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Dangers of Social Media Use

Tomorrow's Teaching and Learning

Message Number: 
1428

The widespread use of laptops, availability of wifi on college campuses, use of cell phones, and use of social media have contributed to the fear that student learning may be affected because of students’ serial switching between tasks.

Folks:


The posting below looks at how cognitive overload is impacted through multitasking via various forms of social media. It is from Chapter 2 – Research on Social Media, in the book, Engaging Students through Social Media: Evidence-Based Practices for Use in Student Affairs, by Reynol Junco. Jossey-Bass, San Francisco. One Montgomery Street. Suite 1200, San Francisco, CA 94104-4594. [www.josseybass.com/highereducation] Copyright © 2014 by John Wiley & Sons, Inc. All rights reserved. Reprinted with permission.

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Regards,

Rick Reis
reis@stanford.edu
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Tomorrow's Teaching and Learning

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Dangers of Social Media Use


Up to this point, the (mostly) positive effects of certain types of social media use on student engagement and academic performance have been discussed. However, there is a growing body of research examining negative factors related to social media use.  It seems that every week, new studies are released showing a relationship between social media use and undesirable constructs such as narcissistic personality traits (Mehdizadeh, 2010; Ong, Ang, Ho, Lim & Goh, 2011).  A popular area of examination is the effect of media multitasking on student learning and academic performance.  In this chapter, the popular term multitasking is understood as often being used to describe the phenomena of divided attention and task switching, concepts from the cognitive sciences literature that are more representative of how humans attend to and process information (Chung, Golomb & Turk-Browne, 2011).  Therefore, multitasking is here defined as divided attention and nonsequential task switching for ill-defined tasks as they are performed in learning situations – for example, when a student is text messaging a friend while studying for an examination.  The widespread use of laptops, availability of wifi on college campuses, use of cell phones, and use of social media have contributed to the fear that student learning may be affected because of students’ serial switching between tasks.

There has been a great deal of research in the cognitive sciences examining the effects of multitasking on human information processing.  This research supports the idea of what has been long termed a “cognitive bottleneck,” a limitation in decision making that slows a second task (Welford, 1967).  For instance, Koch and colleagues (2011) found significant performance costs in both accuracy and reaction time when switching between two auditory stimuli; these costs were not reduced by advance preparation of the participant’s attention.  Tombu and colleagues (2011) found that participants responded more slowly and had poorer accuracy on dual-task trials than on single-task trials for both auditory-vocal and visual-manual tasks.  In summary, trying to attend to or process more than one task at a time “clogs up” the bottleneck and overloads the capacity of the human information processing system, which results in real-world consequences due to the costs of task switching (Koch, Lawo, Fels & Vorlander, 2011; Marois & Ivanoff, 2005; Strayer & Drews, 2004; Tombu et al., 2011; Wood & Cowan, 1995).

Because of the work conducted in the cognitive sciences, researchers have wondered how the costs of multitasking affect educational outcomes (Fried, 2008; Junco & Cotten, 2011; 2012; Mayer & Moreno, 2003; Rosen, Lim, Carrier & Cheever, 2011; and Wood et al., 2012).  Mayer and Moreno (2003) proposed a framework by which we can understand how multitasking might affect the learning process, which is based on four assumptions about the human information processing system:

1.      The human information processing system has two channels by which it can take in information; visual and auditory.
2.      Each channel has a limited capacity for cognitive processing.
3.      This capacity is used when selecting and processing stimuli.
4.      Meaningful learning can happen only by using a substantial amount of capacity (cognitive processing) in either the visual or auditory channels.

Cognitive overload occurs when processing demands evoked by a learning task exceed the processing capacity of the cognitive system (Mayer & Moreno, 2003).  Given the widespread use of technologies and the now-ubiquitous laptop initiatives that encourage or even require students to own a laptop computer, researchers have wondered how much capacity is being taken up by the mere act of using technology (Weaver & Nilson, 2005).  For example, might just having a laptop available increase a student’s cognitive load so that meaningful learning cannot occur?  Indeed, research has shown that unstructured use of laptops (that is, use of laptops without incorporating them into the learning process) is related to performing more off-task activities such as checking e-mail and playing games during class (Kay & Lauricella, 2011).  Research by Fried (2008) has shown that student use of laptops for non-class-related (“off-task”) activities is negatively related to their course performance.  However, laptop initiatives have been touted for helping to support facets of student learning.  Therefore, it is critical to evaluate how student technology use can both help and hinder the learning process.

A number of studies have examined how social media (as well as other technologies) affect learning. One of our early surveys of students at four universities discovered that students who reported studying while sending and receiving instant messages (IMs) were more likely to report that instant messaging interfered with their ability to complete their homework (Junco & Cotten, 2011).  However, the study was correlational and collected only self-reported data.  In a more recent study, we collected GPA data from the university registrar as well as surveys on a large sample of students (n = 1839) (Junco & Cotten, 2012).  Controlling for background variables and high school GPA, we found that using Facebook and texting while studying were negatively related to overall college GPA; however, we found that e-mailing, searching for content not related to courses, talking on the phone, and instant messaging while preparing for class were not related to GPA (Junco & Cotten, 2012).  In yet another study, students frequently sent and received text messages during class, and there was a negative relationship between text messaging and Facebook use during class and GPA even after controlling for gender, race and ethnicity, high school GPA, and Internet skill; however, as we found in our earlier study, e-mailing and searching for content not related to the class were not related to GPA (Junco, 2012c; Junco & Cotten 2012).

