Efficient, helpful, or distracting? A literature review of media multitasking in relation to academic performance

Empirical studies firmly establish a significant drop in academic performance due to media multitasking. According to the Kaiser Family Foundation, heavy media users (exposed to more than 16 h of media content per day, often via media multitasking) report receiving C’s or lower in school, getting in trouble often, frequently feeling sad or unhappy, and frequent boredom (Roberts et al., 2010). Survey data found frequent in-class multitaskers have lower current college GPAs (Al-Menayes, 2015; Bellur, Nowak, & Hull, 2015; Clayson & Haley, 2012; Junco, 2012; Lau, 2017). A longitudinal study examining women’s media use during their first year of college and associations with academic outcomes found that women reported nearly 12 h of media use per day (Walsh, Fielder, Carey, & Carey, 2013). Such amounts of media use imply multitasking; further, media use was negatively related to academic outcomes after controlling for demographics and prior academics, and there were significant, indirect effects of social networking on GPA.

In-class multitasking

In-class, mobile phone multitasking during direct instruction is heavily researched, as it is the technology of choice for many university students and the most prevalent. Rosen, Lim, Carrier, and Cheever (2011) examined the impact of in-class mobile phone usage during course lecture on test performance. Students responded to messages sent by researchers at even intervals throughout a 30-min videotaped lecture (Rosen et al., 2011). Students in the high text messaging group performed worse by one letter grade on an information post-test than the low text messaging group (10.6% lower score). However, the moderate text messaging group showed no score difference compared to the other two groups. Participants who received and sent more words in their texts performed worse on the test; however, this was moderated by elapsed time between receiving and sending a text, with longer delays resulting in better performance. Student metacognitive self-reports reflected test results. Nearly three-fourths of participants felt that receiving and sending text messages during class was disruptive to learning. Despite this, 40% felt it was acceptable to text in class.

Similar studies comparing test performance found the non-texting group outperformed regardless of gender and G.P.A. (Ellis, Daniels, & Jauregui, 2010; Froese et al., 2010). Ellis et al. (2010) examined the effect of texting multitasking on the grade performance of business students. Participants listened to a class lecture in a texting or no-texting condition. Scores on a post-lecture assessment indicated that exam scores of texting students were significantly lower. In a similar lecture format, Froese et al. (2010) demonstrated that students lost roughly 30% of accuracy on a quiz when texting.

A related study found similar results (Kuznekoff & Titsworth, 2013). Participants in three groups (non-multitasking, low-distraction, and high-distraction) watched a video lecture while taking notes and completed two post-lecture assessments. Students in the non-multitasking control group wrote down 62% more information, took notes with more details, were able to recall more detailed information, and scored a full letter grade and a half higher than students in the low-distraction and high-distraction groups. Further analysis found that message content influenced effect on class performance. In addition to participants who did not text, participants who sent texts related to the lecture earned a 10–17% higher letter grade, scored 70% higher on information recall, and scored 50% higher on note-taking than students sending texts unrelated to lecture content (Kuznekoff, Munz, & Titsworth, 2015). These results point to the purpose of usage, rather than multitasking itself, as the culprit for the negative effects of media multitasking on classroom performance. Thus, distinguishing on-task from off-task multitasking redefines the pragmatics of the in-class technology debate.

McDonald (2013) assessed the effect of three different in-class texting behaviors on course grade: (1) mild texting policy; "cell phones are to be turned off and not used during class. This is an issue of respect for others and your professor"; (2) strict cell phone policy; "students will lose 3% of their final grade each time they are caught texting"; (3) no presented texting policy; "students free to have cell phones on and text as desired" (McDonald, 2013, pg. 36). McDonald (2013) found a negative correlation between in-class texting and final grade score, regardless of texting condition. This negative correlation remained after controlling for GPA, ACT, and attendance. However, the higher the levels of in-class texting behavior by a student, the lower their final grade. In-class texting behavior contributed to 22% of the predictor value in final grade. This points to the potential of classroom policy to diminish, but not eliminate, the negative effects of in-class media multitasking.

A similar experiment examining in-class media multitasking with classroom performance expanded the experimental variables to reflect individualized preferences for both media use and notetaking (Wood et al., 2012). The study compared multitasking activities of various mediums to three methods of notetaking during a direct instruction lecture. Technological mediums assessed included texting, emailing, Instant Messaging (IM), and Facebook. All media use was for off-task purposes. Note-taking conditions were paper-and-pencil, word-processing, and a natural use of technology condition in which participants were allowed to use any technology they wished. The natural use of technology condition served to determine whether students choose to multitask during lectures, what technologies students tend to use, and how the choice to multitask affected learning. Across all sessions, only seven participants did not use technology at all. Almost half of participants used technology for every class when permitted. The experiment was conducted over three consecutive lectures. Results indicated that participants who did not use any technologies outperformed students who did multitask—regardless of medium— on a 15-item multiple-choice test. Participants in the Facebook and Instant Messaging conditions performed more poorly than those in the paper-and-pencil control. Wood et al. (2012) surmised that Facebook and IM were more likely to serve as distractors that yield negative impact on learning. Repeated practice with the various technologies did not improve performance over time in any condition.

