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Journal of Digital Life and Learning 2021, Vol 1 No, 1, 56-67
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Examining the Use of Wearable Technologies for K-12
Students: A Systematic Review of the Literature
PHILIP JOVANOVIC1, ROBIN H. KAY1,
1University of Ontario Institute of Technology
Abstract
Wearable technologies such as smartwatches, smart clothing, smart glasses,
fitness trackers, and brain senor headbands are wireless body sensors
designed to record physiological and physical data. Since 2015, their use
has increased in K-12 classrooms, but a comprehensive investigation of
student impact has yet to be conducted. A coherent, big picture perspective
on the use of wearable technology could provide a set of guiding principles
and caveats for K-12 educators. Therefore, we conducted a systematic
review of the literature focusing on the benefits and challenges of using
wearable technologies for K-12 students. Using the Preferred Reporting
Items for Systematic Reviews and Meta-Analyses (PRISMA) approach and
a thematic narrative analysis, we analyzed 29 peer-reviewed articles from
2003 to 2019. The benefits of using wearable technologies for K-12 students
included providing students with voice, ownership of learning and
reflection, increasing engagement and relevance, improving learning,
building social presence, increasing accessibility, and differentiated
instruction. The challenges of using wearable technologies for K-12
students were health and safety as well as diminished perceptions of self-
worth. Finally, we explored future research directions for wearable
technologies in K-12 classrooms, including improved wearables-based
pedagogy, data analysis methods, data ethics, and security policies.
Keywords: Wearable technologies, wearable devices, wearables, elementary school, middle
school, secondary school, learning
INTRODUCTION
earable technologies are wireless body sensors designed to record physiological and
physical data. These devices consist of watches, bracelets, clothing, transferable
tattoos, glasses, fitness tracking devices, virtual reality headbands, and headsets
(Angelides et al., 2018). They collect and store a wide range of data, including heart rate, brain
waves, muscle bio-signals, sleep patterns, blood pressures, and the release of biochemicals
(Angelides et al., 2018). Educators and students use wearable device data to increase self-
W
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awareness, improve self-management, and create a more knowledgeable self (Borthwick et al.,
2015; Labus et al., 2015).
These sensor-rich devices are ideal for promoting constructivist-based learning, also
referred to as production pedagogy, where students learn through developing educational materials
(Jones et al., 2003). Kim and Searle (2017) noted that the growing number of digital makers and
explorers create a culture of viewing students as producers. Recent studies suggest that wearable
technologies to help create engaging and personalized learning experiences for children within a
maker culture (Kim & Searle, 2017; Borthwick et al., 2015).
Flexible design, small size, and reduced costs have led to increased use of and research on
wearable technologies. Researchers have focused on a variety of areas, including using
smartwatches as activity trackers in physical and health education (Casey et al., 2017; Siering et
al., 2019), providing feedback on language learning (Lee et al., 2015), examining cross-curricular
connections (Shadiev et al., 2018), or assessing emotions while learning (Engen et al., 2018). Other
areas of study include monitoring preservice teacher training with miniaturized clip-on cameras
(Lupton & Williamson. 2017), maintaining student security using radio-frequency identification
badges (Borthwick et al., 2015), gamified learning (Borthwick et al., 2015), and student
manufactured smart textiles in makerspaces (Estapa & Amador, 2016).
To date, though, a comprehensive systematic review of wearable technologies in K-12
milieus has yet to be conducted. This type of review could help guide future educators and
researchers in maximizing the potential benefits and minimizing the limitations of using wearable
technologies. To address this need, this paper provides a systematic review of the literature on the
benefits and challenges of using wearable technologies for K-12 students.
METHODS
Overview
In this study, we followed the Preferred Reporting Items for Systematic Reviews and Meta-
Analyses (PRISMA) framework (Liberati et al., 2009) to select articles focusing on the benefits
and challenges of using wearable technologies for Kindergarten to Grade 12 (K-12) students.
PRISMA employs a foundational structure to assess existing literature based on reliability,
transparency, and clarity (Liberati et al., 2009).
In the PRISMA approach, we first determined the type of research and area of focus
(eligibility). Next, we identified the information sources we used, followed by our search strategy
and choice of keywords. Then we documented how we established the final number of articles to
be reviewed (article selection). Finally, we articulated how we analyzed and coded content from
each paper. We will explain each of these areas in more detail.
