Ubiquitous laptop use in higher education: Multitasking and students' perception of distraction in a European setting
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Ubiquitous laptop use in higher education: Multitasking and students' perception of distraction in a European setting by Manuel Goyanes and Daniel Catalan-Matamoros

The present study was conducted in a European setting to examine what undergraduate students do on their laptops during a traditional class and to what extent laptop usage behaviors are associated with academic success, along with social networking use and laptop use as distractions. Based on a survey of 200 Spanish graduate students from a public university, the study led to four conclusions: 1) the most prevalent laptop use during class time was for academic purposes; 2) the perception of computer use during class time as a distraction was a significant and positive predictor of academic performance; 3) all predictor variables of laptop use behavior during class time were statistically significant and were positive predictors of social networking use except for searching for complementary information; and 4) in addition to academic performance, all laptop use behavior variables were statistically significant and positive predictors of laptop as a distraction tool, except for taking notes. Theoretical, academic and implications for practice are discussed.


1. Introduction
2. Literature review
3. Purpose of study and research questions
4. Method
5. Results
6. Discussion
7. Limitations
8. Some ideas for further research



1. Introduction

Portable digital devices are powerful tools, increasingly popularity among university students, even in traditional face-to-face classroom settings. Surveys estimate that 99 percent of students entering a university already own a laptop (University of Virginia, 2009) and about 65 percent bring their laptops to classes (Fried, 2008). The key question for most academics is simply whether these technological innovations have a positive impact on education. Research on educational laptop use addresses both the pros and cons of using these technologies in the classroom.

Laptops, tablet PCs, PDAs and even smart phones all have great potential as classroom learning tools which might enhance student learning, academic performance, collaborative interaction and content delivery (Weaver and Nilson, 2005; Lindroth and Bergquist, 2010). Utilizing laptops in a face-to-face classroom setting may allow students to, for example, instantly access and integrate information, including library resources, course materials and research sites (Fried, 2008; Hoffmann, 2015). Additionally, according to several scholars, laptop use, fundamentally for academic purposes such as taking notes or viewing PowerPoint slides, increases students’ satisfaction, motivation and engagement (Fried, 2008; Hyden, 2005; Weaver and Nilson, 2005).

However, a debate is still active within the academic community whether in-class laptops aid or hinder the learning environment. A quick look from the backside of university students in classes clearly shows that many students with wireless devices are engaged in non-academic activities, including instant messaging, checking e-mail messages, playing games, online shopping or social networking on sites such as Facebook or Twitter (Day, 2007). In fact, reporters have managed to document concerns from students, lecturers and parents regarding the risks of inappropriate utilization of laptops during class time (Day, 2007; McWilliams, 2005; Young, 2006). Consequently, some lecturers have imposed bans on wireless devices in classes. Unfortunately, because universities have spent significant funds on accessible wireless networks, these bans seem, at some point, to be a waste of resources.

Several studies have explored what occurs when students are allowed to use their wireless laptops in traditional classes, specifically in those classes not using these technologies. Fried (2008) conducted a survey of 137 American undergraduate students; 64 percent reported using laptops during classes, spending an average of 23 percent of their class time on the laptop doing anything but taking notes. Their multitasking behaviors included checking e-mail (81 percent), using instant messaging (68 percent), surfing the Internet (43 percent), playing computer games (25 percent) and other activities such as online shopping (35 percent). Other studies have found similar results; for example, McCreary (2009) conducted a survey of 450 law students from three different American universities, and found that 71 percent of the students revealed that they were using a wireless network during classes. The use of a wireless network included e-mailing (87 percent), instant messaging (38 percent), and browsing sites unrelated to a given course (42 percent). Junco (2012) found that students frequently used text messaging during classes, but reported multitasking with other ICTs to a lesser extent.

Other studies conducted self-reporting and classroom observations and found that laptops are being used for non-academic purposes, such as instant messaging, playing games (Barak, et al., 2006), checking e-mail, watching movies (Finn and Inman, 2004) and browsing the Internet (Bugeja, 2007). Inappropriate laptop behavior during classes may produce side effects such as low motivation, disorganized learning, Internet addiction, school disenchantment and low class attendance. Moreover, multitasking on a laptop poses a significant distraction to both the users and fellow students, and can be detrimental to the full comprehension of a given lecture (Sana, et al., 2013). A recent study revealed that task switching and interruption result in reduced effectiveness of learning and notetaking (Wei, et al., 2014).

To our knowledge, these previous studies have been conducted in North American universities primarily from the U.S. and Canada. We can only speculate and generalize the effects wireless networks will have on other regions, such as the Europe, where higher education may be relatively different. Furthermore, as Gaudreau, et al. (2014) pointed out, few studies were conducted in traditional class settings, suggesting that more research is still needed on traditional, lecture-oriented classes where laptops are neither mandatory nor restricted by a given professor. This study was conducted to examine what undergraduate students do with their laptops during traditional classes and to what extent laptop usage behavior are associated with academic success and social networking use, as well as to speculate on distraction in a European setting.



2. Literature review

2.1. Effects of laptop use on academic learning and performance

Positive and negative effects have been reported on laptop use in academic classes. Some researchers have found that the use of laptops in classes increases students’ motivations and their overall academic performance (Fried, 2008). Students in laptop classrooms, in contrast to non-laptop classrooms, reported higher participation rates, more interest in learning and a greater motivation to perform well (Trimmel and Bachmann, 2004). In the same vein, Demb, et al. (2004) found that students felt laptops had a positive effect on their study habits and were important to their academic performances.

