Usage Through Extrinsic Rewards
Thirty-first Americas Conference on Information Systems, Montréal, 2025 1
Reducing In-Class Smartphone Usage
Through Extrinsic Rewards: Learning
Outcomes and Student Perceptions
Completed Research Full Paper
Farhad Mohammad Afzali
University of Nebraska Omaha
farhadafzali@fulbrightmail.org
Kevin Lumbard
Creighton University
kevinlumbard@creighton.edu
Abstract
Smartphones, as one of the fastest-growing technologies, are quickly becoming a ubiquitous part of our
daily lives due to their portability and affordability. One explanation for this is the varied features and
functionalities such as web browsing, playing games, and connecting with one's folks through social media
and messaging. Despite positive features, smartphones are a major source of task-switching and habitual
usage, negatively related to users' working memory, cognitive ability, and learning outcomes. This mixed-
method research investigates the role of extrinsic rewards in regulating in-class smartphone engagement,
quantifies the impact of smartphone visibility, and explores motivations behind smartphone engagement
among university students. Results indicate the motivational model of self-regulation training as a
promising approach to addressing in-class smartphone engagement. Smartphone visibility had a significant
impact on student learning outcomes. Students reported boredom, availability, and consequences as the
primary motivations for engaging with smartphones while in class or studying.
Keywords
Smartphone usage, academic performance, self-regulation, extrinsic rewards.
Introduction
Smartphones have become a habitual presence in our daily lives. Their portability and affordability make
them one of the fastest-spreading technologies of all time (DeGusta, 2012), with over seven billion
smartphone users across the globe (Laricchia, 2024). In the U.S., 98% of people aged 18 to 29 own
smartphones. (Mobile Fact Sheet, 2024). Moreover, 28% report high levels of smartphone usage – being
constantly online on their phones (Perrin & Atske, 2021). This constant usage is evidenced in studies that
found users keep their phones with or near them for 22 hours a day (Facebook, 2017), users spend three
hours and 15 minutes on their phones daily, users touch their phones 2,617 times a day, and users pick their
phones up 58 times a day (Winnick, 2016). Research reports that over half of their phone pickups are within
three minutes of the previous pickup (MacKay, 2019), and their average response time to a smartphone
notification is 90 seconds (Marsden & Lee, 2016). Smartphones maintain high object permanence levels for
users, constantly being on their minds and encouraging habitual usage (Geiger et al., 2023).
While everyone can agree that smartphones are powerful and useful tools, research reports that habitual
smartphone usage may negatively impact users. These negative impacts include observed decreases in
available working memory and cognitive ability (Ward et al., 2017), affecting creativity (Olson et al., 2023),
information overload (Bowman et al., 2010), emotional difficulties (Pielot et al., 2014), and psychological
problems (Lee et al., 2014; Parry et al., 2020). Smartphone usage has been tied to divided attention
resulting in distracted driving (Engelberg et al., 2015), and lower educational outcomes for students (Rosen
et al., 2013; Sapci et al., 2021). These outcomes are often caused by users engaging in chronic multitasking
or task switching, affecting their ability to filter irrelevant information that interferes with their primary
task (Ophir et al., 2009; Zhao, 2023). The widespread use and potential negative impacts of smartphone
Reducing In-Class Smartphone Usage Through Extrinsic Rewards
Thirty-first Americas Conference on Information Systems, Montréal, 2025 2
use create an interesting dichotomy worthy of study. This research explores smartphone usage in academic
settings, an area that has demonstrated a negative relationship between smartphone usage and academic
performance (Bowman et al., 2010; B. McCoy, 2020; Ng et al., 2017; Rosen et al., 2013).
Despite research linking smartphone use to poor academic performance, 94% of students report using or
would like to use their phones in the classroom (Kelly, 2017). Students' non-class-related smartphone
activities increased from 10.96 times per day in 2013 to 11.23 times per day in 2016, accounting for 20.9%
of their class time (McCoy, 2020). Smartphone usage by students continues to increase, and instructors are
limited in their ability to preclude use within the classroom. Self-discipline to limit smartphone usage
during academic tasks may be the answer to allow students to achieve maximum learning and higher
academic performance (Duckworth & Seligman, 2012). Research finds that self-discipline may better
predict students' academic performance than intelligence (as measured through standardized testing)
(Misra & McKean, 2000), and trait self-control is positively linked with academic performance (Troll et al.,
2021). Improving student self-discipline may benefit students in multiple ways.
