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Let's Get Physical: K-12 Students Using Wearable Devices to Obtain and Learn About Data from Physical Activities


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Accessibility to wearable technology has exploded in the last decade. As such, this technology has potential to be used in classrooms in uniquely interactive and personally meaningful ways. Seeing this as a possible future for schools, we have been exploring approaches for designing activities to incorporate wearable physical activity data tracking technologies to help students learn how to interpret data. This article describes four instances of designed learning activities in which wearable physical activity data tracking devices in use with K-12 students. Of special note is how the devices could be used to help students learn both content related to statistics and about physical activities in general. We also identify some of the challenges associated with the use of such devices that others who may use wearable technology in the classroom may wish to consider. © Association for Educational Communications and Technology 2015.
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46 TechTrends • July/August 2015 Volume 59, Number 4
Accessibility to wearable technology has
exploded in the last decade. As such, this
technology has potential to be used in classrooms
in uniquely interactive and personally meaningful
ways. Seeing this as a possible future for schools,
we have been exploring approaches for designing
activities to incorporate wearable physical activity
data tracking technologies to help students learn
how to interpret data. is article describes four
instances of designed learning activities in which
wearable physical activity data tracking devices in
use with K-12 students. Of special note is how the
devices could be used to help students learn both
content related to statistics and about physical
activities in general. We also identify some of the
challenges associated with the use of such devices
that others who may use wearable technology in
the classroom may wish to consider.
Keywords: activity trackers; sensors; statistics
education; wearable computing
Three decades aer the advent of the
calculator watch, wearable technology is
considered to be a rapidly growing sector
in the space of consumer electronics. Wearable
devices oer myriad capabilities in an eort to
ll a niche with consumers that previously went
either unlled or unnoticed. Activity trackers
from Nike, Fitbit, and Jawbone (among others)
are marketed to people trying to improve their
health and physical tness. “Smartwatches”
promise greater convenience in connecting to our
social networks, phones, etc. Google Glass oers
“always-on” connectivity through a “heads up
display” that can digitally augment reality based
on ones location. ese represent just some of the
possibilities in the space of wearable technologies.
While corporations and consumers continue
to negotiate a permanent niche for wearable
devices, let us assume that such wearable
technologies are on track to become a part of our
technological ecosystem in the way that laptop
computers, tablets, and smartphones already
have. If this is the case, we can also expect that
interest in their educational potential will grow
rapidly (e.g., Murray & Olcese, 2011), which leads
us to ask: What might technology-supported
teaching and learning activities look like when
classrooms have access to wearable devices?
In this article, we examine some potential
answers to this question. Although we do not
intend to make an exhaustive treatment of the
subject, we wish to point out that there have been
some noteworthy eorts to incorporate wearable
devices in educational contexts. For example,
Klopfer, Yoon, and Rivas (2005) have been
involved in integrating wearable technologies
into participatory simulations (Colella, 2000).
Using wearable “thinking tags,” students were
able to explore how diseases spread through a
population by looking at the rate of infection as
a “disease” was transmitted from one student’s tag
to another’s during an interpersonal interaction.
In another project, Resnick, Berg, and Eisenberg
Let’s Get Physical:
K-12 Students Using Wearable
Devices to Obtain and Learn
About Data from Physical
By Victor R. Lee, Joel Drake, and Kylie Williamson, Utah University State University
©Association for Educational Communications and Technology 2015
Volume 59, Number 4 TechTrends • July/August 2015 47
(2000) had children attach miniature temperature
sensors to their clothing. e children then
analyzed the accumulated data, uncovering
a few surprises related to how dramatically
temperatures changed in dierent settings that
they had visited throughout the day.
ese studies demonstrated the possibility
of children using wearable devices to gather data
while they go about their familiar routines, then
thoughtfully inspecting and interpreting those
data in such a way that the net total experience
of collecting the data and reviewing it became a
personally meaningful activity. We see the ability
to inspect and reect on experience and, thus,
change how one relates to that experience as one
of the great opportunities for wearable devices.
is opportunity encourages our belief that
wearables have promise in educational settings,
and likely also encouraged the eorts that
preceded us. However, those earlier research and
design eorts took place at a time when wearable
devices, even when congured to have relatively
simple functionalities were ultimately limited in
their long-term use and scalability by “high cost,
low durability, and diculties in programming”
(Klopfer, Yoon, & Rivas, 2004, p. 249). ose
aforementioned research and design teams had
to make very deliberate eorts to have devices
even physically available that could support
instructional goals and a K-12 student population.
Making the recorded data accessible to students
required even more work. Not surprisingly, those
studies represent the extent of early eorts to
bring wearable computing to K-12 education.
Since that time, technology has advanced,
interest in wearable and ubiquitous computing
devices has grown, companies have invested
in mass production, and wearable devices
are becoming highly sought-aer consumer
products. O-the-shelf devices available now
at sports equipment and electronics stores are
already cheaper and more durable than what our
predecessors had at their disposal in the early
2000s. e need for extensive programming
is mitigated as an immediate concern because
many of these devices are already equipped and
designed to store and transfer data.