On the other hand, unrelated laptop behavior might produce a negative impact on academic learning and performance. A study revealed that students who used their laptops more frequently during classes were 1.87 times more likely to obtain lower academic grades, even after controlling other variables such as scholastic abilities and class attendance (Gaudreau, et al., 2014). These findings are comparable to those of experimental studies in which students assigned laptop use obtained lower scores in a subsequent performance test compared to a control group where students were asked not to use their laptops (Hembrooke and Gay, 2003) or to take notes traditionally (Wood, et al., 2012). Another study revealed that laptop use did not statistically improve student performance; and the students reported statistically significantly less satisfaction with their education compared to those who had no laptops (Wurst, et al., 2008). Similar results were reported in a study wherein students were instructed either to exclusively take notes on their laptop or to alternate between taking notes and off-task behavior designed to mimic prototypical usage of laptop by students during classes (Sana, et al., 2013).

2.2. Effects of accessing social networking sites during classes on academic performance

The global popularization of social networking sites as a mean of communication, discussion and management is a reality. The adoption and use of these technologies by students has transformed, consequently, the way in which they learn as well as traditional relationships with their instructors. Advocates of Facebook (e.g., Munoz and Towner, 2009) suggest that it can positively affect lives of students. For example, students can use Facebook to contact other students regarding course assignments and group projects, or teachers can contact students to share useful course links. On the other hand, critics voice their concerns about possible negative effects of Facebook use. For example, students may post inappropriate pictures on Facebook. These pictures may jeopardize opportunities for future employment, since profile data may be mined by potential employers. Others wonder if students who use Facebook might spend fewer hours in studying and learning activities.

There has been a fair amount of academic and professional interest in the effect of social media on college students’ development and success (Abramson, 2011; Kamenentz, 2011; Junco, 2012). Special attention has been paid to Facebook (Matney and Borland, 2009; Kirschner and Karpinski, 2010), one of the most popular social media sites among college students (boyd and Hargittai, 2008). According to some communication scholars, 85–95 percent of college students use Facebook (Jones and Fox, 2009; Matney and Borland, 2009). Particularly, there are three main themes of research empirically studied (Hew, 2011): 1) student Facebook usage profiles (Bosch, 2009; Pasek, et al., 2009); 2) effects of Facebook on academic performance (Pempek, et al., 2009; Kirschner and Karpinski, 2010); and 3) students’ attitudes toward Facebook (Stern and Taylor, 2007; Lewis and West, 2009).

On the second group of studies — the effect of using Facebook on academic performance — findings are not robust: some scholars have found negative influence of Facebook use on academic performance, while others find no relationship. For example, Vanden Boogart (2006) found that intensive Facebook use is observed among students with lower academic performance. According to Junco (2012), social networks (such as Facebook) are negatively related to academic performance. In a similar vein, Kirschner and Karpinski (2010) found that Facebook users have a significant lower academic performance compared to non-users and they spend fewer hours per week (one to five hours) for study than the non-users (11–15 hours).

Conversely, Kolek and Sauders (2008) found no correlation between Facebook use and academic performance in a sample of students from a public university. Similarly, Pasek, et al. (2009) discovered no robust negative relationship between Facebook use and grades, and concluded: “indeed, if anything, Facebook use is more common among individuals with higher grades.” In this context, as Kirschner and Karpinski (2010) pointed out, there was a consensus regarding the need of more research in this area. The questioned relationship between SNS and academic performance remains largely unanswered.

2.3. Multitasking effects on distraction

Laptop use may be an obsessive source of classroom distraction to such as an extent that many faculties have raised concerns and frustration about laptops in classes. In addition, students and parents have also started to discuss the potential problems posed by access to distracting materials available through laptops (Fried, 2008).

In classrooms, students tend to switch back and forth between academic and non-academic tasks (Fried, 2008). Access to online entertainments makes it increasingly difficult for instructors to be “more interesting than YouTube” (Associated Press, 2010), especially if students are not intrinsically motivated by a given classroom subject. Moreover, time dedicated to multitasking is significant. According to Kraushaar and Novak (2010), students multitask approximately 42 percent of class time. Managing two or more tasks at one time requires a great deal of attention. As a result, this multitasking can result in weaker encoding of primary information into long-term memory (Bailey and Konstan, 2006; Ophira, et al., 2009). Established research in the areas of cognitive science and human factors would certainly lead to the prediction that laptop use, particularly with Wi-Fi access, can interfere with learning (Fried, 2008), since human attention is selective and limited (Kahneman, 1973; Posner, 1982). Therefore, too many sources of information can create cognitive overload causing therefore attentional shifts and distraction. Inevitably, when attention is divided, and attentional demands exceed the capacities, task performance suffers (Fried, 2008).

Distractions may also be caused due to the orientation and visual nature of laptops, with pop-ups, instant messages, movement and lighting of text and even low-battery warnings. Hence laptops are inherently distracting (Bhave, 2002; Melerdiercks, 2005; Wickens and Hollands, 2000). Distraction is linked to multitasking and low satisfaction with the education (Wurst, et al., 2008). However, not only laptop users are distracted, laptop distractions due to movement of images and screen lighting (Melerdiercks, 2005) and multitasking activities (Crook and Barrowcliff, 2001) may cause involuntary shifts of attention among other students in close proximity to laptop users (Chun and Wolfe, 2001; Finn and Inman, 2004). Some faculties offer students who use laptops during class to sit in the rear of classrooms to reduce distractions for other classmates (Barak, et al., 2006).



