An Empirical Examination of the Educational Impact of Text ......dar de forma experimental el efecto de los mensajes de texto sobre la memoria en el con-texto del aula. Los participantes

Psicología EducativaVol. 17, n.° 2, 2011 - Págs. 163-177

Copyright 2011 by the Colegio Oficial de Psicólogos de MadridISSN: 1135-755X - http://dx.doi.org/10.5093/ed2011v17n2a4Artículo publicado First online: 01/08/2011

An Empirical Examination of the Educational Impact of TextMessage-Induced Task Switching in the Classroom:

Educational Implications and Strategies to Enhance Learning

Un Estudio Empírico del Efecto de los Cambios de Tarea en elAula Inducidos por los Mensajes de Texto: Implicaciones para

la Enseñanza y Estrategias para la Mejora del Aprendizaje

Larry D. Rosen, Alex F. Lim, L. Mark Carrier, and Nancy A. CheeverCalifornia State University, Domínguez Hills, USA

Abstract. Today’s Net Generation university students multitask more than any prior gener-ation, primarily using electronic communication tools (Carrier et al., 2009). In addition,studies report that many students text during class (Tindell & Bohlander, 2011). Thisresearch examines the impact of receiving and sending text messages during a classroomlecture. Recent laboratory research (Ophir, et al., 2009) reported that multitasking impairedperformance, particularly among heavy multitaskers. Further, experimental research hasshown that “technologically induced” interruptions can be disruptive, causing increasederrors and decreased performance (Monk, et al., 2008). This study is the first to experimen-tally examine the direct impact of text message interruptions on memory recall in a class-room environment. Participants viewed a 30-minute videotaped lecture during which theywere interrupted by receiving text messages requiring responses. Participants in four class-rooms were randomly assigned to three groups receiving no text messages, four text mes-sages or eight text messages. Based on the actual number of texts received and sent—including those not sent by the experimenter—three comparison groups were defined:No/Low Texting Interruption (zero to 7 text messages sent and received), Moderate TextingInterruption (eight to 15 texts), and High Texting Interruption (16 or more texts). Followingthe videotaped lecture, a recall test assessed the impact of text message interruptions onmemory. In addition, participants were asked about their typical monthly texting and theirattitudes toward classroom texting behaviors. Results indicated that the High Texting groupscored significantly worse (10.6% lower) than the No/Low Texting Interruption groupalthough there was no significant difference between No/Low Texting Interruption andModerate Texting Interruption group nor was there a significant difference between theModerate Texting Interruption group and the High Texting Interruption group. In addition,while nearly three fourths of the participants felt that receiving and sending text messagesduring class was disruptive to learning, 40% felt it was acceptable to text in class. Resultsalso indicated that those participants who received and sent more words in their texts per-formed worse on the test although this was moderated by the elapsed time between receiv-ing (or sending) a text with longer delays resulting in better performance. The results ofthese studies are discussed in terms of Salvucci et al.’s (2009) Unified Theory of the

Correspondence on this article should be sent to Lary D. Rosen,e.mail: [email protected]

Multitasking Continuum plus the potential use of metacognitive strategies when faced withpotentially disruptive multitasking. Educational implications are highlighted and strategiesoutlined for maximizing performance in media-rich multitasking environments.Keywords: metacognition, text messaging, multitasking, interruption, threaded cognition,memory-for-goals, ACT-R Theory, education.

Resumen. Comparando con las generaciones anteriores, la actual generación que ahora cur-san estudios universitarios pueden ser denominados generación Internet y generación ‘mul-titarea’, principalmente en lo que se refiere al uso que estos hacen de las herramientas decomunicación electrónicas (Carrier et al., 2009). Además, existen estudios que demuestranque muchos alumnos envían mensajes de texto durante sus clases (Tindell & Bohlander,2011). Estudios recientes llevados a cabo en laboratorios (Ophir, et al., 2009) han constata-do que el efectuar múltiples tareas a la vez perjudica el rendimiento, sobre todo entre los quemás tareas llevan a cabo. Además, otras investigaciones experimentales han mostrado quelas interrupciones inducidas por la tecnología son especialmente perjudiciales, provocandoerrores y un rendimiento menor (Monk, et al., 2008). Esta investigación estudió el efecto deenviar y recibir SMS durante una clase magistral. El presente estudio es el primero en abor-dar de forma experimental el efecto de los mensajes de texto sobre la memoria en el con-texto del aula. Los participantes asistieron a una clase de 30 minutos grabada previamenteen vídeo. Mientras tanto, se les enviaba a los participantes vía SMS preguntas que requerí-an respuestas. Los participantes estaban distribuidos entre cuatro aulas y divididos, dentrode cada aula, y de forma aleatoria, en tres grupos: participantes que no recibieron ningúnSMS; participantes que recibieron cuatro SMS; y participantes que recibieron ocho SMS.Según el número de mensajes recibidos y enviados –incluidos los que no fueron enviadospor el investigador- se definieron tres grupos de comparación: cero/bajo nivel de interrup-ciones (entre cero y siete mensajes enviados y recibidos); nivel moderado de interrupciones(de 8 a 15 mensajes); y nivel alto de interrupciones (16 o más). Después de ver la clase gra-bada, se evaluó mediante una prueba de memoria el efecto de las interrupciones sobre lamemoria. Además, los participantes contestaron preguntas sobre su uso mensual de los men-sajes de texto, y sus actitudes hacia el envío de SMS durante clase. Los resultados indica-ron que el grupo con alto nivel de interrupciones obtuvieron puntuaciones más bajas (un10.6% más bajas) que el grupo con cero o bajo nivel de interrupciones, de forma significa-tiva. Sin embargo, no se encontraron diferencias significativas entre el grupo con cero o bajonivel de interrupciones y el grupo con un nivel moderado de interrupciones, ni tampocoentre este grupo y el grupo con un nivel alto de interrupciones. Además, mientras casi el75% piensan que enviar y recibir SMS en clase es perjudicial para el aprendizaje, un 40%creen que es aceptable hacerlo en clase. Los resultados indicaron también que aquellos par-ticipantes que escribieron más palabras en sus mensajes puntuaban más bajo en la prueba dememoria, aunque moderado por el tiempo entre recibir o enviar, con los participantes quemás tiempo dejaban entre mensajes puntuando más alto en la prueba. Se analizan estosresultados a la luz de la Teoría Unificada del Continuo Multitarea de Salvucci et al. (2009).También se analizan los usos potenciales de estrategias metacognitivas para enfrentarse alas interrupciones de naturaleza multitarea, y se destacan tanto las implicaciones para laenseñanza como las posibles estrategias para maximizar el rendimiento en situaciones-mul-titarea y con múltiples medios.Palabras clave: metacognición, mensajes de texto, multitarea, interrupciones, cognición detareas simultáneas, memoria de objetivos, Modelo ACT-R, enseñanza.