Having o-the-shelf devices that meet our
needs represents an opportunity to shi an
educational technology paradigm. Rather than
purposefully building new devices from scratch
ourselves, we can use the abundantly available
devices in new ways. is oers us a number of
new instructional design opportunities. We are not
alone in recognizing the educational potential of
this new class of wearable technologies. Researchers
have incorporated wearable GPS devices in
aerschool clubs (Taylor & Hall, 2013), wearable
video cameras for classroom teachers to reect on
their practice (Sherin, Russ, Sherin, & Colestock,
2011), and accelerometer enhanced gloves into
immersive and interactive museum simulations
(Lyons, Silva, Moher, Pazmino, & Slattery, 2013).
ese eorts are noteworthy, although their
primary audience has not been K-12 students and
classrooms. In the sections that follow, we describe
eorts we have taken to explore possible uses for
wearable tness tracking devices specically with
that population and context.
Using wearable tness devices
to analyze physical activity data
As part of a multi-year project, our research
and design team has been involved in designing
and implementing new teaching and learning
activities that involve wearable devices that were
developed for displaying and tracking data from
physical activity. While our emphasis is on K-12
school settings and populations, we have also
investigated how adult athletes and other active
adults use such physical activity data devices “in
the wild.” is branch of research was motivated
by the desire to better inform our instructional
design work. We believe that the ways in which
the data from such technologies are made
meaningful within their originating contexts
may provide some purchase for us as designers
attempting to repurpose them for a new
setting, such as a classroom or schoolyard. is
assumption has been a critical driver for what
kinds of learning activities we have designed.
As mentioned above, the wearable technolo-
gies we use most frequently in our work are those
that are promoted to and used by adults who want
to increase their overall wellness or athletic per-
formance. However, as researchers and designers,
we use these devices as tools to create situations
for students to interpret data. e core assump-
tion here is that recall from one’s own body-based
experiences provides a student with an especially
productive set of conceptual resources for under-
standing what would otherwise be complicated
displays of information (Nemirovsky, 2011).
While participating in an activity, we all maintain
an intuitive sense of our level of exertion and how
it changes throughout the activity based on our
perceptions; we can then recall those perceptions
as tools for interpreting the activity represented
as dots on a data display. As designers, we have
sought to harness these interpretive intuitions in
a number of projects. Below, we present two ex-
amples of our eorts to balance athletic activities
as a context for meeting our primary instruction-
al goal—for students to become more adept with
interpreting displays of data.
48 TechTrends • July/August 2015 Volume 59, Number 4
Reecting on Heart Rate Data with
High School Students
e rst example comes from a study we
ran with high school students using Garmin
Forerunner heart rate monitors. In this study,
two groups of high school students participated
in a series of physical activities (e.g., Frisbee,
basketball) and then were asked to interpret
displays of their heart rate data. We observed how
the students’ personal familiarity with the activities
being analyzed supported productive strategies
for reading visual displays of data. at is, the
students used their knowledge of what happened
and how active they each subjectively felt when
they collected the data to push themselves toward
progressively more rened ways seeing patterns
and tendencies in data. rough analysis of video
records of students interacting with their data,
we saw that they gave less weight to outliers and
maximum values and began to focus on areas of
highest data density (i.e., the ‘center’ of the data)
(Lee & DuMont, 2010).
In addition to developing new ways of
seeing tendencies in data, the students in
this study made discoveries with health and
wellness implications—the topical context for
their exploration. Specically, the students in
this study were interested in identifying athletic
training activities that would make them work
harder. (Two of the students were already very
active in organized athletic activities and were
quite competitive in sports.) In response to
these interests, the students worked together
to design and implement comparison studies
to look at how their heart rates diered under
related but dierent exercise conditions. One
student study compared two aerobic exercise
machines that involved continuous cyclical leg
movement: a recumbent stationary bicycle and
an elliptical trainer.
e students collected one workout sessions
worth of data, with each student using both
machines for the same amount of time at what
they considered comparable settings. ey
ultimately found that the elliptical trainer tended
to produce higher heart rates over the same
period of time than a stationary bicycle (Figure
1 on the opposite page). e data varied in how
they were distributed, but the students identied
clear clusters of data for each aerobic exercise
machine, such that they comfortably asserted that
elliptical trainers were more cardio-intensive and,
thus, the machine they planned to use next time
they went to the gym. ese are among the things
one would want to notice when learning about
signicant dierences in inferential statistics.
Beyond successfully designing and executing
their own comparison study and improving
in their ability to interpret data, some of the
participants also demonstrated improvement in
their ability to estimate heart rates for activities
that they did not test. For example, prior to
working with a heart rate monitor, one student
had estimated that they typically had a heart rate
of 30 beats per minute while sleeping and 90 beats
per minute when sprinting. At the end of the study,
aer using a heart rate monitor for less than two
hours distributed across ve days, she was able to
make far more accurate estimates of 80 beats per
minute while sleeping and 190 beats per minute
when sprinting. Other students in this group
also improved in their estimates of heart rates
for activities that they did not actually complete.
is suggests that beyond our goal of helping
the students become more adept with data, the
students also became a bit more knowledgeable
about their bodies as well.
Students exploring data by
quantifying their recess
As a second example of how exercise aware-
ness could be leveraged in a learning activity to
foster data awareness, we refer to a designed ac-
tivity involving pairs of h-grade students that
we refer to as “Quantied Recess” (Lee & Drake,
2013b). e motivation for this activity came
from our observation that competition played a
critical role in motivating data tracking and sub-
sequent data analysis among adult athletes (Lee &
Drake, 2013a). Moreover, many web services that
transfer and store data from these devices foster
virtual competitions among people in the same
social network or in the same geographic region.
For instance, the online athletic community that
formed at awards a “king/queen of
the mountain” or a “course record” to the athletes
who upload the fastest tracker-logged times for
designated routes.