3. Purpose of study and research questions

Research on laptop use during class time and its possible effects on learning, distraction and engagement has mainly been focused on American studies. To our knowledge, there have been very few empirical studies shedding light on these associations in other parts of the world, such as Europe, where higher education is relatively different. In this sense, since the declaration of the ‘European Higher Education Area’ (https://www.ehea.info), European universities have standardized their academic systems. This process aimed to achieve two strategic goals set for Europe: 1) to become the most competitive and dynamic knowledge based society in the world (Martínez-Torres, et al., 2008); 2) and to develop an academic community where all European graduates hold an equivalent academic title regardless of their origin. Since then, rapid technological advances have sparked educational environments where laptops and networks are instructional tools to improve student learning. To our knowledge, this research is one of the first examining what undergraduate students do on their laptops during traditional class time and the extent to which laptop usage are associated with academic success and perceptions of distraction in a European university.

Positive and negative effects of laptop use on academic learning and performance have been reviewed earlier in this paper. The findings are not so robust to empirically assert that either possibility is completely generalizable. This paper is an attempt to step forward in order to provide more evidence on this matter. In doing so, this study might also provide a European-based reference that might support or differ from previous findings based on U.S. or Canadian samples. Moreover, this study provides additional perspectives as reflected in the literature review.

Three main clusters of research emerged from a literature review focused on the use of SNS — especially Facebook — by college students: 1) students’ Facebook usage profile (Bosch, 2009; Pempek, et al., 2009), 2) the effects of Facebook on academic performance (Pasek, et al., 2009; Kirschner and Karpinski, 2010), and 3) students’ attitudes toward Facebook (Stern and Taylor, 2007; Lewis and West, 2009).

From our point of view, this study cannot be integrated into any of these three clusters, although there is some correlation with the second, the effects of Facebook on academic performance. On the one hand, this study not only takes into consideration the empirical analysis of Facebook as a unique social network, but extends over other platforms as well. Therefore, within this framework, other platforms, such as Twitter, Snapchat or Instagram, have garnered much success among the young and are considered in toto. On the other hand, previous empirical efforts were mainly focused on the effects of Facebook on academic performance. The aim of our study is to empirically analyze predictors of SNS use during class time. It particularly focuses on the analysis of different multitasking activities — such as taking notes, surfing the Internet for unrelated school information, checking e-mail — and academic performance, extending our current knowledge about laptop use during class time.

This analysis is important for several reasons. First, it helps educators and academic institutions to understand better which multitasking activities negatively or positively influence students during classes. For example, educators will know empirically whether college students who use SNS extensively during class-time are less or more likely to conduct other multitasking activities for educational purposes, as suggested by advocates of social networking use among students. Second, and most importantly, this study provides empirical evidence on the unexplored relationship between academic performance and social networks use during class time. Therefore, the present research contributes to the discussion of effects of social network use, specifically, during class time, not in general terms, as often examined in previous studies.

Finally, this research empirically studies how laptop use behavior during class time influences the perception of laptops as a distraction. Earlier studies explored how multitasking activities influenced student learning behavior. In this sense, according to Sana, et al. (2013), participants who multitasked with laptops during lectures scored lower in tests compared to those who did not; participants who had a direct view of a multitasking peer scored lower in tests compared to those who did not. By the same token, Wood, et al. (2012) examining the impact of multitasking and found that participants who did not use any technologies in lectures outperformed students who used some form of technology.

Despite the importance of studies in this area in order to understand learning processes during class time, to our knowledge, there is no evidence demonstrating an unexplored relationship between multitasking and perceptions of academic distraction. To address this gap, this study focuses on determining whether the use of different multitasking activities influences student perceptions.

Therefore, the research questions examined were:

RQ1: How does laptop use behavior during class influence academic performance, after controlling for demographics?

RQ2: How does laptop use behavior during class influence SNS use during class, after controlling for demographics and academic performance?

RQ3: How does laptop use behavior during class influence the perception of laptops as a distraction, after controlling for demographics and academic performance?



4. Method

Data for this study were collected with a questionnaire, featuring altogether 200 responses from students at Carlos III University of Madrid. The sample was closely matched to the demographic characteristics of the university population. This study incorporated more women (60 percent, N = 120) than men (40 percent, N = 80) with ages ranging between 18 to 27 years (M = 20.62; SD = 1.76; N = 200). The first question of the survey was whether graduate students normally use their laptops during class time. Respondents who answer this question negatively were excluded from the survey and therefore not part of the final sample. The aim of this preliminary question was to guarantee that all respondents were regular laptop users during classes.

The questionnaire was adapted from Gaudreau, et al. (2014) to empirically investigate laptop use during classes. The questionnaire was first translated into English and then back into Spanish through a translation process by three university academics. The questionnaire was pilot tested on four graduate students and six academics (one post-doctoral fellow, two assistant professors, one associate professor and two full professors). Their comments on the content of the survey questionnaire, item wording, terminology and clarity were incorporated into a revised version. Next, the revised version was tested again on a larger sample of 50 university students, which were not part of the sample. Final adjustments were made.

