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46 TechTrends • July/August 2015 Volume 59, Number 4
Abstract
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 aer the advent of the
calculator watch, wearable technology is
considered to be a rapidly growing sector
in the space of consumer electronics. Wearable
devices oer myriad capabilities in an eort to
ll a niche with consumers that previously went
either unlled 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 oers
“always-on” connectivity through a “heads up
display” that can digitally augment reality based
on one’s 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 eorts 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
Activities
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 dierent 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 reect 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 eorts that
preceded us. However, those earlier research and
design eorts took place at a time when wearable
devices, even when congured to have relatively
simple functionalities were ultimately limited in
their long-term use and scalability by “high cost,
low durability, and diculties in programming”
(Klopfer, Yoon, & Rivas, 2004, p. 249). ose
aforementioned research and design teams had
to make very deliberate eorts 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 eorts 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-aer 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 oers 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
aerschool clubs (Taylor & Hall, 2013), wearable
video cameras for classroom teachers to reect 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 eorts are noteworthy, although their
primary audience has not been K-12 students and
classrooms. In the sections that follow, we describe
eorts we have taken to explore possible uses for
wearable tness tracking devices specically 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 eorts 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
Reecting 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 rened 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. Specically, 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 diered under
related but dierent 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 session’s
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 identied
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
signicant dierences 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,
aer 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 “Quantied 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 strava.com 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 Quantied 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.
Specically, in a pair, one student’s net physical
activity score would be the dierence 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
dierence 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, lemost clumps
of dots are from the bicycle and the lighter, rightmost ones are from the elliptical trainer.
Figure 2. Quantied 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
dierent 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 eective (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 oen encountered
when students are provided with wearable activity
trackers and in particular those that track steps
taken, was whether height inuenced 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 eect
of height dierences among their peers who are
oen 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 conrmed 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 dier 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 aer 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
varies.
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 dierent 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 ecient 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.
Specically, 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 eorts 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 one’s taller parents became a useful and
productive resource for driving an investigation
into how height aected steps.
Yet, in spite on of these benets, 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 benetted 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
activities2.
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 oen
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
rewalls.
Beyond these logistical concerns, privacy
issues must be considered. Because the devices
are designed for personalization, the data can be
tied to specic 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
http://ecds.ed.usu.edu/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
specic 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 benets come in tandem. Balancing the
risks and benets of wearable technologies, the
reception we have seen so far in our own eorts
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 eorts 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 aerschool 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.
Acknowledgments
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 reect 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 victor.lee@usu.edu.
Joel Drake and Kylie Williamson are both aliated with
Utah State University. You may email them regarding this
article at jrichdrake@gmail.com or kyliewill@rocketmail.com.
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