In Quantied Recess, we designed a
competitive activity in which the participating
students wore Fitbit Ultra activity trackers to record
how active they had been during midday recess.
ese particular wearable trackers, which have
become increasingly popular as consumer devices,
combine an embedded three-axis accelerometer
and altimeter to determine activity levels each
minute of the day. Over the course of a week,
the students in this activity would review their
recorded recess activity data and discuss strategies
for increasing their activity levels. However, rather
than simply total overall steps taken or calories
burned, we set up the activity so that it focused on
relative improvement from the rst day to the last.
Volume 59, Number 4 TechTrends • July/August 2015 49
We also required daily scores to be derived from
measures of center from each day’s recess that
paralleled what they were also learning in school.
Specically, in a pair, one student’s net physical
activity score would be the dierence in the mean
number of steps they had taken on the nal day
compared to the mean number of steps taken the
rst day; the other student’s score would be the
dierence in their median number of steps from
day 1 to day 5. us, if one student had a mean
of 6 steps per minute at the beginning of the week
and a mean of 94 steps per minute at the end, she
Figure 1. Data from a student study comparing elliptical training to recumbent stationary bicycling. e darker, lemost clumps
of dots are from the bicycle and the lighter, rightmost ones are from the elliptical trainer.
Figure 2. Quantied Recess data from four students for all ve days of the competition (organized from le to right
in data clumps). All names are pseudonyms.
would contribute 26 points to her team’s score. Her
partner, who had a median of 45 steps per minute
at the beginning of the week and 80 steps per
minute at the end of the week would contribute 35
points. Combined, their team score would be 61;
that score would be compared against the other
participating teams’ scores.
We intended the elaborate formula for
determining scores to encourage students to
explore both their recess activities and also how
dierent measures of center were computed. One
approach to increasing score would be to simply
50 TechTrends • July/August 2015 Volume 59, Number 4
run nonstop for the entire recess. is quickly
proved dull and was not always sustainable for
students who were less athletic, and they realized
this quickly. During daily coaching and data
analysis sessions, we encouraged students to
look for ways to boost their daily numbers by
trying a variety of strategies of their own design,
with some proving more eective (e.g., playing
soccer) than others (e.g., giving piggyback rides
to friends). e combination of these plans and
decisions was important in that students needed
to consider each student’s athleticism and stamina
and how well they matched a given strategy.
It simply was not feasible for some of the less
physically t students to be continuously active
during the entire recess period. Of note, one
pairing of an athletic boy with a less athletically
inclined girl jointly discovered that it made more
sense for the girl who needed to take frequent
breaks from high intensity activities to be scored
by her median value rather than her mean value,
as the median was less sensitive to a few low
scores (Lee & Drake, 2013b). is represents a
level of understanding of measure of center above
and beyond what many older students and adults
typically are exposed to or develop (Cai, Lo, &
Watanabe, 2002; Watson & Moritz, 2000).
Using wearable trackers
to explore measurement
e above examples used only pairs of
students in small workshop settings and explicitly
highlighted physical exercise. However, as stated
above, one of the central premises of this paper is
that wearable technologies can be repurposed for
use within classrooms. In this section, we discuss
activities we have designed and implemented in
partnership with classroom teachers and with
full elementary school classes. ese activities
leveraged the counting capabilities of the Fitbit
Ultra in ways that did not speak directly to health
or wellness, but instead focused on activities related
to topics such as measurement and accuracy.
Students Investigating
Wearable Device Accuracy
One lesson we realized early in our work is
that it is important to provide students with ample
time to “mess around” (Ito, 2010) with these new
wearable technologies. Doing so allowed them to
become familiar with device capabilities and also
generate some of their own questions based on
their experiences using the Fitbit. For example,
following the “messing around” period that
extended for two class periods, several students
in one h-grade class questioned whether Fitbit
activity trackers were actually accurate in their
ability to count steps.1 e persistence of this
question among students in the class who were
skeptical that the Fitbits were even reasonably
accurate created an opportunity for them to
participate in a class-wide test of the accuracy of
the Fitbit Ultra as a step counting instrument.
During the accuracy test, a single student
wore multiple Fitbits and walked a path in the
school building chosen by the class. A group of
peers followed this student, acting as “accurate
step counters silently counting the Fitbit wearer’s
steps. A second group of peers recorded start and
end step counts from each Fitbit. e class then
compared the silent counts of the human step
counters to the Fitbit counts. is procedure was
repeated several times. Data collected from each
run was compiled and given back to the students,
who then used sticky notes and butcher paper to
produce displays of their accuracy assessments
(Figure 3). Class discussion and interpretation
of these displays focused on the observation that
the bulk of the Fitbit step counts were within 10
steps of the students’ own counts. is observation
led the students to conclude that the Fitbits
were reasonably accurate and could be used as a
measurement instrument for elementary students.
Comparing the Steps
of Tall and Short Kids
Another question we have oen encountered
when students are provided with wearable activity
trackers and in particular those that track steps
taken, was whether height inuenced the number
of steps taken. Many kids know from their
previous experiences walking with adults (e.g.,
parents) that they took more steps to cover the
same distance. However, they are not sure how
this would play out when considering the eect
of height dierences among their peers who are
oen in the same height range and shorter than
many adults. To address this, a group of students
in a h grade class ran an experiment to compare
the steps taken by the tallest students in their class
with the steps taken by the shortest students on the
same outdoor walking path. ey then compiled
the data and created two data displays, depicted in
Figure 4, which they subsequently discussed and
analyzed in small groups. By looking at where
there were peaks in the data and concentrations of
data points, they quickly conrmed that shorter
students did tend to take more steps to cover the
same distance. Further, they also noticed other
1 Early research on Fitbit accuracy, not available at
the time of this design experiment, suggests that
calculations of early Fitbit models dier from professional
grade sensing devices by up to 11% (Waltz, 2012).