Since the aim of the survey was to measure different variables related to laptop use during classes, it could be the case that graduate students might not be totally honest in their answers. To avoid bias in this regard, the conductor of the survey in all cases was external, not having any educational or personal relationship with the surveyed graduate students. The survey was conducted on different days during the first week of November 2014. In all cases the procedure was the same: the principal lecturer of the course notified graduate students one week in advance that an individual from outside the class and institution will survey their laptop usage and behavior during classes. During the survey, the external party requested that the principal lecturer to leave the classroom and clearly explained to the students that the outcome of the survey would never be revealed to the lecturer, that their participation was anonymous.

The questionnaire included items to measure laptop usage during class. Therefore, the first six queries measured behaviors considered prototypical of the use that undergraduate students make during classes. Two items were added based on Gaudreau, et al. (2014) to empirically examine other broadband devices and to evaluate the extent to which students perceived their laptops to be a distraction (Fried, 2008). Participants were asked to evaluate how they use a laptop during a typical class on a scale anchored from 1 to 6 (1 = never; 2 = very rarely; 3 = rarely; 4 = often; 5 = very often; 6 = very, very often).

The proposed uses, and therefore variables, were the following: 1) Taking notes; 2) Searching complementary information on the Web; 3) Sending e-mail messages; 4) Navigating Web sites unrelated to school work; 5) Visiting social networking sites; 6) Watching videos or examining pictures; 7) Reading text messages on digital phones or related devices; and 8) Laptop a source of distraction. While the effects of age and gender are important variables, neither of the previous studies on Facebook use and engagement took those into account. In this study, we included these as control variables. Both variables were collected using standard survey measurements. Finally, another key variable in this study was academic performance; it was assessed by asking participants about their cumulative grade point average (GPA).

The survey asked if laptop use was non-related to school work in question 4, “Navigating Web sites that are unrelated to school work”. However we assume that the items asking for multitasking related to school work were: “taking notes”, and “searching complementary information on the Web”. On the other hand, non-related to school work might be: “sending e-mail”, “visiting social networks sites”, “watching photos/videos” and “reading text messages”.

Participants needed assurance that data collection, storage and reporting would guarantee confidentiality and anonymity. Therefore, the study design and protocol was approved by the Ethical Research Committee of the university. All participants received verbal and written information about the study aims, with anonymous data collection.



5. Results

Three regression analyses [1] were conducted, treating the aforementioned research questions. Descriptive statistics and correlations are presented in Table 1. The most prevalent laptop use during classes was for taking notes (M = 4.36; SD = 1.716) followed by searching complementary information on the Web (M = 4.32; SD = 1.290), visiting social networking sites (M = 4.03; SD = 1.597) and WhatsApp use (M = 4.02; SD = 1.61).


Table 1: Descriptive statistics and correlations of quantitative variables.
Note: *p < 0.05; **p < 0.01.
Age (years)20.62
A. Performance7.38


A hierarchical regression analysis was conducted to empirically examine the relationship between student laptop behavior during classes and academic performance. We entered the controls in model 1 (age and gender), and laptop behavior in model 2. This last model accounted for 13 percent of the variance on academic performance suggesting considerable explanatory power. In addition to gender (being male), only the perception of computer use during class time as a distraction was a significant and positive predictor of academic performance (Β = 0.151; p < 0.05). Therefore, the greater the students acknowledge laptop usage during class as a distraction, the greater is academic performance.


Table 2: Hierarchical regression analysis predicting academic performance.
Note: *p < 0.05; **p < 0.01.
Academic performance Age Gender Notes Information E-mail Navigate
Step 10.015–0.317*    
SNS Photos/videos WhatsApp Distraction R2 R2 change Adjusted R2
Academic performance Age Gender Notes Information E-mail Navigate
Step 2–0.013–0.325*–0.056–0.024–0.040–0.081
SNS Photos/videos WhatsApp Distraction R2 R2 change Adjusted R2


The second hierarchical regression analysis was conducted to empirically examine the relationship between academic performance and laptop behavior in class, specifically using SNS. In the first model we entered control variables, in the second academic performance, and in the third laptop behavior. The last model accounted for 69 percent of variance on SNS use during classes. In addition to age (Β = –0.091; p < 0.05), gender (Β = 0.533; p < 0.05) and academic performance (Β = 0.160; p < 0.05), all predictor variables of laptop behavior during class were statistically significant and positive, except for searching for complementary information (Β = –0.120; p < 0.05). Therefore, in this last case, students who habitually search complementary information (that is, related to their courses) using laptops engage less with SNS.


Table 3: Hierarchical regression analysis predicting SNS use.
Note: *p < 0.05; **p < 0.01.
SNSAgeGenderPerformanceNotesInformationE-mailNavigatePhotos/videosR2R2 changeAdjusted R2
Step 10.155**0.111      .03.03**.02
Step 20.150*–0.220–0.346**     .06.03**.05
Step 3–0.091*0.533**0.160*0.209**–0.120*0.208**0.712**0.118*.69.63**.68


Finally, the third hierarchical regression analysis was conducted to empirically examine the relationship between academic performance and laptop behavior during class on the perception of laptop use as a distraction. In the first model, we entered control variables, in the second one academic performance and in the third one laptop behavior. The last model accounted for 45 percent of variance on laptop use as a distraction during classes. In addition to academic performance (Β = 0.264; p < 0.05), all laptop behavior variables were statistically significant and positive except for taking notes (Β = –0.109; p < 0.05). Therefore, in the last case, students who used laptops mainly for taking notes, of course, perceived laptops as non-distracting tools.


