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Submitted 20 February 2014Accepted 19 August 2014Published 16 September 2014

Corresponding authorVeerasathpurush Allareddy,[email protected]

Academic editorFred Muench

Additional Information andDeclarations can be found on

page 7DOI 10.7717/peerj.572

Copyright2014 Nalliah and Allareddy

Distributed underCreative Commons CC-BY 4.0

OPEN ACCESS

Students distracted by electronic devicesperform at the same level as those whoare focused on the lectureRomesh P. Nalliah 1 and Veerasathpurush Allareddy 2

1 Department of Global Health, Harvard School of Dental Medicine, Boston, MA, USA2 Department of Orthodontics, College of Dentistry, The University of Iowa,

Iowa City, IA, USA

ABSTRACTBackground. Little is known about the characteristics of internet distractions thatstudents may engage in during lecture. The objective of this pilot study is to identify some of the internet-based distractions students engage in during in-person lectures.The ndings will help identify what activities most commonly cause students to bedistracted from the lecture and if these activities impact student learning.

Methods. This study is a quasi-experimental pilot study of 26 students from a singleinstitution. In the current study, one class of third-year students were surveyed after alecture on special needs dentistry. The survey identied self-reported utilization pat-terns of smart devices during the lecture. Additionally, twelve quiz-type questionswere given to assess the students recall of important points in the lecture materialthathad just been covered.Results. The sample was comprised of 26 students. Of these, 17 were distracted insome form (either checking email, sending email, checking Facebook, or sendingtexts). The overall mean score on the test was 9.85 (9.53 for distracted students and10.44 fornon-distracted students). There were no signicant di ff erences in test scoresbetween distracted and non-distracted students ( p = 0.652) . Gender and types of distractions were not signicantly associated with test scores ( p > 0.05) . All studentsbelieved that they understood all the important points from the lecture.Conclusions. Every class member felt that they acquired the important learningpoints during the lecture. Those who were distracted by electronic devices duringthe lecture performed similarly to those who were not. However, results should beinterpreted with caution as this study was a small quasi-experimental design andfurther research should examine the inuence of di ff erent types of distraction ondiff erent types of learning.

Subjects Science and Medical Education, HumanComputer InteractionKeywords Flipped class room, Learning methods, Dental education

INTRODUCTIONMost students currently enrolled in dental schools in the United States (US) were bornin the 1980s or 1990s ( Zickuhr, 2010 ; Elam, Stratton & Gibson, 2007 ). This generationis referred to as Generation Y (Gen Y) and they function very di ff erently to previous

How to cite this article Nalliah and Allareddy (2014), Students distracted by electronic devices perform at the same level as those who

are focused on the lecture. PeerJ 2:e572; DOI 10.7717/peerj.572
mailto:[email protected]:[email protected]://peerj.com/academic-boards/editors/https://peerj.com/academic-boards/editors/http://dx.doi.org/10.7717/peerj.572http://dx.doi.org/10.7717/peerj.572http://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/http://dx.doi.org/10.7717/peerj.572http://dx.doi.org/10.7717/peerj.572https://peerj.com/http://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/http://dx.doi.org/10.7717/peerj.572http://dx.doi.org/10.7717/peerj.572https://peerj.com/academic-boards/editors/https://peerj.com/academic-boards/editors/mailto:[email protected]:[email protected]

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choice questions and each question was assigned one point. The primary independentvariables of interest were if the students were distracted, type of distraction, and gender.The distribution of test scores was compared between distracted and non-distractedstudents using Mann-Whitney U tests. Two multivariable linear regression models were

used to examine the simultaneous inuence of gender and distraction on test scores. Inthe rst model, a composite variable (whether the student was distracted by any form)for distraction and gender were used as independent variables. In the second model, thediff erent types of distractions (checking email, sending email, checking Facebook, orsending text) were used separately along with gender. In both models, the test scores werethe outcome variable. Both regression models were t using the Ordinary Least Squaresapproach. All tests were two sided and a p-value of < 0.05 was deemed to be statistically signicant. All statistical analyses were conducted using SPSS Version 22.0 software (IBMCorp, Research Triangle Park, NC).

