Learning Distraction and Smartphones

Learning Distraction

While there is reason to be optimistic about the potential for the smartphone to support learning, the learning distraction potential is unquestioned. What began as the occasional untimely ringing of a phone in class has morphed into dozens of more subtle notifications cascading throughout the room. These notifications might take the form of a gentle vibration of the phone, the buzz of a smartwatch, or a dull blinking light emanating from a corner of the device. Regardless, it indicates to the user that something has happened that requires some level of attention. Up to that point, attention was focused on the topic at hand. This is just one example of how the smartphone might be less than ideal for optimum learning. This post will address three areas of challenges presented by smartphone in the learning environment. These will include cognitive challenges, smartphone addiction and the myth of multitasking. As with the categories presented for learning engagement, these are not intended to be comprehensive. It is also clear that the distinctions between learning distraction categories are less distinct.

Cognitive Challenges

Our cognitive limitations were documented many years ago by George Miller in his famous article, The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information (Miller, 1956).  Sixty years later, neuroscientists have provided an explanation based upon what is known about how the brain transmits information in waves (Miller & Buschman, 2015).

In particular, researchers have identified the limited capacity of working memory. At any given time, learners can only hold and manipulate a limited number of items or tasks in working memory. The limitation is pronounced and has been widely demonstrated as critical to the design and implementation of effective instruction (Mayer, 2014; Van Merrienboer & Sweller, 2005). In particular, instructional designers avoid the introduction of any extraneous cognitive load. This is defined as any design element that does not directly contribute to learning but yet draws attention (e.g., the flashing advertisement on news web-site). One has to wonder how much extraneous cognitive load is introduced by the mere presence of the smartphone.

The Myth of Multitasking

The introduction of multitasking operating systems in the late 1980’s seemed to be a boon for computer users. The capacity to run two programs at once could save time by permitting the switching between tools without having to shut down and restart the necessary programs. Given the propensity to equate computer models with human memory (e.g., information processing model of memory), multitasking seemed destined for a larger role in the human experience. This ongoing human experiment has been a nearly unequivocal failure. With the exception of only the most routine and automated tasks, such as riding a stationary bike, a person’s ability to complete more than one task simultaneously is very limited.

“The path forward is to learn more about our vulnerabilities and design around them. To do that, we have to clarify our purpose. In education, learning is the focus, and we know that multitasking is not helpful. So it’s up to us to actively choose unitasking.” Turkle, 2015. para 11.

Turkle argues that we are becoming too accustomed to bite-sized pieces of information. She notes that the average web video is viewed for 6 minutes, regardless of the total duration. To combat this, we need to actively cultivate the capacity for deep attention. Students need to confront the tendency to parcel out attention to multiple devices and people.

Smartphone Addiction

The level of smartphone use varies. In spite of the seemingly ubiquitous nature there are the rare students who only have a simple flip-phone or no mobile phone at all. At the opposite end of the spectrum is the student who finds it very difficult to have the smartphone out of sight for even a moment. Students at this end of the spectrum may be suffering from what has been termed nomophobia, or no-mobile-phone-phobia (Yildirum & Correia, 2015). Extreme nomophobia is associated with increased anxiety and a strong desire to almost constantly attend to the smartphone. The capacity of these students to attend to a lecture in a meaningful way is highly suspect. Casual observations of college students suggest that most suffer from at least a mild form of nomophobia. For these students, the use of the smartphone becomes more impulsive than conscious. In other words, the smartphone has become so intertwined with daily activities for some that using it is an automatic activity akin to spinning a pencil around the thumb or doodling on the margins of the page.


Weighing the costs and benefits of the smartphone for learning is difficult. An elaborate study of smartphone use amongst undergraduate students by Tossell, Kortum, Shepard, Rahmati and Zhong (2014) provides some interesting insights. 24 undergraduate students who did not have a smartphone were given iPhones with data, text and voice plans for one year. The phones had logging software that tracked the phone usage. Pre and post survey results indicated that the students felt that the phone was more of a distraction than an educational asset. The students also felt that the phone did not help them get better grades. The one positive outcome was that the students felt like it helped them stay up to date with academic work. The study also analyzed the app usage and web browsing activities. 32% of the web-sites visited were education related. However, games were by far the most popular app category. 48% of the users installed game applications. This compared to 3% of users installing educational apps.

