=Paper=
{{Paper
|id=Vol-2733/paper30
|storemode=property
|title=Analysis of the Emotions Experienced by Learning Greedy Algorithms with Augmented Reality
|pdfUrl=https://ceur-ws.org/Vol-2733/paper30.pdf
|volume=Vol-2733
|authors=Maximiliano Paredes-Velasco,Mónica Gómez Rios,Angel Velázquez-Iturbide
|dblpUrl=https://dblp.org/rec/conf/siie/Paredes-Velasco20
}}
==Analysis of the Emotions Experienced by Learning Greedy Algorithms with Augmented Reality==
https://ceur-ws.org/Vol-2733/paper30.pdf
Analysis of the Emotions Experienced by Learning
Greedy Algorithms with Augmented Reality
Maximiliano Paredes-Velasco Mónica Gómez Rios Angel Velázquez-Iturbide
Universidad Rey Juan Carlos Universidad Politécnica Salesiana Universidad Rey Juan Carlos
Móstoles, Spain Guayaquil, Ecuador Móstoles, Spain
maximiliano.paredes@urjc.es mgomezr@ups.edu.ec angel.velazquez@urjc.es
Abstract—Students have difficulties in understanding through teaching methodologies based on gamification
algorithm subjects, in particular how the source code of [21,22] and collaborative learning [23]. The objective of this
algorithms proceeds to solve problems. This article presents an work is to conduct an exploratory study of the advancement
augmented reality tool intended to assist in learning greedy of knowledge and the emotions that students experience while
algorithms. Students use their smartphone’s camera to focus the they study greedy algorithms using augmented reality
source code of Dijkstra’s algorithm as written on paper, and the technologies. An experience was conducted in a classroom
tool shows how the algorithm works. An experience was where students used an augmented reality tool on their
conducted in the classroom to assess students’ emotions and smartphones, called RA-AVD (Realidad Aumentada –
knowledge level. The results show that positive emotions
Algoritmo Voraz de Dijkstra, in English Augmented Reality
experienced by students were almost twice as intense as negative
– Dijkstra’s Greedy Algorithm), along with paper notes
ones. Despite the complexity of the task (i.e. understanding
Dijkstra’s algorithm), the level of enjoyment of students was provided by the teacher.
continuous during the experience. However, the anxiety
II. METHODOLOGY
experienced by students was the double than at the beginning.
Keywords— Emotions, learning, Augmented reality, Greedy
A. Educational objective and context
Algorithm. The objective of the experience is to assess the level of
knowledge and the positive and negative emotions that
I. INTRODUCTION students experience by using the RA-AVD tool in solving
In algorithm design subjects of university computer optimization problem. The experience is conducted in the
science degrees, the development of algorithms that solve context of the Computer Science Degree at the Salesian
optimization problems is usually studied. In this educational Polytechnic University of Ecuador, specifically in the area of
context, learning greedy algorithms is one of the most Data Structures. At some point in this subject, students have
complex topics for the student, not being able to translate into to learn and develop greedy algorithms. In particular, they
source code the elements of their development (i.e., set of must understand Dijkstra’s algorithm [24], which solves a
candidates, selection function, feasibility function, objective classic graph problem: determining the minimum length path
function) [1]. This difficulty may affect the emotional state of from a source node to the rest of the nodes in the graph.
students and cause them to become discouraged during the Students participated voluntarily and they had no previous
learning process. Motivation and emotions in learning play a contact or knowledge about greedy algorithms, although they
fundamental role since they influence memory and logical knew how to program and have basic knowledge of the Java
reasoning and help improve attention [2]. Today, programming language.
neuroscience research helps us in understanding how the brain B. Variables and measurement instruments
works and the influence and importance of emotions to
improve learning [3,4]. Along with this, if a student is not The variables measured were the emotions experienced by
predisposed to learn, or he/she experiences strong negative the students and the level of knowledge they acquired after the
emotions, it is unlikely that he/she will be able to achieve experience. The level of knowledge is measured by a
his/her full potential. Furthermore, learning is characterized as knowledge test on the behavior of Dijkstra’s algorithm. The
a cognitive and motivational process [5], where emotions may test was formed by 5 multiple-choice questions where each
affect both the intrinsic and extrinsic motivation of the student question was scored with maximum 2 points.
