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EFFECTS OF CLASSROOM CELL PHONE
USE ON EXPECTED AND ACTUAL LEARNING
ARNOLD D . FROESE
CHRISTINA N . CARPENTER
DENYSE A . INMAN
JESSICA R . SCHOOLEY
REBECCA B . BARNES
PAUL W . BRECHT
JASMIN D . CHACON Sterling College
Studies of driving indicate that the conversational aspects of
using cell phones generate high risks from divided attention.
Prior surveys document high rates at which students carry
phones to and use them during class. Some experiments have
demonstrated that cell phones distract students from leaming.
The present studies combined survey and experimental methods
to determine student expectations about and actual performance
under cell phone use conditions. On the survey, students estimated the number of questions they could answer out of 10 when
texting and when not texting. For the experiment, we used a
repeated measures design with simulated classroom presentations and measured performance on a 10-item quiz. Students
expected to lose close to 30% on a quiz and actually did lose
close to 30% when texting. We discuss implications of our
methodology and our findings for improving student leaming. Studies of drivers using cell phones
reveal that the cognitive distraction of conversations significantiy increases accident
risk. The National Safety Council (2010)
published a literature review explaining
why cognitive load from cell phones produces inattention blindness for drivers. messages, and manual instead of verbal
"talking" as they reply. If conversational
cognitive load increases accident risk for
drivers, the same cognitive load should
increase errors on tests of lesson material
presented while students are texting. Strayer and Johnston (2001) showed that Survey Research listening to music or even to a recorded
Researchers have explored the disbook did not produce high accident risks, tracting effects of cell phones in classrooms
as did conversing on cell phones.
using surveys. Many students admit to
These findings are important for con- using cell phones for social networking
sidering the potential effects of classroom purposes in the classroom (Bayer, Klein,
texting on students' ability to leam pre- & Rubinstein, 2009; Besser, 2007;
sented material. Texting is conversational, Kennedy & Smith, 2010; Rubinkam,
though it involves visual instead of audi- 2010). Some studies documented perceptory "listening" as students read incoming tions of distraction from phone ringing
323 324 7 College Student Journal (Campbell, 2006) and from texting or sending instant messages during a class or study
session (Besser, 2007; Kennedy & Smith,
2010; Levine, Waite, & Bowman, 2007).
These studies employed survey responses
to evaluate effects.
The typical measurement scales for
such reports are quandtatively weak. For
example. Besser (2007) and Kennedy and
Smith (2010) measured student percepdons of the effects of cell phone use on
class performance using statements with
which respondents either agreed or disagreed. Besser's statement was about
texting drawing attendon away from class,
and Kennedy and Smith's statement was
about these acdvities helping class performance. These nominal measurements
do not provide informadon about the quantity of expected information loss. Other
researchers (Campbell, 2006; Levin, Waite,
& Bowman, 2007) have expanded the number of response options. For example,
Campbell (2006) used a 5-point Likert
scale ranging from strongly agree to
strongly disagree to evaluate student atdtudes about the disruptive effects of ringing
phones. Although these scales increase
response variability, there is no clear reladonship between level of agreement with
a statement such as "when a mobile phone
rings during class, it is a serious distraction" and any quandty of informadon loss.
The absence of clarity about the expected size of the effect presents addidonal
interpredve problems. Some researchers
have found a difference between expressed
attitudes about phone risks and actual
behavior. An American Automobile Association Foundation for Traffic Safety
(2008) survey showed that drivers viewed cell phone use as a serious safety risk. Nevertheless, 46% of those claiming that such
use was an "extremely serious risk" sdll
reported using their phones while driving
within 30 days prior to the interview.
Kennedy and Smith (2010) reported similar discrepancies in student behavior.
Although students generally "agreed" that
cell phones disrupted classroom leaming,
they persisted in using their cell phones in
the classroom. Levels of agreement do not
clearly indicate the size of the expected
effect. If respondents agree that risk is
increased, but perceive that the risk is low,
they may feel jusdfied in ignoring the risk.
