An Affective-computing Approach to Provide Enhanced Learning
Analytics
Javier Dorado Chaparro
a
, Rub
´
en Cantarero Navarro
b
, Ana Rubio Ruiz
c
,
Jes
´
us Fern
´
andez-Bermejo Ruiz
d
, Xavier Del Toro Garc
´
ıa
e
, Mar
´
ıa Jos
´
e Santofimia Romero
f
,
F
´
elix Jes
´
us Villanueva Molina
g
and Juan Carlos L
´
opez L
´
opez
h
School of Computer Science, Computer Architecture and Networking Group, University of Castilla-La Mancha,
Paseo de la Universidad N
o
4, Ciudad Real, Spain
Keywords:
Affective Computing, Learning, Stress, Galvanic Skin Response, Eye-Tracking, Facial Expression, Electroen-
cephalography.
Abstract:
Detecting emotions in a learning environment can make the student-learning process more efficient, avoiding
stressful situations that might eventually lead to failure, frustation and demotivation. The work presented here
describes a perceptive desktop devised to capture the sensations of any person facing learning activities. To
this end, we propose a perceptive environment enhanced with capabilities to perform an analysis of electroen-
cephalography, facial expression, eye tracking and particularly a very distinctive indicator of stress as it is
the galvanic response of the skin. This work focuses on the galvanic response of the skin, comparing the
performance of two devices in the context of the perceptive desktop. One of the devices was very attractive to
our environment as it was a mouse that fit very well to our computer-based desktop, equipped with low-cost
sensors to detect the galvanic response. The other device is more tedious to place and more expensive but we
use it as a reference to know if the mouse is accurate. Four people were exposed to an experiment with the
two devices connected, and observing the results it can be concluded that there is no correlation between the
captures of both devices. Therefore, we could not select the mouse for our environment even though at first it
looks like a very promising device.
1 INTRODUCTION
The use of student-facing learning analytics is gain-
ing attention as it provides information that can help
improving the teaching and learning process (Clow,
2012). Learning analytics could be correlated with
indicators derived from the student biosignal monitor-
ing. This information will eventually provide a more
accurate knowledge of the learning approach that bet-
ter fits every student individual
The term affective computing is used to refer to
a
https://orcid.org/0000-0003-4700-5557
b
https://orcid.org/0000-0003-2893-0392
c
https://orcid.org/0000-0003-0343-8596
d
https://orcid.org/0000-0002-4177-4526
e
https://orcid.org/0000-0002-9996-8008
f
https://orcid.org/0000-0001-6132-1199
g
https://orcid.org/0000-0003-0773-5846
h
https://orcid.org/0000-0002-7372-1568
technological solutions that capture information about
a user’s emotions (Picard, 1995). In this sense, the
objective of this research is to analyze the feasibility
of a perceptive enhanced desktop, with capabilities to
assess students emotions. More specifically, the fo-
cus will be on identifying those symptoms related to
better performance during the learning process.
The proposed desktop is comprised of a set of de-
vices enabling the capturing of different signals. The
analysis of these signals will lead to an understanding
of the situations that generate discomfort or stress as
well as the identification of the environmental factors
that impact the most on the student learning process.
In the first part of this article we detail each of the
analyses that we are going to carry out in our percep-
tive desktop, briefly describing the devices that we are
going to use to capture each emotion. At the end of
this section we will analyze Galvanic Skin Response
(GSR) from the point of view of two devices to be as
accurate as possible in capturing this parameter, as it
Chaparro, J., Navarro, R., Ruiz, A., Ruiz, J., García, X., Romero, M., Molina, F. and López, J.
An Affective-computing Approach to Provide Enhanced Learning Analytics.
DOI: 10.5220/0009368401630170
In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 1, pages 163-170
ISBN: 978-989-758-417-6
Copyright
c
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
163
gives us valuable information on stress points. Later,
the results of the comparison made between the two
devices will be analysed, as well as the placement of
each one inside the perceptive desk. Finally, decisions
will be made as a result of the results obtained to find
the precision in capturing emotions from our desktop
and that our future work through this environment to
provide guidance.
