Programming Instruction Using a Micro Robot as a Teaching Tool TERUYA YAMANISHI, KAZUTOMI SUGIHARA, KAZUMASA OHKUMA, KATSUJI UOSAKI Department of Management Information Science, Fukui University of Technology, Fukui, Japan Received 23 July 2013; accepted 24 July 2013 ABSTRACT:
This practical report analyzes a programming class using a micro robot (MR), the smallest soccer robot in the RoboCup world competition. This class examined the effect of using the MR as the actual equipment employed in programming. Questionnaire results on this class revealed that these teaching materials evoked a heightened programming interest among students. Moreover, the problems related to programming instruction using an MR were better understood; therefore, a strategy for improving the related problems is discussed here. ß 2013 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:109–116, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21582 Keywords: introductory instruction; computer programming; robotics; information science
The transition to an information‐based society through information and communication technology (ICT) is rapidly progressing. There is therefore an increasing need for engineering talent to support the software development technologies at the core of ICT, as well as develop new devices and embedded systems. It is thus important that we foster, in our youth, an interest in information science to encourage them to carry on as the next generation of ICT engineers. A new set of curriculum guidelines for Japanese schools will come into force in April 2013. These guidelines include content related to information technology studies that are to be extensively introduced into elementary (ages 6–11), middle (ages 12–14), and high (ages 15–17) schools in Japan, beginning with the use of ICT and leading to ICT implementation for solving problems. The goals of the curriculum guideline revisions are to increase students’ abilities in ICT and to develop age‐appropriate learning experiences related to needs analysis, algorithm development, and programming. Both the expansion of students’ skills and suitable instruction are necessary at every level of schooling so as to foster the growth of the next generation of ICT engineers. As an example, all middle and high school students will participate in study units titled “Measurement and Control through Computer Programs”
and “Development of Scientific Understanding and Thinking with Regards to Information,” respectively. While we hope that such a study will increase the interest of students in the field of ICT, the introduction of new topics for learning requires the creation of curriculum content that will not only have an educational impact, but also spark participating students’ interest in this field of study. We propose an experiential programming class using actual robots to deepen students’ understanding of embedded systems—a core ICT technology. For high school students who attended this class, we administered questionnaires to investigate the degree to which their intellectual curiosity was stimulated. Also, based on the questionnaires, we explored whether such instruction is an enjoyable activity for students, in addition to it being an effective mechanism toward providing instruction in the humanities, and whether it is possible to study computer programming or embedded systems by selecting certain methods of teaching and teaching materials. Though the concept for this proposal applies to ICT instruction in Japan, we expect that the impact of such instruction will have worldwide applicability. The article is organized as follows: Use of a Robot in the Class Section describes the robots and their usage during instruction. Questionnaire Items and Results Section discusses a case study involving the use of robots in an actual classroom for the proposed course, and relates the results of a questionnaire administered to the participating students. Conclusion Section provides a summary and considers topics for future research.
Contract grant sponsor: Grant‐in‐Aid for Scientific Research; Contract grant numbers: C‐21500962, C‐24501221; Contract grant sponsor: Fukui University of Technology. Correspondence to T. Yamanishi (
[email protected]).
USE OF A ROBOT IN THE CLASS
INTRODUCTION
© 2013 Wiley Periodicals, Inc.
Studies suggest that there are many learning tools involving the use of robots in computer instruction; these studies show significant
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achievements by analyzing questionnaires and follow‐up surveys of students [1–8]. In addition, it has been found that a goal, such as a prize competition, can effectively motivate students [9]. However, many such studies have been conducted using LEGO Mindstorms [10–15]. Therefore, as an alternative teaching tool, we use another type of actual robot called the micro robot (MR) which is the smallest robot in the MR League [16] of the RoboCup [17–19]. The RoboCup is an international scientific initiative to advance the technology of intelligent robots. Thus, considering the development potential that MRs have for individuals to participate in future RoboCups, this study uses MRs as a typical robot of the RoboCup.
