the quantified self and learning implications final edit 671

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 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


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.


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.


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.


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.


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


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


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.


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.


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.


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.


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Appendix A

Educational Data Mining in a Nut Shell (AlMazroui, p. 3)

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