Experimental Research Some researchers have employed experimental techniques to assess actual effects
of cell phone activity on classroom-related performance. Bowman, Levine, Waite,
and Gendron (2009) and Fox, Rosen, and
Crawford (2009) compared comprehension scores for students who were or were
not sending instant messages during a nonclass reading task. Neither study revealed
differences in comprehension, but completing the reading took significandy longer
for those engaged in instant messaging.
These results do not generahze to a lecture or discussion-based classroom
environment where students do not control the dming of informadon.
Other researchers have experimentally
explored distracdon from a cell phone ringing in a classroom. In two studies,
researchers compared classroom scores for
material when no phone was ringing to
scores when a phone was ringing (End,
Worthman, Mathews, & Wetterau, 2010;
Shelton, Elliott, Eaves, & Exner, 2009). In Effects of Cell Phone Use on Learning… / 325 both studies, performance deteriorated significantly for material presented during the
ringing condition. Performance decrements
ranged from 25-40% during ringing
periods. These two studies addressed distraction effects for bystanders and left open
the question of distraction for texting performers.
Ellis, Daniels, and Jauregui (2010) most
directly assessed the effects of texting on
performers in a real classroom context.
Students in the experimental condition sent
three text messages to the instmctor during the lecture. The control group
presumably had tumed their phones off.
Experimental students scored significantly lower than control students did on a pop
quiz at the end of class. Although this
experiment comes directly from a classroom setfing, sending a text message to a
teacher who does not respond is likely not
as distracting as a conversational texting
dialogue.
Purpose The above studies begin to explore how
texting changes classroom leaming. However, their limitations suggested the
following research strategies. First, we
designed both a survey to assess how much
information students thought they would
lose if they were texting, and a corresponding experiment to explore the actual
loss of information. Second, we generated a survey response scale that had stronger
numerical properties than dichotomous or
Likert-scale response options. Third, our
survey response scale had numerical properties that matched those of our
experimental outcome variable. This match
allowed us to compare quantity estimates of expected quiz score changes with experimental performance scores. Finally, we
designed an experiment that approximated both the classroom environment and
students' texting experiences. Hearing a
cell phone ringing in a class distracts leamers from lesson content. However, if
increased cognitive load explains leaming
deficits from texting distraction, the most
invasive distraction should occur for
students actively engaged in texting conversations during a class. Implementing
these developments permitted us to compare expected and actual effects of
non-class-related texting on classroom
leaming. We expected that students would
be aware of leaming decrements produced
by texting, and that their actual performance would confirm that expectation.
Study 1 This study employed a self-report survey to assess students' cell phone activity
in classes and their expectations of the
effects of such activity on leaming outcomes. Unlike previous studies using
self-report measures, we created a measure of anticipated leaming deficits from
texting based on measurements common
to classroom settings.
Method Participants. We collected surveys
from 693 students at seven colleges and
universities across the United States during October through December, 2009.
Seven teachers at these schools administered the surveys in their classes during
class time. Participants' average age was
20.5 years. Ninety-nine percent owned cell
phones. They had owned cell phones an 326 / College Student Journal
Table 1
Verbal and Quantitative Comparison of Self-Described Texting How Would
You Describe
Yourself as a
Text User?
Total How Often Do You Text in a Day?
0 -25 2 6 – 5 0 51 -75 76-100 100+
times
times
times times times
0
0
0
0
Emergency-only
5
1
1
1
7
53
Minimal
14
46
23
87
84
Moderate
139
76
54
70
21
Avid
154
117
100
148
163 average of 5.4 years and used texting functions an average of 4.1 years.
Instrument. Our survey requested
demographic information from students
(summarized above), and information
about frequency of carrying their phones
and texting frequency in various daily
activity contexts. Participants also estimated their expected learning performance
if they texted during class. Our metric for
performance was the question, "If you were
listening to some information, and someone asked you 10 factual questions about
that information, estimate the number of
questions you might be able to correctly
answer?" Participants answered that question for two conditions?if they were and
were not texting while they listened to the
information.