Micro Robots An MR is a cube of approximately 3 cm/side (Fig. 1). Figure 2 shows its component parts. The robot’s upper portion contains a controller board that includes AVR and ARM microcontrollers and an infrared (IrDA) link port. Two lithiumion batteries and motors are connected to the wheels. The robot receives control signals through its IrDA port and transmits those signals to the wheels. The robot can also be operated autonomously, without the use of
infrared signals, through programs stored in its microprocessor. The images to the left and right in Figure 3 show the controller board used to write control programs and the robot recharge unit, respectively. The recharge unit can recharge up to six robots simultaneously. The class described in this article requires an MR, a recharger, a control board for storing programs, and a personal computer. Two stepper motors provide motion. Stepper motors are not structurally simple compared with other motor types, but they have long life and high durability. In addition, it is simple to make a controller for the stepper motor. The MRs move according to control programs stored in the AVR (ATiny84) and ARM (AT91SAM7X256‐100‐BALL‐TFEGA) chips on the control board (Fig. 4).
Experimental Programming Class Using Micro Robots When students learn how to examine situations from the perspective of information processing, it is important that they experience the iterative process of analyzing the problem, systematically constructing a solution, and considering and iteratively refining the results of their endeavors. In the proposed class, students engage in this process by programming a robot control program, executing the program, and verifying the resulting robot operation. It is not easy, however, for students with limited or no programming background to accomplish this within the classroom’s time constraints. It is therefore necessary to establish an environment in which students can perform programming tasks with minimal instruction. Table 1 shows the functions used to send commands to the left and right stepper motors to move the MR’s wheels. These functions take a value from 0 to 30 to specify the speed of wheel rotation; the appropriate selection of values causes the MR to move
Figure 1 A micro robot. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] Figure 3 (Left) A controller board for storing control programs. (Right) A robot recharge unit. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
Figure 2 A disassembled micro robot. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
Figure 4 (Left) AVR (ATiny84) and (right) ARM (AT91SAM7X256‐ 100‐BALL‐TFEGA) chips. The white circles locate these chips on the control board. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
PROGRAMMING INSTRUCTION USING A MICRO ROBOT
Table 1
Default Wheel Control Functions
Commands
Table 2 New Functions for Classroom Use Functions
motor_left (int speed) motor_right (int speed) motor (int left, int right)
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Set left wheel speed (range, 0–30) Set right wheel speed (range, 0–30) Simultaneously set left and right wheel speed (range, 0–30)
straight or turn. Using only these functions‐rotational speed and duration‐in an attempt to realize complex motion is not a simple task for those who are unfamiliar with robot control programming, even though they are not programming amateurs. In general, extensive trial and error is required before appropriate values can be determined. Therefore, we measure motor speed and travel distances to create the functions for classroom use shown in Table 2. Two new functions, straight and turn functions, shown in Table 2, allow the students to begin programming after a simple explanation on how they function. Figure 5 depicts a Figure 8 movement control program that was used to demonstrate how the functions work. Note that we are limited only to explaining the straight and turn functions. Other typical syntaxes, such as “branch” and “loop” primitives are considered as development learning, and are omitted here as this is a class meant for students with limited or no programming background.
Movements
New commands
Functions
Straight
straight (int speed, int length)
Turning
turn (int no, int v, int angle)
Specify speed and distance for straight movement (allowed speed values are 22, 24, 26, 28, or 30) Specify rotational direction, radius, and angle for curved movement
Research Design of Experimental Programming Class We present a teaching plan as follows: (1) Aim (a) Help the students develop an interest in robot and/ or information science through programming training on how to move the robot. (b) Help the students understand the program and flow of processing. (2) Textbook The lesson is presented as a slide presentation without a textbook. (3) Teaching procedure See Table 3.
Method and Practice in Experimental Programming Class An experimental class was conducted to estimate whether the programming of tangible robots fosters students’ interest in information science and ICT. A total of 30 students (20 males and 10 females) not divided into sciences and liberal arts participated.1 The students participated in the programming exercise in groups of five because the number of MRs available was one less than the number of students. To explain how the robots are programmed, we used a slide presentation depicting how such programs are made. Before programming, the students were asked to solve the following practice problems:
1 In Japan, when students enter their second year of high school, they are asked to choose between sciences or liberal arts.
