Journal of Robotics, Networking and Artificial Life
Vol. 8(2); September (2021), pp. 104–107
DOI: https://doi.org/10.2991/jrnal.k.210713.007; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Proposal of Parameter Tuning Skills Evaluation by usingSensor Car Behavior Data in Technology Education
Teruyuki Tamai1,2,*, Keita Murai1, Yoshihiro Ohnishi2, Kazuo Kawada1
Graduate School of Humanities and Social Sciences, Hiroshima University, 1-1-1, Kagamiyama, Higashi-hiroshima, Hiroshima 739-8524, JapanFaculty of Education, Ehime University, 3, Bunkyo-cho, Matsuyama, Ehime 790-8577, Japan

12ARTICLE INFOABSTRACT

Article History

The “Future Investment Strategy 2018” presented by the Cabinet Office in 2018 states that there is an urgent need to develop
human resources that can use Artificial Intelligence to achieve the goal of Society 5.0. As a part of this strategy, programming
education at the primary/secondary level of education is being promoted. However, there are some issues in the quantitativeevaluation of learning and teaching methods based on assessment. In this study, sensor car teaching materials were used toteach programming. Then, a new lesson is proposed to tune the optimal parameters by quantitative evaluation. The results of theuniversity students practice of the proposed lesson are explained.

Received 06 December 2020
Accepted 11 May 2021
Keywords
Junior High Schoolparameter tuning skill
quantitative evaluation© 2021 The Authors. Published by Atlantis Press International B.V.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
The Cabinet Office published “Future Investment Strategy 2018” in2018 [1]. In this strategy, it states that in the Artificial Intelligence(AI) era, in addition to the ability to understand and use AI anddata with high mathematical capabilities, the ability to set up andsolve problems and create heterogeneous human resources that canproduce value with abilities that are difficult to replace by AI (suchas the ability to combine things), are needed. AI education at theprimary/secondary level is strengthened. Teaching materials arebeing developed so that programming education can be effectively
implemented in elementary schools. Also, local environments arebeing developed so that students can learn more advanced programming. Thus, there is an urgent need to improve programming
education and instruction. This is not limited to Japan. In recentyears, there have been many studies on education and lessons inprogramming. For example, it has been shown that there are manydifficulties in learning programming [2] and that beginners inprogramming are more likely to learn in-class than in take-homelearning [3]. Thus, there is an urgent need to improve humanresource development, including programming, both domestically and internationally. In Japan, studying programming at theelementary/secondary level school is mainly conducted in juniorhigh school technology and home economics (in the technologyfield) (hereinafter referred to as junior high school technology).
Teaching materials for programming in junior high school technology include materials for programming the timing of lights andsounds, and robotics [4]. As an index for evaluating programmingskills, it is generally evaluated qualitatively, such as “Can you use
Corresponding author. Email: tamai.teruyuki.xq@ehime-u.ac.jp*

sequential, iterative, and conditional processing?” and “Are theretyping errors?”. There are problems with these types of evaluations,such as the fact that they are time-consuming and depend on theteacher’s discretion. To solve this problem, quantitative evaluation
studies have been conducted. The “effect” of the program is visualized by having the learner complete tasks of varying difficulty [5].
This method of evaluation allows students to feel their programming skills improve. On the other hand, to develop the competencyof junior high school technology, it is important to learn how tooptimize the solution to problems. There is no learning programming practice that incorporates quantitative evaluation into thislearning process. In this study, a new evaluation method to quantitatively evaluate a sensor car’s linear motion in terms of time andleft-right swing during motion is proposed. A lesson for beginners
in programming to develop the skills to tune the optimal parameters is also proposed.

2. TEACHING MATERIALSIn this study, a sensor car (made by Artec) as shown in Figure 1 wasused. The Studuino mini which is an Arduino-compatible microcontroller board with input/output interfaces for education wasused as a microcontroller board. An infrared photoreflector (RPR220, Made by ROHM) was used as a sensor to identify the line to betraveled. It was powered by three AA batteries at 4.5 V.

3. LEARNING TASKA learning challenge aimed at the beginning student of programming was devised.

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105

3.1. Evaluation Items
As shown in Figure 2, a black line with a width of 10 mm was
drawn on the white surface of the field. The sensor car ran straight
500 mm along the black line in this field. The running time andleft-right swing were measured. Task 1 shortened the running time,
while Task 2 aimed to reduce the left-right swing. The learning task
involved tuning the parameters to solve these two tasks.

3.2. Parameters to be Tuned
Figure 1 | Sensor car.

As shown in Figure 3, the Studuino mini software is capable of­Scratch-based visual programming. The learner tuned the fiveparameters. It is the threshold of the sensor (threshold), and theratio of pulse width modulation (PWM) to adjust the speed of theleft and right motors (M1y, M2y, M1n, M2n).

3.3. Parameter Evaluation
Figure 2 | Field to run.

The running time t in Task 1 was measured using a stopwatch. The
left-right swing of the sensor car in Task 2 was recorded using a
digital camera (IXY 92015, made by CANON). Then, it was analyzed using the video analysis software PV Studio 2D ver2 (madeby L.A.B). To measure the left-right swing, markers were attachedat two points as shown in Figure 4. The x- and y-axis were defined
as shown in Figure 5, and the positions of the markers were read.
The left-right swing y12 was calculated using Equation (1). This iis the index of extracted data from the video.		     y12i = y1i − y 2 (1)The variance s 2 y12 was calculated using Equation (2). However, n is thenumber of data extracted from the video from the start to the goal using
video analysis software. This s 2 y12 was used as the left-right swing.
Figure 3 | Studuino mini programming.	    ∑ ( y12 − y12) =2n2s y12i =1i(2)nIf the running time t and the degree of left-right swing s 2 y12
became small, the learner was able to choose a good parameter. In
other words, parameter tuning skill was high.