Table 4: Hierarchical regression analysis predicting academic distraction.
Note: *p < 0.05; **p < 0.01.
Distraction Age Gender Performance Notes Information
Step 10.202**–0.497*   
Navigate Photos/videos WhatsApp R2 R2 change Adjusted R2
Distraction Age Gender Performance Notes Information
Step 20.196**–0.354–0.453**  
Navigate Photos/videos WhatsApp R2 R2 change Adjusted R2
Distraction Age Gender Performance Notes Information
Step 3-0.062–0.3310.264**–0.109*–0.020
Navigate Photos/videos WhatsApp R2 R2 change Adjusted R2




6. Discussion

This study examined what the activities of undergraduates on their laptops during traditional classes and the extent to which behaviors related to laptop use were associated with academic success, social networking and distraction in a specific European university. Data were collected based on responses from 200 undergraduate students to questionnaires at Carlos III University of Madrid. The survey was conducted on different days during the first week of November 2014. In order to answer this study’s three research questions, we conducted a correlation analysis and three hierarchical regressions.

First, a brief analysis of the descriptive statistics highlight the importance of the apparently “good use” that undergraduate students made of laptops. Like the study conducted by Ragan, et al. (2014), our study found that the predominant laptop use during class is for academic purposes, such as taking notes and searching complementary information on the Web. Immediately after that, we noted non-academic, and therefore problematic, usages, such as visiting social networking sites and WhatsApp. Our findings suggest that although inappropriate and non-academic uses of laptops during classes might raise some concerns among lecturers, parents, public legislators an even delegations of students (Day, 2007; McWilliams, 2005; Young, 2006), their use for academic purposes dominants. This is not to say that we declare an advocacy for all laptop use, but we outline some value in its appropriate and deliberate use by a majority of students in traditional lecture-oriented classes, where laptops are neither mandatory nor restricted.

Several studies have examined the link between laptop use in classes and academic achievement. One of the major effects of laptop use is an increase in distractions, which decrease academic performance. Many studies found that academic success is negatively related to laptops as distractions (Sana, et al., 2013; Wurst, et al., 2008; Kirschner and Karpinski, 2010). Other studies discovered that laptops improve academic success when used as instructional and learning tools. For example, Gulek and Demirtas (2005) found that students using laptops illustrated significantly higher achievement in nearly all measures after one year in a program. With respect to differences in academic performance in this study, regression analysis revealed that the greater that the students acknowledge laptop usage during class as a distraction, the greater their academic performance. This finding might indicate that simply making students aware of negative effects may allow them to make informed choices, rather than assuming they were immune to laptop use during classes. However, a recent study (Barry, et al., 2015) revealed that students aware of these distractions interfering with their studies continued to undertake them. Therefore, our findings may support the need to develop campaigns aimed not only to increase awareness and concern among students about the negative effects of laptops as distractions, but also to assume the challenge of behavioral changes.

The second research question aimed to explore the association between laptop use during classes and SNS use, after controlling for demographics and academic performance. To our knowledge, previous empirical studies mainly focused on analyzing the effects of SNS on academic learning or performance. In this sense, these findings were not robust (Kirschner and Karpinski, 2010). Some have found negative influences of Facebook use on academic performance (Vanden Boogart, 2006; Junco, 2012), while others show no empirical relation (Kolek and Sauders, 2008; Pasek, et al., 2009). In our analysis, we demonstrated how academic performance and all the variables included in “multitasking” were statistically significant and positive predictors of SNS use during class. Only one exception was found empirically to be a negative predictor of SNS, namely, searching for complementary information. These findings have several empirical and educational implications.

The first strength of this study is that it addressed the positive relationship between academic performance and SNS use during class. Students with higher grades tend to engage more in SNS during class time than students with lower grades. In this sense, as Pasek, et al. (2009) pointed out regarding the association between Facebook use and academic performance, “indeed, if anything, Facebook use is more common among individuals with higher grades”, could also be extrapolated to the relation between academic performance and SNS use during class. This apparently counter-intuitive finding can be explained by several factors such as lack of educational incentives, lack of engagement with courses and active learning and a perception of ease to obtain specific degrees. We strongly suggest that this association be explored in other populations, which also challenges the general assumption of negative outcomes of SNS use during class. According to our results, this hypothesis is not supported, especially if no other moderator or mediator variables are considered.

Secondly, taking notes during class was the unique negative and significant predictor of SNS use. Therefore, students whose main use of laptops during classes is for taking notes are less engaged in SNS. This finding is supported by other studies, which stated that using laptops for academic purposes, such as taking notes in class, can increase satisfaction, motivation and engagement among students (Fried, 2008; Hyden, 2005; Weaver and Nilson, 2005). Nevertheless, students frequently admit spending considerable time in using laptops for other activities during lectures (Fried, 2008). Our findings support the importance of in-class use of laptops for specific academic purposes, suggesting that responsible use of laptops should be promoted among students.

Multitasking on laptops poses significant distractions to students (Sana, et al., 2013). In general, students find the temptation for content on the Internet too strong to alter their behavior in classes. Wurst, et al. (2008) found that students were heavily involved in Internet activities and therefore were inattentive to the substance of lectures. However, according to this study, when students use their laptops only for taking notes, of course, they perceive laptops as non-distractive tools; this conclusion is consistent with results of previous research (Fried, 2008). Some other studies (Fitch, 2004; Partee, 1996; Stephens, 2006) have found some positive outcomes related to the use of laptops, such as facilitating faculty-student interactions, stimulating in-class participation, increasing engagement and active learning.