Considering the large percentage of students engaging with their smartphones while presumably studying
(Afzali & Morrison, 2018; Kim et al., 2021; McCoy, 2020), this research utilized the motivational model of
self-regulation training (Berkman, 2016) to investigate the effectiveness of course policy and academic
rewards as extrinsic motivation to curb smartphone engagement in academic settings. We define
smartphone engagement as having one's phone within their visibility or using it actively. We also explored
the impact of smartphone engagement during lectures on learning outcomes related to the lecture material.
Finally, we explore student motivations for smartphone engagement in academia via interviews.
Specifically, this research asks the following research questions:
RQ1: Do extrinsic rewards impact students' in-class smartphone usage?
RQ2: What impact does smartphone engagement have on students' learning during lecture?
RQ3: What motivates students to use their smartphones in class and while studying?
This research explores smartphone engagement in academic settings. It examines the motivational model
of self-regulation theory by explicating the relationship between extrinsic motivation and in-class
smartphone engagement and student motivation for smartphone usage. Extrinsic motivation involves
performing an activity for a separable outcome, contrasting with intrinsic motivation, which is driven by
inherent satisfaction itself (Ryan & Deci, 2000). Practically, this research can inform decisions about course
design and instructor interactions with students regarding in-class smartphone engagement.
Theoretical Background
Smartphone Usage in Academia: Students’ perceived ability to multitask using smartphones has been
linked to poor academic performance (Rosen et al., 2013; Sapci et al., 2021). One potential reason for this
negative relationship is that smartphones provide features and functions that allow students to perform
many tasks and remain constantly connected to communication platforms. This constant connection results
in students switching between studying and secondary tasks and may overload their ability to process
information and engage in deeper learning (Zhao, 2023). Task-switching, in general, may impact task
accuracy and performance of both primary and secondary tasks (Monsell, 2003). Rosen et al. (2013) studied
students' switching frequency between studying and using a technological device, the rationale for
switching, and its impact on students' learning ability. The authors recorded whether students were on-task
(reading, writing) or off-task (Facebook, Instant Messaging, texting, television, music, or using a computer
for something other than reading or writing). Unsurprisingly, the study reported a significant relationship
between a student's ability to stay on task and a higher GPA. Keeping students on-task and away from
smartphones for non-academic purposes or task-switching can improve student learning outcomes.
Communication applications on smartphones promote task switching by design. The constant connection
and notifications are designed to grab attention in various settings. However, in academic settings, these
notifications can cause undesired consequences. Social networking and instant messaging through
Reducing In-Class Smartphone Usage Through Extrinsic Rewards
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smartphones significantly distract students during lectures (Leysens et al., 2016) and significantly predict
lower GPAs (Zhao, 2023). In one study, 74% of students reported that smartphones distract them during
lectures (Brennan & Dempsey, 2018). Adverse outcomes from in-class smartphone engagement result in
students’ inattention, possibly missing instructions, and distracting others(McCoy, 2020).
Students are increasingly realizing that they could achieve higher academic grades if they reduce their
smartphone distractions and subsequent usage. Yet smartphone usage by students continues to grow.
Approximately 50% of students admit to using their phones for more than five hours daily (Brennan and
Dempsey, 2018), and 36% self-report as smartphone addicts(Afzali et al., 2025). Students generally use
smartphones in class for non-class related activities on average 11.43 times a day on campus (McCoy, 2020).
Even when students use their smartphones for learning purposes, there still seems to be an adverse impact
on their GPAs (Ng et al., 2017), possibly due to the mere presence of a smartphone in one's visibility can
occupy cognitive resources and reduce learning capacity (Ward et al., 2017).