Figure 5 An example program of Figure 8 movement. “static int act” and “switch case” primitives are briefly are limited only to explaining the straight and turn functions. Other typical syntaxes, such as “branch” and “loop” primitives are considered as development learning, and are omitted here as this is a class meant for students with limited or no programming background.
Problem 1: Create a program that moves the robot 30 cm at a speed of 24. Problem 2: Create a program that makes the robot turn 180 degrees to the left, following a circle of radius 5 cm. After discussing the solutions to these problems, the students were divided into groups and were allowed to develop their own ideas for moving the robot. The students wrote programs and loaded them into the robots themselves. As the participants were first‐year high school students, we assumed a middle school level of information literacy.2 The students performed the task with significant within‐group discussion, and some groups developed programs for intricate robot behavior. Some students used video cameras to record their robots executing programs, indicating a significant interest in the learning task. Figure 6 shows images of students working on the task.
2 The information literacy level for first‐year high school students is high enough for them to surf the Internet and use word processors.
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Table 3 Methods and Class Designs in Experimental Programming Class Using Prepared Materials Suggested for First-Year High School Students Time (min)
Contents
Activities of students
0
Lecture on the robotics and ICT
Listen to a teacher’s instruction
10
Programming explanation and solving practice problems
Listen to a teacher’s instruction and consider problems
30
Program creation to move the robot
Editing the program and correcting errors
50
Embed the program in the robot and execute it
Implement the experiment and observe the robot’s motion
Points of evaluation Whether students understand computer control and robot systems (A, B, D) Whether students understand the structure of the program and use commands required for the program (A, B, C) Whether students create the program resulting in movement of the robot and whether they challenge the subject positively (A, B, D) Whether students analyze the extent of their knowledge and technology by watching other programs and motions of robots (A, C, D)
The methods and curriculum of our experimental programming class used prepared materials suggested for first-year high school students to interest them in the field of information science. Class time was limited to 1 h. First, we began with a slide presentation, giving a simple overview of the robot and explaining the programming process. Then, the students created and tested programs for moving the robot. Finally, we administered the post-instruction questionnaire. “A,” “B,” “C,” and “D” in parentheses denote “interests, concerns attitude,” “ability to understand,” “ability to express,” and “knowledge,” respectively.
Upon the students’ completion of the experiential programming class, we received feedback on the lesson and noted the comments made by the students on every item in the comments column at the end of the questionnaire. We discuss the results obtained from the questionnaire: Feedback – – – –
–“It was a pleasant lesson,” –“The robot is small and cute,” –“The program that moves the robot is simple,” –“It turned out that we tried to make a program just by imagining the motion of the robot in our head,” etc.
Comments – –“It moved well. I would like to know more about programming,” – –“I understood the procedure of program creation,” – –“Studying the core of a program was enjoyable,” – –“It turned out that every single word and number of an imperative sentence had meaning,” – –“The input of words in the imperative sentence was difficult,” etc. From these results, we conclude that the program’s aim for beginner programmers was achieved through this class in experiential programming. However, we received some feedback of the difficulty of editing the program because of many English typographical errors during the input of the imperative sentence. This problem is peculiar to Japanese individuals. So, the problem would be resolved in the future by using an interface in a tile type language.
Figure 6 Participants performing the learning task. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
The next section presents in detail a quantitative statistical analysis of the questionnaire.
