=Paper=
{{Paper
|id=Vol-2555/paper20
|storemode=property
|title=Robotics in First Year Engineering Students: An Experience in Learning Concepts of
Linear Motion
|pdfUrl=https://ceur-ws.org/Vol-2555/paper20.pdf
|volume=Vol-2555
|authors=Eveling Castro-Gutierrez,Sebastián Bobadilla-Chara,Diego Mendoza-Pinto,Whinders Fernandez-Granda,Caterine Chara-Barreda
}}
==Robotics in First Year Engineering Students: An Experience in Learning Concepts of
Linear Motion==
https://ceur-ws.org/Vol-2555/paper20.pdf
Robotics in First Year Engineering Students: An
Experience in Learning Concepts of Linear Motion
Eveling Castro-Gutierrez1, Sebastián Bobadilla-Chara2, , Diego Mendoza-Pinto3,
Whinders Fernandez-Granda4 and Caterine Chara-Barreda 5
1,2,3,4,5
Universidad Católica de Santa María
{ecastrog, 70669699, 70799893, wfernandezg, ccharaba}@ucsm.edu.pe
Abstract. Given the complexity of the learning process, it is a great challenge
getting students to be actively involved in it. There is a concern for professors to
use new teaching-learning strategies that playfully approach, motivate and
increase the attention span of students in the learning sessions. The objective of
this study is to use educational robotics (RE) for the teaching of "concepts of
particle movement in one dimension". The sample is made up of 69 students of
the Physics course of the third semester (second year), of the Professional School
of Systems Engineering. The differences found between the pre and post-test of
both groups are not statistically significant. From that we conclude that a single
learning session is not enough to obtain results similar to the actual values.
However, the use of educational robotics "improves the attitude" towards
learning in students.
Keywords: Educational robotics, learning outcomes, learning environment,
learning tool, pedagogical approach, robotics, physics.
1 Introduction
The “Ability to apply knowledge of mathematics, science and engineering” [1], is one
of the eleven skills that engineering students must possess when they finish their
studies, based on the Criterion 3 of ABET, an entity that granted Accreditation in 2019,
to the Systems Engineering degree1 of the Catholic University of Santa Maria (UCSM).
Mathematics framework according to PISA 2021, involves engagement in the
application of knowledge of the number, the understanding of measures, magnitudes,
units, estimation, etc.[2], in a wide variety of environments. This leads to the interest
1
https://www.ucsm.edu.pe/ingenieria-de-sistemas/
Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
of using different teaching and learning techniques in [3],[4],[5] and [6], Science,
Technology, Engineering and Mathematics (STEM) students.
Achieving a more personalized and inclusive education is one of the challenges in
today's education. It is of most importance to know which learning style is better
according to the different profiles of our students [7].
The new learning models to transmit and build knowledge, require tools, resources, and
instruments that help the students shape their ability to think and act with scientific
criteria in solving the different situations presented to them.
Given the complexity of the processes related to learning, especially in physics related
courses, it is a great challenge to get students to be actively involved in their learning,
and a challenge for the present research, because the transmission of knowledge is
highly theoretical with just a few practical sessions. This motivated us to propose new
teaching and learning strategies that playfully approach the student and at the same time
motivate and increase the attention span in the learning session.
This article is organized as follows: Section 2 presents the related works; Section 3
describes the materials and methods used. Then in Section 4 the results and discussion
according to the proposed experience are presented, we finish by presenting our
conclusions.
2 Related Works
During the review of the state of the art, several studies were found that conceptualize
Educational Robotics (ER):
2.1 Educational Robotics
According to Pittí et. al [8] consider ER as a learning tool that has the potential to
improve creativity and learning skills, being its ultimate goal for the student to "achieve
learning". ER is described as a systematic and organized process in which robotic
platforms and software participate. It is applied in the study of three relevant
components in the teaching and learning process: a) concepts: robotics, technology,
computer science, mathematics, and physics; b) procedures, managing to strengthen
some cognitive, social, and metacognitive skills, among others; c) attitude, and attitude
changes towards science and technology.
In another work, the authors demonstrated [9], that ER or pedagogical robotics try to
create conditions of "appropriation of knowledge", so that students manufacture their
own representations of real-world phenomena and make their transfer to different areas
of knowledge.
Suggested by Gonzales, J et. al [5] and Eteokleous, N et.al [10], the constructivist
theory asserts that knowledge is not transmitted but is constructed, meaning that it is
actively created in the student's mind, however constructionism also considers that in
order to achieve it, the individual should build something tangible which has a personal
meaning for him. This last pedagogical theory was based on many of the main
advancements in educational robotics.