Volume 59, Number 4 TechTrends • July/August 2015 51
Figure 3. Student display of Fitbit step count deviation
from mentally counted steps over a xed course. Note that
the students wrote on this poster “is proves it [the Fitbit
Ultra activity tracker] is mostly accurate.
features of the data aer they were displayed, such
as the spread of the data, as illustrated from the
recorded conversation excerpted below.
J: is one [data for short students]
looks like it has more steps here,
cause, well, it does. And it’s clumped
together. And this one [data display
for tall students] is spread out.
E: So it just depends cause some tall
people have short legs, and it’s just
the upper part of their body. at’s
why it varies. at’s probably why it
J: It’s about how long their legs are.
E: Yeah.
J: Cause some people can be really tall
cause they have longer legs and
some people can be tall cause they
are longer on top.
In this short exchange excerpt, the students
from this class explicitly acknowledged that the
data had dierent overall shapes. In the data for
the shorter students, the steps data were “clumped
together,” but they were more “spread out” for
the taller students. While the students’ primary
goal for this experiment was to nd out if shorter
students took more steps over the same distance
than taller students, they were able to turn a
peculiarity (i.e., the wide range of data for tall
students) into an opportunity to consider causes
of variation. is is noteworthy in that variation
in data has not historically been the purview of
elementary mathematics and science teaching,
Figure 4. Step data for tall (a) and short (b) students from a h-grade experiment.
52 TechTrends • July/August 2015 Volume 59, Number 4
but has been documented as a real learning
possibility in carefully designed data-intensive
learning environments (Lehrer & Schauble, 2004).
From one class period obtaining data by simply
walking and another class period organizing
and looking at their data, we saw a situation
arise where students were indeed engaging
with variation as a way to understand the shape
of their data. Our view from experiences like
the one described here is that wearable devices
enable this to happen by making the process of
collecting data both ecient and familiar.
What is next?
In this article, we have examined the prospect
of using wearable activity tracking devices
to support teaching and learning in schools.
Specically, we have described ways we have tried
to support teaching and learning science and
mathematics content, with a special emphasis on
students making sense of visual displays of activity
data. e approaches we have taken as designers
of instruction has involved understanding how
these devices are used in their intended contexts
(by adults and by athletes) and capitalizing on
students’ own questions that arise as they become
familiar with the devices. Both of these strategies
have shown promise in our eorts to import and
integrate wearable devices into classrooms.
e two main advantages of using wearable
tness tracking devices are that 1) students
can passively acquire a large amount of data
and 2) students will be intimately familiar with
the activities in which the data were generated.
As most teachers know, collecting data for an
experiment or investigation can demand a
great deal of classroom time and coordination.
Understanding where data came from and
why they look the way they do also requires a
substantial time investment, especially when the
students were not involved in collecting the data
being examined (Hug & McNeill, 2008). Wearable
devices have the potential to reduce those time
investments. Using wearable tness devices, the
students can collect data while participating in
familiar activities (e.g., recess, Physical Education
class). When analyzing the data, the students
can draw on explicit recall of the experiences
that produced data and what they know from
participating in a broader set of related activities.
For example, in the “Comparing Tall and Short
Kids” example above, the everyday experience
of being shorter and having to walk extra steps
alongside ones taller parents became a useful and
productive resource for driving an investigation
into how height aected steps.
Yet, in spite on of these benets, using
wearable technologies in classrooms is not
without challenges, including logistical and
privacy concerns. For example, commercial
devices use proprietary services to provide access
to the abundance of data records. ese services
are designed for use by adult tness enthusiasts
and athletes; as such, these services cannot always
be expected to provide the exibility or level of
detail required for classroom investigations. For
example, the Fitbit web service displays time
series data in 15-minute intervals for a single
user. For our projects, we needed the data to be
displayable in frequency plots that combined
data from multiple users. Additionally, some
of the students’ investigations benetted from
one-minute interval data. As a result, we had to
do some behind the scenes work to obtain the
information in a usable format for our classroom
An additional concern is that wearable devices
require a space where data can be transferred.
Many currently available devices upload data to
their web services wirelessly in the background.
However, school rewalls intended to protect
students from inappropriate content can oen
disallow access to these services. Ensuring that
the schools and classrooms are able to send and
obtain data from third party services requires
additional legwork to make sure that the proper
sites and services are allowed through existing
Beyond these logistical concerns, privacy
issues must be considered. Because the devices
are designed for personalization, the data can be
tied to specic individuals in the classroom. is
is both an advantage and an important ethical
consideration. In our research, we always make
sure to obtain informed consent to use, access, and
share these activity data. e students we worked
with were enthusiastic and eager to see where
their data t into the class dataset. ey also drew
on their knowledge of themselves and the other
students in the class when interpreting the data
displays. However, data made public within the
classroom and that they have consented to making
available may still include details that students
do not wish to share. For example, because we
work with h-grade and high school students
(i.e., adolescents) who may be self-conscious
about their bodies, we have treated data related
to body weight3 as private and avoid making that
information publicly available. It is important to
be aware of data that might be sensitive for the
2 For access to a webform we have developed to get such
minute by minute data from Fitbit tracking devices, go totbit.