QUESTIONNAIRE ITEMS AND RESULTS Thirty students participated in the learning activity described in the previous section, and each student completed a post‐instruction questionnaire. This section describes the questionnaire items and results. The questionnaire was designed to gauge student interest in information science to extract correlations with student interest in other major academic areas. Therefore, the questionnaire items were made to correspond to the content in the “Points of evaluation” column in Table 3, and a psychological measure was added to them to quantitatively analyze the data. Table 4 shows the questionnaire items and the number of respondents. For example, item A1 corresponds to the content shown at the bottom right of Table 3. Statistical analyses indicate a positive learner response to the class content. In particular, over 70% of the students answered “Strongly agree” or “Agree” for items A3 (Interest in programming) and A6 (Enjoyment of the class) shown in Table 4. Furthermore, over half of the students answered “Strongly agree” or “Agree” for item A7 (Increased interest in programming), indicating that the class was at least partially successful as an experiential class for programming. Conversely, there were few positive responses for item A8 (Increased interest in robots). We can therefore predict that the students regarded robots as just being a tool for learning programming. Next, we present a 3D bar chart in Figure 7 as a 3D diagram to derive correlation coefficients between items on the basis of the number of respondents for the items, for example, A1–A8 and B1. Table 5 shows correlation coefficients obtained from the results of the number of respondents for each item in Figure 7 with bidirectional significance at any significance level. We performed significance testing (non‐correlation testing) with the null hypothesis “the correlation coefficient is 0” and the alternative hypothesis “the correlation coefficient is not 0.” and in Table 5 denote significance levels at 1% and 5%, respectively. We found the meaningfulness of correlations between the class‐related items (p 0.01 except for A4–A7 relation) and the emergence of strong positive correlation coefficients from Table 5
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Table 4
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Questionnaire Items and Responses Response
Item Class‐related questions (A1) I understood the meaning of a “program” in today’s class (A2) I understood the meaning of “embedded system” in today’s class (A3) I am more interested in programming after today’s class (A4) I am more interested in robotics after today’s class (A5) I was able to create a program to move a robot (A6) I had fun during today’s class (A7) I want to learn more about programming (A8) I want to learn more about robotics Questions related to participants (B1) I like mathematics classes (B2) I like English classes (B3) I like Japanese classes (B4) I find computers hard to use
5 Strongly agree
4 Agree
3 No opinion
2 Disagree
1 Strongly disagree
Average
Standard deviation
7 (23.33%)
11 (36.67%)
8 (26.67%)
2 (6.67%)
2 (6.67%)
3.633
1.110
5 (16.67%)
12 (40.00%)
8 (26.67%)
2 (6.67%)
3 (10.00%)
3.467
1.147
7 (23.33%)
15 (50.00%)
5 (16.67%)
1 (3.33%)
2 (6.67%)
3.800
1.046
5 (16.67%)
13 (43.33%)
8 (26.67%)
2 (6.67%)
2 (6.67%)
3.567
1.055
8 (26.67%)
8 (26.67%)
7 (23.33%)
3 (10.00%)
4 (13.33%)
3.433
1.334
7 (23.33%)
15 (50.00%)
4 (13.33%)
2 (6.67%)
2 (6.67%)
3.767
1.086
7 (23.33%)
10 (33.33%)
6 (20.00%)
4 (13.33%)
3 (10.00%)
3.467
1.258
3 (10.00%)
11 (36.67%)
10 (33.33%)
3 (10.00%)
3 (10.00%)
3.267
1.093
2 (9.52%)
5 (23.81%)
7 (33.33%)
2 (9.52%)
5 (23.81%)
2.857
1.283
4 (19.05%) 4 (19.05%)
3 (14.29%) 9 (42.86%)
6 (28.57%) 6 (28.57%)
4 (19.05%) 1 (4.76%)
4 (19.05%) 1 (4.76%)
2.952 3.667
1.362 0.992
4 (19.05%)
1 (4.76%)
8 (38.10%)
5 (23.81%)
3 (14.29%)
2.905
1.269
Internet
Spreadsheets
Other
7 (33.33%)
0 (0.00%)
Educational materials 1 (4.76%)
Gaming (B5) What do you use computers for? (Check all that apply)
(B6) How often do you use computers? (Check the answer that is closest) (B7) Have you ever written a computer program before?