Given the challenges presented and apparently limited benefits, some have chosen to ban smartphones from the classroom (Mandel, 2015). There is limited research about the prevalence of this view and it is something worthy of pursuit. However, given the recent adoption of more sophisticated wearable technologies (e.g., Apple Watch and FitBit) and the likelihood that embedded technologies will follow, the decision to ban the use of these types of technologies may be moot.

Note: This post is based upon a previously published paper from the Proceedings of the Building Bridges 2016 Conference.


Mandel, H. (2015, July 6) No phones, please, this is a communications class. Chronicle of Higher Education. Retrieved from http://chronicle.com/article/No-Phones-Please-This-Is-a/231235

Mayer, R. E. (2014). Cognitive theory of multimedia learning. In Mayer, R. (Ed.), The Cambridge handbook of multimedia learning, (31-48). New York: Cambridge University Press.

Miller, E. K., & Buschman, T. J. (2015). Working memory capacity: Limits on the bandwidth of cognition. Daedalus, 144(1), 112-122.

Miller, G. A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.

Roberts, J., Yaya, L., & Manolis, C. (2014). The invisible addiction: Cell-phone activities and addiction among male and female college students. Journal of Behavioral Addictions, 3(4), 254-265.


Tossell, C. C., Kortum, P., Shepard, C., Rahmati, A., & Zhong, L. (2014). You can lead a horse to water but you cannot make him learn: Smartphone use in higher education. British Journal of Educational Technology, 46(4), 713-724.

Turkle, S. (2015, October), How to teach in an age of distraction. Chronicle of Higher Education. Retrieved from http://chronicle.com/article/How-to-Teach-in-an-Age-of/233515

Van Merrienboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147-177.

Yildirim, C., & Correia, A. P. (2015). Exploring the dimensions of nomophobia: Development and validation of a self-reported questionnaire. Computers in Human Behavior, 49, 130-137.

Learning Engagement and Smartphones

The smartphone has become a ubiquitous part of the college experience. A Pew Research Center survey found that 85% of adults aged 18-29 had a smartphone (Smith, 2015). Observations will suggest that the percentage is higher on any college campus. The college student without a smartphone is rare. These devices bear little resemblance to the common cell phone of ten years ago. These are powerful computers with GPS, HD video cameras, multiple high-resolution still cameras, highly sensitive microphones, accelerometers, and gyroscopes. For many students, this device has become the hub that manages his or her life. A survey of undergraduate student cell-phone use found that when all of the reported activities and times were tallied (calls, texting, email, Facebook, Pinterest etc.) the average times spent approached nine hours a day (Roberts, Yaya, & Manolis, 2014).

While students are generally supportive of utilizing the smartphone in support of learning, indications are that faculty are less enthusiastic (Abachi & Muhammed, 2014). The potential educational benefits of these devices are numerous. However, the negative impact on learning engagement of these devices might overwhelm any benefits. A cost-benefit analysis is needed to better understand the potential of these devices for higher education. The purpose of this paper is to describe the positive, or learning engagement, implications and negative, or learning distraction, implications of the smartphone for higher education.

Learning Engagement

The merits of smartphone use in support of learning (also referred to as mobile learning or m-learning) are seemingly boundless. The capacity of the device to extend and augment our thinking is substantial. For the purposes of this paper, the affordances provided by smartphone will be categorized as cognitive support, reference, self-regulation, and accessibility. These categories are intended to be illustrative rather than comprehensive.

Cognitive Support

The need to scaffold complex concepts is important but often difficult in some situations. For example, science teachers have long struggled to help learners visualize the relative movements of the sun, moon, and earth. Programmers have taken advantage of the GPS, accelerometer, and camera in the smartphone to develop a powerful augmented reality tool to support learning about the orientation and movements of the sun, moon, and earth. For example, when the user to points the smartphone towards the moon the program can show an extrapolated view of the moon’s path. In addition, the user can switch between an earth view and universe view in real time (Tian et al. 2014).