Procedure. Instructors read an introductory script to their classes that provided
instructions and the informed consent
option of not completing the survey. Surveys were confidential, and students
completed them during a 6-minute time
limit. Total
5
63
254
360
682 Results More than half (52.8%) of our respondents described themselves as "avid users"
and 90% described themselves as moderate or avid users. These verbal categories
corresponded with reported number of
texts sent per day, r^ (682) = .612,p < .01,
as shown in Table 1.
Most students carry their phones to
class. Seventy-five percent reported carrying phones to class "always," and another
16.4% said "most ofthe time." These carrying frequencies were lower than when
students performed daily errands (87%
reported "always"), but higher than when
in leisure activity (72% reported "always"),
at work (61% reported "always"), or
attending church (46% reported "always").
Students predicted scoring significantly better if not texting (M = 8.93, SD =
1.68) than if texting (M= 6.01, SD = 2.25),
i(676) = 31.31,/? < .01, effect size (t/ ^/Ñ)
= 1.20. Low-frequency users expected
greater decrements from texting (M=4.16,
SD = 2.77) than did moderate (M = 3.01,
SD = 2.24) or higher-frequency users (M
= 2.61, SD = 2.41), F(2, 672) = 12.14, p Effects of Cell Phone Use on Learning… / 327 < .01, effect size (rf) = .035. A Tukey posthoc test indicated that the low-frequency
users differed significanüy from both higher-frequency users.
Discussion These data confirm prior reports of the
ubiquity of cell phones in the classroom
(Bayer, Klein, & Rubinstein, 2009; Besser, 2007; Kennedy & Smith, 2010;
Rubinkam, 2010). They add contextual
informadon to classroom frequency data,
indicating that the classroom presents
fewer inhibidons to phone use than do
church and work setdngs.
More importandy, these data present a
strong metric for expected leaming effects
of phone use in the classroom. Researchers
can directly compare expected point losses on a 10-item quiz to actual performance
from a classroom experiment.
Study 2
We designed a simulated classroom in
which we manipulated student texdng. Otir
goal was to establish actual effects of texdng on quiz performance, and compare
this performance with expectadons derived
from the survey in Study 1.
Method Participants. We randomly selected 82
names from a complete college student list,
and 40 of these students (21 men and 19
women) agreed to pardcipate. We believe
this procedure produced a much better
sample of students than the typical General Psychology student sample receiving
course credit for participadon. Our sample
derived from random selecdon, and par- dcipants received no incendves for participation beyond being involved in and
receiving informadon about the results of
the project.
Materials. Pardcipants brought their
personal cell phones to a classroom that
contained a computer, a projector and
screen, and sound connecdvity. Students
had access to pencils and blank paper so
they could take notes. Another room across
the hallway was available for break periods between sessions and for
co-experimenters who texted participants
during tesdng.
We prepared two lessons for participants. Each lesson provided author and
content information about the books,
"Young Men and Fire" by Norman
Maclean (1992), and "Let the Great World
Spin" by Colum McCann (2009). No pardcipant indicated any prior knowledge of
either book. Each presentadon consisted
of a prerecorded narrative and accompanying, self-timed, PowerPoint presentadon
that lasted about 6 minutes. The presentations simulated classroom teaching. For
each presentadon, we prepared a 10-item
multiple-choice quiz. We pretested the
quizzes with people who had not read the
books and modified them so that pretest
scores were close to chance levels.
Procedure. We tested all participants
twice?once while texdng and once while
not texdng. We counterbalanced all story
and condition orders, and each story
appeared an equal number of dmes in each
order condidon. We tested texdng and nontexting pardcipants simultaneously in small
groups depending on when pardcipants
could attend. Texdng and non-texdng par- 328 / College Student Journal ticipants sat on different sides of the room
to reduce distraction. Co-experimenters
sat in the room across the hall.
We told all participants that they would
watch an informational presentation; they
could take notes if they desired; and they
should try to retain the presented information for a quiz following a 5-minute
break. During the break, participants had
access to refreshments. They were told not
to discuss the content of the presentation.
We identified the texting condition for
each participant before each presentation.