Finding ways to mitigate student smartphone engagement during academic tasks seems to be a worthwhile
goal for all concerned. This paper primarily investigates the role of bonus points as extrinsic motivation in
reducing smartphone engagement under the lens of the motivational model of self-regulation training.
Study Method
Two studies were completed to answer the research questions. The first study, the rewards experiment,
focused on whether extrinsic rewards were enough to impact students' in-class smartphone usage (RQ1)
and the associated impact on learning outcomes (RQ2). The follow-up study, student perceptions, explored
student motivation to use smartphones in academic settings (RQ3) and provided further qualitative insight
into RQ1 and RQ2. The follow-up study also explored the role of course policy on in-class smartphone usage.
Extrinsic Rewards Experiment
Participants were recruited from two sections of an undergraduate database management course at a
Midwestern university in the Spring of 2019. In this database management course, students learned about
general database concepts and SQL statements. In one section (rewards group, n = 20) students were
informed that they would receive two bonus points for each lecture session where their smartphone was not
visible. The reward had a maximum of 50 points (5% of overall course grade) if a student attended all classes
and did not use their phone for all 25 semester sessions. Students in the other section (control group: n =
27) were taught as usual, with no reward offered. The students were in their third or fourth year, majoring
in Management Information Systems, Information Technology Innovation, or Cybersecurity. Class
attendance was not required and was not calculated as part of the final course grades for either section. The
researcher taught the rewards group section, while the control section was taught by another instructor who
was not part of this research project.
Instrument: An attendance sheet collected the students' smartphone engagement data. The attendance
sheet had three columns: name (printed beforehand), signature, and phone usage. As student signatures
were collected on the form, the phone usage column was intended to remind of the potential bonus points
available. During lectures, the instructor could easily see all the students' desks. The instructor would put
an indicator in the "phone usage" column for any student observed looking at or using their phone or had
their phone sitting on their desk. Students were not told their phone usage was continuously observed or
related to the research project. Only students without phone usage indicators were awarded bonus points
for that lecture. Attendance and phone usage data from the control group was not collected.
A quasi-experiment was designed to investigate the impact of the external reward on in-class smartphone
engagement behavior and learning outcomes. In week 15 of the 16-week semester, the same instructor gave
both class sections an identical lecture. The lecture content and same lecturer was chosen to control for
other factors, such as teaching style, teaching skills, etc., that could affect learning outcomes. The lecture
content included data warehousing and business intelligence. At the conclusion of the lecture, students
were given a 15-minute quiz on the content of the lecture. The quiz questions were written and agreed upon
Reducing In-Class Smartphone Usage Through Extrinsic Rewards
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by both instructors, the researcher, and the control group instructor. Thirteen students from the rewards
group and 17 students from the control group were present during the lecture sessions and completed the
quiz. A t-test was conducted to compare the quiz scores of the two groups.
Follow-Up Study
To understand student perceptions of motivation and policy, the research team conducted semi-structured
interviews (Spradley, 2016). Eight students from the rewards group of the classroom experiment were
selected following a convenience sampling technique. An email was sent to the whole group, and the first
eight to respond were selected. The sample included seven male and one female student (ages 19-34). The
rationale for the sample being drawn from the experiment group was due to the focus on students'
reflections on the role of the bonus points in their smartphone engagement behavior.
Procedure: The interview protocol was created to explore student motivation to use their smartphones in
academic settings and their perceptions about the regulation of smartphone engagement (Ajzen, 1991;
Berkman, 2016; Elpidorou, 2018; Ryan & Deci, 2000; Silvia & Duval, 2001). To verify that the interview
protocol aligned with the research questions, the research team discussed and collectively edited the
protocol over multiple meetings. The interview protocol was approved by the Institutional Review Board
(IRB), and participants signed consent forms before being interviewed. Interviews took 30-45 minutes
each. Sample interview questions include:
1. Do you think using a smartphone has an impact on learning? (why / why not)?
2. Do your course syllabi include instructions on in-class smartphone usage?
3. Have you noticed a difference in your concentration between when you have your phone visible
and when you do not see it?
