the quantified self and learning implications final edit 671
Post on 14-Apr-2017
152 Views
Preview:
TRANSCRIPT
Running Head: THE QUANTIFIED SELF AND LEARNING IMPLICATIONS 1
The Quantified Self and Learning Implications:
How it can be used to Make Learning More Effective
Bredell M. Evans Jr
University of Maryland Baltimore County
THE QUANTIFIED SELF AND LEARNING IMPLICATION 2
Abstract
The Quantified Self (QS) is a relatively new movement that is gaining traction in the personal
health industry. There are currently a variety of devices that measure such things as your heart
rate, sleeping patterns, stress levels, weight gain/loss, how many steps you take on a particular
day and how many calories you ingest per day. All of this data is gathered for the purpose of
creating a better quality of life. QS, however, has the potential to do even greater feats especially
in the educational field. The educational field includes e-learning, m-learning, and conventional
instruction (E.g. learning in a classroom). This paper will 1) Go into depth about how QS can be
used to make learning more efficient for the typical learner and how teachers can use this
technology to improve the quality of his/her teaching through the use of learner analytics, 2)
Examine various case studies and their implications, and 3) Discuss the pros and cons of using
learner analytics in regards to QS.
THE QUANTIFIED SELF AND LEARNING IMPLICATION 3
The Quantified Self and Learning Implications:
How it can be used to Make Learning More Effective
Introduction
The quantified self (QS) movement is one that is in constant change. Every day there are
new devices and technologies that appear which allow a user to easily check one’s stress levels,
how many calories one intakes in a day, brainwaves, etc. These technologies are not just limited
to medical institutions. With the use of smartphones, apps can easily be downloaded and utilized
right on the spot. Furthermore, these apps connect to various portable devices that track, store,
and process the collected information so that a person can “take a look” into his/her body. A
person can then use that information to become healthier or change habits. Looking back to an
era where smartphones were non-existent, internet being accessed through dial-up, and
computers that used Windows 96, the advances that have been seen in today’s data collection is
one that most people only dreamed about.
The educational field is one of particular interest because the use of these technologies
have the potential to revolutionize the way in which teaching is implemented and the way in
which learners acquire knowledge. Imagine being able to visually see exactly what your students
understand and do not understand in real-time. Or, imagine being able to receive personalized
learning through the analyzing of habits and personality. All of these are possible through the use
of the quantified self.
QS Technology
THE QUANTIFIED SELF AND LEARNING IMPLICATION 4
Just how big is the QS movement? As of October 2012, there were over 500 devices
available on the market dealing with QS (Melanie Swan, 2012). For future reference, here is a
list of a few that are out there now:
Fitbit- Fitbit is a small device that fits in your pocket and tracks movement and sleep.
(Quantified Self Guide)
Digifit- Digifit is collection of different apps that measure blood pressure, heart rate, and fitness
effort. (Quantified Self Guide)
MoodScope- MoodScope is an application that allows people to track their moods while getting
feedback as to what is causing a particular feeling. (Quantified Self Guide)
BodyMonitor- Body Monitor is a wearable armband that tracks how you are feeling. It can tell if
you are happy, if you are sad, if you are stressed and if you are angry. (Quantified Self Guide)
GreenGoose- GreenGoose is a company that has created tiny device that can be attached to
almost anything. It can be attached to a toothbrush to track how long you brush your teeth and
can even be attached to your cell phone to track how many times to pick it up in a day
(Technology Quarterly, 2012).
While QS is mostly known for its use in the personal health field, the average person
does not think about QS in an educational sense. Given time and further research, QS has the
potential to give new meaning to learning and develop new methods into the way facilitators
devise/implement lessons. Not only does it have the potential to revolutionize learning, but the
analytics that are involved in the display of QS data is profound.