Downs, Tran, McMenemy, and Abegaze (2015) manipulated the multitasking environment rather than the note-taking method, finding that participants performed worst on a post-lecture exam when distracted with social media. Two-hundred and four university students were randomly assigned to one of six classroom conditions: (1) Facebook distracted; (2) paper note-taking; (3) no media use control group; (4) mixed distraction; (5) laptop note-taking; and (6) distracted combination. Participants in the Facebook condition used laptops to join a Facebook chat group created for the study through which they received questions to respond to at two-minute intervals. Participants in the note-taking condition received a sheet of notebook paper and instructions to take notes as they normally would during a lecture. Participants in the no media use control group were instructed to only watch the documentary. The mixed distraction group approximately half of the participants (every other seat) were asked to join the aforementioned Facebook chat group, while the other half watched the documentary without an additional distraction. Participants in the laptop note-taking group used a word processing program to take notes. Participants in the distracted combination condition followed the Facebook protocol for condition one while simultaneously taking notes on their laptop during the video. In all conditions, participants viewed a documentary video for 25 min and completed a subsequent, 15-question, multiple-choice exam assessing lecture content. Participants in distracted conditions (1, 4, & 6) performed worse on the post-test than participants in the non-distracted control, paper-and-pencil, and laptop note-taking groups.

Brooks (2015) further examined mobile phone multitasking in a natural classroom setting. Participants completed a pre-task survey before watching a 15-min video lecture. Participants received no instructions or study regarding social media or mobile phone usage prior to observing a 15-min video lecture. Following the video, participants completed a quiz over video content and a survey regarding social media use, attentional control, multitasking computer self-efficacy, technostress, and happiness. Participants were instructed to complete the tasks on their own time so that they would have access to their personal machines. Like prior studies, this study found that social media usage on mobile phones negatively affected performance. Attentional control and multitasking computer self-efficacy did not yield significant effect on this relationship. Thus, students are not as skilled at multitasking as they perceive themselves to be.

Conard and Marsh (2014) examined the effect of interruptions via Instant Messaging and situational interest on learning during multitasking. Participants viewed a video presentation in a simulated environment meant to emulate a standard working environment such as a business meeting, a training presentation, or a classroom lecture. During the 16-min video, participants responded to eight Instant Messages sent at specific times by research assistants. Following the video, participants completed a 22-item multiple choice test assessing lecture comprehension and responded to measure of situational interest. Multitasking interruptions reduced learning; furthermore, interest level was as strong a predictor of learning as being interrupted. However, interest did not moderate the effect of interruptions. This indicates a need for further research examining individual difference factors, such as interest levels, when assessing the effects of multitasking on learning.

Like mobile phone use, laptop use is commonplace in the university setting, yet presents unique issues concerning multitasking. Laptops provide a convenient means to connect with the lecture while simultaneously providing a major source of distraction. Self-report data by Fried (2008) showed that students using laptops in class spent considerable time multitasking. On average, students generate more than 65 new active windows on laptops per lecture, with 62% of these windows being classified as distractive and irrelevant to lecture content (Kraushaar & Novak, 2010). Laptop use negatively related to multiple learning outcomes including course grade, focus on lectures, reported clarity of lectures, exam performance, and comprehension (Fried, 2008; Kraushaar & Novak, 2010; Wood et al., 2012; Zhang, 2015). In a study by Hembrooke and Gay (2003), laptop use during lecture resulted in significantly lower recall and recognition test scores. Students in two conditions (laptop multitasking or no multitasking condition) listened to a lecture and completed a comprehension exam. Exam results indicated that multitasking students suffered memory decrements.

Students habitually using laptops in class report low satisfaction with their education, are more likely to multitask in class, and are more distracted (Wurst, Smarkola, & Gaffney, 2008). According to information processing theory, humans process stimuli, rather than merely responding, employing attention mechanisms such as working memory. Thus, laptops provide additional stimuli for students to process, distracting them from the academic task. This accounts for the decrements in performance seen as a result of in-class laptop multitasking. Because most technological mediums serve as a gateway to both productive and unproductive tasks, students are likely to engage in both over the course of a class period and struggle to resist temptation. These distractions prevent processing and learning of material.

Research involving undergraduate students indicate that laptop multitasking can hinder class learning for both users and nearby peers (Fried, 2008; Sana, Weston, & Cepeda, 2013). Fried (2008) administered surveys to a university course, assessing various aspects of class such as class attendance, classroom experiences, and laptop use, finding that students frequently cited personal and external laptop use as major sources of classroom distractions and hindrances of learning. Experimental evidence affirms this: Sana et al. (2013) simulated a classroom with 40 undergraduate students in which students viewed a 45-min PowerPoint lecture in a multitasking or non-multitasking condition and completed a multiple-choice comprehension test. Participants who multitasked on a laptop during lecture scored lower on the test than non-multitaskers. Moreover, participants in direct view of a multitasking peer scored 17% lower than those who were not. Distractions due to movement of images and laptop screen lighting, as well as multitasking activities, may cause involuntary shifts of attention among students in close proximity to laptop users. Thus, proximity to a multitasker—and not solely active multitasking—can be detrimental to academic performance. Understanding the effects of multitasking on others is an area of research deserving replication and further investigation.

Overall, this body of research regarding in-class multitasking affirms ideas purported by the bottleneck theory of attention as well as the scattered attention hypothesis, and presents evidence against the trained attention hypothesis. The literature indicates that attempting to attend to class material (mostly lectures) and engage in technologies simultaneously can have a detrimental impact on learning, likely due to inattention to course information. These negative effects on academics were demonstrated with varied outcomes-- test performance, grades, comprehension, recall, and note-taking. Yet, many of these studies involved measuring the impact of media multitasking during a short time span, so longer follow-up studies are needed to more fully investigate the claims of the trained attention hypothesis which states that repeated practice of media multitasking will improve performance over time.