Content Analysis: Both authors transcribed and analyzed the interviews using qualitative content
analysis techniques to identify recurring themes related to smartphone usage (Creswell & Poth, 2018). The
transcribed text was uploaded into Dedoose, a browser-based software tool for content analysis
(Consultants, 2019). The first and second authors created an a priori codebook based on literature (Ajzen,
1991; Berkman, 2016; Elpidorou, 2018; Ryan & Deci, 2000; Silvia & Duval, 2001) (see Figure 1). One of the
interviews was individually coded by the first and second authors, deductively using the codebook and
inductively using open coding methods. Both authors then discussed the results and edited the codebook
(see Figure 1) based on a mutual understanding of the existing codes and the identification of new codes.
The first and second authors then used the codebook to deductively pair code the corpus of interview text.
Pair coding involved the cooperative coding of the text, and disagreements were argued for consensus.
Themes were identified through an exploration of code frequency, code co-occurrences, and analysis of text
content. A co-occurrence matrix was created that captured code frequencies and linked to specific text. The
text associated with individual codes and code co-occurrences was then analyzed by the research team to
capture context and descriptive information about recurring themes. Interesting and relevant themes (See
Follow-Up Study Results) were identified and described through this stage.
Results
Rewards Experiment
The semester data for smartphone engagement in the rewards group was analyzed. Smartphone
engagement during the identical lecture within each group was compared for the identical lecture session.
Additionally, descriptive statistics of the groups were compared, and an independent samples t-test was
conducted on the quiz scores.
Reducing In-Class Smartphone Usage Through Extrinsic Rewards
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Figure 1. Interview Coding Handbook
Smartphone Engagement in Rewards Group: Four of 20 students in the rewards group engaged with
their phones at least once in the 25 class sessions. Two students engaged with their phones only once.
Student one (S1) attended 15 sessions and engaged once with their phone (session 7), a 6% occurrence rate.
Student 21 (S21) attended nine lectures and engaged once with their phone (session 6), with an 11%
occurrence rate. S13 attended 12 sessions and engaged twice with their phone (sessions 8 & 9), a 16%
occurrence rate. S7 had the highest number of engagements with a 23% occurrence rate (13 sessions
attended, with 3 engagements in sessions 9, 14, and 20). Sixteen students earned all possible bonus points
based on attendance rate (26-48 points earned), with no smartphone engagement during class all semester.
Smartphone Engagement Comparison: Comparing the rewards and control groups' phone
engagement for one specific lecture near the end of the term showed that none of the thirteen present
students from the rewards group engaged with their phones during the lecture. However, eight of 17
students (47%) from the control group engaged with their phones during the lecture.
Learning Outcome Impacts: Results for the quiz given at the conclusion of the identical lecture were
analyzed. As Figure 2 illustrates, the lowest score for both groups was 1.5 (out of 6.0). The highest score for
the control group was 5.0, and the highest score for the rewards group was 6.0. The frequency distribution
(see Figure 3) illustrates that 61.5% (8/13) of the rewards group participants scored above 50%. Only 29.4%
(5/17) for the control group scored higher than 50%. An independent samples t-test (n = 30) was conducted
to examine the difference in quiz performance between the groups.
The results indicate the reward group had significantly better learning outcomes (4.07 ± 1.51) compared to
the no-reward group (2.88 ± 0.91), a mean difference of 1.19 (95% CI, 0.25 to 2.13), t(28) = 2.6, p = 0.00,
effect size (Hedge's g = 0.96). Final course grades for both groups were compared using a t-test. Results
indicate (t = -0.1928, df = 42.982, p-value = 0.848) that the groups were not statistically different. In other
words, both groups of students had similar course performance, implying that the groups were not
dissimilar in nature.
Reducing In-Class Smartphone Usage Through Extrinsic Rewards
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Figure 2. Quiz Score Boxplot
Figure 3. Quiz Frequency Distribution
Follow-Up Study
Following the initial study, a selection of participants were interviewed to provide further insight into the
impact on student motivation of the extrinsic reward and general student smartphone engagement. In our
thematic content analysis of these interviews, three key themes around motivation for smartphone
engagement emerged: student boredom, smartphone availability, and consequences of usage. These
findings confirm and extend our understanding of motivators for in-class smartphone usage, as reported
by previous research (B. McCoy, 2020).