164 TEXT MESSAGE INTERRRUPTIONS


Copyright 2011 by the Colegio Oficial de Psicólogos de MadridISSN: 1135-755X - http://dx.doi.org/10.5093/ed2011v17n2a4

Artículo publicado First online: 01/08/2011

According to a recent national study (Zickuhr,2011) 85% of Americans ages 18 and older and 95%of 18- to 34-year-olds own a cell phone. An ongoingnational survey of 60,000 phone bills (NielsenCompany, 2011) found that in the beginning of 2007teens sent and received an average of 435 text mes-sages a month and that by the final quarter of 2010this number had increased to 3,705 text messagesper month. Overall, the Nielsen study found that66% of cell phone users send text messages withyoung adults (1,707 per month) and preteens (1,178per month) following teens in their burgeoning SMSusage. Further, a recent study by the Pew Internet &American Life Project (Lenhart, 2010) found thattexting is now the most common way that teenagerscommunicate with their friends with 54% textingfriends followed by 38% talking on a cell phone,and 33% talking face-to-face.

Cell phone use has also expanded to the class-room where note passing has been supplanted bytexting as surreptitious communication. Tindell andBohlander (2011) reported that 91% of college stu-dents in their study had sent or received a text mes-sage in their university class and 62% felt textingshould be allowed in class if it does not disturb otherstudents. The Pew report (Lenhart, 2010) found that58% of cell phone owning teens whose school bansthe devices have sent a text message during class;64% of those teens have texted in class at least once;and 43% of all cell phone-toting teens text in classat least once a day. With texting being the typicalmeans of communication for teens and youngadults, it is not surprising that students text in class.The question remains whether this impacts learning.

Classroom Interruptions

Internet-based technologies, communication tools,and other forms of media have made interruptionscommonplace. According to Oulasvirta andSaariluoma (2006), interruptions can disrupt theencoding of content and cause difficulty when askedto retrieve the information. In contrast, Monk,

Trafton, and Boehm-Davis (2008) stated that formost people, “dealing with interruptions is not aproblem to be overcome as much as it is an inevitablepart of life. In fact, the ability to ‘multitask’ is consid-ered a desirable job skill by many employers, whichis not surprising given that, on average, workers shiftbetween tasks every 3 minutes” (p. 299). The presentstudy provides an extension to this field of researchby examining how one handles technological inter-ruptions in a real classroom environment.

Personal mobile technologies have proven a diffi-cult transition for educators. On the one hand,schools want to integrate these technologies into thecurriculum. However, according to Lenhart, Ling,Campbell and Purcell (2010), “most schools treatthe phone as a disruptive force that must be man-aged and often excluded from the school and theclassroom” (p. 7). In the classroom, cell phones, e-mail, instant messaging (IM), and other technologyand media compete for attention and can ultimatelydisrupt the learning process (Wijekumar & Meidin-ger, 2005).

Research by Garrett and Danziger (2008) foundthat multitasking led to a high occurrence of interrup-tions which may prove problematic for college stu-dents attempting to attend to classroom materialwhich, by its nature, requires extensive workingmemory. Further, recent research (Carrier, Cheever,Rosen, Benitez, & Chang, 2009) demonstrated thatwhile multitasking is common among people of allages, it is most prominent among members of the NetGeneration—those born between 1980 and 1989(Rosen, Carrier & Cheever, 2010). Carrier et al.(2009) found that across all generations, tasks suchas playing video games or reading a book, whichinvolve more working memory, provide more multi-tasking difficulty. According to Carrier, et al. (2009),texting was found to be one of the more difficulttasks to multitask.

In addition, not only do interruptions impact pri-mary task performance, but studies have shown thatinterruptions that occur in various modalities havedifferent performance outcomes when it comes toaccomplishing the primary task (Ratwani, Andrews,


LARRY D. ROSEN, ALEX F. LIM, L. MARK CARRIER, AND NANCY A. CHEEVER 165


Sousk & Trafton, 2008). Further, Oulasvirta andSaariluoma (2004) stated that it is common for indi-viduals who are studying material to divert theirattention to interrupting messages such as e-mails orinstant messages which, as they demonstrated intheir study, may negatively impact learning. In addi-tion, the increased time required to accomplish atask has been shown to lead to more errors (Ratwaniet al., 2008) and greater feelings of stress and anxi-ety (Gonzales & Mark, 2004; Ratwani et al., 2008).

A recent study conducted by Ophir, Nass andWagner (2009) suggests that multitasking is detri-mental to performance, particularly for members ofthe Net Generation who simultaneously use multiplemedia sources. Ophir et al. (2009) examined chronicheavy media multitaskers compared to light mediamultitaskers on a battery of tests requiring task-switching such as identifying whether a number isodd when it is coupled with a letter distractor on thescreen. Based on the differences in dual-task perform-ance between groups, Ophir et al. (2009) concludedthat “heavy multitaskers are distracted by the multiplestreams of media they are consuming or, alternative-ly, that those who infrequently multitask are moreeffective at volitionally allocating their attention inthe face of distractions” (p. 15585). Although theirresults were compelling, the study was performed ina laboratory with tasks that may not represent real-world media multitasking environments.