THE QUANTIFIED SELF AND LEARNING IMPLICATION 5
Swan defines QS as “any individual engaged in the self-tracking of any kind of
biological, physical, behavioral, or environmental information” (Swan 2013, p. 86). Looking at
this definition through an educational context, I define QS as the use of any device or the
collection of any kind of information that causes change in behavior through awareness. If
facilitators are aware of learners’ biological behaviors (attention spans, how well they currently
understand the material, etc.), they can change their actions accordingly. The same goes for
learners. If learners are aware of their own behaviors such as what times during the day their
energy levels are the lowest or the way in which they best learn through the study of their own
brain patterns, they can change their actions to make their learning more meaningful. There are
technologies out now that are going in that direction.
The Augmented Lecture Feedback System (ALFS) is a method that is being researched
which allows the facilitator to see the thoughts of their students through the use of augmented
glasses. These glasses display symbols above each student’s head which show how well they are
understanding information. This information is created by the student interacting with their
smartphone and inputting how they feel about a particular matter. The facilitator sees this
information in real-time and is able to change his/her teaching method. The facilitator might see
that it is necessary to reiterate information or might see that the material needs to be taught in a
slower format (Universidad Carlos III de Madrid). This research is particularly intriguing
because of its implications. Once this technology is perfected, there will no longer be a need for
a separation of level 1 and level 2 evaluations. Students are showing how they are reacting to the
material (level 1) and the facilitator is receiving feedback on how well information is being
received (level 2). This type of data collection and transmission, however, would not be possible
without the use of learning analytics.
THE QUANTIFIED SELF AND LEARNING IMPLICATION 6
Learning Analytics
Learning analytics is not a new phenomenon. Its early uses can be dated back to a 1995
experiment on retention and performance (Harmelen & Workman 2012, p. 6). It was not until
mid-2000 when learning analytics started to gain traction. Learning analytics can be defined as
“the use of predictive modeling…to support the achievement of specific learning goals” (Bach
2010, p. 2). Or it can be defined as “the measurement, collection, analysis and reporting of
data...for purposes of understanding and optimizing learning ….” (Roberson 2014, p. 2). Both
definitions can be summarized into the following statement: By predicting actions and thoughts
of a person through collected data, behavior is able to be changed or shaped. In this context,
learning analytics is very much education through observation. However, learning analytics does
have its limits. An example of its limits is shown the study that was done in Amsterdam.
The study involved 24 people who wore both a GPS tracker and a Fitbit for a month. The
purpose of the study was to determine if technology could give a better picture into one’s
physical activity and whether collected information would change the participants’ future
behavior. After the study concluded, it was determined that while a clear picture of physical
activity could be seen, analytics alone could not determine whether the participants changed their
behavior (Riphagen, Hout, Krijnen, & Gootjes, 2013). The results indicate that in an educational
context, data collection is only truly effective when the person who the data is taken from is
motivated to use it. If data are collected by the educator, the educator needs to explain/show the
data to the learner in a way that will motivate the learner to change. If the data are collected by
the learner him/herself, then motivation to change is created by the understanding of the data’s
implications.
THE QUANTIFIED SELF AND LEARNING IMPLICATION 7
Another case study that had similar results was a study involving the qualified recess.
The qualified recess study was carried out with elementary students in an attempt to
measure recess activity and whether or not this data helped students to increase their knowledge
of means and medians. It also attempted to see if these data points brought an interest in the
elementary students to learn more about the subject (Lee & Drake, 2013). What was interesting
about this study was that the students tended to be more involved in the results when there was
some type of competition included. This competition comprised of the comparison of each
groups’ data and changing their behavior in order to change their median/mean scores. When the
data was just shown to them with only explanation, there was little enthusiasm to learn more.
What these two studies imply is that data is only data unless meaning is put behind it. Learning
analytics is only capable of expressing data, not explaining it. Educationally speaking, that is the
job of the facilitator or the facilitator. This brings the paper to the next section. How do
facilitators/ facilitators actually define the various data they receive from leaning analytics? In
addition, how does this data help to better the learning environment of the student? The answer is
through the use of Educational data mining.