When applying the experimentation, in order to achieve our goals, we aim to strengthen
significant knowledge going from the abstract to the tangible.
In the production of new knowledge imagination and creativity are very relevant. In
order to develop these characteristics students need to make use of the information
captured by their senses, test their limits, and obtain feedback [11],[12].
2.2 Learning Object (LO)
Ibarra et al. [13] showed that the development of the learning object is based on a
strategy oriented to student’s learning and to fulfill the objective its design must have
an internal structure that has different elements. The design of the learning object has
the following aspects: a) pedagogical reference which is the pedagogical practice
developed based on the competences of the course based on the theory of
constructivism learning and playful learning; b) technological reference, tangible
digital objects such as robots are used; c) contextual reference: The LO is designed
according to the following sequence: Curriculum design, Learning Routes, Learning
Unit and finally the Learning Session.
2.3 Robotic Kit
The main objective of this research is to evaluate a learning session including
methodological innovation through the programming of a technological element (a
robot), available in the market called Dash and Dot ® robotics kit, to fulfill a specific
function in the topic of: Movement of Particles in one Dimension, as a learning resource
in physics, and replicating it with other work groups. We use educational robotics (ER)
as support for the development of learning [11] (one of its many uses). This approach
uses robots within the class as a teaching resource where learning is facilitated by
inquiry and errors are taken as a learning opportunity. The code for this project is in
GitLab2 and the schema for the robot session is in Fig. 1.
2
https://gitlab.com/lokdex/learning-retention
Fig. 1. Proposal schema of robot kit (Dash & Dot) for learning physics concepts
3 Materials and Methods
A longitudinal, analytical study was carried out with 69 students of the third semester
of the Systems Engineering degree at the Catholic University of Santa María.
For this research, a programmable Dash and dot ® robotics kit was used. Robot
programming was done with the module provided by the manufacturers, obtained from
the GitHub repository of Wonder Workshop called WonderPy. This module gives
access to all the sensors and actuators of the robot, thus allowing us to give instructions
and obtain data from the various sensors that these devices have.
The programming language used was Python 2.7, used to develop an application
executed through a command line.
This program already includes the different options necessary to carry out the
experimental part of the topic proposed in this investigation, being necessary to enter
data such as the distance you want the robot to travel or the speed at which you want it
to move.
3.1 Sequence of the learning session
Learning competence of the course in the topic “Movement of Particles in One
Dimension”:
Analyzes, interprets and exemplifies the movement of particles in free space by
establishing relationships in problem solving, executing experiments, assuming critical
and reflective attitude, valuing the importance of particle movement in their
professional training, respecting international standards.
Pedagogical reference:
Chapter 2: Movement of Particles in a Dimension, topics to be discussed: displacement,
time, and average speed, instant speed medium and instantaneous acceleration.
Movement with constant acceleration. Bodies in free fall.
Instruments
In this item, we presented two session of practices that we formulated, see figure 2.
Fig.2. From left to right, we have the practices session of the physics Learning of
Movement of Particles in one Dimension with the videos and subsequently
evaluated with the support of a robot. At the bottom there is a final survey
regarding the practical session.
First experiment
A. Participants
The research was conducted with the students of the two sections of the third semester
of the UCSM Professional School of Systems Engineering, distributed as follows:
Experimental group (EG): section A, composed of 31 students and Control group (CG):
section B, composed of 38 students.
B. Learning Object
The development of the learning object is based on a strategy oriented to the student's
learning of the Physics course. The design of the Learning Object has the following
steps:
a) Problematic situations
Both groups (EG, CG) were exposed to 7 seven situations, the first four referring
to the issue of one-dimensional speed and the remaining three to the issue of
acceleration in a straight line, which were numbered as shown in Table 1.
Activity
N URL of the video for each activity
description
1 People walking https://www.youtube.com/watch?v=bX4ag0ocAMI
2 Cars moving https://www.youtube.com/watch?v=17IhMKtAPN8
3 Man jogging https://www.youtube.com/watch?v=HcHZbgBIGYU
4 Vehicle moving https://www.youtube.com/watch?v=A1pxxwDajQU
5 Motorcycle https://www.youtube.com/watch?v=V6NS-tHQewM
accelerating
6 Man, in Fall https://www.youtube.com/watch?v=l0XjrJlod3M
7 Moto accelerating https://www.youtube.com/watch?v=hKiKtLmvJ1Q
Table 1. Situations of one-dimensional movement and their URL
b) Process
The following activities are proposed:
First: Both the EG and the CG, simultaneously but in separate rooms, watch
each of the videos.