Some studies have found a negative relationship between Facebook use and academic performance (Junco, 2012b; Kirschner & Karpinski, 2010), whereas others have found no relationship between the two (Kolek & Saunders, 2008; Pasek, More & Hargittai, 2009).  As reported in my Facebook and academic performance paper, the ways that students use Facebook are perhaps the most important factors in influencing academic outcomes (Junco, 2012b).  Additional research has discovered that the negative relationship seen between Facebook use and academic performance is likely predicted by multitasking.  In a manuscript currently under review, the time students spent doing schoolwork while using Facebook was split from other time spent on Facebook (Junco, under review).  Time spent on Facebook was negatively related to student overall GPA for first-year students, but not for sophomores, juniors, and seniors.  Furthermore, time spent multitasking while on Facebook was negatively related to GPA for students at all levels except seniors.  Not only does multitasking predict the negative relationship between Facebook use and academic performance, then, but also perhaps first-year students have not developed the appropriate self-regulation skills to keep their Facebook use from impacting their academic performance.

The aforementioned studies were based on self-report measures of multitasking (Junco & Cotten, 2011; 2012; Junco, under review). As has been discovered with technology use (Junco, 2013) and in other areas ranging from health-related behaviors (Celis-Morales et al., 2012; Hald, Overgaard & Grau, 2003) to TV viewing (Otten, Littenberg & Harvey-Berino, 2010), self-reported measures of behavior are notoriously inaccurate in capturing actual behaviors.  In methodological terms, self-reported measures lack evidence of criterion validity. Furthermore, the aforementioned studies were correlational in nature – perhaps students with poorer academic skills are more likely to multitask.  In order to extend these findings, Rosen and colleagues (2013) observed students during fifteen-minute study periods in their natural environments.  The researchers found that students who accessed Facebook during the study period had lower GPAs than those who didn’t and concluded that the drive to check Facebook is related to the emotional gratification derived from reading posts, commenting on content, or posting status updates (Rosen, Carrier & Cheever, 2013).

Other studies have examined social media, multitasking, and academic outcomes using experimental designs.  For instance, a study by Wood and colleagues (2012) used an experimental design to examine the effects of Facebook, text messaging, IM, and e-mail during a simulated lecture.  The researchers found that students who used Facebook while attending to a lecture scored significantly lower on tests of lecture material than those who were only allowed to take notes using paper and pencil; however, the scores of students who texted, e-mailed, or sent IMs did not differ significantly from students in control groups.  In a related study, Rosen and colleagues (2011) had students watch a thirty-minute lecture video.  The researchers asked students to respond to text messages sent out at even intervals throughout the lecture by researchers.  Students were split into a low text messaging group (which received 0-7 messages), a moderate text messaging group 8-15 messages), and a high text messaging group (16 or more messages).  The researchers found that the high text messaging group performed worse (by one letter grade) on an information post-test than the low text messaging group; however, the moderate text messaging group showed no difference on the post-test compared with the other two groups.  These studies leave open questions about the interaction of technologies with multitasking behaviors to produce differential outcomes.  Future research may elucidate the process by which some activities lead to negative outcomes and others to positive outcomes.

References

Celis-Morales, C.A., Perez-Bravo, F., Ibanez, L., Salas, C., Bailey, M.E.S., & Gill, J.M.R. (2012). Objective vs. self-reported physical activity and sedentary time: Effects of measurement method on relationships with risk biomarkers. PLOS ONE 7(5).

Chun, M.M., Golomb, J.D., & Turk-Browne, N.B. (2011).  A taxonomy of external and internal attention.  Annual Review of Psychology, 62, 73-101.

Fried, C. (2008).  In-class laptop use and its effects on student learning.  Computers & Education, 50(3), 906-914.

Junco, R., & Cotten, S.R. (2012), No A 4 U: The relationship between multitasking and academic performance. Computers & Education, 59, 505-514.

Junco, R., & Cotten, S.R. (2011). Perceived academic effects of instant messaging use. Computers & Education, 56(2), 370-378.

Kay, R.H., & Lauricella, S. (2011). Unstructured v. structured use of laptops in higher education.  Journal of Information Technology Education, 10, 33-40.

Koch, I., Lawo, V., Fels, J., & Vorlander, M. (2011). Switching in the cocktail party: Exploring international control of auditory selective attention.  Journal of Experimental Psychology: Human Perception and Performance, 37(4), 1140-1147.

Kolek, E.A., & Saunders, D. (2008). Online disclosure: An empirical examination of undergraduate Facebook profiles. National Association of Student Personnel Administrators Journal, 45(1), 1-25.

Marois, R., & Ivanoff, J. (2005).  Capacity limits of information processing in the brain.  Trends in Cognitive Sciences, 9(6), 296-305.

Mayer, R., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43-52.

Mehdizadeh, S. (2010). Self-presentation 2.0: Narcissism and self-esteem on Facebook. Cyberpsychology, Behavior, and Social Networking, 13($), 357-64.

Ong, E., S., Ang, R., Ho, J., Lim, J., & Goh, D. (2011). Narcissism, extraversion, and adolescents’ self-presentation on Facebook. Personality and Individual Differences, 50, 180-185.

Rosen, L.D., Lim, A.F., Carrier, L.M., & Cheever, N.A. (2011). An empirical examination of the educational impact of text message-induced task switching in the classroom: Educational implications and strategies to enhance learning.  Psicologia Educativa, 17(2), 163-177.

Strayer, D.L., & Drews, F.A., (2004). Profiles in driver distraction: Effects of cell phone conversations on younger and older drivers. Human Factors, 46(4), 640-649.

Tombu, M.N., Asplund, C.L., Dux, P.E., Godwin, D., Martin, J.W., & Marois, R. (2011). A unified attentional bottleneck in the human brain.  Proceedings of the National Academy of Sciences, 108(33), 13426-13431.

Welford, A. (1967). Single-channel operation in the brain. Acta Psychologica, 27, 5-22.

Wood, E., Zivcakova, I., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374.