Eligibility Criteria
Eligible articles for this study included peer-reviewed empirical research or positional
papers from 2003 to 2019, focusing on student use of wearable technologies and sensors in K-12
environments. No restrictions on language or country of origin impacted eligibility. Our rationale
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for the selection criteria was to obtain a broad, holistic body of literature representing an
orchestration of teacher and student voices in the K-12 context.
Information Sources
We searched six information databases for this project: Online Library Database powered
by Summons Service, ERIC via EBSCOhost, Education Source via EBSCOhost, IEEE Xplore,
LearnTechLib, and Google Scholar. We also reviewed references from each article to identify
additional sources.
Search Strategy
The university library database provided an initial test ground for an iterative series of
probing searches recommended by Liberati et al. (2009, p.48). The test phrase wearable
technologies for education exposed the need for fewer, more specific search terms using Boolean
operators (e.g., AND, OR, NOT). The final search term was wearables AND education. Filters
applied to the search ensured that the results were scholarly, peer-reviewed journal articles from
the education discipline, with children as the subject term. After scanning titles and abstracts, we
added the search term sensors and expanded the full search to five target databases: ERIC via
EBSCOhost, Education Source via EBSCOhost, IEEE Xplore, LearnTechLib, and Google
Scholar.
Article Selection
The initial search from the six databases produced 2609 articles. We first skimmed records
by title and then by abstract when titles were unclear or inconclusive. This initial screening
revealed 127 articles for full-text review. We then removed 43 articles after a secondary screening
of the abstracts because they did not focus explicitly on the use of wearable technologies and
sensors in K-12 environments. We removed an additional 55 papers that referred to specific
technologies that did not fit the criterion of discreet wearable technologies (e.g., smartphones or
tablet computers) or referenced devices particular to the medical field. This process produced 29
articles for this systematic review.
Data Analysis and Coding
Narrative depth is the focus of data analysis in this review. Many articles reported empirical
evidence, whereas others offered positional narratives. We used thematic narrative analysis with
inductive, data-driven coding to increase the voices or perspectives previously silent in the data
(Boyatzis, 1998). In-text narrative analysis helped extract a deeper thematic structure by
identifying and summarizing key article highlights.
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RESULTS
Study Context
Twenty-five out of the 29 articles (86%) reviewed were published after 2014. Key
populations identified included elementary school (n=13), secondary school (n=7), middle school
(n=3) and special needs students (n=1) as well as preservice and in-service teachers (n=3). Note
that a number of studies focused on multiple age groups. Key areas for using wearable
technologies in K-12 environments included student health and safety, learning, body movement,
environment monitoring, predicting learning outcomes, and diagnosing academic emotions.
Benefits of Using Wearable Technologies for K-12 Students
We identified seven key benefits for using wearable devices in K-12 environments,
including increasing engagement, increasing relevance, modifying the learning experience,
improving learning, offering new learning spaces, increasing accessibility, and providing
differentiated instruction. A summary of benefits with the number of studies is presented in table
1. We will discuss each of these benefits in detail.
Increasing Engagement. Six articles reported that wearable technologies enhanced
engagement for students in a range of learning contexts. Siering et al. (2019) noted that real-time
feedback from touch devices increased student engagement in learning activities. Borthwick et al.
(2015) added that wearable technologies allowed learners to be engaged in customizable learning
anywhere and anytime, extending the time and places where learning can occur. Kim and Searle
(2017) reported that 12 to 14-year-olds using wearable devices in collaborative learning were more
engaged with and better understood their community issues. Drew and Gore (2014) stated that
76% of teachers noticed a lasting, positive effect of wearable devices on student engagement and
attitudes toward physical activity in the Global Children's Challenge. Additionally, Hughes and
Morrison (2018) observed that students in grades 3-9 who interacted with wearable devices,
specifically with e-textiles in constructivist-based makerspaces, demonstrated improved
engagement. Finally, Lindberg et al. (2016) indicated that the novelty factor of learning with
exciting new technology (e.g., wearable devices) increased student motivation to learn and overall
engagement.
Increasing Relevance. Two studies suggested that wearable devices can provide students
with data that makes learning more relevant and personal. For example, Lee et al. (2015) observed
that wearables increased learning relevance by using body data for analysis in mathematics and
science classes. Mehmood and Lee (2017) added that wearable devices provide multimodal
methods of accurate data analysis, especially when measuring the emotional states of children
while learning. However, the relevance of the data also relies somewhat on its accuracy. Without
accurate data, students cannot make meaningful interpretations of the information they produce.