RESULTSThere were 27 students who participated in this lecture. One student did not complete thetest or survey and was omitted from the evaluation leaving a nal sample of 26 students.The nal sample was comprised of 8 males and 18 females.

During the lecture, 57.7% reported that they checked their email and 11.5% reportedsending an email, 15.4% checked Facebook, and 7.7% sent a text message. Of those whochecked their email, 69% used their smartphone, 18% used their laptop and 13% usedan iPad. Seventeen of the 26 students (65.4%) were distracted in some form during thelecture.

There were 12 post-lecture multiple choice questions related to the lecture materials andthe proportion of the class that got each question correct is listed: Q1, 73%; Q2, 65%; Q3,69%; Q4, 96%; Q5, 81%; Q6, 81%; Q7, 85%; Q8, 92%; Q9, 96%; Q10, 58%; Q11, 88%;Q12, 100%. On a scale of 0 to 12 (with 1 point for each question), the mean score for theclass was 9.85 (standard deviation is 2.89). The distribution of test scores are summarizedin Table 1. The mean score amongst those that were distracted was 9.53 (compared to10.44 for students that were not distracted. Overall, there was no signicant di ff erence indistribution of test scores between students that were distracted and not distracted ( p-valufrom Mann-Whitney U test is 0.652). In the survey, 100% of students believed that they understood all the important points from the lecture.

Results of the multivariable linear regression analysis examining the simultaneousassociation of gender and distraction on test scores are summarized in Table 2. After

adjustment for the e ff ects of distraction, males were associated with 2.6 points higherscores compared to females. However, this was not statistically signicant ( p = 0.052) .After adjustment for the e ff ects of gender, those who were distracted did not have asignicantly diff erent score when compared to those who were not distracted ( p = 0.955) .Genderand distraction explained 17.5% of variance in test scores.

Results of the multivariable linear regression examining the e ff ects of diff erent typesof distractions and gender on test scores are summarized in Table 3. Overall, there were

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Table 1 Distribution of Scores (Scale of 0 to 12).

Measure All students Students that weredistracted ( N = 17)

Students that were notdistracted ( N = 9)

p-value

Mean 9.85 9.53 10.44 0.652Standard deviation 2.89 3.20 2.24Minimum 4 4 625th percentile 7.75 6 8.50Median 12 12 1275th percentile 12 12 12Maximum 12 12 12

Notes.Mann-Whitney U test was used to compare distribution of scores between distracted and non-distracted students.

Table 2 Multivariable linear regression analysis for examining the e ff ects of gender and distractionon test scores.

Independent variable Parameter estimate (95% CI) p-value

GenderMale 2.60 ( 0.025.21)Female Reference

0.052

DistractedYes 0.07 ( 2.472.61)No Reference

0.955

no statistically signicant diff erences in test scores for the diff erent types of distractions:checking email (estimate is 0.88, p = 0.476), sending email (estimate is 2.40, p = 0.166),

checking Facebook (estimate is 2.16, p = 0.293), or sending text (estimate is 3.66, p = 0.199) after adjusting for the e ff ects of gender (estimate is 2.44, p = 0.007). In thismodel, gender anddi ff erent typesof distractions explained 32% of variance in test scores.

DISCUSSIONLecture theaters used to be the only source of information but education is moving in thedirection of readily available information that is convenient and accessible at all timesthrough electronic resources. Current generations of students are thought to requiremore engaged teaching modalities ( Massachusetts Institute of Technology, 2014). In fact,a Pew report found that 87% of teachers believed modern technology was creating aneasily distracted generation of students with short attention spans ( Purcell et al., 2012).Another Pew study showed that 24% of Gen Y report that technology use is what makestheir generation unique ( Pew Research Center, 2010 ). However, little is known about theimpact on learning of being engaged in electronic activities during lecture. This paperreports outcomes of a small study that was designed to evaluate the learning outcomes of atraditional lecture among Gen Y students.