The texting participants set their phones
on vibrate, and were free to respond immediately to any texts that arrived. The
non-texting participants turned off the
vibrate function, placed their phones out
of sight and did not use their phones during the presentation. Following the first
quiz, the groups switched conditions for
the second presentation.
The co-experimenters confirmed phone
functionality with participants before the
experiment began. Following confirmation, the experimenter signaled
co-experimenters to begin texting the participants. When all texting participants
received their first message, the experimenter started the PowerPoint presentation.
Co-experimenters exchanged messages as
quickly as possible with assigned participants throughout the presentation. We
prepared a list of texting topics involving
general introductory information, but
allowed texting content to develop spontaneously throughout the interactions. Results Quiz scores were significantly lower
when students texted (M = 6.02, SD =
2.224) than when they did not text (M =
8.25, SD = 1.597), i(39) = 5.34, p < .01,
effect size (t/ ^//V) = .84. The difference in
scores represented a 27% decline during
texting from the non-texting performance.
Neither the story during which they texted,
nor the order of texting and non-texting,
produced different results.
For a convenience sample of 15 students, we recorded the time participants
actually spent reading or texting on their
phone during the texting phase. Participants spent an average of 2.69 minutes
engaged in texting during the presentation.
The range of texting times was from 1.5 to
4.25 minutes. Time engaged in texting was
negatively, though not significantly, correlated with quiz score in the texting phase,
076
Discussion

Our data support a prior report (Ellis,
Daniels, & Jauregui, 2010) of deleterious
effects of texting on classroom leaming..
Score reductions for texting conditions
were greater in our experiment than in the
prior experiment. Our methodological
addition of conversational texting may
account for our greater score reductions.
Although the correlation between texting time and texting score was not
significant, the direction and size of the
correlation leave open possibilities that
level of engagement in texting is a factor
in losing classroom information.
Our method presents a strong tool for
evaluating the effects of texting on leam- Effects of Cell Phone Use on Learning… 7 329 ing. The counterbalanced, repeated-measures design controlled subject and order
variables. The pre-recorded presentadons
equated lesson materials for all pardcipants across tesdng sessions. Nevertheless,
due to phone connectivity differences, pardcipants spent widely differing amounts
of dme actually engaged in texdng. We
expect that methodological refinements
could demonstrate even greater loss of
informadon than we found.
General Discussion
Our research successfully implemented a survey measure of students'
expectadons about the effects of texdng
on leaming that was comparable to typical classroom measures?predicted quiz
scores. The measure is quantitatively
strong?a rado measurement scale?and
easy for respondents to understand. The
data confirmed that self-report measures
can provide informadon that is verified in
experimental outcome studies. One
remaining limitadon is that students may
fail to account for chance performance levels associated with multiple-choice
quesdons. With four response altemadves,
that chance level?25%?represents no
significant leaming. It is likely that those
students who predicted scores lower than
chance did not understand this baseline
minimum.
The texdng manipuladon in the simulated classroom environment more closely
approximated texting during real class sessions than previous experiments. Students
in the texdng condidon responded to messages from their own friends as well as
from co-experimenters. The messages engaged pardcipants in conversadon, a procedure that the driving studies (Nadonal
Safety Council, 2010; Strayer & Johnston,
2001) suggested as a source of distraction
and one that was missing from the Ellis,
Daniels, and Jaurgui (2010) study. This
engagement likely accounted for more
informadon loss in our study than Ellis,
Daniels, and Jaurgui (2010) found. Furthermore, the conversations occurred
simultaneously with the lesson presentation, unlike the studies reported by
Bowman, Levine, Waite, and Gendron
(2009) and Fox, Rosen, and Crawford
(2009). The differences in informadon loss
that we obtained, in contrast to Bowman,
Levine, Waite, and Gendron (2009) and
Fox, Rosen, and Crawford (2009) support
the idea that cognitive load increases when
informadon presentadon conflicts with texting communications. One remaining
difference between our experimental setting and a real classroom is that some
students commented about how different
it was to freely text during a classroom
presentation.