2 METHODS
Previous studies have used affective computing to
capture emotions and thus improve the user experi-
ence. In this way, some findings aim to increase the
motivation and engagement of users in learning tasks,
detecting their emotions within a multimodal environ-
ment (Garc
´
ıa-Garc
´
ıa et al., 2018). However, as tech-
nological solutions are currently in full development,
there are many challenges to be faced such as the
identification of the state of emotions or the integra-
tion of information in multimodal environments (Wu
et al., 2016). We plan to extend the affective comput-
ing paradigm to promote learning activities adapted
to the student individual. To this end, we have de-
signed a perceptive desktop equipped with different
devices that, whenever possible will be unobtrusive
and non-invasive. First, the user will be prompted
to tailored stimuli, provoking them specific emotions.
This will be used to train the system on identifying
specific emotions. Then, the system will be able to
identify those emotions for which it has been trained
for and, when possible, will provide the teacher and
student the appropriate means to handle the situations.
One of the most significant features to identify
stress and cognitive load is the GSR (Sriramprakash
et al., 2017) , but in other cases is studied with
some other factors such as speech to detect chronic
stress (Kurniawan et al., 2013). A desktop environ-
ment intended to be aware of stress symptoms has to
therefore be equipped with at least a device enabling
the GSR measurement. The analysis of the commer-
cial solutions brought about a mouse equipped with
the appropriate sensors to measure this feature. There
were very attractive features for this device, as it was
mainly its low-cost and unobtrusiveness. This de-
vice is initially devoted to gamers who want to track
their constants during a game. For this reason, we de-
cided to evaluate the performance of this mouse with
another device specifically devoted to measure GSR.
This other device will work as the gold standard, with
a more tedious setup but higher precision. The first
step in designing the perceptive desktop is to evaluate
whether the performance of the mouse is enough to
evaluate stress symptoms.
The GSR provides relevant information about our
subconscious. Our skin reacts to different stimuli by
sweating and we can determine the conductance (or
resistance that is the inverse) of the skin at that mo-
ment. When the conductance is high, the skin sweats,
so we will dictate that the stimulus has provoked a
high activation or excitement. In other words, it indi-
cates a high emotional charge, although it is unknown
whether the effect is positive (happiness, excitement)
or negative (stress, fear, worry). Because of this, al-
though GSR is an ideal measure to track emotional
activation, it does not provide information about the
emotional valence, i.e. the quality of emotions.
In an effort to capture all the emotional data we
have designed a perceptual desktop depicted in Fig. 1.
This desktop is comprised of: 1) an electroencephalo-
gram headset to capture the electrical impulses of the
brain (Electroencephalogram (EEG)); 2) a depth cam-
era located above the monitor that will be used to de-
tect facial expressions; 3) an eye-tracking camera, lo-
cated in the lower frame of the monitor that will allow
us to determine where our user’s attention is focused;
4) the two aforementioned devices to measure GSR
and also the Electrocardiogram (ECG).
Figure 1: Design of a Perceptive Desktop with Devices to
Collect Data from Facial Expression, Eye-Tracking, Elec-
troencephalography, Galvanic Skin Response and Electro-
cardiogram.
The following subsections describe, in detail, the
main characteristics of the device and how they will
be used in order to detect emotions and, more specif-
ically, symptoms associated to stress.
2.1 Electroencephalography Analysis
The Brain Computer Interfaces (BCI) is a technol-
ogy based on obtaining the electrical activity of the
brain in order to control an external component. This
type of interface allows us to transform our thoughts
CSEDU 2020 - 12th International Conference on Computer Supported Education
164
into real actions, and its field of application is very
wide-ranging: from the medical field for the control
of robotic prostheses (Mcfarland and Wolpaw, 2010),
to the field of leisure and video games (L
´
ecuyer et al.,
2008).
There are many devices created by different re-
search groups or manufactured by different compa-
nies. However, there are little differences among them
as they all have a similar basic function: measure
brain activity using sensors, process the signal obtain-
ing its most important characteristics and even inter-
act with the environment as desired by the user.
