Evaluation of The Skill Succession Support System by The
ARCS Model in Fisheries High Schools
Tsukasa KATO
1,2, Itaru NAGAYAMA
11 University of The Ryukyus, Japan 2 Okinawa Fisheries High School, Japan
Abstract
In 2019, the authors proposed an industry-school education skill succession process, and used it to develop a learning system for self-study of skills1). In this study, we evaluated the appeal of the teaching materials in this system using the ARCS model from the perspective of learning motivation and searched for learning motivation factors. We used a teaching material evaluation sheet based on the ARCS motivation model to conduct a survey of instructors and students at a fisheries high school after using the skill succession system. From the results, we obtained many positive answers with all ARCS model factors and confirmed a high degree of usefulness for instructors and students. In addition, based on the results of the factor analysis, we confirmed that in this system, instructors focus on the factors of “Confidence” and “Attention,” and students on the factors of “Satisfaction” and “Relevance.” As such, we confirmed that learning motivation factors differ between instructors and students.
Keywords : Skill Succession Support System, ARCS Model, Learning Motivation Factors, Factor Analysis
1. INTRODUCTION
Fisheries and marine high schools (hereafter, fisheries high schools) have been producing engineers in various fields through specialized education in maritime skill. However, the recent decrease in education budgets and retirement of highly skilled experts may pose obstacles to the transfer of skills to replacement instructors and students requiring skills training. The problem of skill succession in various industries was a topic of discussion in 2007 alongside the retirement of a large number of baby boomers. It reemerged in 2012, as many retired experts were no longer eligible for re-employment. This has been particularly felt in the manufacturing sector, which has proceeded to systematize skill succession processes. As far as we know, no vocational high schools are working to systematize processes for skill succession. Therefore, the authors proposed an industry-school education skill succession (I-SSS) connecting the skill succession processes for school education and industry through a learning system modeled on fisheries high schools (Fig.1)1). This process is characterized by linking skill succession in educational and industrial settings through a skill succession support system (hereafter, the system). The system is designed from the perspective of self-training in the workplace and of training successors in the industrial field; thus, it is possible to support skill succession in after-graduate industrial settings.
Skills are considered to be passed on when they are at the same level as those of an expert2). Consequently, continuous autonomous learning is sought for the transfer of skills. Intervention experiments conducted by the authors confirmed that learner autonomy improves as a result of using the
system1). However, often the novelty of e-learning teaching materials like this system motivates learning. Therefore, forms of motivation other than novelty are needed to modify learning environments to ensure the system is used continuously. Thus, we think it is required to evaluate the appeal of this system as an e-learning teaching material by surveying the motivation for learners..
The purpose of this study is to evaluate the system from the perspective of the instructional design theory of learning motivation, and to search for and identify learning motivation factors.
2. RELATED WORK
In this section, we outline the past studies on skill succession systems in industrial and educational settings. In industrial setting, these include the proposal of an OJT system utilizing remote learning from skilled engineers and moveable arm robots for transferring skills related to machine tool operating methods3), and the proposal of skills learning combining training using a VR system combining three-dimensional stereoscopic devices and force display devices with remote learning from skilled engineers4). Furthermore, learning support systems for transferring skills for the maintenance of large-scale machines have been tested and evaluated5), and an e-learning system to support skills learning in small-to-mid-sized manufacturing has been developed6). In educational settings, steps have been taken in school nursing programs to develop e-learning teaching materials incorporating visual aids into skills training, as well as imaging target subjects to verify the degree of skills acquisition7). Thus, there have been many studies on skill succession in industrial settings, but only a few in educational settings. These studies on skill succession focus on system development, but there are Correspondence to Tsukasa Kato, Graduate School of Engineering and Science,
University of the Ryukyus,1,Sembaru,Nakagami Gun Nishihara Cho,Okinawa 903-0219, Japan; E-mail: katouts@open.ed.jp
Fig.1 Industry-School Education Skill Succession Process
Fig.3 Screen layout of this system “Fisheries and marine skill training”
no examples of evaluation from an instructional design perspective.
Against this backdrop, the authors developed and verified a skill succession system modeled on fisheries high schools. Studies on skill succession focus on system development, but there are no examples of evaluation from the learning motivation perspective.
3. SKILL SUCCESSION SUPPORT SYSTEM
In this section, we explain the outline of our previous study on the development of the skill succession system1)
The URL of this system is shown below.
<http://www.okisui-h.open.ed.jp/suisan-jissyuu/>
3.1 System configuration
This system consists of two parts, “Teaching Material Module” and “User Interface Module” (Fig. 2).