Student Boredom: Boredom was one of the most frequently occurring codes (76 occurrences) in our
analysis. Boredom was coded whenever students expressed a lack of task focus or dissatisfaction with their
environment. This theme confirms McCoy’s (2020) findings, which reported boredom as one of the reasons
for in-class digital distraction. Boredom had high co-occurrences with the codes of intrinsic motivation,
social pressure, and liminality.
Boredom and intrinsic motivation was observed in responses when students discussed losing focus on
academic activities or lectures. For example, one student noted that when they felt lost in the lecture, their
smartphone provided them with an outlet to engage with the outer world. As per state boredom's definition,
disengagement is a form of boredom.
Sometimes [boredom] actually triggers it. As soon as I'm lost in the lecture, like that second, just flips
me. I would be using my phone instead to feel comfortable. - (S2)
Boredom and social pressure was observed in conversations when students talked about social pressures to
stay connected on social media or instant messaging. This confirms prior survey findings by (B. R. McCoy,
2020). For example, when asked if other students' engagement with their phones impacted their own
behavior, one student reported that:
Yeah, [other students' smartphone engagement] definitely does [impact] because I get distracted. - (S3)
Boredom and liminality occurred when students felt disengaged with their environment (in class), and a
gap allowed them to shift focus to their smartphones. The reasons given for disengagement included a lack
of interest in the class, a boring lecture, or the complexity of the topic being too difficult or too easy.
Because I am bored, I am lost, and I don't know what's going on. As soon as the professor says like okay
we're going to move to the next topic and he starts typing stuff and it appears …. I have a few seconds.
I'm lost anyways, and I don't know what's going on. I gotta check my phone. - (S6)
In general, students expressed boredom when the lecture material was either too easy or too difficult. This
insufficient meaning or non-optimal challenge (Elpidorou, 2018) resulted in their disengagement with their
current environment, which lead to engagement with their smartphones.
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Availability: Convenience was one of the most frequently occurring codes (94 occurrences) in our
analysis. Convenience was coded when the visibility, persistence, or ubiquitous nature of smartphones was
mentioned. It had high co-occurrences with the codes perceived control, impact, failure attribution and
intrinsic motivation.
Convenience and perceived control was coded when students reported turning off their phones, disabling
notifications, enabling “Do Not Disturb” mode, or putting their smartphones out of sight. These strategies
helped them focus on their academic work.
If I'm working on something, and I don't want to be busy or just distracted, I would actually leave it in a
separate room and would go and work on my tasks. Leaving it in a separate room keeps me protected,
otherwise, when I have a smartphone next to me, I'm gonna mess with it. - (S2)
Convenience and impact was present when students reported that smartphone availability had a negative
impact on their focus and the time to complete tasks. Students stayed focused, and it was less distracting
when they did not have their phones in front of them. When asked if their phone's availability had an impact
on their learning, one student said:
Yes. Because for obvious reason right you're paying attention and your mind goes somewhere else and
you're not listening. And you see something pop up, even though you don't check it, but you see it and
your mind just absorbs the information there. You switch over to just whatever gets on your phone,
instead of listening to your professor. - (S4)
Convenience and failure attribution was coded when students mentioned using their phones
unintentionally because it was visible. Many students reported engaging with their phones just because they
were visible. Students had better focus when their phones were away.
If I have a jacket with like pockets then I keep it in there. If I don't, it's usually like put it on my chair
between my legs so easy access. Sometimes I put it around on a table. If I do that, then I usually end up
grabbing it. (S2)
Convenience and intrinsic motivation was coded when students discussed using their phones because of an
intrinsic impulse, and having their phones with them made acting on that impulse possible. These intrinsic
impulses came from waiting for a call or text or the urge to check their social media accounts.
