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.
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.
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).
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.