In the workplace, interruptions have also beenshown to impact performance by requiring more timeto complete an interrupted task. For example,Altmann and Trafton (2004) studied resumption lag,defined as the time interval between the completionof the secondary (interrupting) task and the firstaction in returning to the primary task, and found sub-stantial resumption lags after being interrupted. In anobservational study over a three-day period, Gonzalesand Mark (2004) found that computer programmerswere interrupted every three minutes whileBenbunan-Fich and Truman (2009) found that usinglaptops during course meetings led to nearly oneinterruption every two minutes. Parnin and Rugaber(2009) examined 10,000 sessions of 85 computer pro-

grammers and found that more than half of resump-tion lags were five minutes or longer and only one insix were less than one minute. Similarly, Judd andKennedy (2011) reported that medical studentsswitched tasks every five minutes while studying inthe school computer lab. Finally, Czerwinski, Horvitzand Wilhite (2004) showed among their sample ofknowledge workers, a weeklong diary revealed that23% of interruptions were self-initiated and 23% ofthe time interruptions were due to e-mail.

Not only does technology lead to numerous inter-ruptions, but returning to task from those interrup-tions requires additional time. Mark, Gudith, andKlocke (2008) reported that on average their pro-grammers took more than 25 minutes to return to theoriginal task after interruption. Jackson, Dawson,and Wilson’s (2003) study of interruptions in a 500-employee British company found that 70% of e-mails were responded to within six seconds of arriv-ing and 85% within two minutes. Upon responding,each e-mail required an average of two minutes ofinterruption—one minute to respond to the e-mailand another minute of “recovery time.”

There is limited research on the impact of memo-ry recall of lecture material with interruptions in aclassroom environment. A study by Oulasvirta andSaariluoma (2004) examined the memory effects ofinterruptive video messages in an undergraduatecourse and found that when participants divertedtheir attention to an interrupting message, theirmemory accuracy decreased by 16%, particularlywhen the interruptive material was semanticallysimilar to the to-be-learned material. Fried (2008)found that students who used laptops more in classmultitasked more, were more distracted and hadworse classroom performance. Interestingly, class-room students rated in-class laptop use as more dis-tracting than other students talking in class.

In contrast to previous research demonstrating thedetrimental effects of interruptions, Bowman,Levine, Waite and Gendron (2009) demonstratedthat in a real-world setting, interruptions may notnegatively affect task performance. Bowman et al.(2009) examined instant messages (IMs) as a form





of interruption during a learning task. Participantswere randomly assigned to one of three conditions:a control group received no IMs while reading a pas-sage, a first experimental group received IMs beforereading the passage, and a second experimentalgroup received IM interruptions while reading thepassage. Upon completion of the task—reading onlyfor the control group and reading plus IM conversa-tion for the two experimental groups—all partici-pants took a memory recall test. Students took sig-nificantly longer to read the passage in the secondexperimental condition than in the other conditions;however, the students’ test performance did not dif-fer between all conditions suggesting that they mayhave consciously dealt with the interruption byspending additional time reading the material fol-lowing the interruption. Similarly, Fox, Rosen andCrawford (2009) found that IM users during a read-ing comprehension task required more time to com-plete the task than those not using IM, yet compre-hension scores were unaffected.

Subsequent studies have shown the positiveimpact of interruptions. Salvucci and Bogunovich(2010) found that when their primary task requiredparticipants to attend to an e-mail customer-supporttask and the interrupting task involved an IM chat,interruptions during higher workloads were moredisruptive than interruptions during lower cognitiveworkloads. However, during higher mental work-loads participants switched only 6% of the time tothe interrupting task, while they switched 94% of thetime during lower mental workloads. Their resultssuggest that interruptions can be delayed during ahigh cognitive load until the mental workload of theprimary task has been minimized.

Metacognition

As further support for peoples’ ability to delayinterruptions, Wijekumar and Meidinger (2005)examined the effects of IM interruptions in a pro-gramming course as a function of “metacognition.”Metacognition is defined as being aware of one’s own

mental processes or, as suggested by Hacker,Dunlosky, and Graesser (1998) “knowledge of one’sknowledge, processes, and cognitive and affectivestates; and the ability to consciously and deliberatelymonitor and regulate one’s knowledge, processes andcognitive and affective states” (p. 11). According toBowler (2010), metacognition involves knowingwhat tasks are easy or difficult for a person, knowingwhich learning strategies work better in which situa-tions and monitoring and self-regulation of theknowledge and learning process. The importance ofmetacognitive control in a high-tech learning envi-ronment was shown in Wijekumar and Medinger’s(2005) study in which students worked on program-ming exercises while they were allowed to use IM ontheir desktop computers. Students with moremetacognitive skills typically turned off the soundson their IM and only responded to the interruptionswhen they had a break. Those with lower metacogni-tive skills allowed themselves to be interrupted by theIMs. Garret and Danziger (2008) suggested other keyways that people manage IM interruptions. Accordingto the authors, other than IM providing a means ofgaining task relevant information rapidly, IM pro-vides a relatively unobtrusive way to test availability.Thus the sender knows that the recipient can ignore ordismiss the IM notification easily or can provide aclear indication of status quickly (e.g., “I’m busyright now and away from my computer.”). Theseinterruption managing (metacognitive) skills couldalso transfer to those who encounter interruptionssuch as text messaging in classrooms.

Unified Theory of the Multitasking Continuum

Salvucci, Taatgen and Borst (2009) presented theUnified Theory of the Multitasking Continuum(expanded in Salvucci and Taatgen, 2011) to explaintask switching behaviors ranging from concurrentmultitasking (e.g., listening and note-taking) requir-ing near simultaneous processing to sequential mul-titasking (e.g., writing a paper and reading an e-mail) which allow more time (and control) between




switches. The crux of the model is based on ACT-Rarchitecture (Anderson, 2007) which posits thatinformation is processed by relatively independentbut interacting modules including: (a) a declarativememory module that handles factual knowledge,task instructions and episodic information; (b) a goalmodule which tracks progress; (c) a problem repre-sentation module which contains the informationderived during learning that may be needed later inthe process; and (d) a procedural module that con-nects all of the modules together and monitors theflow of information between modules. In ACT-R, allmodules can work separately at the same time buteach module can only work on a single task at atime. The second part of Salvucci et al.’s theoryinvolves Threaded Cognition Theory (Salvucci &Taatgen, 2008) which allows for multiple tasks to beperformed concurrently. This theory explains howdifferent tasks compete for resources (modules) andinterfere with each other to the extent that they shareneeded resources. If two tasks are similar and/orrequire complex problem representation and/orrequire use of the same module at the same time,threaded cognition theory predicts that one threadmust wait its turn to use necessary resources or mod-ules which would slow processing.