3 Body weight has been necessary to obtain to calibrate
some of the devices that students use
Volume 59, Number 4 TechTrends • July/August 2015 53
specic groups with which we work. It is also
important to establish norms in the classroom
such that there is a mutual and continually
reinforced understanding that the purpose of
seeing everyone’s data is to help answer particular
questions and not to single out students in a way
that could make them needlessly uncomfortable.
Concerns such as these are not unique to
wearable technologies. As with any new technology
brought into schools and other learning spaces,
risks and benets come in tandem. Balancing the
risks and benets of wearable technologies, the
reception we have seen so far in our own eorts
has been encouraging. As the capabilities and uses
of wearable technologies continue to develop,
such as with wearable cameras and with wearable
GPS tracking devices, we anticipate eorts will
be similarly made for those to also expand into
educational settings. e possibility also exists
to bridge across subject areas and settings, such
as from PE to physics or from aerschool soccer
practice to math class, is certainly there. Some
intrepid teams have begun to bridge daily activity
to virtual game environments (Ching & Hunicke,
2013). We are eager to see what other designers
and technologists discover as wearable devices
eventually establish their own educational niche
in the classroom and beyond.
Work reported in this article was supported
by funding from the National Science Foundation
(Grant No. DRL-1054280). e opinions
expressed herein are those of the authors and
do not necessarily reect those of the National
Science Foundation.
Victor R. Lee is a faculty member in the Department of
Instructional Technology and Learning Sciences at Utah State
University in Logan, UT. Address correspondence regarding
this article to him at via email at
Joel Drake and Kylie Williamson are both aliated with
Utah State University. You may email them regarding this
article at or
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... assessing students' PA in physical education, PA wearables have caught the attention of professionals in the field of PA measurement and assessment, and physical education (Dauenhauer et al., 2017;Lee, Drake, & Williamson, 2015;Melton, Buman, Vogel, Harris, & Bigham, 2016;Rote, 2017). As a matter of fact, PA wearables have gained a great deal of popularity in educational settings and hence are suggested to be a solution for assessing PA in physical education (Lee et al., 2015;Lindberg, Seo, & Laine, 2016;Rotich, 2016). ...
... assessing students' PA in physical education, PA wearables have caught the attention of professionals in the field of PA measurement and assessment, and physical education (Dauenhauer et al., 2017;Lee, Drake, & Williamson, 2015;Melton, Buman, Vogel, Harris, & Bigham, 2016;Rote, 2017). As a matter of fact, PA wearables have gained a great deal of popularity in educational settings and hence are suggested to be a solution for assessing PA in physical education (Lee et al., 2015;Lindberg, Seo, & Laine, 2016;Rotich, 2016). Previous research indicated that PA wearables can produce objective PA data for teachers to enhance teaching effectiveness in physical education (Dauenhauer, Keating, & Lambdin, 2016;Kang et al., 2016;Rote, 2017;Sgrò et al., 2017) and promote PA during recess in elementary schools (Van Camp & Hayes, 2017). ...
... Previous research indicated that PA wearables can produce objective PA data for teachers to enhance teaching effectiveness in physical education (Dauenhauer, Keating, & Lambdin, 2016;Kang et al., 2016;Rote, 2017;Sgrò et al., 2017) and promote PA during recess in elementary schools (Van Camp & Hayes, 2017). It was found that K-12 students could better learn how to analyze their own PA patterns using data collected by their PA wearables (Lee et al., 2015). Sgro and associates (Sgrò et al., 2017) used a PA wearable to assess elementary students' standing long jump performance and quantified key elements in skill acquisition. ...
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The Official Publication of the International Council for Health, Physical Education, Recreation, Sport, and Dance ( ICHPER.SD)
... This integration can include hardware devices such as laptops, mobile phones, and televisions, but can also include the use of wearables. Wearable technology has been utilized within a broad educational setting, enabling students to relate directly to the data presented [10]. Therefore, based on the user's movement and the data presented within a practical class setting, the student can conceptualize the information and interpret the data presented [10]. ...
... Wearable technology has been utilized within a broad educational setting, enabling students to relate directly to the data presented [10]. Therefore, based on the user's movement and the data presented within a practical class setting, the student can conceptualize the information and interpret the data presented [10]. This enables what was performed within a physical education class to be a topic addressed within mathematics, physics or biomechanics classes and also provides a broad educational experience for the student, possibly enhancing the student's understanding. ...
... Many other sports today also utilize wearable sensors, giving a large pool of applications to engage people, as well as apply within an educational setting. The utilization of wearables, as previously outlined, allows individuals to emotionally connect with the technology and product due to personal interest [10], which is seen as key to the adoption of new technologies [21]. Sports using wearables include snowboarding [20], athletics and the biomechanics of running [22], cross-country skiing [23] and team-based sports [24]. ...
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Background: Adding new approaches to teaching curriculums can be both expensive and complex to learn. The aim of this research was to gain insight into students' literacy and confidence in learning sports science with new wearable technologies, specifically a novel program known as STEMfit. Methods: A three-phase design was carried out, with 36 students participating and exposed to wearable devices and associated software. This was to determine whether the technology hardware (phase one) and associated software (phase two) were used in a positive way that demonstrated user confidence. Results: Hardware included choosing a scalable wearable device that worked in conjunction with familiar and readily available software (Microsoft Excel) that extracted data through VBA coding. This allowed for students to experience and provide survey feedback on the usability and confidence gained when interacting with the STEMfit program. Outcomes indicated strong acceptance of the program, with high levels of motivation, resulting in a positive uptake of wearable technology as a teaching tool by students. The initial finding of this study offers an opportunity to further test the STEMfit program on other student cohorts as well as testing the scalability of the system into other year groups at the university level.