4. LESSON SUGGESTIONS

Figure 4 | Marker position.

To examine the time required to implement the proposed learning evaluation method and learning effects, university students­practiced a class in teacher training. Two people could use one
sensor car. Eight groups of 16 people were included in the study.
Table 1 presents the learning plans.
Table 1 | Lesson plan
Hours (min) Main learning contents

Figure 5 | Set axis.

Lesson 160

Lesson 230

Lesson 3

90

Check the relationship between the adjustmentof parameters and the movement of thesensor car’s wheels.
Know how to program a line trace using asensor car.
Understand the learning task.
Tuning the optimal parameters.

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Table 2 | Sensor car resultsGroup 1t(s)First time
Second timeThird time
Fourth timer 2 y 12Group 22Threshold M1y M2y M1n M2n t(s) r y 12 Threshold M1y M2y M2n M1n4.17 0.085
4.12 37.80
4.02 0.24
3.98 0.18

80
60
60
6055
7580
90

100
100
100
100

In Lesson 1, students experienced how changing the direction
and speed of rotation from left to right changed the sensor car’s
behavior. Most of the students had never previously experienced
programming a sensor car. In Lesson 2, students were shown theprogram shown in Figure 3 and were taught the steps of the program to perform a line trace. In Lesson 3, the students first predicted the sensor car’s movement and designed the parameters tocomplete the task. They then programmed the sensor car according
to the design and made it work. In this way, Lesson 3 incorporated
the cycle of design, creation, evaluation, and improvement so thatthe solution to the problem would be optimal, which is considered
important in the learning process.

5. RESULTS AND DISCUSSION
Table 2 shows the results of university students’ practice. In thisstudy, university students carried out the learning process ofLesson 3 four times. Some of the results are presented shown inTable 2. For Group 1, the fourth time was the fastest for Task 1.
For Task 2, the second time was the worst. However, it improvedby the third and fourth times. For Group 2, the third and fourthtimes were the same for Task 1. However, the left-right swing
improved from 0.25 to 0.16. For the groups shown here, it wasconcluded from the quantitative evaluation that the parameters
were better regulated.

6. CONCLUSION
In this study, a lesson and an evaluation method were proposed todevelop the skills required to tune the parameters. The proposed
lesson and evaluation methods were implemented by university
students. It was shown that parameter regulation skills could bequantitatively evaluated. The evaluated data were compared and100
1001001005575
6075

4.01
4.05
3.73
3.73

0.32
0.31
0.25
0.16

46
45
43
43

100
100
100
100

2030
80
90

2030
80
90

100
100
100
100

statistically analyzed. The goodness of parameter tuning is demonstrated. In future studies, learning time will be organized and supplementary materials will be developed so that this study can beimplemented in junior high schools.

CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

ACKNOWLEDGMENT
The authors are grateful that this work was supported by the Ehime
University Challenge Support Fund.

REFERENCES
[1]
[2]
[3]
[4]
[5]

Cabinet Office, Future Investment Strategy 2018, (2018).
Available from: https://www.kantei.go.jp/jp/singi/keizaisaisei/pdf/miraitousi2018_en.pdf. (accessed April 20, 2021).
Y. Bosse, M.A. Gerosa, Why is programming so difficult to learn?:
Patterns of Difficulties Related to Programming Learning MidStage, ACM SIGSOFT Software Engineering Notes 41 (2016), 1–6.
O. Karnalim, G. Kurniawati, S.F. Sujadi, R.A. Nathasya,Comparing the impact of programming assessmennt type:in-class vs take-home, iJEP 10 (2020), 125–132.
Y. Ohnishi, K. Honda, R. Nishioka, S. Mori, K. Kawada, Robotics
programming learning for elementary and junior high school
students, J. Robot. Mechatron. 29 (2017), 992–998.
Y. Ohnishi, T. Tamai, K. Kawada, Estimation of programming learning achievement by line tracing robot, Proceedings of the 2019
International Conference on Artificial Life and Robotics, ALife
Robotics Corporation Ltd., Beppu, Oita, Japan, 2019, pp. 431–434.

AUTHORS INTRODUCTION
Dr. Yoshihiro Ohnishi

Mr. Teruyuki Tamai
He received the M.A. degrees in Educationfrom Hiroshima University in 2012. He iscurrently a Doctoral course student in theGraduate School of Education, Hiroshima
University. He is an Assistant Professor
in the Faculty of Education, Ehime
University.

He received his Dr. Eng. from Osaka
Prefecture University in 2002. He is a­professor at Faculty of Education in Ehime
University.

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Dr. Kazuo Kawada

Mr. Keita Murai
He received his B.Ed. degree in Educationin 2020 from the Faculty of Education,Hiroshima University. He is ­acquiring
the M.Ed. in the Graduate School ofHumanitiesandSocialSciences,Hiroshima University.

He received his PhD degree at Graduate
School of Education in Hiroshima
University. He is an Associate Professor
in the Graduate School of Humanities andSocial Sciences, Hiroshima University.