Despite the many technologies for the acquisition
of brain activity, the most effective tool nowadays is
the EEG due to its price, flexibility and response time
for device control.
The Emotiv EPOC helmet is a non-invasive type
of BCI, i.e. its use does not involve physically dam-
aging or penetrating the skin or scalp. A kit including
the following items is provided with the product:
An Emotiv EPOC headset (rechargeable lithium
battery).
A Universal Serial Bus (USB) receiver.
A hydration pack with 16 sensors.
A saline solution.
A USB charger.
The Emotiv EPOC headset has 14 channels that
are distributed on the basis of the international 10-20
system. The positioning of the channels is achieved
thanks to the plastic branches and two references that
ensure the correct position of the electrodes.
Figure 2: Emotiv EPOC Electrode Helmet for the Study of
Brain Electrical Activity or Electroencephalography.
The electrodes used by Emotiv EPOC (see Fig. 2)
have a small metal disc which is fixed with a conduc-
tive paste, enabling very low contact resistance to the
signal. In addition to the electrodes, the headset has
a gyroscope and two accelerometers that provide in-
formation on the movements of the person’s head. It
also has a wireless transmitter that enables informa-
tion to be sent to the USB receiver and a battery that
powers it. Through these electrodes, treated with a
saline solution, we collect the information from each
sensor for later analysis and, most importantly, con-
trast this data with those collected by other sensors on
the desktop.
2.2 Facial Expression Analysis
Facial analysis aims to detect the muscle movements
that occur in the face, as it is the interface between the
world and the emotions. It is therefore the one that
allows us to have an image of what we feel, although
this can be provoked voluntarily or involuntarily.
There are several ways to retrieve data from fa-
cial expression. The Electromiography (EMG) is the
most accurate method of capturing data. However,
the desktop is equipped with a depth camera able to
capture facial movements and to identify and analyze
the user’s expression in a non-intrusive way. Depth
cameras basically work on the basis of two elements,
an infrared light projector and an infrared light sen-
sor. In this way, the camera projects beams of infrared
light, which the camera sensor will detect, being able
to collect the distance to each point and generate a
projection of what the camera captures.
Figure 3: RealSense D435 Depth Camera for the Study
of Facial Expression Located on the Top of the Perceptive
Desktop Monitor.
There are multiple depth cameras available on
the market for which different facial recognition al-
gorithms have been implemented. RealSense (see
Fig. 3) is one of them, enabling the acquisition of
depth images up to 90 fps with a resolution of up
to 1920x720. In order to properly capture the data,
the camera’s location and placement, the room’s light-
ing and the face’s visibility have to be taken into ac-
count. The information collected from the facial re-
sponse will indicate the validity of the emotional re-
sponse and, as with other devices on the desktop, we
An Affective-computing Approach to Provide Enhanced Learning Analytics
165
can compare the results with information obtained by
other sensors on the desktop.
2.3 Eye Tracking Analysis
The aim of the eye-tracking analysis is to establish
a relationship between the movements of the eye
and human cognition. Many fields are involved in
eye-tracking to analyze behaviors, and even people’s
trends. Some of these fields are: neuroscience, ad-
vertising, simulation, website testing, learning and
education, gaming, medical research. . . This technol-
ogy is also used for interacting with a computer sys-
tem without having to use the traditional keyboard or
mouse.
The dilatation of the pupil, the distance to the
screen, the direction of our gaze or eye blinking can
give us a lot of information about our visual attention,
concentration or even the things that we give more im-
portance. The operation of a non-intrusive eye tracker
is described by the Pupil Centre Corneal Reflection
(PCCR) technique, which basically consists in send-
ing a light to illuminate the eyes, so that the cameras
of the device collect those reflections, and through the
use of algorithms, the device can identify where you
are looking. It is important to have good lighting con-
ditions and a good positioning of the device when col-
lecting data.
Figure 4: Eye Tracking Tobii 4c to Do the Eye-Tracking
Study Located in the Lower Frame of the Perceptive Desk-
top Monitor.