[6,7]. The instrument used to measure emotions was AEQ
Not only emotions can influence learning outcomes. The (Achievement Emotions Questionnaire), which is a consistent
use of new technologies plays a relevant role in learning [8]. and validated scale in the educational context [25]. Taking
Specifically, augmented reality (AR) technology may into account the principles of neuroeducation [26], the
improve learning performance [9] and constitutes a emotional variables to be measured can be classified into two
technology option with great potential and effectiveness to types: 1) activation emotions, which are the emotions that
activate positive emotions in students [10]. Augmented reality produce a higher degree of agitation (fear, anxiety, anger, etc.)
not only provides immersive experiences of visibility or and 2) deactivation emotions, which produce lower agitation
observation [11-13], but it may also contribute to student’s (depression, calm, boredom, etc.). In addition, these emotions
feeling of greater satisfaction [14], improved usability [15] can be classified by the positive (pleasant sensation) or
and reduction of his/her cognitive load [16] in the use of negative impact (unpleasant or uncomfortable sensation) that
technological tools during the learning process. Augmented they produce on participants. Overall, up to four classes of
reality technologies have been applied to programming emotions could be identified.
learning at different educational levels, showing satisfactory The AEQ scale measures these emotions by offering a
results: from early ages [17,18], high school and university series of statements about the participant’s emotional state and
students [19] to professional adults [20], being applied mainly students must assess the degree to which they describe their
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
emotions and feelings. Each statement is rated in a Likert Figure 1). In summary, the student can read code in the sheets
scale, which ranges from very little (value 1) to extremely (a static description of the solution) and can watch an
(value 5). In Table I, the variables of emotions measured with animation of the algorithm on the mobile (a dynamic
AEQ are detailed (the number in parentheses corresponds to demonstration of the solution).
the total of items to be assessed for that emotion). Note that
the test only addresses three kinds of emotions and that it
consists of 75 items which measure 8 emotions.
TABLE I. MEASURED VARIABLES OF EMOTIONS AND THE NUMBER
OF ITEMS
Activation
Emotion
Activation Deactivation
Positive Enjoyment (10)
Hope (6)
Pride (6)
Negative Anger (9) Hopelessness (11)
Anxiety (11) Boredom (11)
Embarrassment (11) Fig. 1. Extension of the source code with the visualization of the graph
AEQ scale is organized so that students assess at the end detected by means of augmented reality.
of the session their emotional state at three different times:
The tool has a user interface that allows to control step by
1) Before starting the learning task: the student assesses step the execution of the algorithm, obtaining a display of its
how he/she feels right at the beginning of the experience. execution trace, as in a debugger (see Figure 2). This trace
2) During the task: the student assesses how he/she feels screen shows the graph declared in the source code at the top
while doing the learning task. and the trace table along with the step-by-step execution
3) After the Task: the student rates how he/she feels after controls below (see Figure 2).
completing the experience. Figure 2 shows the trace in an intermediate state to
Finally, the opinion of participants about the experience calculate the minimum paths from node 0 to the other nodes.
was collected by asking some of them their opinion in a short Notice that some nodes present a label formed by brackets in
interview. the form of [A,B]N, where A is the distance of the path from
the source node to the node, B is the predecessor node from
C. Phases that path and N is the resolution stage number. The algorithm
In the first phase, students attended at a laboratory and they solves the problem in stages, in such a way that at each stage
received on printed sheets the markers to present the it analyzes possible new paths between the source node and
information with augmented reality. These sheets contained each remaining node, recording a new path if it is shorter than
the Java source code for Dijkstra's algorithm. the current path. Therefore, these labels are dynamically
generated as tracing progresses, and they are replaced every
Subsequently, students downloaded the application to
time the algorithm finds a shorter path. In this case, the path
their mobile devices and a brief explanation was given about
that partially expresses the bracket label is discarded (by
the task to be carried out (for 10 minutes), which consisted of
displaying a horizontal line that crosses it out) and a new label
reading and interpreting the source code that appeared on the
is shown by the node, representing the new path.
sheets provided.