The final part of Salvucci et al.’s (2009) theory,necessary for handling sequential multitasking, isMemory for Goal Theory (Altmann & Trafton,2002), which explains how during an opportunity orrequirement to multitask, the new task goal must beactivated above the old task goal which then beginsto lose activation and fade away. This means thatwhen the interruptive task is completed the originaltask will take more time to reactivate and result inadditional time to complete than if it were attempt-ed without interruption. Salvucci et al. (2009) haveapplied their model successfully to a variety ofexternal and internal (self-generated) interruptions.In the current study receiving a text message con-tains both an external interruption (auditory, visualor kinesthetic vibration) and an internal interruption(the decision when to read and respond to the mes-sage).

In the this study, students in a classroom situa-tion were sent zero, four, or eight text messagesduring a 30-minute videotaped lecture to whichparticipants were asked to respond as promptly aspossible. Based on Salvucci’s Unified Theory ofthe Multitasking Continuum, Hypothesis 1 pre-dicts that the more texts that the learners had toread and respond to, the lower their memory recall.However, based on the dissimilarity between thevideotaped lecture material and the text messagecontent (e.g., What was the last movie you saw andwhat is your favorite movie?), it was predicted,based on the lack of overlap in shared resources,that this disruption should be minimal. Althoughparticipants were asked to respond “promptly” noconstraints were set on exactly when they chose toread the text or to respond to the text. Based on the metacognition theory as well as the Uni-fied Theory of the Multitasking Continuum,Hypothesis 2 predicts that those who waited toread a text or respond to a text interruption until atime when memory and attention to the lecture wasnot deemed as important—presumably demon-strating better metacognition and lower need formemory resources—would produce better memoryrecall scores than who responded immediately anddid not wait for an appropriate time to self-inter-rupt.

Methods

Participants

A total of 185 college students in four undergrad-uate psychology courses participated in this study.All participants were given course extra credit.Participants included 80% females and averaged 25years of age (range: 18 to 66 with 83% Net Genersborn between 1980 and 1989 distributed evenlyacross groups). The participants’ self-reported aver-age GPA was 3.06 and their self-reported currentcourse grades were primarily “As” (57%) and “Bs”(47%).





Materials and Procedure

Participants received a form one week before thestudy providing informed consent and requestingtheir cell phone number, their carrier, their typicalmonthly texting behavior, and permission to textthem during the class period. Participants were toldthat they were to view a videotaped lecture relevantto the course and during the lecture some wouldreceive text messages from the investigator.Participants were asked upon receiving the text mes-sages to respond as promptly as possible. To controlfor other confounding interruptions, participantswere asked to place their cell phone on their desk onthe vibrate setting along with paper and pen to takelecture notes. Immediately after the videotaped lec-ture was complete, participants were given thepaper-and-pencil memory recall test.

Each class was shown a 30-minute videotapedlecture on life-span development and participantswere informed that they would be tested on thematerial following the lecture. Videotaped presenta-tion, typically used in each course, was selected tomaximize the ability to time text message deliveryto lecture content; overall, 79% of the participantsrated the videotape as important to their overallgrade and 97% rated it as interesting. An 18-itemexam was developed with questions covering mate-rial from the entire 30-minute period. Eight pre-arranged text messages were developed to elicitmultiple-word responses from the participantsincluding: “Why did you choose your major?” “Ifyou won $100,000 in the lottery, what would you dowith the money?” and “What was the last movie yousaw and what is your favorite movie?” The text mes-sages were timed to arrive at the same time that thevideotaped lecture covered material that was includ-ed on the posttest1.

Each classroom was randomly divided into threegroups. One third received no text message interrup-tions, one third received four text message interrup-tions at timed intervals throughout the videotapedlecture and the final third received the same four textmessages plus an additional four text message inter-ruptions spread evenly throughout the lecture buttimed to coincide with videotaped lecture contentthat was assessed on the posttest.

Following the posttest, participants were asked tolist information about text messages received by theinvestigator including exact time received, whethera response was sent and the time it was sent, and thenumber of words in that response. In addition, par-ticipants noted personal text messages they receivedduring the videotaped lecture including the timereceived, whether a response was made to that mes-sage and the time it was sent, and the number ofwords in the text both received and sent. Participantswere also asked questions about their typical textingbehaviors in the classroom, and their attitudes aboutwhether it was acceptable to text during class andwhether texting during lectures was harmful to theirability to learn the material.

Results

Group Composition

Participants were sent zero, four, or eight textmessages from the investigators. However, someparticipants did not receive every text message and,as part of the posttest survey, participants indicatedthat they had received—and often responded to—additional text messages from people other than theinvestigators. In fact, during the 30-minute video-taped lecture, participants received an average ofbetween one and two additional texts each (M =1.41; SD = 2.59) with 64% receiving no texts andthe other 36% averaging 3.90 text messages (SD =3.00). Accordi-ngly, the total number of textsreceived and sent were tallied and participants wereplaced into the following groups: No/Low Texting




1 A pilot test determined that the only way to ensure close to imme-diate text message delivery was to send it from the same mobile phonecarrier as the receiver’s mobile phone carrier. Five student researchassistants, each using a cell phone with a different major mobile phonecarrier, assisted the investigator in sending and receiving text messagesduring the videotaped lecture. Based on the information collected priorto the study, senders were matched to receivers on the basis of cellphone carrier. Following the posttest, participants consulted their pho-nes and reported the exact time each text message was received.