... Empirical evidence also indicates that wearable technologies are flourishing in consumer markets with growing momentum in the business sector [5], [12]. This momentum covers different segments such as healthcare, wellness, sports, fitness, lifestyle, fashion, gaming, and security [13]. ...
... This momentum covers different segments such as healthcare, wellness, sports, fitness, lifestyle, fashion, gaming, and security [13]. However, in education, there is a need to review the adoption of wearables as innovation in education, due to specific needs of courses such as physical education [14], for advancing novel educational technology paradigms that promote seamless and interactive learning experiences [10], [12]. Yet, knowledge on the innovation prospects of wearable technology in education remains limited. ...
... Data extraction, data computation, data classification, and signal processing, Data analysis models, data utilization, activity recognition, movement estimation, big data models, benchmarking, and activity recognition algorithms. [11], [12], [25], [32], [37], [43], [47]- [83] Conceptual methods Theoretical and conceptual frameworks, and models for wearable technology [6], [8], [10], [13], [27], [33], [36], [44], [84]- [101] Mixedmethods Combination of qualitative and quantitative data on wearable technology [1], [3]- [5], [7], [9], [14], [20], [26], [29]- [31], [34], [35], [38], [41], [42], [102]- [117] Mathematical models Quantitative approach to develop and/or improve wearable technology data collection processes [45], [46], [118]- [120] Workshops ...
Wearables, like smart watches for fitness and virtual reality sets for entertainment, are technological innovations transforming everyday life and offer benefits for education. This study presents a systematic review of literature on wearable technology in the field of education. The review identifies and critically analyses 115 peer-reviewed publications between 1999 and 2019. The review captures research methodologies, theories, wearable technology types and applications, and contributions of the studies in the context of education. The review then analyzes different research foci for educational research and loci for technological innovation. Using insights from the review process, the study concludes by discussing theoretical foundations and outlining directions for future research.
... 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. ...
... 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. ...
... 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. ...
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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.
... Empirical evidence suggests that wearable technologies are flourishing in consumer markets and currently have momentum in the business sector (Lee et al., 2015;Motti, 2019). It is also the case in different sectors such as healthcare, wellness, sports, fitness, lifestyle, fashion, gaming, security, and prevention (Montagner et al., 2017). ...
... There is, however, a need to adopt the technology as an innovation in education, especially physical education (Miao et al., 2018). Researchers, innovators, and innovative educators could grasp an opportunity to further investigate and integrate wearable solutions in order to advance novel educational technology paradigms that promote seamless and interactive learning experiences (Borthwick et al., 2015;Lee et al., 2015). Yet, knowledge on wearable technology readiness in education remains limited. ...
This empirical research aims to explore physical education teachers’ readiness and develop an evaluation platform for wearable technology as a digital innovation in physical education. The research adopts a mixed-method approach with qualitative and quantitative methodologies for collecting data through systematic literature review, case study, survey questionnaire, and an evaluation platform. 115 peer-reviewed articles formed the basis of the systematic review. Responses of 38 expert physical education teachers in Kuwait, collected from semi-structured interviews and cognitive task analysis, formed the basis for qualitative analysis. The responses of 346 physical education teachers in Kuwait, collected from a questionnaire, formed the basis for quantitative analysis. In addition, usability testing and responses of 13 physical education teachers shaped the evaluation platform. The systematic review identifies different research foci for educational research and loci for technological innovation with insights that pose theory-based behavioral and technological conundrums. The case study findings identifies cognitive tasks, organizational and technological conditions for wearables. The survey develops a measurement scale for teacher readiness and tests the influence of readiness on perceived innovativeness. Using case study and survey insights, an evaluation platform was developed to support physical education teachers. The research offers theoretical, practical, and policy implications, shaping policy for strategies to introduce digital innovations in physical education.
... With a now well-established body of research showing that gesture and embodiment can support and reveal learning (Alibali & Nathan, 2018;Lira & Stieff, 2018;Williams-Pierce et al., 2017), advances in wearable technologies are also allowing researchers to explore ways to integrate these into STEM teaching (Lee, Drake, & Williamson, 2015;Norooz et al., 2016). While new technologies offer more accessible ways to engage in STEM, they also risk widening opportunity gaps; any bringyour-own-device approach runs this risk. ...
... Artificial intelligence identifies spam email messages (Bhowmick & Hazarika, 2018), filters results in online searches (Chau & Chen, 2008), and determines which posts social media users see (Lada, Wang & Yan, 2021). Wearable technologies gather data about people's physical activities (Lee, Drake & Williamson, 2015) while implantable medical devices improve people's health and wellbeing (Fitzpatrick, 2015). Millions of robotic vacuum cleaners help households keep their homes clean (Loup Ventures, 2019). ...
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The technological advances in the last decades are transforming the global economy and society paving the way for the automated economic system, aka robonomics. This editorial provides a brief overview of automation technologies, their application in various sectors of the economy and society, and elaborates on robonomics as an economic system and a scientific field. Robonomics is introduced as an economic system that relies almost entirely upon automation as a production factor rather than human labour. As a new scientific field, robonomics goes beyond the economic aspects of the automated economy and focuses on the social, cultural, demographic, political, environmental, legal, geographic, psychological and other issues raised by automation technologies as well. Finally, the editorial introduces ROBONOMICS: The Journal of the Automated Economy, and the publications in the inaugural issue.