When I have it out and I'm waiting for something, I am usually focused more towards that because I'm
waiting for whatever is important at that time, as opposed to being in my backpack [or] fully attentive
to the instructor. - (S7)
Consequences: Policy was another code that occurred frequently (59 occurrences) in the analysis. It was
coded when the university, course, or instructor’s policy on in-class smartphone usage was mentioned.
Policy had high co-occurrences with extrinsic motivation, punishment, and rewards.
Policy and extrinsic motivation was observed when the existence of specific instructions on in-class
smartphone engagement was mentioned. Several interviewees stated that there was a no-phone usage
policy in most courses they had taken. However, even if a policy existed, it was rarely taken seriously by the
instructor or students. The only time instructors would raise the issue was when one student's smartphone
engagement would cause distraction and disruption to others.
Students thought including smartphone usage in the course policy has become a social etiquette that
everyone knows about but does not follow. Students indicated they would not follow the policy unless there
were consequences for violations. When asked about the impact of course policies on regulating in-class
smartphone usage:
Not really. Even though they're saying don't get your phone, unless there's any consequences to it. Like
one of the classes I took, you won't get a grade if you are on your phone, that was just, I guess like it
would really get me in trouble and that type of thing. - (S3)
Policy and punishment occurred when students discussed impractical punishments for addressing in-class
smartphone engagement. Students saw punishments as negative reinforcement doing more harm than
good in regulating smartphone usage. One student said:
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A lot of teachers have extremely strong feelings about smartphones. I actually just dropped a class this
semester because the teacher said, if one person's phone goes off during somebody's speech, then
everybody loses points. People have kind of extremist views about phones and classrooms. And so, I've
seen a lot of that side of things where they try to put really harsh punishments on smartphone usage or
try and take points away from you, which is pretty bad from an academic perspective. - (S3)
Policy and rewards was observed when students discussed rewards or positive reinforcement within course
policies with respect to smartphone engagement. Students thought positive reinforcements would lead to
better results than punishments:
In my mind, it is like conditioning. We're just like animals we need to be conditioned to just not do
something that's bad. They know if they do something good, reward will enforce good behavior. - (S3)
Discussion
The adverse effects of smartphone engagement, especially in academia, warrant attention. Engaging with
one's phone impacts the quality and time spent on academic tasks. While some students may have found a
way to keep their smartphones away during lectures (Bonneville & Riddell, 2023), a high percentage of
students do not see smartphone engagement as a problem and would still prefer to engage with their
smartphones in academic settings (Kelly, 2017; B. McCoy, 2020). Leveraging the motivational model of
self-regulation training to mitigate in-class smartphone engagement (RQ1) proved promising. This could
be a potential solution for smartphone dependence among university students. The frequency distribution
and t-test results (Figures 1 & 2) show that the rewards group had better learning outcomes than the control
group. Students appear to learn more when they do not engage with their smartphones while in class.
We suggest conducting such interventions in early introductory courses so students learn to self-regulate
and be more attentive for the rest of their academic journey and beyond.
Our experiment, conducted in an academic setting, supports prior lab research (Stothart et al., 2015; Ward
et al., 2017) and validates findings from self-report studies (Ames, 2013; B. McCoy, 2020). A later study of
the same participants will possibly validate the long-term impact of the intervention and examine if these
students manage to self-regulate without extrinsic rewards.
While engaging with one's smartphone may not seem problematic, its usage can unintentionally extend
beyond the time or space intended. For instance, interviewees reported that they like to use their
smartphones in liminal times and spaces. However, such usage can leak into other formal and informal
structures. Students reported even in classes where they are required to use their smartphones to answer
poll questions, they switch to social media or instant messaging right after answering the poll question.
Implications
Looking at the outcomes of smartphone engagement in learning, the best way to encourage student focus
and attention is to keep their smartphones away from them during lectures. The literature supports the idea
that the availability of a smartphone in class may interfere with students' cognitive ability and working
memory, which are necessary for processing and learning new material.