Our data confirm that students expect
texdng to dismpt their classroom leaming,
and that texting does dismpt leaming. The
real score declines (27%) approximated
the expected dechnes (33%). The somewhat higher expected declines could have
occurred as students failed to account for
the 25% chance baseline and from texdng
requirements that did not occupy all ofthe
lesson dme. The corresponding declines
for self-report and experimental measurements suggest that students are aware that
using cell phones for personal communication in class compromises classroom 330 / College Student Journal leaming. Thus, our data support the value
of self-reports of the effects of using cell
phones on leaming, at least as presented
with the measurement tools we used.
Survey participants varied considerably
in their score predictions under texting conditions. Some participants expected no
detrimental effects of texting. Similarly,
experimental participants varied considerably in their quiz scores under texting
conditions. Some texting participants
answered all questions correctly. We do
not know if each participant's expected
and actual performance measures were correlated because different participants
completed the survey and the experiment.
These data could reflect the same kind of
discrepancies reported by the American
Automobile Association Foundation for
Traffic Safety (2008) between participants'
expectations of safety risks for others but
false immunity from risk for self. Further
research could solicit information loss
expectations from experimental participants to determine whether students can
accurately predict their own distractibilityStanovich (2009) summarized two
aspects of rationality?epistemic and
instmmental. Epistemic rationality exists
when a person's view ofthe way the world
works matches the way it actually works.
The correspondence of average expected
and actual losses in our studies suggests a
degree of epistemic rationality. Participants
really do know what happens when students text. Instrumental rationality is
evident when a person sets a goal and follows appropriate steps to achieve that goal.
Our data suggest deficits in instmmental
rationality for students who pay to become educated, yet choose to engage in counterproductive behaviors.
Given that students generally expect
texting to dismpt their leaming, researchers
can reasonably ask why students risk
potential failure to maintain social contact? Wei and Wang (2010) recently
explored two models of student motivation for classroom texting. They predicted
that instmctor immediacy?making eye
contact, calling students by name, talking
with students outside of class, among other
behaviors?could enhance students' motivation to learn and thus reduce texting.
Altematively, students' habits and gratifications they receivefi-omthe activity could
maintain texting. Their data confirmed that
immediacy enhanced motivation to leam,
but that motivation did not correlate with
texting rates. They concluded that the
habits and gratifications model better fits
their data. These results raise questions
about how phone carrying habits and phone
checking impulses relate to instmctional
variables. Students may benefit from knowing whether carrying their phones to class
increases their impulses to check for messages. Likewise, teachers may want to
know if interruptions to lesson flow
increase students' urges to check their
phones. These possibilities present fertile
ground for future research.
Finally, faculty variations in handling
texting events in classrooms may affect
student behaviors in ways that alter leaming. Further research could explore
differences between faculty and students
in perceptions of the effects of texting as
well as of techniques for handling unwanted texting in class. Knowing such
perceptions and the effectiveness of inter- Effects of Cell Phone Use on Learning… / 331 venfion techniques in the context of the
demonstrated effects of texting could
improve classroom environments and
enhance student learning. Bowman, L. L., Levine, L. E., Waite, B. M., &
Gendron, M. (2009). Can students really multitask? An experimental study of instant
messaging while reading. Computers & Education, 54, 927 – 931 .doi : 10.1016/j .compedu 2
009.09.024. Author Note
Denyse A. Inman is now at the School
of Behavioral Sciences, California Baptist
University. Christina N. Carpenter is now
at Bailey, Colorado. Jasmin D. Chacon is
now at the Department of Psychology, Gallaudet University.
We thank Brian Allen, Andrea
Mehringer, and Jeffi-ey Ropp for assistance
in conducting the research, coding data,
and discussing our ideas.
Address correspondence concerning
this article to Arnold D. Froese, Psychology Department, Sterling College, Sterling,
KS 67579. E-mail: [email protected] Campbell, S. W. (2006) Perceptions of mobile
phones in college classrooms: Ringing, cheating, and classroom policies. Communication
Education, 55, 280 – 294. doi: 10.1080/0363
4520600748573. References
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