Interruption group (n = 44; received plus sentbetween zero and seven texts; Mean text messages1.61; Mdn = 0), Moderate Texting Interruptiongroup (n = 76; 8-15 texts; M = 10.09; Mdn = 8), andHigh Interruption Text group (n = 65; 16 or moretexts; M = 19.22; Mdn = 16). Overall, 79% of thesample remained in their original group.

Attitudes Toward Texting in Class

Participants were asked several questions con-cerning their attitudes toward texting during acourse lecture. While 75% of the participants agreedthat receiving and sending texts ruins one’s ability tolearn from a lecture, 40% of the sample agreed itwas acceptable to text during a lecture. In addition,only 18% of the sample claimed they never respond-ed to a text in class and a most stated that theyresponded if they received a text from a friend(67%) or family member (75%) during class. Therewere no differences in these responses across textinggroups.

Typical Texting Behavior

Participants were asked about the number of textsboth sent and received during a typical month.Overall, participants received and sent an average of1,513 texts per month (SD = 967.00). The data, how-ever, were positively skewed (skewness = 4.60) andwere truncated at three standard deviations abovethe mean. These data were then partitioned intothirds and a group comparison revealed a statistical-ly significant difference between groups with themajority of the No/Low Texting Interruption groupin the lower third of monthly texting, the ModerateTexting Interruption group showing an even distri-bution of monthly texting, and the High TextingInterruption group’s typical texting being predomi-nantly in the top two monthly texting groups [χ2 (4,N = 181) = 12.43, p < .05]. Based on these results,the monthly texting behavior was included as a vari-

able in the test of Hypothesis 1. No other demo-graphic variables, including age, gender, grade pointaverage, and ethnic background, were correlatedwith the test score nor showed differences betweengroups.

Hypothesis 1

Hypothesis 1 predicted that the more text mes-sage interruptions the participant received the worsethey would perform on the posttest. The hypothesiswas tested with a 3 (text interruption group) x 3(monthly texting practices groups) ANOVA andshowed that the only significant difference wasbetween texting interruption groups [F(2, 182) =3.21, p < .05] and that difference had a small effectsize (partial η2 = .034) according to Cohen (1988).A post hoc Tukey B Test showed that the High TextInterruption group [M = 11.71 (65% of the total pos-sible on the test); SD = 3.45] performed significant-ly worse than the No/Low Texting Interruptiongroup [M = 12.95 (72%); SD = 2.01] with theModerate Texting Interruption group [M = 12.58(70%); SD = 2.28] not significantly different fromeither the No/Low Texting Interruption group or theHigh Texting Interruption group. Overall, theNo/Low Texting Interruption group performed10.6% better than the High Texting Interruptiongroup.

The impact of texting behavior was also exam-ined in terms of the amount of words sent andreceived with a prediction that those participantswho sent and received more words (requiring morecognitive workload and more use of all resources inthe Unified Theory of the Multitasking Continuum)would perform worse on the posttest. First, a com-parison showed that the average number of wordsreceived and sent per text was constant betweengroups, averaging 9.22 words per text sent andreceived [F(2, 153) = 2.13, p > .05] as was the aver-age number of words sent [M = 9.26; F(2, 147) =0.23, p > .05] and the average number of wordsreceived [M = 9.42; F(2, 154) = 1.77, p > .05]. A





correlation analysis revealed that the posttest scorewas significantly negatively related to total wordsreceived (r = -.158, p < .05) and total number ofwords received and sent (r = -.125, p < .05) whilethe total words sent was also negatively correlatedwith posttest score but not statistically significant (r = -.082, p > .05).

Hypothesis 2

To maximally disrupt learning, text messageswere timed to coincide with the videotaped lecturepresented material that was included on the posttestand participants were asked to respond promptly tothe text messages. Since participants noted the exacttime a text was received and their response was sent,the time elapsed between the presentation of a testfact from the videotape and the receipt of andresponse to a text message was calculated for eachtext message received or sent. Due to the nature ofthe videotaped lecture, which often took up to oneminute or more to present information that was onthe posttest, the calculations for lag between testedlecture material and message receipt and responsewere made in minute-by-minute increments. Forexample, suppose the videotaped lecture containedmaterial that was on the posttest at two minutes and30 seconds from the start of the lecture. If the mes-sage was received at, say, two minutes and 45 sec-onds from the start of the videotaped lecture and aresponse sent at three minutes and 45 seconds thenthat message would be considered received 15 sec-onds following the videotaped material (or withinone minute) and a response sent one minute and 15seconds following the videotaped material (orbetween one and two minutes). Figure 1 displays thepercentage correct for each test question that wasinterrupted by a text message under two conditions:(1) the time between when the to-be-tested materialappeared on the videotaped lecture and receipt of atext message and (2) the time between the testedmaterial appearing on the videotaped lecture andsending a text message response. The percentages

correct for posttest questions are plotted in terms ofminutes following the tested videotaped materialthat a text message was received or sent. It is impor-tant to note that the experimental manipulationinvolved the participants receiving a text messageeffectively during the lecture material presentation,which would nearly always be included in the 0 to60 second time frame unless the text message deliv-ery was delayed. In addition, participants were toldto respond promptly to the text message but exami-nation of the times that they received a text messageand sent a response varied from a few seconds toseveral minutes. Figure 1 includes all text messagesfor all participants since no between group differ-ences were found.

Although only suggestive, Figure 1 shows twointeresting trends. First, text messages receivedwithin the first three to four minutes following test-ed material showed a consistent 71% to 78% correctanswers while messages received more than fourminutes after tested material demonstrated morethan 90% correct answers. Second, for texts sent bythe participants, performance ranged between 70%and 76% when that response was made within fourto five minutes following the tested material andballooned to 85% when the participants waited formore than five minutes to respond to a text message.

Discussion

The college classroom is home to young NetGeneration adults who have been involved in tech-nology and media from a very early age. They areaccustomed to multitasking and, therefore, experi-ence a daily spate of interruptions. In addition, theyare continually communicating electronically, pri-marily through sending and receiving text messagesand other digital messages. In the classroom, thiscould be a problem as students continously checktheir cell phones for messages and, in general,respond to those messages as soon as they arrive.The present study examined the impact of classroomtexting on learning and retention of lecture material.