... Studies of these kinds of wearables examine the monitoring of physical performance with heart-rate monitors to maintain healthy heart rate within a target zone (Furger, 2001), and the use of inertial sensors and heart rate meter to detect physical activity (Lindberg et al., 2016). There are also research interests in obtaining and learning about data from physical activities using wearable fitness trackers (Lee, Drake, & Williamson, 2015), and examining the feasibility of using wearable devices to measure physical activity in physical education (Schaefer, Van Loan, & German, 2014). ...
The purpose of this article is to explore physical education teachers’ perspectives on their readiness to use and integrate wearable technology as an innovation in physical education. The article presents a case study grounded on an analytic induction logic with a constructivist epistemology and involves semi-structured interviews with 38 public school physical education teachers. Based on a thematic analysis of interview data, the study identifies eight themes on attitudinal shifts, adequate capabilities, convenient use, injury prevention, effective exercises, non-sedentary behavior, and system access. These themes reflect the technological and organizational conditions that enable physical education teachers’ readiness to use and integrate wearable technology in physical education. The article concludes with discussions on theoretical and practical implications of the research, limitations and future directions.
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The present doctoral research is related to OnLIFE Education and is contextualized in the investigations developed by the International Research Group on Digital Education - GPe-dU UNISINOS/CNPq, within the scope of the Education, Development and Technologies research line, of the Postgraduate Program -Graduate in Education UNISINOS. As a problematic field that was constituted, we have the social project, Grupo Explosão da Dança, as a research territory. In the problematization of the present time, the research question arises: How does the creation/invention process emerge in the appropriation of wearable technologies (wearables)? The thesis articulates in the digital technological perspective, the concept of wearables; from the theoretical epistemological perspective, Reticular and Connective Epistemology in an atopic dwelling (Di Felice), Inventive Cognition (Kastrup), OnLIFE Education (Schlemmer and Moreira) and results of research developed by GPe-dU UNISINOS/CNPq. This thesis aims to understand how invention emerges from/in the appropriation of wearable technologies. The research is qualitative, exploratory and descriptive and uses the Cartographic Research-Intervention Method, proposed by Passos, Kastrup, Escóssia and Tedesco, as a hodos-goal for data production and analysis. As research instruments will be presented records in photos, recordings, transcripts and videos and a journey diary (researcher). While results are presented clues that indicate the emergence of an inventive process, as well as the Pulsus wearable invention. A presente pesquisa de doutorado está relacionada à Educação OnLIFE e se con textualiza nas investigações desenvolvidas no Grupo Internacional de Pesquisa em Educação Digital – GPe-dU UNISINOS/CNPq, no âmbito da linha de pesquisa Educa ção, Desenvolvimento e Tecnologias, do Programa de Pós-Graduação em Educação UNISINOS. Enquanto campo problemático que foi se constituindo, temos o projeto social, Grupo Explosão da Dança, enquanto território da pesquisa. Na problematização do tempo presente, se origina a questão de pesquisa: Como emerge o processo de criação/invenção na apropriação de tecnologias vestíveis (wearables)? A tese articula na perspectiva tecnológica digital, o conceito de wearables; na perspectiva teórico epistemológica a Epistemologia Reticular e Conectiva num habitar atópico (Di Felice), a Cognição Inventiva (Kastrup), a Educação OnLIFE (Schlemmer e Moreira) e resultados de pesquisas desenvolvidas pelo GPe-dU UNISINOS/CNPq. Esta tese tem por objetivo compreender como emerge a invenção, a partir da/na apropriação de tecnologias vestí veis (wearables). A pesquisa é de natureza qualitativa, do tipo exploratória e descritiva e apropria-se do Método Cartográfico de Pesquisa-Intervenção, proposto por Passos, Kastrup, Escóssia e Tedesco, como uma hodos-meta para produção e análise de dados. Como instrumentos da pesquisa serão apresentados registros em fotos, gravações, transcrições e vídeos e um diário de percurso (pesquisador). Enquanto resultados são apresentadas pistas que indicam a emergência de um processo inventivo, bem como o invento wearable Pulsus.
Given growing interest in K-12 data and data science education, new approaches are needed to help students develop robust understandings of and familiarity with data. The model of the quantified self—in which data about one’s own activities are collected and made into objects of study—provides inspiration for one such approach. By drawing on what one already knows about their self and their prior experiences, it may be possible to bootstrap students’ abilities to interpret and make sense of data. Taking that possibility seriously, this article describes some of the gains observed in students’ statistical reasoning following a quantified self, wearables-based elementary statistics unit and provides a theoretical framework drawing from cognitive psychology, embodiment, and situative perspectives to characterize how prior experience is used as a resource in data sense-making when the data are about students’ own physical experiences. This framework centralizes and interrogates the work of “remembering” prior experiences and articulates how remembering is involved in interpreting quantified self data. Specifically, the framework emphasizes that remembering in service of data interpretation is a reconstructive act that draws from both general and specific embodied resources and that the work of reconstructive remembering in the classroom is both individual and multi-participant work. To demonstrate measured learning gains and illustrate the framework, written assessment results and descriptive cases of student and teacher discussions about quantified self data from two sixth-grade classes participating in a classroom design experiment are provided. Both a discussion of and recommendations for ethical considerations related to quantified self data in education are also provided.