The “Teaching Material Module” consists of thumbnail, technique name, the outline of a technique, learning targets, and video contents. The structure is a tree structure and is classified into three categories: major category (Major Category), medium category (Medium Category), and video contents. In addition, the display order of the medium category and each technique video is arranged by ascending order of difficulty to acquire. The “User Interface Module” acquires the screen information and video information of the classification node selected by the user to generate HTML data and display it in the browser.
3.2
Screen configuration
The screen layout of this system is shown in Fig.3. We summarize each training item on the main screen. The main menu is located on the menu bar at the top of the screen, and sub-menus are located on a side bar to the left of the screen. Major categories are arranged on the main menu, and middle categories are arranged on the sub-menus. Web pages are prepared for each major and middle category item, each containing the names of major categories and explanatory text summarizing the techniques. On the pages for middle category
items that can be selected from the sub-menus, thumbnails of
relevant techniques videos, the names of techniques, and summary explanatory text appears below the names of middle categories and summary explanations of techniques. Furthermore, replay buttons are present. Selecting the replay button replays the technique video. In addition, this system is designed so that videos can be replayed in a browser, meaning they can be viewed regardless of whether a terminal has a video player. Furthermore, the top page is designed with thumbnail images of each fishery and maritime engineering
Fig.4 Fonts and images are resized to adjust to the screen size of various devices.
Fig.5 Quantification of skills
item together with major category names. Unnecessary text or images have been omitted, making it easy to get to a desired item quickly and avoid incorrect selections. The Twitter Bootstrap used in this system is a responsive web design (Fig.4) in which designs automatically change to suit various devices. Fonts and images are re-sized to adjust to the screen size of a particular device, making it easier to select items.
3.3 Development of the technique video
In this section, we introduce our previous study on the development of technique video in 20198). First, we extracted skills from skilled teachers and retired teachers, and then edited technique videos. In extracting skills, we recorded their movements and sounds. Then we interviewed them and recorded work tools and work results. The extracted data was edited to focus on their hand movements and insert animation. We also displayed the numerical information to express the techniques more detail (Fig.5).
The technique video is constituted of three parts: “Introduction part,” “Knowledge development part,” and “Knowledge review part.” In “Introduction part,” guidance of the techniques is presented, and the purpose of the video is conveyed. In “Knowledge development part,” the knowledge of procedures and notes are provided. In “Knowledge review part,” the knowledge is reviewed. In the video, it is displayed as a review screen that summarizes the technical points. Therefore, learners can check their skills (Fig.6). We edited using the editing software Adobe Premiere Pro CS5.5.
3.4
Types of video content
Types of video content are categorized as follows with reference to the past ten years of educational information and textbooks.
① Knot information: Rope work is categorized as knotting, tying, and braiding. These are basic techniques learned in fisheries training.
② Splicing information: Splicing refers to technique in making loops (eyes) at the ends of rope, and knitting rope for joining.
③ Net-making information: Contains information on learning how to make fishing nets. Includes learning two
net-making methods (Reef-knot and Sheet-bend), net repair methods, and how to braid a fisheries buoy. ④ Fishing gear production information: Contains information on learning techniques to make bait logs and other contrivances.
⑤ Marine training information: Contains information on learning the preparation, procedures, and methods when implementing knowledge and techniques learned in classroom lessons and lectures at sea.
4. EVALUATION AND ANALYSIS METHODS
4.1
About ARCS models
The ARCS motivation model proposed in 1983 by John M Keller takes its name from the first letter of the four learning motivation factors “Attention,” “Relevance,” “Confidence,” and “Satisfaction.”9) “Attention” is defined as the factor by which interest is attracted. This factor has “Perceptual Arousal,” “Inquiry Arousal” and “Variability” as subclass. “Relevance” as the factor by which the needs and objectives acting on positive attitudes among learners are fulfilled. This factor has “Goal Orientation”, “Motive Matching” and “Familiarity” as subclass. “Confidence” as the factor aiding in learning and feeling confident about and truly experiencing success. This factor has “Learning Requirements”, “Success Opportunities” and “Personal Control” as subclass. “Satisfaction” as the factor by which achievement is
enhanced through intrinsic and extrinsic rewards. This factor has “Intrinsic Reinforcement”, “Extrinsic evaluation”
and “Equity” as subclass8). This model was developed as a
framework for making teaching materials appealing without relying on novelty, and has gained attention as a classroom design model10). In addition, it has proven effective in designing and developing appealing teaching materials to systematically address learners’ desires. As such, it has been utilized as a means for evaluating e-learning teaching materials according to the above mentioned four factors11),12).
This system was designed for the systematic learning of fishery and maritime engineering to support skill succession but was not designed based on the ARCS model. However, since the system and video content configuration was developed based on classroom design including lesson planning and instructor guides, we consider the four factors of the ARCS model intrinsic to this system.
In this study, we evaluate the system based on the above four factors, and search for the factors that affect learning motivation.