An example may assist in better understanding this
While reading the Java code on paper, students could use
notation. In Figure 2 we can see that node 3 has the label [8,1]4
the mobile to focus parts of the code and receive assistance
with a crossing line, which means:
from the application. This phase lasted 30 minutes.
Number 8: length of the path from source node to
Once this phase was finished, the students proceeded to
node 3 (labeled node).
carry out the evaluation of acquired knowledge and they
measured the emotions experienced before, during and after Number 1: node predecessor to node 3 in the path
the learning task, using 15 minutes for this. The whole from the source node.
experience was organized in a single session.
The subscript 4: step or stage in the solution
III. APP DESIGN construction process.
RA-AVD is a tool created for learning Dijkstra’s Strikethrough label (crossing line): it means that the
algorithm. The use of the tool is based on the following idea. path denoted by that label is discarded. Node 3 has
The student uses the teacher's notes where the Java code that two labels since, at that time, the algorithm had found
implements Dijkstra’s algorithm is shown (for the tool to two alternative paths (namely, [3,2]2 and [8,1]4). The
recognize it, it has to be a specific source code). When the path with a longer distance from the source to node 3
student has doubts about how the code solves the problem, was discarded. In this case, the label [8,1]4, with
he/she uses the tool, focusing on the source code with his/her distance 8, was discarded since the other path was
mobile. At that point, the tool shows the source code that the shorter, with length 3.
student is viewing on paper, augmenting it with comments and
explanations in order to understand how it works. If the
student focuses the source code where the graph is declared,
RA-AVD tool draws the graph on the mobile’s screen (see
"Fixed-Origin Length". Subsequently, the algorithm selects
the smallest of these paths (4, 2 and 8), in this case the smallest
path is 2, and its corresponding node (node 2), so it is marked
as a fixed node for the next stage (step 2). That would be the
end of the stage, hence a new stage would start until the
minimal path to all nodes is reached.
At the top of the trace table, the "Optimal route" and
"Length" fields show, at each stage, the optimal path and its
length respectively for the selected, fixed node. Initially, all
nodes are painted in one color and as they are processed in
the construction stages their color changes to show which part
of the graph is being processed.
IV. RESULTS
This section presents the statistical analysis of the data
obtained during the experience, in which 18 students aged 19
to 22 participated. Eleven students (61.1%) were men and 7
(38.9%) were women. The analysis was performed with the
IBM SPSS tool and the Pandas Matplotlib library in Phyton.x.
A. Acquired knowledge
Table II shows the level of knowledge acquired by the
students after the experience. Students had neither previous
contact nor knowledge about greedy algorithms at the
beginning of the experience. However, at the end of
experience they obtained an average score close to 7 out of 10
(specifically 6.95). Table II displays the mean scores per
question (maximum 2 points per question) and the total mean.
It should be noted that half of the students in the group
(50.5%) obtained a score higher than 8, the maximum score
being 10 and the lowest 3. Furthermore, we can see that in
Fig. 2. Running the algorithm step-by-step in RA-AVD three of the five questions students scored more than 1.7 out
of 2 points. Therefore, that the level of knowledge is quite
The trace table has the following columns (see Figure 2): satisfactory.
Step: number of the stage of construction of the TABLE II. KNOWLEDGE EVALUATION QUESTIONNAIRE AND SCORES
solution. OBTAINED BY QUESTIONS
Fixed node: the candidate node selected by the Number Statement of the task or question
selection function at each stage (of all the candidate Select the distance of two adjacent nodes when they
1 1.72
are equal
nodes, the one with the shortest path length from the Indicate the sequence of edges that Dijkstra would
source node is selected). 2 1.78
calculate from the origin to node X
3 Find the shortest path from vertex X to vertex Y 0.56
Adjacent nodes: the nodes adjacent to the selected Identify which is the predecessor node of node X in
node. 4 1.89
the graph.
Starting from the origin node, what would be the
Fixed-origin length: for each adjacent node, it 5
predecessor node Not selected for node X in step N.