Fig. 4 shows the Tobii 4c eye tracker, which will
enable the capture of eye tracking data. These data
will be fused with the rest of the data collected from
the other devices on the desktop.
2.4 Galvanic Skin Response Analysis
The variation of human sweat can give us enough in-
formation about how emotionally activated we are. In
order to analyse this, we have to look at the GSR, also
known as Electrodermal Activity (EDA) or Skin Con-
ductance Level (SCL). The interesting part of mea-
suring this parameter is that the sweat generated is
not consciously produced, but the nervous system ac-
tivates the sweat glands in the face of an alert. The
sympathetic nervous system is in charge of producing
this response.
The way to collect the data from SCL is basically
to place two electrodes on two fingers, on the sole of
the foot or on the sole of the hand. The sensors are
usually silver/silver chloride and a constant voltage
(low level) is applied to them to measure the voltage
difference between the two electrodes. With this non-
invasive process you can collect emotional responses
that are useful for countless domains, such as psy-
chology, psychotherapy, marketing or even usability
studies.
The design used in this research will provide a
comparative analysis between two devices that mea-
sure the GSR with the aim of being able to have these
data available and make a further analysis of the stress
peaks. First, we will analyze the data collected with
Shimmer3 GSR+ Unit, then we will see in detail how
the data is collected with the Mionix mouse. This
comparison aims to evaluate different characteristics
or parameters of the devices used in order to select
one or the other depending on what is needed. Fi-
nally, the methodology to collect data from both de-
vices will be discussed in order to compare both sys-
tems as well as the assembly of the different sensors in
what will be the perceptual desktop designed to cap-
ture emotions.
2.4.1 Accuracy Evaluation using Shimmer3
GSR+ Unit
Shimmer is an Irish company that was originally con-
ceived in 2006, although it was not founded until
2008. Since then, it has been regarded as a pioneer
in portable sensor technology and solutions.
One of its products is the GSR Unit+, which
measures galvanic skin response (GSR) through two
reusable electrodes placed on two fingers of one hand.
In response to internal and external stimuli, sweat ap-
pears on our skin that makes the glands more active,
increasing the moisture content in the skin and al-
lowing the electrical current to flow more easily by
changing the balance of positive and negative ions in
the secreted fluid. In this way, the conductance of the
skin increases and the resistance decreases.
To gather the measured data the Consensys pro-
gram, provided by Shimmer company, is employed,
following the next steps:
CSEDU 2020 - 12th International Conference on Computer Supported Education
166
Figure 5: Shimmer3 GSR for the Capture of Galvanic Re-
sponse Signals from the Skin, Located in the Proximal Pha-
lanx of the Left Hand.
Table 1: Shimmer3 GSR+ Unit Characteristics.
Current draw 60 µA
Measurement range
8k - 4.7M
(125µS - 0.2µS)
+/- 10%,
22k - 680k
(45µS - 1.5µS)
+/- 3%
Frequency range DC-15.9Hz
Input protection
RF/EMI filtering;
Current
limiting; GSR
inputs include
defibrillation
protection.
Connections
GSR 1 input (red),
GSR 2 input
(black):
Hospital grade
1mm
contact proof
IEC/EN
60601-1 jacks
DIN42-802.
Analog/digital
auxiliary
input: 4 position
3.5 mm connector.
Bias voltage
across GSR inputs
0.5V
EEPROM memory 2048 bytes
Weight 30g
1. With the device turned on and plugged into the
dock by USB, we start the Consensys program.
2. We select our Shimmer and put the firmware that
interests us, in this case we record the LogAnd-
StreamShimmer v0.11.0 to obtain data by blue-
tooth and to obtain information of registry in the
SD card.
3. The next step is the configuration to measure what
we need. For example we can change the data
record, the name of our test and device, the sam-
pling frequency and what we want to measure. At
the end we have to write the configuration in the
device.
4. Place the sensors before collecting the data and
then place them where they belong.
5. The last step is to go to live data, connect the blue-
tooth and look at the data we are getting.