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Personal mobility is a mundane characteristic of daily life. However, mobility is rarely considered an opportunity for learning in the learning sciences, and is almost never leveraged as relevant, experiential material for teaching. This article describes a social design experiment for spatial justice that focused on changes in the personal mobility of six non-driving, African-American teenagers, who participated in an afterschool bicycle building and riding workshop located in a mid-south city. Our study was designed to teach spatial literacy practices essential for counter-mapping—a discursive practice in which youth used tools similar to those of professional planners to “take place” in the future of their neighborhoods. Using conversation and multimodal discourse analyses with video records, GPS track data, and interactive maps authored by youth, we show how participants in our study had new experiences of mobility in the city, developed technically-articulate criticisms of the built environment in their neighborhoods, and imagined new forms of mobility and activity for the future.
Conference Paper
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Recess is often a time for children in school to engage recreationally in physically demanding and highly interactive activities with their peers. This paper describes a design effort to encourage fifth-grade students to examine sensitivities associated with different measures of center by having them analyze activities during recess over the course of a week using Fitbit activity trackers and TinkerPlots data visualization software. We describe the activity structure and some observed student behaviors during the activity. We also provide a descriptive account, based on video records and transcripts, of two students who engaged thoughtfully with their recess data and developed a more sophisticated understanding of when and how outliers affect means and medians.
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The introduction of sensor technologies to sports has allowed athletes to quantify and track their performance, adding an information-based layer to athletic practices. This information layer is particularly prevalent in practices involving formal competition and high levels of physical endurance, such as biking and running. We interviewed 20 athletes who participated in distance cycling or endurance running and also had experience using these technologies. This paper presents two cases and a number of shorter descriptive examples from these interviews that illustrate the factors salient to the introduction of these athletes to their respective sports, their continued participation in running or cycling, and their use of physical activity data. The effects of these data and logging practices among these individuals are examined, including some of the tensions that these athletes have with respect to quantifications of their performance and how they see themselves as athletic individuals in light of the increased presence of digital data. Educational implications are also discussed.
Conference Paper
This paper describes an empirical study conducted to explore the expressive design space for what we dub "effortful interaction." Effortful interaction is a form of human-computer interaction where different degrees of physical effort are intentionally incorporated into the interaction design in order to communicate information. In this sense effortful interaction is similar to haptics, where devices provide physical force feedback to users, except the perceived feedback occurs entirely within users' proprioception: i.e., users' perception of their own bodies. Our larger design goal is to use effortful interaction to communicate quantitative information to children who aren't yet proficient with graphs. First, though, we had to establish if we could reliably induce sensations of effort. This study examined the sensitivity of children to three different design parameters (the duration of interaction, the intensity required by the interaction, and the physical modality of the interaction), and compares their responses to those of adults.
One warm night last February, I lay down to bed feeling like a lab mouse. A heat-and motion-sensing arm-band gauged my energy expenditure, another activity tracker clipped to my waistband recorded movement, a blood-pressure cuff connected to my iPad squeezed my right arm, and a brainwave-sensing headband would soon monitor my sleep. A scale linked by Bluetooth to an app on my iPad sat on the bathroom floor. With consistent use, these devices would provide a numeric picture of my general health and behaviors. They would give me intimate knowledge of my physical self, with all the information displayed neatly in graphs and charts. Not too many years ago, you had to go to medical specialists to get this kind of biological data. Now, whether your problem is migraines or mood swings, you can keep track of your ailment with a consumer device that costs around US $100. As these healthand-wellness gadgets proliferate, a “quantified self” movement is gaining strength: It's attracting athletes, fitness buffs, data lovers, hypochondriacs, and people just trying to lose some weight.
In this paper, we examine how students discuss and interpret data and whether these actions vary depending on the type of data they analyse. More specifically, we are interested in whether students perform differently when analysing first-hand data, which they collect themselves, compared with second-hand data provided to them. Our data analysis focused on two classrooms using two different curriculum units, chemistry in Grade 7 and biology in Grade 8, collected during the 2002/03 school year from a Mid-western urban middle school in the USA. We analysed classroom videotape associated with lessons in which students discussed first-hand and second-hand data both in small group settings and full class discussions. We found the two types of data offer different benefits and limitations, suggesting that both types of data are important for students to work with as they develop skills in scientific inquiry practices. We discuss the characteristics of classroom discussions around different data sources as well as implications for the design of curriculum materials, instructional environments, and student learning in science.
Within weeks of becoming available, the iPad reportedly sold over 3 million units, a brisker pace than other tablets in the personal computer realm. Much of the early success might be attributed to the almost 250,000 applications that could run on the device and a similar interface to the popular iPod Touch and iPhone. This article considers whether the sales spark that has ignited a hardware revolution (numerous device manufacturers have launched–e.g., HP, RIM, Samsung, Motorola, and HTC–or have plans to launch tablet devices over the next year) is being matched on the software front, with a particular focus on K-12 teaching and learning. Authors consider the potential affect both the iPad and its applications might have on teaching and learning in K-12 settings and whether these technologies allow educators and students to accomplish what they otherwise could not, from a teaching and learning perspective.
The development of the understanding of average was explored through interviews with 94 students from Grades 3 to 9, follow-up interviews with 22 of these students after 3 years, and follow-up interviews with 21 others after 4 years. Six levels of response were observed based on a hierarchical model of cognitive functioning. The first four levels described the development of the concept of average from colloquial ideas into procedural or conceptual descriptions to derive a central measure of a data set. The highest two levels represented transferring this understanding to one or more applications in problem-solving tasks to reverse the averaging process and to evaluate a weighted mean. Usage of ideas associated with the three standard measures of central tendency and with representation are documented, as are strategies for problem solving. Implications for mathematics educators are discussed.