6.1. Implications for practice

Our study reinforces previous recommendations (Fried, 2008) that lecturers who do not use laptops in an integrated ways should consider efforts to limit or control laptop use, informing students about potential pitfalls and related distractions. indeed, there have been suggestions that laptops should not be used in classes where they are not integrated into a given course (Barak, et al., 2006; Gay, et al., 2001). According to our findings, we believe students need to be aware of the distractions of laptop use in class but we are not calling for the ban of laptops. Laptops are important tools for learning and education, such as for taking notes and other in-class activities.



7. Limitations

Our conclusions are based on self-reports, and therefore cannot be generalized to other populations with different educational systems and academic incentives. Furthermore, the present study did not analyze confounding factors consisting of personal characteristics (such as school motivation, personality or Internet addiction) that could predispose students to use laptops leisurely during classes.



8. Some ideas for further research

Due to the small sample size, this study did not analyze data for students enrolled in different undergraduate programs. Further research may examine diverse programs and disciplines, to test for variations in behavior and use. Future research may also be improved by monitoring laptop use more directly, avoid the limitations of self-reporting. Accurate monitoring would undoubtedly give a clearer picture of why and when laptop use interferes with learning. Such data collection methods would complement self-reporting used in this study and would improve our understanding of the nature of laptop use and its influence on learning. End of article


About the authors

Manuel Goyanes is Assistant Professor of Media Management at Universidad Carlos III de Madrid.
E-mail: mgoyanes [at] hum [dot] uc3m [dot] es

Daniel Catalán-Matamoros is Assistant Professor of Science Communication at Universidad Carlos III de Madrid.
E-mail: dacatala [at] hum [dot] uc3m [dot] es



1. In the three models the constant was statistically significant. Furthermore, due to problems of multicollinearity, WhatsApp use and perception of laptop as a distraction, were removed from the analysis in the second regression, as well as in the third regression, E-mail and SNS use were removed.



L. Abramson, 2011. “Can social networking keep students in school?” NPR (9 February), at http://www.npr.org/2011/02/09/133598049/can-social-networking-keep-students-in-school, accessed 5 November 2014.

Associated Press 2010. “At universities, is better learning a click away?” Education Week (7 April), at http://www.edweek.org/ew/articles/2010/04/07/28clickers.h29.html, accessed 13 September 2017.

B. Bailey and J. Konstan, 2006. “On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state,” Computers in Human Behavior, volume 22, number 4, pp. 685–708.
doi: https://doi.org/10.1016/j.chb.2005.12.009, accessed 13 September 2017.

M. Barak, A. Lipson and S. Lerman, 2006. “Wireless laptops as means for promoting active learning in large lecture halls,” Journal of Research on Technology in Education, volume 38, number 3, pp. 245–263.
doi: http://dx.doi.org/10.1080/15391523.2006.10782459, accessed 13 September 2017.

S. Barry, K. Murphy and S. Drew, 2015. “From descontructive misalignment to constructive alignment: Exploring student uses of mobile technologies in university classrooms,” Computers & Education, volume 81, pp. 202–210.
doi: https://doi.org/10.1016/j.compedu.2014.10.014, accessed 13 September 2017.

M. Bhave, 2002. “Classrooms with wi-fi,” T.H.E. Journal, volume 30, number 4, pp. 17–20.

T. Bosch, 2009. “Using online social networking for teaching and learning: Facebook use at the University of Cape Town,” Communicatio: South African Journal for Communication Theory and Research, volume 35, number 2, pp. 185–200.
doi: http://dx.doi.org/10.1080/02500160903250648, accessed 13 September 2017.

d. boyd and E. Hargittai, 2010. “Facebook privacy settings: Who cares?” First Monday, volume 15, number 8, at http://firstmonday.org/article/view/3086/2589, accessed 13 September 2017.
doi: http://dx.doi.org/10.5210/fm.v15i8.3086, accessed 13 September 2017.

M. Bugeja, 2007. “Distractions in the wireless classroom,” Chronicle of Higher Education (26 January), volume 53, number 21, pp. 1–5, and at http://www.chronicle.com/article/Distractions-in-the-Wireless/46664, accessed 13 September 2017.

M. Chun and J. Wolfe, 2001. “Visual attention,” In: E. Goldstein (editor). Blackwell handbook of sensation and perception. Oxford: Blackwell, pp. 272–310.
doi: http://dx.doi.org/10.1002/9780470753477.ch9, accessed 13 September 2017.

C. Crook and D. Barrowcliff, 2001. “Ubiquitous computing on campus: Patterns of engagement by university students,” International Journal of Human–Computer Interaction, volume 13, number 2, pp. 245–256.
doi: http://dx.doi.org/10.1207/S15327590IJHC1302_9, accessed 13 September 2017.

T. Day, 2007. “Digital distraction,” University Affairs (7 November), at http://www.universityaffairs.ca/features/feature-article/digital-distraction/, accessed 30 November 2014.

A. Demb, D. Erickson and S. Hawkins-Wilding, 2004. “The laptop alternative: Student reactions and strategic implications,” Computers & Education, volume 43, number 4, pp. 383–401.
doi: https://doi.org/10.1016/j.compedu.2003.08.008, accessed 13 September 2017.

S. Finn and J. Inman, 2004. “Digital unity and digital divide: Surveying alumni to study effects of a campus laptop initiative,” Journal of Research on Technology in Education, volume 36, number 3, pp. 297–317.
doi: http://dx.doi.org/10.1080/15391523.2004.10782417, accessed 13 September 2017.