The combination of relevant and accurate data from wearable devices can increase the significance
of learning and help students gain rich insights.
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Table 1. Benefits and Challenges of Using Wearable Technologies
Theme
No. Studies
Authors
Benefits
Increase in engagement
6
Borthwick et al., 2015; Drew and Gore, 2014;
Hughes and Morrison, 2018; Kim and Searle,
2017; Lindberg et al., 2016; Siering et al.,
2019
Increase in learning
relevance
2
Lee et al., 2015; Mehmood & Lee, 2017;
Modified learning
experience (increase in
student voice, ownership,
and self-reflection)
5
Engen et al., 2018; Kim and Searle, 2017;
Jones et al., 2003; Lee et al., 2015; Lupton
and Williamson, 2017
Increase in learning
performance
5
Grawemeyer et al., 2017; Merkouris et al.,
2017; Ng & Ryba, 2018; Shadiev et al., 2018;
Williamson, 2015
New spaces for learning
2
Bower and Sturman, 2015; Jones et al., 2003;
Increased accessibility
1
Borthwick et al., 2015
Differentiated instruction
2
Borthwick et al., 2015; Ng & Ryba, 2018
Challenges
Health & Safety
1
Drew & Gore (2014)
Altered perceptions of self-
worth
1
Drew & Gore (2014)
Modifying the Learning Experience. This section identified three themes as potential
benefits for K-12 students using wearable devices to modify their learning experience: student
voice, ownership, and self-reflection. The first theme, student voice, refers to student control over
the data collected from wearable devices about their bodies and the interpretation of this data. Kim
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and Searle (2017) noted that digital technologies amplify student voice, allowing them to be digital
makers and "blurring boundaries between students' voices and the digital tools with which they
engage." (p.150). The convergence of more enabled and engaged students in more relevant
learning experiences points to wearable device's potential to improve K-12 learning experiences
(Lupton and Williamson, 2017). Engen et al. (2018) added that the summary and analysis of
oneself through body data or datafication could be enjoyable for children, especially if they are
self-motivated and voluntarily create the data.
The second theme, ownership, refers to students owning their data and having more control
over how they engage in learning contexts based on this data. Examples of ownership include
recording personally meaningful audio and visual material while learning and communicating with
other students in real-time about their learning goals, as well as leaving electronic annotations on
digital maps about learning experiences (Jones et al., 2003). Because students construct their
knowledge through their use of wearables, they gain a more profound sense of ownership over the
overall learning process and outcomes Merkouris et al. (2017).
The third theme emphasizes the importance of self-reflection. Lee et al. (2015) outlined
three benefits of using wearable sensor data to promote self-reflection and student learning. First,
students learn to interpret data in meaningful and unique ways. Second, students use the data to
reflect on how to design educational activities for self-improvement. Finally, students employ
data to predict their academic outcomes because they have a clearer understanding of their abilities.
Lee et al. (2015) suggested that, over time, students using wearable devices become more self-
reflective about the data collected, data production, and what the data means.
Improving Learning. Three studies presented evidence that wearable technologies offer
benefits for student learning across different learning domains in the K-12 context. Shadiev et al.
(2018) assessed the use of smartwatches to learn English as a foreign language (EFL) and reported
three outcomes. First, learning performance with smartwatches was significantly higher than
learning performance without smartwatches (Shadiev et al., 2018). Second, smartwatches
significantly promoted student EFL learning. Third, students completed learning tasks more
efficiently (Shadiev et al., 2018). In addition, Merkouris et al. (2017) reported that wearable
technologies helped improve programming skills for 36 middle-school students. Finally,
Grawemeyer et al. (2017) tested affective-based feedback and performance-based feedback
delivered through wearable technologies on 8 to 12-year-olds. They found that students who
received affective-based feedback showed improved learning outcomes (Grawemeyer et al., 2017).
The results of these three studies suggest that wearable technologies can improve student learning
across a range of learning contexts.
Using wearable technologies can also impact learning due to changes in pedagogical
approaches. One example of an emerging pedagogy, biopedagogy, is synthesizing sensor data into
pedagogical thinking about health and physical education (Williamson, 2015). The authors suggest
that wearable sensors could encourage students to view their bodies as personal laboratories for
experimentation and encourage individual students to develop a healthier body image
(Williamson, 2015). A survey of 437 high school athletes supports this biopedagogy approach,
where individual students associated their ownership of a wearable fitness tracker with a stronger
self-concept as a successful athlete (Ng & Ryba, 2018).