At the beginning of the session, in each group, the teacher explains the activity
they will develop, with the objective of quantifying the degree of accuracy of
their perception of speed or acceleration that a certain object has, according to
the videos listed in table 1.
Then the participants write down the results on the worksheet.
Second: The CG received the traditional learning session, inside the classroom
the teacher solves problems on the blackboard. On the other hand, the EG,
conducted this session outside the classroom, with the "robot" as a teaching
resource.
Third: Again, both groups (EG and CG) watched the videos. Subsequently, they
completed the questions proposed in the worksheet.
Second Experiment
A. Participants
Only students from EG take part in this activity.
B. Learning Object
The design of the Learning Object has the following steps:
a) Problematic situations
Constant Speed: the robot is programmed to move at two speeds: 0,2 m/s and
0,4 m/s.
Constant acceleration: the robot is programmed to move with 0,1 m/s2 and 0,3
m/s2 of acceleration.
b) Process
The following activities are proposed:
First: Outside the classroom, participants observe the movement of the robot in
each of the problematic situations.
Second: Each participant makes an intuitive calculation of the speeds and
accelerations proposed in the worksheet (fig. 2).
Third: Finally, using instruments, they performed the measurements of space
and time, data that they will use to perform the respective calculations.
Then the students write the results on the worksheet (fig. 2).
Data Analysis
The data analysis was performed with the Statistical Package for the Social Sciences
(SPSS) software, version 26. The calculation of descriptive and analytical statistics was
included. Nonparametric statistics were used: Wilcoxon to analyze the results of the
experiment before and after using the technological resource; U Mann-Whitney to
compare the results of the two groups. A value p ≤ 0,05 was considered statistically
significant.
4 Results Analysis
Of the 69 students with whom the study began, 23 (8 from the experimental group and
15 from the control group) were excluded because they presented incomplete
assessments.
Table 2 (first experiment) shows the results obtained for each of the problematic
situations raised in the didactic experience, see Appendix A.
When performing the statistical treatment for related samples, before and after the
application of the didactic resource, only statistically significant difference (p <0.05)
was found in the control group of the situation 2.
Likewise, when comparing the control and experimental group, only a significant
difference was found (p < 0,05) after the application of the didactic resource in the
situation 1, observing smaller differences between the real and calculated value of the
experimental group (𝑥̅ control = 0,64 m/s; 𝑥̅ experimental = 0,31 m/s).
It is important to mention that in situations 4, 5, 6 and 7, after using the didactic
resource, there is greater dispersion, especially between the third quartile and the
maximum value of the results.
Table 3 (second experiment) shows the results obtained intuitively and after taking
measurements, finding that there is a significant difference (p < 0,05) between the
values calculated for the two accelerations, the differences between the real value and
the one calculated in the group that performed the measurements.
Figure 3 shows a greater dispersion of the intuitive results corresponding to the
measurement, both of acceleration 1 and 2.
Results of second experiment
Statistics Speed 1 (m/s) Speed 2 (m/s) Acceleration 1 (m/s) Acceleration 2 (m/s)
Intuitive Measured Intuitive Measured Intuitive Measured Intuitive Measured
As 3,08 1,05 2,95 4,09 1,62 3,05 1,27 2,88
Vmin 0,00 0,01 0,00 0,03 0,00 0,00 0,10 0,00
Vmax 3,80 0,30 6,60 7,38 2,90 0,40 4,70 0,66
Q1 0,10 0,06 0,10 0,07 0,05 0,02 0,20 0,01
Mean 0,42 0,11 0,78 0,62 0,65 0,06 1,24 0,10
Median 0,10 0,10 0,15 0,20 0,40 0,05 0,70 0,04
Q3 0,15 0,16 0,40 0,37 0,90 0,06 1,70 0,14
Riq 0,05 0,10 0,30 0,30 0,85 0,04 1,50 0,13
P valor
0,106 0,749 0,001 0,000
(related)
Table 3: Comparison between solving methods using a technological tool.
Fig 3: Box and whisker plot for intuitive and calculated results of the experimental group (second
experiment)
5 Discussion
The educational environment it is quite known that subjects such as physics,
mathematics, among others, generate in students feelings of demotivation due to the
difficulty that comes along with the resolution of different situations that they have to
face in reality. That is why, numerous researches make proposals for innovative
strategies that allow improved success in the teaching and learning process in these
areas, one of them being the use of technological resources. Due to their inherent
characteristics they capture the attention of the students, allowing to develop different
skills with their manipulation. Hypothetically, we think that the use of a technological
resource would surely improve student performance, however, according to the results
obtained in this research, it was not possible to obtain significant differences (p > 0,05)
when comparing the results. This may be due to the fact that students are not familiar
with its handling, and mainly because a single learning session would not be enough to
achieve full understanding of the topics treated in the practice session. Similarly, when
comparing intuitive results with calculated results, significant differences (p < 0,05)
were found for acceleration measurements, which were measured at the end of the
experiment, being an important observation that allows us to mention that students need
to repeat the same practice more than once in order to acquire the desired competence.