Even when out of view, a smartphone's persistent and always-on notifications act as a beacon to draw users'
attention. While limitations on notifications may help keep smartphones out of mind, they do not
completely address the availability issue. The key difference between the solution presented in this research
and other approaches discussed in the literature is the availability of smartphones. Most solutions
introduced by the research and available in the app stores require the smartphone itself to intervene with
the user's interaction with it. This can be problematic because even though the students want to block
notifications, the mere availability of the smartphone can act as a distraction that could seamlessly lead to
an interruption, affecting their focus on their primary task (studying).
Furthermore, completely keeping smartphones away from students during class is not practical or feasible.
In a survey, 93.1% of university students voted against banning smartphones in the classroom (B. McCoy,
2020). In the same study, however, students indicated they would turn off all "non-class digital
Reducing In-Class Smartphone Usage Through Extrinsic Rewards
Thirty-first Americas Conference on Information Systems, Montréal, 2025 9
distractions" for an average of 7.8% extra credit toward their final grades. This study expands on the
mentioned self-report survey and objectively measures the impact of bonus points as extra credit.
Regarding punishment and reward structures, we observed that many students experienced a lack of
consequences for using their smartphones in class despite existing course policies limiting or banning their
use. The lack of consequences or negative reinforcement was, in essence, condoning in-class smartphone
engagement. Our results indicate that reward structures positively correlated with reduced smartphone
usage and student learning outcomes in a single instance. We found self-regulation training's motivation
model promising in addressing in-class smartphone engagement. The bonus points (extrinsic motivation)
overcame the urge to use the smartphone in the classroom (intrinsic motivation).
We argue that if removing smartphone availability is not a viable option, another way to address the in-
class smartphone engagement issue may be using positive reinforcement, such as rewarding students for
not using their phones. However, we theorize that an optimal solution may include a mix of both positive
and negative reinforcement with consistent enforcement of the policy.
Conclusion
This research examined extrinsic motivation’s in regulating in-class smartphone engagement, focusing on
its impact on learning outcomes and sought student perceptions of smartphone usage in academic settings.
Results indicated that offering bonus points helped mitigate the urge to use smartphones, suggesting a
potential solution for managing smartphone engagement in academia. Students who refrained from phone
use during lectures scored significantly higher on quizzes than those who did engage with their phones.
These findings validate previous lab and self-report studies through a semester-long experiment. Follow-
up interviews revealed that students often reported boredom, convenience, and lack of consequences as key
reasons for in-class smartphone use.
REFERENCES
Afzali, F. M., & Morrison, B. B. (2018). Cellphone Usage in Academia: The Problem and Solutions. ACM
Conference on Computer Supported Cooperative Work and Social Computing, November 2018, 325–328.
Afzali, F. M., Rastegari, E., & Ning, W. (2025). Students and Smartphone Usage: Influencing Factors. 2025
ACM Southeast Conference (ACMSE 2025), April, 2025, Cape Girardeau, MO, USA, 1, 156–163.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes,
50, 179–211.
Berkman, E. T. (2016). Self-regulation training. Handbook of Self-Regulation: Research, Theory and
Applications, 3rd Ed. Guilford, New York.
Bonneville, L., & Riddell, D. (2023). Smartphones in the university classroom: less problematic than we
tend to think? Information Communication and Society, 26(5), 1008–1022.
Bowman, L. L., Levine, L. E., Waite, B. M., & Gendron, M. (2010). Can students really multitask ? An
experimental study of instant messaging while reading. Computers & Education, 54(4), 927–931.
Creswell, J. N., & Poth, C. N. (2018). Qualitative Inquiry: Choosing Among Five Traditions. Sage.
DeGusta, M. (2012). Are Smart Phones Spreading Faster than Any Technology in Human History? - MIT
Technology Review. MIT Technology Review. https://bit.ly/2GOpU9h
Duckworth, A. L., & Seligman, M. E. P. (2012). Self-Discipline outdoes IQ in predicting academic
performance of adolescents. Psychological Science, 16(12), 939–944.
Elpidorou, A. (2018). The bored mind is a guiding mind: toward a regulatory theory of boredom.
Phenomenology and the Cognitive Sciences, 17(3), 455–484.