In four university classrooms, students were sentzero, four, or eight texts during a 30-minute video-taped lecture with the text messages sent to coincidewith the presentation of information that wouldappear on a posttest immediately following the lec-ture. Participants were requested to respond prompt-ly to the texts.

Based on the number of texts sent and receivedincluding those sent by the investigator and thosereceived from other people, participants were divid-ed into three groups which included a No/LowTexting Interruption group (with more than halfreceiving no texts), a Moderate Texting Interruptiongroup, and a High Texting Interruption group.Hypothesis 1, based on Salvucci et al.’s (2009)Unified Theory of the Multitasking Continuum, pre-

dicted that the more texts that learners had to readand respond to, the lower their memory scores onthe posttest. However, the Threaded CognitionTheory and Memory-for-Goals Theory also predict-ed that since the text message content had little over-lap with the lecture material (and the text messagecontent was really quite innocuous and simple toanswer), this memory disruption should be minimal.This hypothesis was supported since only the HighTexting Interruption group differed significantlyfrom the No/Low Texting Interruption group andthat difference only represented a 10.6% reductionin memory and a small effect size (Cohen, 1988).This is supported by and validates research byOulasvirta and Saariluoma (2004) who found thatinterrupting messages decreased memory accuracy





Figure 1. Percentage correct on memory recall posttest by the time a text message was received or by the time a text message response was sent as afunction of the time information for the most recent posttest question appeared on the videotaped lecture

by 16%, particularly when the interruptive materialwas semantically similar to the to-be-learned mate-rial. Finally, the significant negative correlationsbetween the total number of words sent andreceived, and the number of words received but notsent adds further support to the Unified Theory ofthe Multitasking Continuum. According to this theo-ry, the arrival of a text message would first requirean additional thread which would then compete forshared resources with any threads begun by watch-ing to the videotaped lecture material. Second, thetext message arrival would deactivate the goal ofattending to the videotaped lecture material and,instead, activate attending to answering the ques-tions from the investigator. It is not surprising at therelatively small effect the interruptions had on per-formance is not surprising given the fact that thesequestions were unrelated to the lecture material and,by their nature, did not require extensive cognitiveworkload. Attending to the text message did impactperformance, but the impact was quite small due tothe minimal nature of the disruptions to the thread-ed cognitions and the memory for goals.

Participants received text messages throughoutthe videotaped lecture. For the High Text groupthose eight text messages occurred during materialto be tested while the two other groups eitherreceived no texts to disrupt learning or received halfthe number of texts as the High Text group. Manyparticipants also received texts from other peopleduring the lecture. Although participants wereinstructed to respond to the text messages promptly,some, as seen in Figure 1, chose not to do so. Inaddition, when texts from toher people arrived, par-ticipants could choose when to respond. A detailedanalysis demonstrated that those participants whochose to wait more than four to five minutes torespond to a text message did substantially betterthan those who responded more rapidly. Althoughthese data are merely suggestive, this lends somesupport to the idea that participants may have con-sciously employed metacognitive strategies, perhapsindicating, as shown in an IM study by Wijekumarand Meidinger (2005), that participants were aware

that if they responded to the interruption during lec-ture periods that were more likely to contain testableinformation they would not learn the material aswell as if they waited for a time when the lecturematerial was less likely to be on the exam.

Overall, this study supports the idea that class-room texting may not be as interfering as assumedby teachers. Even when participants were inundatedwith text messages—receiving and sending 16 ormore texts in a 30-minute period—their perform-ance was only slightly (albeit significantly) worsethan those receiving no texts or a few texts. The factthat the Moderate Texting Interruption group did noworse than the No/Low Texting Interruption grouplends further support to the minimal interference ofclassroom texting. However, it must be noted that a10.6% difference between the group receiving themost text messages and the group receiving the leasttext messages is equivalent to one letter grade.

It is important to note, however, the prevalence oftexting in the typical university classroom. AsTindell and Bohlander (2011) reported, nearly allstudents have sent or received a text message inclass. During this short 30-minute videotaped lec-ture 36% of the participants received at least onetext message—and averaged nearly four text mes-sages—in addition to those sent by the experi-menters. This confirms that rampant texting in classmay be a problem for some students and this is anissue that must be evaluated with each teacher andeach classroom.

Educational Implications

The results of this study will undoubtedly baffleteachers and parents who insist that multitasking inthe classroom and at home is extremely harmful tocomprehension. Even with constant texting the HighText group only performed just under 11% worsethan the group with no texts. Surprisingly, theModerate Text group, which sent and received 8texts in 30 minutes, did not do any worse than thosewho got essentially no texts. In defense of teens’ and




young adults’ propensity to multitask at all timeswith multiple forms of media, Rosen et al. (2010)suggests that teachers and parents allow children,teens and young adults to multitask, particularlywhen the task demands of the secondary task arelow (e.g., familiar music) or when the tasks requiredifferent sensory modalities (music vs. reading).

In the classroom, however, the problems are mag-nified. Students who constantly check their technol-ogy for text messages, Facebook updates, etc. cando so surreptitiously since research has shown that42% of teens can text blindfolded (HarrisInteractive, 2008). Some schools have opted torequire all cell phones to be turned off or even, in theextreme, deposited in a box by the classroom door.The problem is that just because the student’s tech-nology is “out of sight” it is not “out of mind.” Forexample, Dumontheil, Gilbert, Burgess, and Otten(2010) found no neurological differences betweenexternally driven task switching (e.g., responding toa text message beep) and internally driven switches(e.g., “thinking about” a text message). This study,plus other neurological research, suggests that keep-ing technology away from students will not removeit from the students’ brain activation. In fact, Foerde,Knowlton, and Poldrack (2006) found that duringtask switching there is a shift in neural activity fromthe hippocampus, which is the seat of thought andmemory to the striatum, which is a brain regionassociated more with rote or habitual learning. Ifstudents are continually internally task switchingthen it is entirely possible that even during periodswhere they are supposed to be paying attention, theymay be doing so primarily using a brain area that isnot conducive to analytical learning.