Educational Data Mining
Educational data mining is explained as discipline that develops “methods for exploring
the unique types of data that come from educational settings, and using those methods to better
understand students, and the settings which they learn in” (AlMazroui 2013, p.3). Looking at the
visual of Appendix A, 4 categories that are included are Educational Information Systems,
Educational Data, Educational Data Mining Tasks, and Discovered Knowledge. To put this
graph visual into context, let’s change the words used.
THE QUANTIFIED SELF AND LEARNING IMPLICATION 8
Let’s replace educational information systems with QS, educational data with learning
analytics, and EDM Tasks with behavior. Now look at the visual again. Facilitators create a
better learning environment by doing the following: Use QS devices and gather data from the
learners. Once the data is collected and shown in an understandable visual format through
learning analytics, the facilitator compares the data with learners’ behavior. The facilitator then
starts to see patterns emerge (e.g. during 2 pm students’ focus is at its lowest or students get lost
when information is explained in this particular way. From here, all that is left to be done is
adapt the environment with what the data is communicating. When the facilitator changes his/her
behavior to fit the needs of the learner, the learner will start to do the same. Everything falls on
the ability of the facilitator to correctly interpret data. If data is interpreted incorrectly, the
facilitator might change behavior based on a false conclusions. A great example can be taken
from a Brigham Young University (BYU) independent study.
Brigham Young University
BYU conducted a study in which graduate students used Google Analytics to determine
how students were engaging with course material in distant education. The study was a
conducted for a term of 4 months and results of the various learners’ page visits were analyzed.
What they found out was that out of the thousand or so learners, only eight bothered to complete
the online evaluation. However, the reason behind the low completed evaluation rate was due to
the fact that a paper form of the same evaluation was handed out as well. Since learners already
have filled out the paper version, they did not feel a need to do it one more time online (George
Veletsianos 2010, p. 240). If the graduate students only interpreted the evaluation data without
asking the question “why”, all of their work would have been for nothing because their results
THE QUANTIFIED SELF AND LEARNING IMPLICATION 9
would not affect change to the real problem. The asking of why is what will help facilitators
analyze data correctly.
Harmelen & Workman reference this in a chart on what questions provide information
and insight. The chart speaks of asking three questions when it comes to insight: 1) How and
why did it happen?, 2) What’s the next best action?, and 3) What’s the best/worst that can
happen? (Harmelen & Workman 2012, p. 5) Question number one is the most important of the
three because its premise is around the simple idea that things are not as they appear. While
learning analytics display a variety of different data, there is a reason for everything. Looking
only at the face value of data without knowing context is a recipe for ineffectiveness when it
comes to changing learner behavior. Question number two can simply be explained as a
facilitator making a decision on how he/she will change their behavior. Whether it be adding
extra break times during course discussion or changing to an open-teaching approach, a decision
has to be made. This leads into question three which is the effect of the given decision. The
effect should solicit a positive outcome since question one flows into question two which flows
into question three. If the outcome is not positive, the facilitator will need to re-examine the how
and why of the original data.
Learner’s Role
Switching from facilitator to learner, what is the learner’s role in all of this? As
previously stated, learners must be motivated in order for QS to be effective in the educational
sphere. How do learners motive themselves? Watson says that motivation occurs when one
perceives something to be relevant. (Knowles, Holton, & Swanson, p. 88) Since relevance is
subjective, motivation all depends on how important the subject is for the learner. Taking David
as an example, he has worried that because of his poor sleeping habits, concentration at work
THE QUANTIFIED SELF AND LEARNING IMPLICATION 10
was declining. He used a QS device to track how long he REM slept and tracked the foods,
supplements, and alcohol he was consuming. From the analytics of the data, he determined that
his drinking caused a negative effect on his quality of sleep. Furthermore he saw that the
supplements he was taking had a positive effect on his quality of sleep. Now, he has changed his
behavior to the point where he is able to feel a difference in his concentration. (Technology
Quarterly, 2012) What we learn from this is that because David was aware of his problem
through the data he collected, his desire to change affected his behavior which allowed him to do
exactly that.
David is just one example. There is a plethora of many other examples in which people
perceive their collected data as relevant to their particular situation.