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New Spaces for Learning. The theme of social presence includes the benefits of using
wearable technologies to overcome restrictions on where and when learning occurs. For example,
Jones et al. (2003) observed 10 to 12-year-old students' use of wearables to make digital comments
on digital maps of public spaces, which provided other students with digital content to explore
these spaces. Their study illustrated how wearable technologies could empower students to explore
new spaces, convert those spaces into areas for collaboration and communication, and bring digital
presence and physical space closer together (Jones et al., 2003). On a more fundamental level,
Bower and Sturman (2015) added that wearables free up space for learning by removing the
traditional wired connection to a workstation, or desktop computer, thus making the learning
experience more mobile and active for students.
Increasing Accessibility. Another benefit of wearable technologies is bridging gaps in
accessibility to learning materials, especially for students with unique physical needs or living in
geographically remote areas. For example, wearable gesture-capture devices help students who
cannot physically input information through a keyboard participate in physical activities and
assessments remotely (Borthwick et al., 2015). Thus, a key population of students can benefit from
the affordances of wearable technologies to make learning more accessible and to help students
overcome limitations of physical ability or physical distance.
Differentiated Instruction. The final thematic benefit of students using wearable
technologies to learn is developing student profiles to support differentiation of instruction.
Student profiles, created from data collected by wearable devices, evolved based on a desire to
create datafication of each student into a quantified self (Borthwick et al., 2015). A key benefit of
datafication is developing evidence-based, targeted instruction and assessment (Borthwick et al.,
2015). Ng and Ryba (2018) noted that student profiles developed from wearable enhance
understanding of student performance and provide pre-emptive protection against school-related
stress or burnout.
Challenges of Using Wearable Technologies for K-12 Students
We identified two challenges for using wearable devices in K-12 environments, including
healthy and safety and altered perceptions of self-worth (Table 1). We will discuss both of these
challenges.
Health and Safety
One of the key reasons to use wearable technologies for learning in K-12 contexts is to
protect student health and safety. However, some evidence points to the possibility of wearable
devices to create unhealthy, risky behaviour in children. Drew and Gore (2014) reported that one
unintentional outcome of using wearable technologies is creating antagonistic body relations,
where extreme competitiveness between students introduces a risk to their health and safety. As
students push their bodies to achieve comparable and quantifiable outcomes with their data, they
risk physical illness or injury to achieve better results than their peers. The challenge for students
is to learn about healthy body relations and exercise self-control and balance in their use of
wearable devices, especially in physical education contexts.
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Altered Perceptions of Self-Worth
The use of wearable devices can encourage altered and unhealthy perceptions of self-worth.
For example, Drew and Gore (2014) reported four challenges students face when interpreting their
body data. These challenges include the psychological conceptualization of oneself as normal or
abnormal, the pressure to conform to the group ideal, emphasis on popular learning activities, and
exclusion of unique student-created learning activities. Student voice can be lost when their data
are compared to the whole group or when novel ways of learning are disregarded. Drew and Gore
(2014) summarized the challenges associated with maintaining student voice, noting that wearable
devices should not be used to measure student norms. Instead, this data could help students reflect
on how they learn and promote a broader range of individualized learning experiences.
CONCLUSIONS
Summary
In this study, we conducted a systematic review of the research to examine the benefits
and challenges of using wearable technologies for K-12 students. The thematic analysis of the
literature identified seven benefits including increasing student voice in and ownership over
learning, encouraging self-reflection, making learning more engaging and relevant, improving
the quality of learning, building new learning spaces, providing more accessibility, and
supporting differentiated instruction. The same analysis revealed two challenges included
creating unhealthy, risky behaviour and negative perceptions of self-worth. These themes
provide a starting point for educators to assess how to best use wearable devices and minimize
risk.
Limitations
We observed several limitations in the studies analyzed in this literature review that
should provide future research opportunities. First, a fundamental lack of empirical research
and quantifiable data exists, with only 32 empirical studies collected data regarding the use of
wearable devices in K-12 settings. Second, the specific grade levels and subject areas within
K-12 environments are not evenly represented, with most research focusing on elementary
school classrooms. Third, few studies examined the blending of various wearable technologies
and subject matter content to create multimodal learning contexts. Finally, limited analysis of
individual differences (e.g., gender, grade level) has been conducted on the use of wearable
devices.