One strategy which may help this process is whatthe authors refer to as a “technology break.” Similarto a coffee break where a person is feeling low onenergy and needs to get a dose of caffeine to feelmore motivated and energized, a technology breakcan supply the student the knowledge that within acertain time period a break will be available to checkin with their connected world. On a neurologicallevel this process should promote the student pro-

cessing information more in the hippocampus, whichwill lead to better understanding and memory for thecourse information. Trials of this strategy are beingimplemented with good anecdotal results. For exam-ple, in one high school, students are told that every15 minutes they will get a one to two minute break(depending on the teacher) to check their e-mail, textmessages, Facebook posts using any approved tech-nology (the school has a laptop program for all stu-dents and 96% of the students text). In a typical 45-minute class this amounts to two short tech breaksand rather than a waste of two to four minutes oflearning, teachers are reporting that the students aremore alert during the 15-minute teaching time.

Along these lines we must also consider the inter-esting results displayed in Figure 1 where studentswho waited a period of time to either open and reada text or respond to a text performed better on thetest. This suggests that we should be teaching ourstudents metacognitive strategies that focus on whenit is appropriate to take a break and when it is impor-tant to focus without distractions. In one study cur-rently underway by the authors (Rosen, Carrier, &Cheever, 2011), high school and university studentswere observed for 15 minutes (the same amount oftime we recommend for focusing before taking atech break) while they studied in their natural envi-ronments (usually their bedroom or a den area).Their expected grade on the material as well as theirgrade point average were predicted by five vari-ables: how often they stayed “on task” rather thanswitching to another task (almost always technolo-gy-based), whether they had strategies for studying,their preference for task switching, the total mediahours per day and whether they checked theirFacebook page once during the study period. Ifthese students applied a metacognitive strategy ofstaying on task for 15 minutes, learning not toembrace task switching as a normal part of studyingand not switching to their Facebook page whilestudying, they would perform better in school.Perhaps knowing that after the 15-minute study peri-od they could switch to check Facebook or othertechnology learning might be enhanced.





In a typical classroom setting, requiring studentswho multitask or task switch when they are not inschool to sit and unitask can and will lead to bore-dom and lack of attention or, as some teachers havediscovered, students surreptitiously using their cellphones from their laps. Rosen et al. (2010) conclude,“The bottom line is that our students are multitaskingand we cannot stop them without placing them in aboring, unmotivating environment. The trick is todevelop educational models that allow for appropriatemultitasking and that improve learning” (p. 95).

Limitations

There are several limitations to this study includ-ing an inability to precisely establish an exact timewhen relevant, tested material was on the videotapelecture since that material spanned from seconds toa minute or more. This is inherent in a real class-room environment and could be controlled by pre-senting fact-based information that is stated in sec-onds during the lecture although this would distortthe typical lecture style. In addition, the fact thatparticipants received and responded to texts fromother people may have interfered with their respons-es to texts sent by the investigators; however, thetiming of those texts was recorded and synchronizedwith the videotaped lecture and they representedonly a small percentage of overall texts. The factthat the group that was supposed to receive no textsdid receive an occasional text message from some-one else presented that group with a source of inter-ference. Given that these occurrences were rare—more than half this group received no texts but someparticipants in this group did send and/or receive asmany as seven texts—it may be helpful to controloutside texting. This could be accomplished byreplacing texting to cell phones with a more con-trolled use of classroom response systems as inter-ruption tools (Kay & LeSage, 2009). Another limi-tation involved with the texting environment is theinterruptive quality of the text messages themselves.Participants were asked to have their phones on

vibrate and close at hand, but it may be the case thatthe constant vibrations were disruptive to them (orother students in the classroom) and reduced theirlearning ability. The fact that the average score onthe exam was around 70% and the scores wereequally distributed within each group, it is unlikelythat the vibrating sounds differentially affected theparticipants in any particular group.

This study was done in a university classroom.Given the excessive texting data for teens, it wouldbe important to replicate the results in a high schoolclassroom. Finally, it would be important to pilottest both teaching students metacognitive strategiesfor when to focus and when it is not harmful to taskswitch, coupled with the implementation of techbreaks, to determine if these teaching strategies doindeed improve classroom performance.

References

Altmann, E. M. & Trafton, J. G. (2002). Memory forgoals: An activation-based model. CognitiveScience, 26, 39-83.

Altmann, E. M. & Trafton, J. G. (2004). Task inter-ruption: Resumption lag and the role of cues. NY:Erlbaum. Proceedings of the 26th annual confer-ence of the Cognitive Science Society.

Anderson, J. R. (2007). How can the human mindoccur in the physical universe? New York:Oxford University Press.

Benbunan-Fich, R. & Truman, G. E. (2009). Multi-tasking with laptops during meetings. Communi-cations of the ACM, 52, 139-141.

Bowler, L. (2010). The self-regulation of curiosityand interest during the information search processof adolescent students. Journal of the AmericanSociety for Information Science and Technology,61, 1332-1344.

Bowman, L. L., Levine, L. E., Waite, B. M., &Gendron, M., (2010) Can students really multi-task? An experimental study of instant messagingwhile reading. Computers & Education, 54, 927-931.




Carrier, L. M., Cheever, N. A., Rosen, L. D.,Benitez, S., & Chang, J. (2009). Multitaskingacross generations: Multitasking choices and dif-ficulty ratings in three generations of Americans.Computers in Human Behavior, 25, 483-489.

Cohen, J. (1988). Statistical power analysis for thebehavioral sciences (2nd ed.). Mahwah, NJ:Lawrence Erlbaum.

Czerwinski, M., Horvitz, E., & Wilhite, S. (2004). Adiary study of task switching and interruptions. InProceedings of the ACM Conference on HumanFactors in Computing Systems (pp. 175-182).New York: ACM.

Dumontheir, I., Gilbert, S. J., Burgess, P. W., &Otten, L. J. (2010). Neural correlates of task andsource switching: Similar or different? BiologicalPsychology, 83, 239-249.