1.00
indicates its distance from the source node. Total 6.95
Pending lengths: the lengths of the paths from the B. Positive activation emotions
source node that have not been selected so far. It is
Figure 3 shows the mean of the positive emotions
made up of values from the column "Fixed Origin
(enjoyment, hope and pride) valued by the students at three
Length" that have not been selected.
different times: before, during and after the experience of use
Minimum length: the shortest length of the lengths of of the tool. Not all emotions were measured at all times. For
the adjacent nodes of the fixed node and the pending example, measuring proud about accomplishment in the task
lengths. does not make sense before having done it, or there is no point
in measuring the students’ level of hope about what they will
Let's see an example to better understand the meaning of learn once they had learnt it. Therefore, in Figure 3 not all the
these columns (note that the source node is 0). Step 0 indicates positive variables appear at all times.
the initial state. In step 1, the algorithm marks the origin node
0 as a fixed node (in the first step the origin node itself is We can see that the average enjoyment remains roughly
chosen as the fixed node), and determines the adjacent nodes, the same throughout the experience (between 3.72 and 3.70).
which are nodes 1, 2 and 4, writing them down in the We also found out that hope decreased in students while they
"Adjacent Nodes" column. Next, the algorithm determines the did the task from 4.13 to 3.96 (i.e. 4.12%), and that pride felt
distance from these nodes to the origin, whose lengths are 4, once students finished the task decreased from 3.86 down to
2 and 8 respectively, and write them down in the column 3.67 (4.92%).
Fig. 4. Negative activation emotions
Fig. 3. Positive activation emotions
It was also observed that students felt worried and
The fact that enjoyment remained constant can be anguished about having to deal with too many study materials
interpreted as a favorable symptom, since the learning task and about having little time, since the items of the AEQ
entailed concepts that were new and difficult to understand for questionnaire related to this aspect of anxiety received the
students, and despite this fact they did not stop enjoying highest ratings (items 86, 96, 111 and 132 in Table III). This
during learning. The authors wonder whether the use of AR may have partly increased anxiety during the experience,
may have been the reason of keeping constant the levels of reaching a high value at the end of the experience compared
enjoyment. In relation to this feeling of enjoyment, it should to the beginning (Figure 4). Note that at the end, anxiety was
be noted that item 110 of the AEQ questionnaire (“I study the negative emotion that was experienced with the greatest
more than necessary because I enjoy it a lot”) obtained the intensity. Therefore, this finding constitutes an important
lowest score with an average of 2.89, which indicates that the aspect that should be taken into account in the design and
student is not interested in studying more than strictly construction of educational tools in order to reduce this feeling
necessary. Note that we numbered the items same way as the for the student.
AEQ questionnaire. Rather, it seems that they were interested Besides, we may observe in Figure 4 that the emotional
in acquiring new knowledge that seems significant to them, level of embarrassment decreased throughout the experience,
judging by the evaluation of question 139 (“I enjoy acquiring which probably means that the student started feeling more
new knowledge”), which obtained the highest score (4.33 out confident as he/she moved forward in the learning activity.
of 5).
Regarding the decrease in pride, it should be noted that the TABLE III. ANXIETY ITEMS AND THEIR RATINGS
highest score of the questions of the AEQ questionnaire to Number
Description Time
measure this emotion is item 135 (“When I excel in my work item AEQ
I feel proud”), which had an average of 4.00 out of 5. Thus, I get so nervous that I don't even want to
82 1,83
BEFORE
students value the work they do and feel proud of it. It is start studying.
85 When I have to study I start to feel dizzy. 1,78
possible that they did not value the task they had to do When I look at the books that I have yet to
(analyzing Dijkstra’s algorithm) as an interesting task and this 86 2,44
read, I feel distressed.
could have caused that pride decreased at the end of the I worry about being able to deal with all
96 2,44
experience. my work.
While studying, I want to distract myself
C. Negative activation emotions 102 2,94
to reduce my anxiety.
DURING
In relation to these emotions we could observe that the When time is running out my heart begins
111 2,83
to race.
average anger decreased during learning and increased when
118 I get tense and nervous while studying. 1,78
the task was finished by 7.65% (see Figure 4). On the other This subject scares me because I can't
hand, the level of anxiety increased notably from the 125 1,83
quite understand it.
beginning of the experience, being 15.83% higher during the Worrying about not completing the course
132 2,56
experience and 28.5% at the end of it (Figure 4). makes me sweat.