Data Security
With various people collecting their personal data and with the data that more people will
be collecting in the future, where does all of it go? After we finish using the data, what happens
to it? Internet being the way that it is, once something is posted or uploaded, it is there forever. It
cannot be deleted. Furthermore, with a computer, even if data is deleted from the hardware, it is
not permanently erased. If someone has the right skills and software, he/she will be able to
recover the data with little difficulty. With the various apps storing data to computers and smart
devices, how do we know that the data is protected from unwanted strangers who want to get
their hands on it? The answer to these questions is that we do not. We cannot say with 100%
certainty that our personal data is safe. Nor can we say that our data will 100% be used for
purposes that we intended them to be used for. That is where QS and learning analytics have
their drawbacks.
THE QUANTIFIED SELF AND LEARNING IMPLICATION 11
In a survey done of a DNA service provider, 76% percent of the subscribers said that they
would be willing to use their data for research (Swan 2012, p. 91). This is intriguing because it is
amazing how people are so willing to give up personal information. This trend can be seen
through the scouring Facebook. Millions of people post their pictures, locations, likes, dislikes,
old boyfriends, etc. on the website. This gives anyone easy access to your personal information.
The CIA even said in a statement that the greatest gift they have ever received is Facebook.
There have also been cases where stalkers know exactly where to find someone through pictures
that he/she posts.
An example can be taken from an acquaintance of mine. She always went to a café for
lunch and took a picture of the scenery. Her stalker was able to locate where that particular café
was through the observation of the scenery. She had no clue that this was possible and found out
the hard way. This is just one example of how simple data can be used for destructive purposes.
Used in the right way and this information can be very beneficial for the research and possible
curing of various diseases. Used in the wrong way, however, and personal privacy will become
an issue. Gerstein says that freely giving data away is pretty harmless now, but that same data
might be harmful in the future (Karen Weintraub, 2013). In fact, a NMZ Horizon Report states
that the vulnerability of personal information possible with QS will have to be addressed within
the next four to five years (NMZ, 2014). This is not surprised given that we already have
problems now with internet security and keeping our credit card information safe from hackers
such as the ones who hacked the PlayStation 3. In the attack, millions of PS3 users’ personal
information was compromised. It is not a stretch to say that the same thing might happen with
QS data as well.
THE QUANTIFIED SELF AND LEARNING IMPLICATION 12
Conclusions and Future Study
The quantified self is a phenomenon that is only going to get more technologically
advanced. NMZ Horizon identifies it as one of the 18 technology trends that will enter
mainstream within the next four years (NMZ, 2014). Learning analytics go hand in hand with
QS and the possibilities are endless. However, analytics can only be useful if it is interpreted
correctly. Furthermore, the data that analytics collect must be one of importance to the learner in
order for a change in behavior to occur. Importance, in this sense, can be exchanged with
motivation since motivation is a necessity for learners. Facilitators have a responsibility to create
this motivation through giving meaning to data collected from the learner. If a facilitator wants to
create change in the learner, the facilitator has to change as well. That can only be done by
asking the whys and hows of the data. Data in itself, however, is not inherently safe. The more
people freely give away their personal data, the more likelihood of that data being used for
purposes not intended by the person who gave the data. This problem will be hard to solve due to
the internet and the freedom it provides.
Future research should encompass both an in-depth look into the functions of various QS
technologies and how each one can be used in a way that will benefit educational learning.
Included, should also be examples of analytic representations that can show exactly how data is
being translated visually. Finally, there should be more study done on the use of predictive
analytics. Baylor University, for example, is using predictive algorithms to determine which high
school students are like to enroll at the school. They focus on the ones who are seen most likely
to attend by sending more flyers to those people and calling them more often (Harmelen
&Workman 2012, p. 13). These kinds of algorithms will sweep the educational community by
storm if fully funded and researched. There would be no need to take the SAT, ACT, or GRE.