Fig. 5 depicts the appropriate emplacement of the
electrodes. In this case, they are placed on the index
and middle fingers in the proximal phalanges. After
pressing the orange button the device will start cap-
turing data. The last step would be to download and
visualize the data, and for it, the Consensys program
can be used to download the data in a file Comma-
Separated Values (CSV) so we can analyze the GSR.
2.4.2 Accuracy Evaluation using Mionix QG
Mionix is a Swedish company that is known for the
development and sale of high performance gaming
accessories designed for perfect ergonomics. Their
products include keyboards, headphones, and mouse.
The product that we will use in our perceptive en-
vironment is the Mouse Mionix Naos QG which has
additional sensors that will allow us to monitor heart
rate, GSR and the performed activity.
If we look at the sides of the mouse logo, we find
on the left the optical sensor for measuring heart rate,
and on the right two small metal connectors to get
the response of the skin. The operation of the opti-
cal sensor is similar to the operation of sensors found
in smartwatches, on the other hand, the galvanic skin
response sensor measures the conductivity of the skin
detecting the level of stress, because the more stress
the user has, the more sweat in the palm of the hand
and therefore more conductivity.
It can be seen that the features, design and the
market to which Mionix is directed are designed for
gamers or players to play in a comfortable, accurate
way and in this particular model is obtained data on
how the player is reacting during the game. However,
in the design of our perceptive desktop we thought
about this mouse to identify activities in which the
user is comfortable or not.
The software that has the mouse is called Mionix
Hub, which can be downloaded for free for Windows
and MAC to monitor what the sensors read at every
moment of the activity. However, there is a Web-
socket Application Programming Interface (API) for
the data to be accessible from any environment and
An Affective-computing Approach to Provide Enhanced Learning Analytics
167
Figure 6: Mionix Naos QG Mouse for the Capture of Gal-
vanic Response Signals from the Skin Located in the Right
Hand.
for any activity for which we need this data(Wulff-
Abramsson, 2017).
A Unity program has been developed for data ac-
quisition using this API. This program allows us to
collect the information from the GSR in a text file
with JavaScript Object Notation (JSON) format.
2.4.3 Experiment Design
The experiment was conducted with 4 volunteers (2
men and 2 women). We have chosen a game that
keeps the user stressed during the five or ten min-
utes of the test, in order to see sudden changes in
the galvanic response of the skin, and thus be able
to more clearly identify and compare the skin con-
ductance levels in both devices. The test consisted of
playing slither.io(Howse, 2016), which is a massive
online game involving multiple users which is aimed
at growing your worm or snake through the food on
the map or other worms or snakes that have been elim-
inated by hitting their head on the body of another
worm or snake. The game, therefore, ends when you
hit another worm or snake.
In order to collect data on the galvanic response
of the skin, an environment with these components is
proposed:
GSR with Shimmer3: Connected to the computer
via Bluetooth for data collection, and placed on
the left hand of the user who performs the test.
The placement of the device is carried out as ex-
plained in the sub-section 2.4.1.
GSR with Mionix QG: Connected to the computer
via USB the user will handle it with the right hand
placing the palm of the hand correctly on the elec-
trodes of the mouse.
Camera for recording user’s expressions: The
camera is used to get user snapshots and be able
to compare their expressions with the data GSR.
Screen snapshots: The screen is captured to iden-
tify the moment in which any alteration may occur
in the user.
Once the devices are ready, we run the program
to collect data with Shimmer (Consensys), and we
also run the program we developed in Unity, taking
into account that the sampling frequency of the two
devices are the same. For this experiment we have
decided to capture 1 sample every second (1Hz), be-
cause according to (Geddes and Baker, 1991) the low
frequencies are ideal for obtaining the GSR (a range
of 0 Hz to 5 Hz is recommended for the collection of
this type of measures).
The test takes between 5-10 minutes, and all data
and visuals are saved for further analysis. It should be
noted that the videos will only be used to make a com-
parison with the data collected from the GSR devices
and therefore will be nonpublished material. Never-
theless, the test participants signed a consent form that
makes it clear that their images will not be published
at any time, and that they will only be used for com-
parative testing in this study.