Reducing In-Class Smartphone Usage Through Extrinsic Rewards
Thirty-first Americas Conference on Information Systems, Montréal, 2025 10
Engelberg, J. K., Hill, L. L., Rybar, J., & Styer, T. (2015). Distracted driving behaviors related to cell phone
use among middle-aged adults. Journal of Transport & Health, 2(3), 434–440.
Facebook. (2017). Always Connected: How Smartphones and Social Keep Us Engaged. In How
Smartphones and Social keep us engaged. https://tinyurl.com/pj8pvvct
Geiger, M., Dinh, H., & Remus, U. (2023). Constantly Distracted - Understanding How Users Cope With
Smartphone Distractions. ECIS.
Kelly, R. (2017). Survey: 94% of Students Want to Use Their Cell Phones in Class. Campus Technology.
Laricchia, F. (2024, June 12). Smartphones - statistics & facts | Statista. Statista.
MacKay, J. (2019). Screen time stats 2018: How your phone impacts your workday -- RescueTime.
Marsden, E., & Lee, P. (2016). There’s no place like phone. In Deloite Global Mobile Consumer Survey 2016.
McCoy, B. (2020). Gen Z and Digital Distractions in the Classroom: Student Classroom Use of Digital
Devices for Non-Class Related Purposes. Journal of Media Education, 11(2), 5–23.
Misra, R., & McKean, M. (2000). College students’ academic stress and its relation to their anxiety, time
management, and leisure satisfaction. American Journal of Health Studies, 16(1), 41–51.
Mobile Fact Sheet. (2024, November 13). Pew Research Center. https://tinyurl.com/57959mxf
Olson, J. A., Sandra, D. A., Langer, E. J., Raz, A., L, S. P., L Veissière, S. P., & L Veissi ere, S. P. (2023).
Creativity and Smartphone Use: Three Correlational Studies. International Journal of Human-Computer
Interaction, 39(14), 2920–2925.
Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the
National Academy of Sciences of the United States of America, 106(37), 15583–15587.
Parry, D. A., le Roux, D. B., & Bantjes, J. R. (2020). Testing the feasibility of a media multitasking self-
regulation intervention for students: Behaviour change, attention, and self-perception. Computers in
Human Behavior, 104, 1–17.
Perrin, A., & Atske, S. (2021). 31% of American adults are online almost constantly. Pew Research Center.
Pielot, M., Church, K., & de Oliveira, R. (2014). An in-situ study of mobile phone notifications. Proceedings
of the 16th International Conference on Human-Computer Interaction with Mobile Devices & Services -
MobileHCI ’14, 233–242.
Rosen, L. D., Mark Carrier, L., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced
task-switching while studying. Computers in Human Behavior, 29(3), 948–958.
Ryan, R. M., & Deci, E. L. (2000). Self-Determination Theory and the Facilitation of Intrinsic Motivation,
Social Development, and Well-Being. American Psychologist, 55(1), 68.
Sapci, O., Elhai, J. D., Amialchuk, A., & Montag, C. (2021). The relationship between smartphone use and
students` academic performance. Learning and Individual Differences, 89, 102035.
Silvia, P. J., & Duval, T. S. (2001). Objective self-awareness theory: Recent progress and enduring problems.
Personality and Social Psychology Review, 5(3), 230–241.
Troll, E. S., Friese, M., & Loschelder, D. D. (2021). How students’ self-control and smartphone-use explain
their academic performance. Computers in Human Behavior, 117.
Ward, A. F., Duke, K., Gneezy, A., & Bos, M. W. (2017). Brain Drain: The Mere Presence of One’s Own
Smartphone Reduces Available Cognitive Capacity. Journal of the Association for Consumer Research, 2(2),
Winnick, M. (2016). Putting a Finger on Our Phone Obsession. Dscout. https://tinyurl.com/3w98cwt8
Zhao, L. (2023). Social media multitasking and college students’ academic performance: A situation–
organism–behavior–consequence perspective. Psychology in the Schools, 60(9), 3151–3168.