In the current study, students attended a traditional lecture and were given a post-testabout the lecture topic and a questionnaire. The questionnaire asked the students whether


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Table 3 Multivariable linear regression analysis for examining the e ff ects of gender and di ff erenttypes of distractions on test scores.

Independent variable Parameter estimate (95% CI) p-value

GenderMale 2.44 ( 0.235.10)Female Reference

0.07

Distracted by checking emailYes 0.88 ( 3.391.64)No Reference

0.476

Distracted by sending emailYes 2.40 ( 1.085.87)No Reference

0.166

Distracted by checking Facebook Yes 2.16 ( 6.322.01)No Reference

0.293

Distracted by sending textYes 3.66 ( 2.089.39)No Reference

0.199

they checked/sent email, checked Facebook accounts or sent text messages during thelecture. Certainly, there is a possibility that students were not completely honest withtheir answers and our ndings may be an underrepresentation of the actual amount of involvement with electronic devices and the Internet that was unrelated to the lecture. Wefound that 57.7% of students checked their email; 11.5% sent an email; 15.4% checkedtheir Facebook accounts and 7.7% sent a text message during the lecture. Remarkably,

the distracted group (those that engaged in one of these activities during the lecture)performed similarly in the post-test to the undistracted group.A total of 65.4% of the class reported engaging in distracting behavior such as

emailing, using Facebook or texting. Nonetheless, 100% of the students believed thatthey had understood the important concepts discussed in the lecture. However, in somequestions, only 58% of students knew the correct answer. The major concern is that allstudents believed they understood the important concepts but there were three questionsin the post-test where less than 70% knew the correct answer. Overall learning outcomeswere not ideal, however, the group that reported being distracted performed similarly to the group that said they were not distracted. Existing research concurs with thisnding and reports that media multitasking was not related to self-reported di ffi culties indistractibility ( Pew Research Center, 2010 ). In the current study, 58.8% of the distractedgroup and 55.6% of non-distracted answered all questionscorrectly.

Notably, in the current study all males who were engaged in a distracting behaviorscored 100% in the post-test. However, among females engaged in distracting behaviorsonly 50% got all questions correct. The mean score for males was 11.63 points compared to9.06 in females. However, the overall scores were not statistically signicantly diff erentbetween males and females. The small sample size in the current study could have


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variance in test scores. This clearly shows that there could be a multitude of other variablesapart from gender and distractions which could inuence test scores. Consequently, theissue of omitted variables bias should not be discounted. Finally, this is a small, single sitestudy and information may not be generalizable.

CONCLUSIONSSixty ve percent of students in a traditional lecture reported being distracted by email,Facebook or text messages. Those who were distracted during the lecture performedsimilarly in the post-lecture test to the undistracted group. However, results should beinterpreted with caution as this study was a small quasi-experimental design and furtherresearch should examine the inuence of di ff erent types of distraction on di ff erent types oflearning.

ADDITIONAL INFORMATION AND DECLARATIONS

FundingThis study was not funded.

Competing InterestsVeerasathpurush Allareddy is an AcademicEditor forPeerJ.

Author Contributions Romesh P. Nalliah conceived and designed the experiments, performed the experiments,

analyzed the data, contributed reagents/materials/analysis tools, wrote the paper,prepared gures and/or tables, reviewed drafts of the paper, gave nal approval of themanuscript.

Veerasathpurush Allareddy conceived and designed the experiments, analyzed the data,contributed reagents/materials/analysis tools, wrote the paper, prepared gures and/ortables, reviewed draftsof the paper, gave nal approval of the manuscript.

Human EthicsThe following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):

Harvard School of Dental Medicine - Harvard Medical School exempt status wasobtained (IRB #: 13-1300).

Supplemental InformationSupplemental information for this article can be found online at http://dx.doi.org/10.7717/peerj.572#supplemental-information .

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