3 RESULTS
3.1 Shimmer3 vs. Mionix Naos QG
When the experiment was over, we could observe the
different characteristics of the two devices. First, the
range in which the Shimmer3 conductance values are
found is between 125µS and 0.2µS as shown in Tab. 1.
However, the values we found in the Mionix measure-
ment range are between 0µS and 1µS. Measurement
ranges did not match even though we set Shimmer3
to range 3 (Tab. 2) which places values between 1.5µS
and 0.2µS.
Table 2: Scale Conductance Range Shimmer3.
Range 0 125µS to 15.9µS
Range 1 15.9µS to 4.5µS
Range 2 4.5µS to 1.5µS
Range 3 1.5µS to 0.2µS
Range 4 Auto-Range
On the other hand, we found that when correlating
the data obtained in each device, there is no linear
relationship in any of the users exposed to the test. As
can be seen in Tab. 3 the results obtained are weak
negative (User 1, User 2, User 4) and positive (User
3) relationships.
For a clearer view of the comparison, we can see
in Fig. 7 and Fig. 8 that the collected data have no
CSEDU 2020 - 12th International Conference on Computer Supported Education
168
correspondence, and furthermore, in Fig. 8, we can
clearly see the moments in which user 1 had higher
values of conductance, however in Fig. 7 we are not
able to detect the moments in which the user has some
kind of excitation.
Figure 7: Results Collected on User 1 Using the Mionix
Naox QG Mouse.
Figure 8: Results Collected on User 1 Using the Shimmer3
GSR Device.
3.2 Perceptive Desktop
The integration of all the devices described for the de-
velopment of the perceptive desktop can be seen in
Fig. 9.
Figure 9: Final Appearance of the Perceptive Desktop with
All Devices for Capturing Data.
First, the depth camera is placed on top of the
monitor at an ideal angle for collecting facial expres-
sions. Second, the eye tracker is placed in the lower
frame of the monitor for eye tracking. On the other
hand, when the user is seated on the desktop we will
place him/her the EEG headset, the shimmer for the
detection of the GSR and ECG and the mouse will be
used by the user to carry out the different experiments.
Table 3: Correlation Coefficient.
User 1 -0.026811939
User 2 -0.083538697
User 3 0.066392937
User 4 -0.008630607
4 DISCUSSION
According to the obtained results in the comparison
of the two GSR devices, we have observed that the
mouse reveals limitations when it comes to perceive
changes in GSR. The first fact that we observed to
determine the limitations that the mouse had with re-
spect to the other device was that the range of val-
ues in which the mouse was moved was a range of
values that did not correspond to the values obtained
with the Shimmer3 GSR. To determine this first limi-
tation we based on the fact that typical skin resistance
values vary from 47 k to 1 M (21µS to 1µS con-
ductivity) (ShimmerSensing, 2015). This fact, made
us repeat the tests on the same users to be sure that
everything was being carried out correctly, since for
example readings may be altered by hand movements
during the test or be affected by the position of the
palm on the mouse. This leads to a lack of consistency
on the observed reading during the testing phase.
It is observed that even in Shimmer, when there is
some movement in the electrodes there is fluctuations
in data that could not be taken as good. For the assem-
bly of the other devices of the desktop, it is necessary
to bear in mind that they are well placed to collect the
data, but also the different programs that collect the
data have the same sampling frequency for accurate
analysis.
In our quest to get an explanation of the values we
were getting from the mouse, we sent an email to the
Mionix Support Portal to find out if what we were get-
ting was in other units or if the data we were getting
was from some conversion. The only answer we re-
ceived was that the units should be in microsiemens.
Finally, we decided not to take any more samples
in the experiment because we saw that the mouse gave
us unreliable results, even though we repeated the test
over and over again or even collected data from dif-
ferent users.
An Affective-computing Approach to Provide Enhanced Learning Analytics
169
5 CONCLUSION
The use of learning analytics is gaining attention as it
support teachers towards improving the learning pro-
cess of each student, by shaping the learning process
to the approach that better fits the student cognition
process. This work presents a desktop environment
setup, enhanced with a set of devices capable of per-
ceiving the student emotions, with special focus on
identifying the symptoms associated to stress.