I am concerned that I did not understand
AFTER
141 3,56
the subject correctly.
When I can't keep up with my studies, it
147 3,17
scares me.
D. Negative deactivation emotions
The results show that the hopelessness of students
decreased while the experience with the AR tool was carried
out. However, it increased again to the levels of the beginning
when the task was finished (Figure 5). The authors cannot
explain well the reason for this effect, although they believe
that it could be related to the fact that the use of the AR tool The decrease in these negative emotions during the
gave them hope of learning while they were using it. experience could be related to the use of the tool, while the
increase detected once the task was finished could be related
to the fact that they had to take a knowledge assessment test
at the end of the task. This could make them anxious, angry,
or hopeless.
F. Students’ opinion
It was observed that students preferred to work
collaboratively during the task. They spontaneously came
together in pairs and in some cases even in groups of three
students. Some of the students pointed out the lack of any
collaborative interaction support in the tool. On the other
hand, one of the problems that was observed is that some
students did not have a smartphone with minimal
characteristics and this caused that the camera focus was not
optimal, causing the student to be bringing the mobile closer
to the paper several times until the tool detected the source
code. The students commented that they were surprised to
learn a new subject matter through the tool and even more so
Fig. 5. Negative deactivated emotions
because some did not know the augmented reality technology
Regarding boredom, it was detected that it decreased and it caused them great interest and curiosity, since they had
while using the tool (Figure 5). This is an important aspect not commonly used it in the classroom. In general, they
since boredom leads to reduced intrinsic motivation and indicated that they were satisfied with the tool and that it had
cognitive withdrawal from the task [27]. The authors think been useful in the experience.
that the use of augmented reality could have awaken the level
V. CONCLUSIONS AND FUTURE WORK
of attention and interest of the student and therefore could
have reduced boredom, perhaps increasing student’s This article has presented a classroom experience to learn
motivation. greedy algorithms using an augmented reality tool called RA-
AVD. In this experience, both the emotions and feelings of the
E. Comparison of positive and negative emotions students and the level of knowledge acquired have been
Figure 6 shows the emotional levels grouped by positive evaluated. The knowledge evaluation was satisfactory. The
activation (enjoyment, hope and pride), negative activation students managed to obtain an average of almost 7 (6.95) out
(anger, anxiety and embarrassment) and negative of 10, which means that most of the students managed to
deactivation emotions (hopelessness and boredom). It should understand the operation and behavior of the algorithm under
study (Dijkstra’s algorithm). Note that the students had no
be noted that the positive activated emotions experienced by
previous knowledge nor contact with greedy algorithms, thus
the students as a whole are almost twice as intense as the
the learning curve was high.
negative ones. The authors wonder if it could be that the use
of AR in learning algorithms has something to do with this In relation to emotions experienced by using the tool, the
fact of experiencing positive emotions more intensely than experience revealed that the students experienced positive
negative ones. Which raises an interesting line of future emotions (enjoyment, hope and pride) much more intensely
research. Regarding negative emotions (both activation and than negative ones (such as anxiety or anger), being almost
deactivation), it could be seen that they decreased slightly twice as intense. In addition, boredom and embarrassment
while students were using the tool in the task (Figure 6, decreased notably during the use of the tool, keeping the
“During” section of the diagram). However, at the end all feeling of enjoyment roughly constant during its use.
these emotions increased. However, the student's level of anxiety increased from the
beginning while using the tool, being almost the double at the
end of the experience. This last aspect is especially relevant
and it should be taken into account in the design and
construction of future educational tools.
As future projects, we plan to analyze in greater depth the
results obtained by carrying out a correlation analysis between
emotions and learning. Furthermore, we intend to replicate the
experience with a control group and compare traditional
learning with the use of augmented reality.
ACKNOWLEDGEMENTS
This work has been supported by research grants iProg (ref.
TIN2015-66731-C2-1-R) and e-Madrid-CM (ref.
P2018/TCS-4307) with FSE and FEDER funds. The support
of the GIIAR group of the Salesian Polytechnic University is
appreciated.
Fig. 6. General average of emotions according to time
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