(B8) If you answered “yes” to question (B7), where/how did you write it? (Multiple answers OK)
9 (42.86%)
Word processing 1 (4.76%)
Almost every day 7 (33.33%)
Every 2–3 days
Once a week
Once a month
Only at school
3 (14.29%)
4 (19.05%)
3 (14.29%)
4 (19.05%)
Yes 6 (28.57%)
No 15 (71.43%)
As a school assignment
As a club activity
On my own, as a hobby
Other
3 (50.00%)
0 (0.00%)
3 (50.00%)
A friend or relative taught me 0 (0.00%)
0 (0.00%)
0 (0.00%)
Response options to questions A1–B4 are set in five steps, such as “Strongly agree” to “Strongly disagree” with weights 5–1. The figures and percentages in parentheses denote the number of answers and the ratio of one to the total answers, respectively.
(r 0.56 except for A4–A7 relation). These results are reasonable because there is generally a direct relationship between understanding and interest. Also, notably there is a significant correlation between the items related to the class and item B1 (Interest in mathematics; r 0.52, p 0.05), except for the A4–B1 relation. In particular, correlations between B1 and items A1 (Understanding of the content), A2 (Understanding of embedded systems), A3
(Interest in programming), and A7 (Increased interest in programming) are positively significant at the 1% level (r 0.58, p 0.01). These results suggest that students with a relatively high (low) interest in mathematics will show a relatively high (low) interest in the class as well as in programming. Figure 8 represents the significant correlation between the items: that is, one item group consists of items A1, A2, A3, A7, and A8, with which the
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Figure 7 The 3D bar charts (a–h) on the number of answers for the relation, for example, between A1–A8 and B1, respectively. The horizontal and vertical axes denote the frequencies of A1–A8, B1, and the number of respondents. The correlation coefficient between A1–A8 and B1 is calculated from each number of respondents in the figure below. Providing the number of respondents increases with an increase in the frequencies of items A1–A8 and B1, the correlation coefficient has a positive value.
PROGRAMMING INSTRUCTION USING A MICRO ROBOT
Table 5
Correlation Coefficients Between All Items (N ¼ 21) Q (A1)
Q Q Q Q Q Q Q Q Q Q Q Q Q
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Q (A2)
Q (A3)
Q (A4)
Q (A5)
Q (A6)
Q (A7)
Q (A8)
Q (B1)
Q (B2) Q (B3)
Q (B4)
Q (B6)
(A1) (A2) 0.9062 (A3) 0.7079 0.8221 (A4) 0.5906 0.7310 0.8391 (A5) 0.7599 0.8083 0.6764 0.7036 (A6) 0.7615 0.8923 0.8846 0.7970 0.8180 (A7) 0.8242 0.7751 0.7270 0.4433 0.5644 0.6962 (A8) 0.7321 0.8136 0.7490 0.7220 0.5967 0.7099 0.7233 (B1) 0.6339 0.6239 0.6605 0.4034 0.5358 0.5241 0.7415 0.5818 (B2) 0.2548 0.3660 0.0086 0.1967 0.1907 0.0686 0.0791 0.3994 0.1051 (B3) 0.0765 0.0292 0.1318 0.0641 0.0616 0.1318 0.0844 0.0446 0.0374 0.4817 (B4) 0.2427 0.0016 0.2521 0.3471 0.0602 0.0699 0.0943 0.2642 0.0501 0.2729 0.3153 (B6) 0.3991 0.2906 0.0462 0.1168 0.3023 0.1155 0.3196 0.0793 0.2657 0.0988 0.0317 0.4809
Double asterisk ( ) and single asterisk ( ) indicate bidirectional significance at 1% and 5% by significant testing (non-correlation testing) with the null hypothesis “the correlation coefficient is 0” and alternative hypothesis “the correlation is not 0,” respectively.
correlation is significant at the 1% level, and the group has a relation with item B1. For other items, A5 and A6 correlate with B1 at the 5% level, but A4 has no relation with B1.