J. Fitch, 2004. “Student feedback in the college classroom: A technology solution,” Educational Technology Research and Development, volume 52, number 1, pp. 71–77.

C. Fried, 2008. “In-class laptop use and its effects on student learning,” Computers & Education, volume 50, number 3, pp. 906–914.

P. Gaudreau, D. Miranda and A. Gareau, 2014. “Canadian university students in wireless classrooms: What do they do on their laptops and does it really matter?” Computers & Education, volume 70, pp. 245–255.
doi: https://doi.org/10.1016/j.compedu.2013.08.019, accessed 13 September 2017.

G. Gay, M. Stefanone, M. Grace-Martin and H. Hembrooke, 2001. “The effects of wireless computing in collaborative learning environments,” International Journal of Human-Computer Interaction, volume 13, number 2, pp. 257–276.

J. Gulek and H. Demirtas, 2005. “Learning with technology: The impact of laptop use on student achievement,” Journal of Technology, Learning and Assessment, volume 3, number 2, pp. 3–38.

H. Hembrooke and G. Gay, 2003. “The laptop and the lecture: The effects of multitasking in learning environments,” Journal of Computing in Higher Education, volume 15, number 1, pp. 46–64.
doi: https://doi.org/10.1007/BF02940852, accessed 13 September 2017.

K. Hew, 2011. “Students’ and teachers’ use of Facebook,” Computers in Human Behavior, volume 27, number 2, pp. 662–676.
doi: https://doi.org/10.1016/j.chb.2010.11.020, accessed 13 September 2017.

M. Hoffmann, 2015. “An exploratory study: Mobile device use for academics,” doctoral dissertation (education in learning technologies), Pepperdine University; version at https://search.proquest.com/docview/1664843577, accessed 13 September 2017.

P. Hyden, 2005. “Teaching statistics by taking advantage of the laptop’s ubiquity,” New Directions for Teaching & Learning, volume 2005, number 101, pp. 37–42.
doi: https://doi.org/10.1002/tl.184, accessed 13 September 2017.

S. Jones and S. Fox, 2009. “Generations online in 2009,” Pew Research Center (28 January), at http://www.pewinternet.org/2009/01/28/generations-online-in-2009/, accessed 10 November 2014.

R. Junco, 2012. “In-class multitasking and academic performance,” Computers in Human Behavior, volume 28, number 6, pp. 2,236–2,243.
doi: https://doi.org/10.1016/j.chb.2012.06.031, accessed 13 September 2017.

D. Kahneman, 1973. Attention and effort. Englewood Cliffs, N.J.: Prentice-Hall.

A. Kamenentz, 2011. “Gates Foundation bets on Facebook app to help kids graduate,” Fast Company (9 February), at http://www.fastcompany.com/1725665/gates-foundation-bets-on-facebook-app-to-help-kids-graduate, accessed 10 February 2015.

P. Kirschner and A. Karpinski, 2010. “Facebook and academic performance,” Computers in Human Behavior, volume 26, number 6, pp. 1,237–1,245.
doi: https://doi.org/10.1016/j.chb.2010.03.024, accessed 13 September 2017.

E. Kolek and D. Saunders, 2008. “Online disclosure: An empirical examination of undergraduate Facebook profiles,” NASPA Journal, volume 45, number 1, pp. 1–25.

J. Kraushaar and D. Novak, 2010. “Examining the effects of student multitasking with laptops during the lecture,” Journal of Information Systems Education, volume 21, number 2, pp. 241–251, and at http://jise.org/Volume21/21-2/Pdf/vol21-2-pg241.pdf, accessed 13 September 2017.

J. Lewis and A. West, 2009. “‘Friending’: London-based undergraduates’ experience of Facebook,” New Media & Society, volume 11, number 7, pp. 1,209–1,229.
doi: https://doi.org/10.1177/1461444809342058, accessed 13 September 2017.

T. Lindroth and M. Bergquist, 2010. “Laptopers in an educational practice: Promoting the personal learning situation,” Computers & Education, volume 54, number 2, pp. 311–320.
doi: https://doi.org/10.1177/1461444809342058, accessed 13 September 2017.

M. Martínez-Torres, S. Toral Marín, F. Barrero García, S. Gallardo Vázquez, M. Arias Oliva and T. Torres, 2008. “A technological acceptance of e-learning tools used in practical and laboratory teaching, according to the European higher education area,” Behaviour & Information Technology, volume 27, number 6, pp. 495–505.

M. Matney and K. Borland, 2009. “Facebook, blogs, tweets: How staff and units can use social networking to enhance student learning,” paper presented at the annual meeting of the National Association for Student Personnel Administrators (Seattle, Wash.), version at https://studentlife.umich.edu/research/article/facebook-blogs-tweets-how-staff-and-units-can-use, accessed 13 September 2017.

J. McCreary, 2009. “The laptop-free zone,” Valparaiso University Law Review, volume 43, number 3, pp. 989–1,044, and at http://scholar.valpo.edu/vulr/vol43/iss3/, accessed 13 September 2017.

G. McWilliams, 2005. “The laptop backlash,” Wall Street Journal (14 October), at https://www.wsj.com/articles/SB112924976699568321, accessed 13 September 2017.

K. Melerdiercks, 2005. “The dark side of the laptop university,” Journal of Ethics, volume 14, number 1, pp. 9–11.

C. Munoz and T. Towner, 2009. “Opening Facebook: How to use Facebook in the college classroom,” Society for Information Technology & Teacher Education International Conference, at https://www.learntechlib.org/p/31031/, accessed 13 September 2017.

