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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




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


























































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




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










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,







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




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




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: 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


American Automobile Association Foundation for


Traffic Safety. (2008). 2008 Traffic Safety Culture Index. Washington, DC: AAA Foundation


for Traffic Safety. Downloaded from






Bayer, J. B., Klein, R. D., & Rubinstei...


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