CONCLUSION A new set of curriculum guidelines for Japanese elementary, middle, and high schools will be effective from April 2013 (stepwise, in the case of high schools). We proposed a class for the middle school unit, called “Measurement and control through computer programs,” and for the high school unit, called “Development of scientific understanding and thinking with regards to information” that are inexpensive, durable, and easily maintained learning materials. We conducted the class in a way that we believe will result in increased student interest and will stimulate students’ curiosity concerning embedded systems‐one of the foundations of ICT. The class uses robots, a focus of interest for many children, and allows students to experience the process of creating programs to control them and load programs onto their controller boards. Participating students were able to practice needs
Figure 8 Relational diagram of items with significance levels represented at and indicated in Table 5. The blue and red circles denote significance levels with (p 0.01) and (p 0.05) for correlation coefficients between each item included in these circles. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
assessment, algorithm creation, and programming, thereby gaining experience related to aspects of the new guidelines that call for learning an information‐processing approach to problem analysis, systematic development of solutions to problems, consideration of results, and iterative refinement method. In the implementation of the proposed class with a mixed group of 30 students from technical and non‐technical backgrounds, time constraints did not facilitate adequate opportunity for iterative design, such as the development of new actions or the fine‐ tuning of programs, as well as information sharing and integration with other groups. On administering a post‐instruction questionnaire, we found that over 70% of students indicated a positive response to the contents of the curriculum. From the number of answers on B1–B3 in Table 4, we predict that the students who were in favor of Japanese classes will probably take up liberal arts rather than the sciences. Therefore, we find that use of the robot in programming classes is helpful in encouraging students to have an interest in information science and ICT, including not only those who will go into the sciences but also liberal arts, and furthermore those who have seldom used computers. We believe that the majority of the students indicated an interest in the class, despite programming being a subject that is typically difficult for newcomers, because an actual robot was the subject of the programming. Correlations between participants’ academic interests and interest in programming—the relation between A3 and B1—indicate that students with a relatively high (low) interest in mathematics tended to have a relatively high (low) interest in programming (r 0.66, p 0.01). Nonetheless, many students with no interest in mathematics found the class interesting, according to the students’ feedback and comments, suggesting that further refinement of class methods and content may result in increased interest in programming for non‐technically oriented students. We also believe that the teaching materials introduced here would be useful for teachers of ICT throughout the world. The questionnaire results show that the class contents attracted the students’ interest. However, because the present programming environment has a Character User Interface (CUI), the students, especially the beginners, took substantially longer to make the control program. As a result, the amount of time that the students required to move the MR decreased, and the students might not have been able to experience a sufficient sense of accomplishment. Therefore, to solve this problem, we came up with the idea that movement of the MR should be controlled
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through use of the Graphical User Interface (GUI)‐based programming environment (e.g., Scratch [20]) (now in progress). Of course, as this idea is not a unique solution to the problem, many other possibilities should be discussed. This article is not only a practical report that addresses how to set up instructional tools for an embedded system but also a proposal related to programming instruction. We hope to expand this research to elementary and middle school students, and use the results as feedback for the further development of educational materials that will result in students’ increased interest in information science and advance their understanding of information processing.
ACKNOWLEDGMENTS This study was supported by Grant‐in‐Aid for Scientific Research (C‐21500962, C‐24501221) and the Fukui University of Technology, to whom we express our gratitude.
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BIOGRAPHY Teruya Yamanishi received his MS degree in educations of science from Kobe University in 1991, and the PhD degree in physics from Kobe University in 1994. He is a Professor at Fukui University of Technology where he studies mathematical information science for the brain, and develops optimization tools on behaviors of the autonomous robots.
Kazumasa Ohkuma received his BS degree in education from Kagawa University in 1996, his MS degree in Arts from University of Tokushima in 1998, and his PhD in Science from Kobe University in 2001. He is now an associate professor at Fukui University of Technology. His research interests include science education, information science, and theoretical physics.
Kazutomi Sugihara is an associate professor at Fukui University of Technology. He received his PhD in engineering from Osaka University. His research interests include data analysis based on the concept of Rough sets, and operational research and its applications to real issues.
Katsuji Uosaki received his BS, MS, and PhD degrees, all in applied physics, from Osaka University, in 1965, 1967, and 1972, respectively. He was a professor at Faculty of Engineering, Tottori University, and Graduate School of Information Science and Technology, Osaka University, and now is a visiting professor at Fukui University of Technology. His research interests include computational intelligence, stochastic optimization, and estimation and identification of stochastic systems.