E. Ophira, C. Nass and A. Wagner, 2009. “Cognitive control in media multitaskers,” Proceedings of the National Academy of Sciences, volume 106, number 37 (15 September), pp. 15,583–15,587.
doi: https://doi.org/10.1073/pnas.0903620106, accessed 13 September 2017.

B. Partee, 1996. “The development of formal semantics in linguistic theory,” In: S. Lappin (editor). Handbook of contemporary semantic theory. Oxford: Blackwell, pp. 11–38.

J. Pasek, e. more and E. Hargittai, 2009. “Facebook and academic performance: Reconciling a media sensation with data,” First Monday, volume 14, number 5, at http://firstmonday.org/article/view/2498/2181, accessed 13 September 2017.
doi: http://dx.doi.org/10.5210/fm.v14i5.2498, accessed 13 September 2017.

T. Pempek, Y. Yermolayeva and S. Calvert, 2009. “College students’ social networking experiences on Facebook,” Journal of Applied Developmental Psychology, volume 30, number 3, pp. 227–238.
doi: https://doi.org/10.1016/j.appdev.2008.12.010, accessed 13 September 2017.

M. Posner, 1982. “Cumulative development of attentional theory,” American Psychologist, volume 37, number 2, pp. 168–179.

E. Ragan, S. Jennings, J. Massey and P. Doolittle, 2014. “Unregulated use of laptops over time in large lecture classes,” Computers & Education, volume 78, pp. 78–86.
doi: https://doi.org/10.1016/j.compedu.2014.05.002, accessed 13 September 2017.

F. Sana, T. Weston and N. Cepeda, 2013. “Laptop multitasking hinders classroom learning for both users and nearby peers,” Computers & Education, volume 62, pp. 24–31.
doi: https://doi.org/10.1016/j.compedu.2012.10.003, accessed 13 September 2017.

P. Stephens, 2006. “Validation of the business computer self-efficacy scale: Assessment of the computer literacy of incoming business students,” Journal of Educational Computing Research, volume 34, number 1, pp. 29–46.
doi: https://doi.org/10.2190/CFHJ-MJXN-8LWA-P3QC, accessed 13 September 2017.

L. Stern and K. Taylor, 2007. “Social networking on Facebook,” Journal of the Communication, Speech & Theatre Association of North Dakota, pp. 209–220.

M. Trimmel and J. Bachmann, 2004. “Cognitive, social, motivational and health aspects of students in laptop classrooms,” Journal of Computer Assisted Learning, volume 20, number 2, pp. 151–158.
doi: https://doi.org/10.1111/j.1365-2729.2004.00076.x, accessed 13 September 2017.

University of Virginia, 2009. “UVa first year student computer inventory,” at http://itc.virginia.edu/students/inventory/2009/, accessed 12 March 2015.

M. Vanden Boogart, 2006. “Uncovering the social impacts of Facebook on a college campus,” Master’s thesis, Kansas State University, at http://krex.k-state.edu/dspace/handle/2097/181, accessed 13 September 2017.

B. Weaver and L. Nilson, 2005. “Laptops in class: What are they good for? What can you do with them?” New Directions for Teaching & Learning, volume 2005, number 101, pp. 3–13.
doi: https://doi.org/10.1002/tl.181, accessed 13 September 2017.

F.-Y. Wei, Y. Wang and W. Fass, 2014. “An experimental study of online chatting and notetaking techniques on college students’ cognitive learning from a lecture,” Computers in Human Behavior, volume 34, pp. 148–156.
doi: https://doi.org/10.1016/j.chb.2014.01.019, accessed 13 September 2017.

C. Wickens and J. Hollands, 2000. Engineering psychology and human performance. Third edition. Upper Saddle River, N.J.: Prentice Hall.

E. Wood, L. Zivcakova, P. Gentile, K. Archer, D. De Pasquale and A. Nosko, 2012. “Examining the impact of off-task multi-tasking with technology on real-time classroom learning,” Computers & Education, volume 58, number 1, pp. 365–374.
doi: https://doi.org/10.1016/j.compedu.2011.08.029, accessed 13 September 2017.

C. Wurst, C. Smarkola and M. Gaffney, 2008. Ubiquitous laptop usage in higher education: effects on student achievement, student satisfaction, and constructivist measures in honors and traditional classrooms,” Computers & Education, volume 51, number 4, pp. 1,766–1,783.
doi: https://doi.org/10.1016/j.compedu.2008.05.006, accessed 13 September 2017.

J. Young, 2006. “The fight for classroom attention: Professor vs. laptop,” Chronicle of Higher Education (2 June), 52, pp. A27–A29, and at http://www.chronicle.com/article/The-Fight-for-Classroom/19431, accessed 13 September 2017.


Editorial history

Received 20 December 2016; revised 19 August 2017; revised 28 August 2017; accepted 1 September 2017.

Licencia de Creative Commons
“Ubiquitous laptop use in higher education: Multitasking and students’ perception of distraction in a European setting” by Manuel Goyanes & Daniel Catalán-Matamoros is licensed under a Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional License.

Ubiquitous laptop use in higher education: Multitasking and students’ perception of distraction in a European setting
by Manuel Goyanes and Daniel Catalán-Matamoros.
First Monday, Volume 22, Number 10 - 2 October 2017
doi: http://dx.doi.org/10.5210/fm.v22i110.7268

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