WolframAlpha Mathematics Application Screen Grab
WolframAlpha Mathematics Application Screen Grab

In mathematics courses, scientific calculators have provided significant support for students in advanced mathematics courses. Today, smartphone apps such as Wolfram’s Alpha provide extensive calculation and visualization capacity that far exceeds the capability of early scientific calculators.

Just in Time Information / Reference

Possibly the most common use of the smartphone in support of learning is to simply retrieve information. Despite criticisms, Wikipedia is the seventh most visited website in the world (Alexa, 2016). Even if students are not permitted to use Wikipedia as a source, they undoubtedly refer to as a quick way to get an overview of virtually any topic. The use of Wikipedia in a classroom may only be surpassed by Google, the default reference for all of the world’s knowledge.

The smartphone can also support very discipline-specific reference tasks. For example, the Google Play Store currently lists 192 Periodic Table apps. The most popular Periodic Table app has over a million installations and 40,000 user reviews. Inevitably geography plays a role in almost all courses and the incredibly detailed map applications that are included also become valuable resources.


Learners can also benefit from the use of a smartphone as personal information manager. Task managers such as EndNote combined with a calendar is generally one of the first must-have apps for adults. Educators have also taken advantage of the smartphone to support learners with special needs. There are numerous examples of applications used to support students with Autism Spectrum Disorders (Nelson, 2013; Lofland, 2016). While little of this research has been conducted in higher education settings, the implications are similar.


Computer use for individuals with certain disabilities took a great leap forward when the Macintosh Operating System (specifically OSX) introduced new integrated accessibility features. This greatly expanded the number of software programs that could support accessibility features such as text to speech and magnification. By integrating the functionality into the operating system software developers could defer to the existing functionality rather than having to develop it themselves.  The major players in smartphone operating systems, Android and iOS for iPhones, have followed this lead with many accessibility features being integrated into the operating system. Many of the new user options found in smartphones (e.g., predictive text text-to-speech, screen magnifiers and voice controls) were previously only available with special software and hardware designed for those with a disability (Boone & Higgins, 2015).

It is worth noting that this is apparently much more functional in iOS as it has a clear adoption advantage for those with visual impairments.  In a survey of smartphone users, 91% of participants with a visually impairment used an iPhone compared to 55% of sighted participants (Ye, Malu, Oh, & Findlater, 2014).

Note: This post is based upon a previously published paper from the Proceedings of the Building Bridges 2016 Conference.


Abachi, H. R., & Muhammad, G. (2014). The impact of m-learning technology on students and educators. Computers in Human Behavior, 30, 491-496.

Alexa (2016). Top 500 sites on the web. Accessed on January 6, 2016. http://www.alexa.com/topsites

Boone, R. & Higgins, K. (2015), Refocusing instructional design. In Dave L. Edyburn (ed.) Accessible instructional design (Advances in special education technology, volume 2) (95-120). Bingley UK: Emerald Group Publishing Limited.

Lofland, K. B. (2016). The use of technology in the treatment of autism. In Cardon, T.A. (Ed.) Technology and the treatment of children with autism spectrum disorder (27-35). New York: Springer International Publishing.


Nelson, L. (2013). Using a mobile device to deliver visual schedules to young children with autism (Doctoral dissertation) Retrieved from Digital Scholarship@UNLV (# 1947).

Roberts, J., Yaya, L., & Manolis, C. (2014). The invisible addiction: Cell-phone activities and addiction among male and female college students. Journal of Behavioral Addictions, 3(4), 254-265.

Smith, A. (2015). US Smartphone Use in 2015. Pew Research Center. Available online: http://www.pewinternet.org/files/2015/03/PI_Smartphones_0401151.pdf

Tian, K., Endo, M., Urata, M., Mouri, K., & Yasuda, T. (2014). Multi-viewpoint smartphone AR-based learning system for astronomical observation. International Journal of Computer Theory and Engineering, 6(5), 396-400.

Van Merrienboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147-177.

Ye, H., Malu, M., Oh, U., & Findlater, L. (2014, April). Current and future mobile and wearable device use by people with visual impairments. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (3123-3132). ACM.