The use of the robot, as a pedagogical resource, has allowed us to clearly observe that
students become active actors in their learning, improving their attitude towards the
learning of physics. The 23 students of the experimental group mentioned that “it is
more didactic, entertaining, dynamic, and the best way to approach reality”. Although
they still lack intuitive and critical thinking when assessing a real problematic situation.
6 Conclusions and Future Works
The use of a technological resource improves the attitude of learning. A single learning
session is not enough for the student to develop an accurate intuition of the speed and
acceleration of objects. Both methodologies successfully teach the required
competences for the student. However, by using the “robot” they actively take part in
the learning-teaching process.
The future work will be to apply this technique for more than one session in the same
physical topic for the next edition of the course.
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Appendix A
p valor4, 5
Situations GROUP TEST As Vmin Vmax Q1 Median Q3 RIQ p valor3
(U Mann-
(Wilcoxon)
Whitney)
Before 0,13 0,05 0,95 0,2 0,45 0,84 0,64
CONTROL 0,281
After 0,30 0,05 1,45 0,25 0,45 0,95 0,70 0,231
1
Before 0,17 0,01 0,95 0,05 0,45 0,75 0,70 0,017
EXPERIMENTAL 0,069
After 0,19 0,01 0,85 0,05 0,35 0,45 0,40
Before 1,57 0,17 9,00 1,00 1,56 2,89 1,89
CONTROL 0,035
After 1,65 0,00 5,00 1,00 1,22 2,00 1,00 0,774
2
Before 0,58 0,11 5,72 0,67 1,56 3,17 2,50 0,886
EXPERIMENTAL 0,550
After 1,19 0,00 6,00 0,20 1,56 2,06 1,86
Before 0,51 0,10 2,00 0,67 1,00 1,50 0,83
CONTROL 0,235
After 0,19 0,50 2,00 1,00 1,00 1,50 0,50 0,774
3
Before - 0,06 0,00 2,00 0,50 1,10 1,50 1,00 0,062
EXPERIMENTAL 0,522
After 0,31 0,00 2,00 0,50 0,94 1,22 0,72
Before 0,22 0,67 7,61 1,28 3,44 4,89 3,61
CONTROL 0,099
After 3,40 0,67 54,00 0,67 4,75 9,00 8,33 0,207
4
Before - 0,16 0,67 9,00 2,11 4,89 6,22 4,11 0,938
EXPERIMENTAL 0,356
After 2,04 0,67 22,00 2,11 4,00 6,22 4,11
Before - 0,32 3,00 13,00 7,00 9,00 10,00 3,00
CONTROL 0,051
After 1,80 7,00 17,00 9,00 9,33 10,00 1,00 0,921
5
Before 0,25 1,00 17,00 5,00 8,00 11,00 6,00 0,441
EXPERIMENTAL 0,754
After 2,13 2,00 39,00 3,00 8,00 12,90 9,90
Before - 0,52 0,00 15,00 7,00 9,00 10,00 3,00
CONTROL 0,925
After - 1,00 0,00 14,00 7,00 9,00 9,00 2,00 0,955
6
Before 1,17 0,99 24,00 7,00 9,00 9,50 2,50 0,674
EXPERIMENTAL 0,394
After 2,58 0,90 89,00 3,00 8,80 14,00 11,00
Before - 0,32 0,00 10,00 0,00 6,00 10,00 10,00
CONTROL 0,183
After 0,32 0,00 10,00 2,00 5,00 6,00 4,00 0,430
7
Before 1,81 0,00 30,00 5,00 6,00 10,00 5,00 0,065
EXPERIMENTAL 0,737
After 2,28 0,00 40,00 2,00 7,00 10,00 8,00
Table 2: Comparison of the results obtained before and after a learning session with traditional
teaching resources and tangible digital object: robot (first experiment).
Legend
As: asymmetry coefficient Vmin: minimal value
Vmax: maximal value Q1: first quartile
Q3: third quartile RQ: interquartile range
3
The values represent the comparison of related samples: data before and after the learning session.
4
Superior result, represents the comparison of independent samples, values of EG and CG before the
learning session.
5
Lower result, represents the comparison of independent samples, EG and CG values after the learning
session.