Foerde, K., Knowlton, B. J., & Poldrack, R. A. (2006).Modulation of competing memory systems by dis-traction. Proceedings of the National Academy ofSciences of the USA, 103, 11778-11783.

Fox, A. B., Rosen, J., & Crawford, M. (2009).Distractions, distractions: Does instant messagingaffect college students‘ performance on a concur-rent reading task? CyberPsychology, 12, 51-53.

Fried, C. B. (2008). In-class laptop use and itseffects on student learning. Computers & Educa-tion, 50, 906-914.

Garrett, R. K. & Danziger, J. N. (2008). IM=Interruption management? Instant messaging anddisruption in the workplace. Journal of Com-puter-Mediated Communication, 13, 23-42.

Gonzalez, V. M. & Mark, G. (2004). Constant, con-stant, multitasking craziness: Managing multipleworking spheres. Proceedings of CHI ’04, pp.113-120.

Hacker, D. J., Dunlosky, J. and Graesser, A. C.(1998). Metacognition in educational theory andpractice. Mahwah, NJ: Erlbaum.

Harris Interactive (2008). A Generation Unplugged.Research report retrieved from http://files.ctia.org/pdf/HI_TeenMobileStudy_ResearchReport.pdf.

Jackson, T., Dawson, R. & Wilson, D. (2003). Re-ducing the effect of email interruption on employ-

ees. International Journal of Information Ma-nagement, 23, 55-65.

Judd, T. & Kennedy, G. (2011). Measurement andevidence of computer-based task switching andmultitasking by ‘Net Generation’ students. Com-puters & Education, 56, 625-631.

Kay, R. H. & LeSage, A. (2009). The benefits andchallenges of using audience response systems: Areview of the literature. Computers & Education,53, 819-827.

Lenhart, A. (2010). Teens, cell phones and texting:Text messaging becomes centerpiece communica-tion. Washington, DC: Pew Internet & AmericanLife Project. Retrieved from http://pewresearch.org/pubs/1572/teens-cell-phones-text-messages.

Lenhart, A., Ling, R., Campbell, S., & Purcell, K.,(2010, April 20). Teens and mobile phones: Textmessaging explodes as teens embrace it as thecenterpiece of their communication strategieswith friends. Washington, DC: Pew Internet &American Life Project. Retrieved from http://www.pewinternet.org/Re ports/2010/Teens-and-Mobile-Phones.aspx.

Mark, G., Gudith, D., & Klocke, U. (2008). The costof interrupted work: More speed and stress. InComputer-Human Interactions (CHI), Florence,Italy: ACM, 107-110.

Monk, C. A., Trafton, J. G., & Boehm-Davis, D. A.(2008). The effect of interruption duration anddemand on resuming suspended goals. Journal ofExperimental Psychology: Applied, 14, 299-313.

Nielsen Company (2011). State of the media 2010:U.S. audiences & devices. Retrieved fromhttp: / /b log.nie lsen.com/nielsenwire/wp-content/uploads/2011/01/nielsen-media-fact-sheet-jan-11.pdf.

Ophir, E., Nass, C.I. & Wagner, A. D. (2009). Cog-nitive control in media multitaskers. Proceedingsof the National Academy of Sciences, 106, 15583-15587.

Oulasvirta, A. & Saariluoma, P. (2004). Long-termworking memory and interrupting messages inhuman-computer interaction. Behaviour & Infor-mation Technology, 23, 53-64.





Oulasvirta, A. & Saariluoma, P. (2006). Survivingtask interruptions: Investigating the implicationsof long-term working memory theory. Interna-tional Journal of Human-Computer Studies, 64,941-961.

Parnin, C. & Rugaber, S. (2009). Resumption strate-gies for interrupted programming tasks. SoftwareQuality Journal, 19, 5-34.

Ratwani, R. M., Andrews, A., Sousk, J., Trafton, G.(2008). The effect of interruption modality on pri-mary task resumption. In Proceedings of theHuman Factors and Ergonomics Society 52nd

Annual Meeting, (pp. 372-375). Santa Monica,CA: Human Factors and Ergonomics Society.

Rosen, L. D. (2007). Me, MySpace, and I: Parentingthe Net Generation. NY: Palgrave Macmillan.

Rosen, L.D., Carrier, L. M., & Cheever, N. A.(2010). Rewired: Understanding the iGenerationand the Way They Learn. NY: Palgrave Macmil-lan.

Rosen, L.D., Carrier, L. M., & Cheever, N. A.(2011). How do today’s students task switchwhile studying? Lessons for technological dis-tractions and metacognitive strategies for learn-ing. Unpublished manuscript.

Salvucci, D. D. & Bogunovich, P., (2010).Multitasking and monotasking: The effects ofmental workload on deferred task interruptions.In Proceedings of the SIGCHI Conference on

Human Factors in Computing Systems: CHI2010. New York: ACM Press.

Salvucci, D. D. & Taatgen, N. A. (2008). Threadedcognition: An integrated theory of concurrentmultitasking. Psychological Review, 115, 101-130.

Salvucci, D. D. & Taatgen, N. A. (2011). TheMultitasking Mind. New York: Oxford UniversityPress.

Salvucci, D. D., Taatgen, N.A., & Borst, J. P. (2009).Toward a unified theory of the multitasking con-tinuum: From concurrent performance to taskswitching, interruption, and resumption. InProceedings of the SIGCHI Conference onHuman Factors in Computing Systems: CHI2009. New York: ACM Press, pp., 1819-1828.

Tindell D. R. & Bohlander, R. W. (2011). The useand abuse of cell phones and text messaging inthe classroom: A survey of college students.Manuscript submitted for publication.

Wijekumar, K. & Meidinger, P. (2005). Interruptedcognition in an undergraduate programmingcourse. Proceedings of the American Society forInformation Science and Technology, 42(1).

Zickuhr, K. (2011). Generations and their gadgets.Washington, DC: Pew Internet & American LifeProject. Retrieved from http://www.pewinternet.org/~/media//Files/Reports/2011/PIP_Generations_and_Gadgets.pdf




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