Future Research
Pedagogy. One of the overarching challenges in K-12 education is to understand how
wearable technology impacts pedagogy. One risk for teachers is using wearable devices for the
sake of using the latest technology, thereby creating a school culture of technology before
pedagogy (Bower & Sturman, 2015). Bower and Sturman (2015) added that an overreliance on
technology can limit the development of critical thinking skills and may result in teachers
treating technology as a one-size-fits-all, cure-all solution to learning. Instead of focussing on
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the technology, we need more research on identifying and evaluating effective learning
straggles for using wearable technologies.
Data Analysis Methods. Another practical issue of using wearable technologies to
collect data from students in K-12 environments is understanding how the data is analyzed.
Specifically, there is a potential for underlying social biases or ulterior motives in interpreting
student data. Byun et al. (2018) suggested that the challenge of overcoming data analysis bias
is developing a socially constructed, democratic synthesis of student data that accounts for
social biases. However, Williamson (2016) contended that even socially constructed,
democratic data synthesis is not neutral, and underlying social values may skew the data
outcomes.
Furthermore, Roberts-Mahoney et al. (2016) reported a general trend in the education
field toward reducing all learning and behaviours into quantifiable skills to compare students.
Di Mitri et al. (2018) added three complications to data analysis, including a lack of
understanding of how to support learning with data, difficulty in synthesizing increasing
amounts of data, and the ambiguity of literature from different professions about data analysis.
More research is required to inform the development of efficient data analysis methods that
account for social biases and the accurate synthesis of student data for clear educational goals.
Data Ethics and Data Ownership. Data ethics and ownership require careful
consideration before implementing the use of wearable technologies in K-12 schools. Lupton
and Williamson (2017) stated that companies profit from the data analysis services they provide
to schools and may retain ownership of student data for non-academic, commercial purposes.
Commercial ownership of student data may then limit how children, parents, and teachers
access and control the privacy of the data (Borthwick et al., 2015; Lupton, 2015).
Furthermore, Selwyn (2015) warned that the algorithmic analysis of students and
educational problems might shift schools away from a holistic understanding of students to a
statistical modelling approach. The concern is that students will become the producers of data,
and commercial entities or schools will use data to control students. Selwyn (2015) also
identified four potential dangers of statistical solutionist management styles in education:
reproduction of social inequity in data ownership, intensive management of schools through
strict data analysis, increased statistical profiling and discrimination of students, and reduction
of contextual, environmental, and unique student voice in data analysis. Therefore, we need to
be aware of and examine the role of data ethics and ownership as they pertain to the use of
wearable devices in K-12 education.
Security Issues. Lupton and Williamson (2017) are critical of potential security issues
that could emerge from using wearable technologies. They noted that hacking attacks are
increasingly commonplace events that threaten the security and privacy of student data. The
authors add that the absence of research informing teachers about effective ways to protect
student rights. Schools may have to take on the burden of ensuring the protection of student
data. However, the immediate solution for implementing strict network security (such as
internet firewalls) may not be effective. For example, Lee et al. (2015) suggested that relying
heavily on digital firewalls to prevent hacking can limit access to learning information, other
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devices and ultimately frustrate teachers and students. We need more research focussing on
security issues related to the use of wearable technologies.
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AUTHORS
Philip Jovanovic graduated from the Master of Education program at Ontario Tech University.
He is an elementary school teacher in Seoul, Korea. Philip also teaches ESL to adults. He has
been studying the use of wearable technologies for several years.
Robin Kay is currently the Dean and a Full Professor in the Faculty of Education at Ontario Tech
University in Oshawa, Canada. He has published over 200 articles, chapters and conference
papers in the area of technology in education. He has taught in computer science, mathematics,
and educational technology for over 30 years at the high school, college, undergraduate and
graduate level. Current projects include research on mobile devices use in higher education,
online learning tools in K-12 education, online learning in secondary and higher education, video
podcasts, scale development, emotions and the use of computers, the impact of social media tools
in education, and factors that influence how students learn with technology. Dr. Kay received his
M.A. in Computer Applications in Education at the University of Toronto and his Ph.D. in
Cognitive Science (Educational Psychology) at the University of Toronto.
Corresponding Author:
Philip Jovanovic
philip.jovanovic@ontariotechu.net