This work presents the desktop devices along with
the purposes to which each device is intended. Special
attention has been devoted to the Galvanic Skin Re-
sponse (GSR) as it is a reliable sign to detect changes
on emotions. Two devices have been evaluated and
compared in order to capture GSR data. One is based
on mouse-like device which, a priori, looks like a
very attractive device for being an element present in
every computer-based desktop. The other implies a
more tedious setup, with electrodes tied to two fin-
gers.
An experiment was conducted to evaluate the ac-
curacy and performance of the mouse-based device
(the Mionix) using the Shimmer device as the gold
standard. Results have demonstrated that despite be-
ing an attractive device for the construction of a per-
ceptive desktop, the obtained measures are not reli-
able enough for the sought purpose. Further works
will consist in combining the data obtained from the
different devices and obtain a consistent pattern of
work-related stress singnals of individuals.
ACKNOWLEDGEMENTS
This work was supported by European Union’s Hori-
zon 2020 programme under grant agreement ID
857159, project SHAPES (Smart and Healthy Age-
ing through People Engaging in Supportive Sys-
tems); Spanish Ministry of Science, Innovation
and Universities under Grant PLATINO (TEC2017-
86722-C4-4-R); Spanish Ministry of Economy and
Competitiveness under Grant CITISIM (TSI-102107-
2016-8 ITEA3 N
o
15018); Regional Government
of Castilla-La Mancha under Grant SymbIoT (SB-
PLY/17/180501/000334); Spanish Ministry of Edu-
cation, Culture and Sport under Grant FPU Program
(ref. FPU 16/06205); and Xavier del Toro Garc
´
ıa
has received financial support from the European Re-
gional Development Fund (Fondo Europeo de Desar-
rollo Regional, FEDER).
REFERENCES
Clow, D. (2012). The learning analytics cycle: Closing the
loop effectively. In Proceedings of the 2Nd Interna-
tional Conference on Learning Analytics and Knowl-
edge, LAK ’12, pages 134–138, New York, NY, USA.
ACM.
Garc
´
ıa-Garc
´
ıa, J. M., Penichet, V., Lozano, M., Garrido, J.,
and Law, E. (2018). Multimodal affective computing
to enhance the user experience of educational software
applications. Mobile Information Systems, 2018:1–10.
Geddes, L. A. and Baker, L. E. (1991). Principles of Ap-
plied Biomedical Instrumentation. Wiley, London, 3rd
edition.
Howse, S. (2016). Slither.io. urlhttp://slither.io/. Accessed
15-07-2019.
Kurniawan, H., Maslov, A., and Pechenizkiy, M. (2013).
Stress detection from speech and galvanic skin re-
sponse signals. In Proceedings of the 26th IEEE In-
ternational Symposium on Computer-Based Medical
Systems, pages 209–214.
L
´
ecuyer, A., Lotte, F., Reilly, R. B., Leeb, R., Hirose, M.,
and Slater, M. (2008). Brain-computer interfaces, vir-
tual reality, and videogames. Computer, 41(10):66–
72.
Mcfarland, D. J. and Wolpaw, J. R. (2010). Brain–computer
interfaces for the operation of robotic and prosthetic
devices. Advances in Computers, 79:169–187.
Picard, R. W. (1995). Affective computing. Technical re-
port, Massachusetts Institute of Technology.
ShimmerSensing (2015). GSR+ User Guide Revision 1.8.
Sriramprakash, S., Prasanna, V., and Murthy, O. (2017).
Stress detection in working people. Procedia Com-
puter Science, 115:359–366.
Wu, C., Huang, Y., and Hwang, J. (2016). Review of af-
fective computing in education/learning: Trends and
challenges. British Journal of Educational Technol-
ogy, 47:1304–1323.
Wulff-Abramsson, A. (2017). Communication framework
for the mionix naos qg mouse for online and offline
usage. Technical report, Augmented Cognition Lab at
Aalborg University Copenhagen.
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