THE QUANTIFIED SELF AND LEARNING IMPLICATION 13
All schools would have to do is use an algorithm to analyze/shift through the colossal amount of
data and then pick and choose who is the best fit.
THE QUANTIFIED SELF AND LEARNING IMPLICATION 14
References
AlMazroui, Y. (2013, January-February). A survey of data mining in the context of e-learning.
International Journal of Information Technology & Computer Science (IJTIS). 7 (3): 8-
18. Retrieved from http://ijitcs.com/volume%207_No_3/Yousef+Almazroui.pdf
Bach, C. (2010). Learning analytics: Targeting instruction curricula and student support.
Retrieved from
http://www.iiis.org/CDs2010/CD2010SCI/EISTA_2010/PapersPdf/EA655ES.pdf
Harmelen, M., &Workman, D. (2012). Analytics for learning and teaching. JISC CETIS
Analytics Series. 1 (3). Retrieved from
http://publications.cetis.ac.uk/wp-content/uploads/2012/11/Analytics-for-Learning-and-
Teaching-Vol1-No3.pdf
Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2014). NMC Horizon Report: 2014
Higher Education Edition. Austin, Texas: The New Media Consortium.
Knowles, M., Holton, Elwood, & Swanson, R. (2012). The adult learner: The definitive classic
in adult education and human resource development. London and New York: Routledge
Lee, V. (2013, November-December). The quantified self (QS) movement and some emerging
opportunities for the educational technology field. Educational Technology. 39-42.
Retrieved from
https://www.academia.edu/5184844/The_Quantified_Self_QS_Movement_and_Some_E
merging_Opportunities_for_the_Educational_Technology_Field
Lee, V. R., & Drake, J. (2013). Quantified recess: Design of an activity for elementary students
involving analyses of their own movement data. In J. P. Hourcade, E. A. Miller & A.
THE QUANTIFIED SELF AND LEARNING IMPLICATION 15
Egeland (Eds.), Proceedings of the 12th International Conference on Interaction Design
and Children 2013 (pp. 273-276). New York, NY: ACM.
Riphagen, M., Hout, M., Krijnen, D., & Gootjes, G. (2013). Learning tomorrow: Visualising
student and staff's daily activities and reflect on it. Retrieved from
https://medialab.hva.nl/wp-content/uploads/2013/11/ICERIE2013_Paper_M_Riphagen_
AUAS.pdf
Roberson, J. (2014). Learner-centric learning analytics. Retrieved from
https://www.academia.edu/7167228/Learner-centric_Learning_Analytics
Collapse
Swan, M. (2012). Sensor mania! The internet of things, wearable computing, objective metrics,
and the quantified self 2.0. Journal of Sensor and Actuator Networks. 1, 217-253: DOI:
10.3390/jsan1030217. Retrieved from www.mdpi.com/2224-2708/1/3/217/pdf
Swan, M. (2013). The quantified self: Fundamental disruption in big data science and biological
discovery. Big Data. Vol. 1, No. 2, 85-95. DOI: 10.1089/big.2012.0002. Retrieved from
http://online.liebertpub.com/doi/abs/10.1089/big.2012.0002
The Economist. (2012). The quantified self: Counting every moment. Technology Quarterly, Q1.
Retrieved from http://www.economist.com/node/21548493
Universidad Carlos III de Madrid. (2013). Intelligent glasses designed for professors. OIC
Oficina De Informacion Cientifica. Retrieved from
http://portal.uc3m.es/portal/page/portal/actualidad_cientifica/noticias/professors_glasses
THE QUANTIFIED SELF AND LEARNING IMPLICATION 16
Veletsianos, G. (2010). Emerging Technologies in Distance Education. Athabasca University:
AU Press
Weintraub, K. (2013, January). Quantified self: The tech-based route to a better life?. BBC: In
Depth. Retrieved from http://www.bbc.com/future/story/20130102-self-track-route-to-a-
better-life
THE QUANTIFIED SELF AND LEARNING IMPLICATION 17
Appendix A
Educational Data Mining in a Nut Shell (AlMazroui, p. 3)
top related