We used 15 items from the “ARCS motivation model-based teaching materials evaluation sheet”12)13) (hereafter, teaching material evaluation sheet) developed by Kogo for the evaluation, and created the six-answer questionnaire shown in Table 1. The reason for the six cases is to avoid respondents answering “Neither.” The ARCS model was originally proposed as an index of appealing classroom design, but Kogo developed the teaching materials evaluation sheet assuming that “it is possible to regard the teaching materials guiding classes and intended for self-study as classrooms themselves.”13) Accordingly, this system serves as
teaching materials for self-study through an industry-school education skill succession process. Therefore, we believe it is valid to evaluate the system using the teaching materials evaluation sheet.
4.2 Analysis method
We used the teaching materials evaluation sheet to survey high school instructors (hereafter, instructors) and fisheries high school students (hereafter, students) to search the learning motivation factors when using this system. We targeted second grade about fisheries high school student. The reason is that the second grade in many schools in begin to take specialized courses.
The class design using this system is the following. First, the instructor explained only how to use this system after distributing the tablet device to students at the beginning of class. And then, the target instructors and students used this system within 50 minutes of class time in one specialized subject “Fisheries practical training” class, and completed the teaching material evaluation sheet immediately after use. The survey was taken place from January 20 to March 10, 2017, and the teaching materials evaluation sheet was sent to fisheries high schools throughout Japan. The responses were anonymous, and we clearly indicated that participation would involve no sharing of information or disadvantages to participants. We received answers from 78 instructors at 21 schools, and 276 students at 14 schools. Excluding those with missing answers or ceiling and floor effects, we analyzed 67 instructors and 220 students. Furthermore, statistical processing for the analysis in this study was performed using Excel statistical analysis software (Social Survey Research Information Co.).
The analysis method involved tallying the evaluation results of instructors and students, after that the percentages of positive and negative answers were examined. This enabled analyzing the evaluation results for the four factors in the ARCS model and for each question item. Next, we conducted a factor analysis, and searched for ARCS model factors intrinsic to the system. We then determined the effects of these factors on each question item for instructors and students. This made it possible for instructors and students to use the system and identify the learning motivation factors during learning.
5. ANALYSIS RESULTS
5.1 Verification of the reliability of the questionnaire
About the reliability of the questionnaire, the Cronbach's coefficient α (hereinafter referred to as α) of the questionnaire to the instructor was 0.936, and it was 0.929 to 0.935 for each item of the questionnaire. The Item-Total (hereinafter referred to as I-T) correlation was 0.592 to 0.761. The α of the questionnaire to the students was 0.948. For each item of the questionnaire, it was 0.943 to 0.947. The I–T correlation was 0.617 to 0.794. A certain level of reliability was confirmed because the questionnaires given to instructors and students and α for each item showed a sufficient value of 0.9 or more. Table 1 ARCS motivation model-based teaching materials
evaluation sheet No Contents Classification Q1 Freshness Attention Q2 Piqued interest Q3 Variety Q4 Looked interesting Q5 Feel an affinity Relevance Q6 Voluntary
Q7 Enjoy the process Q8 Worthwhile Q9 Goal was clear
Confidence Q10 Steady progress Q11 Controllable Q12 Had confidence Q13 Satisfactory Satisfaction Q14 Had fun Q15 Mastered Choices
1. Strongly agree 2. Mostly agree 3. Somewhat agree 4. Do not agree much 5. Mostly disagree 6. Completely disagree
0% 20% 40% 60% 80% 100% Q15 Q14 Q13 Q12 Q11 Q10 Q9 Q8 Q7 Q6 Q5 Q4 Q3 Q2 Q1 Instructors n=67
Strongly agree Mostly agree Somewhat agree Do not agree much Mostly disagree Completely disagree
0% 20% 40% 60% 80% 100% Q15 Q14 Q13 Q12 Q11 Q10 Q9 Q8 Q7 Q6 Q5 Q4 Q3 Q2 Q1 Students n=220
Strongly agree Mostly agree Somewhat agree Do not agree much Mostly disagree Completely disagree
Fig.7 Questionnaire tally results
25.9% 24.6% 29.9% 25.7% 35.3% 36.9% 32.8% 34.3% 29.9% 27.2% 28.0% 28.4% 7.5% 8.6% 6.3% 10.4% 0.5% 1.9% 1.9% 1.1% 1.0% 0.7% 1.1% 0% 20% 40% 60% 80% 100% Satisfaction Confidence Relevance Attention Instructors n=67
Strongly agree Mostly agree Somewhat agree Do not really agree Mostly disagree Completely disagree
31.9% 21.9% 20.1% 28.4% 25.7% 27.2% 27.5% 25.7% 26.6% 34.1% 35.0% 33.3% 8.8% 11.5% 13.2% 9.3% 4.3% 3.4% 2.6% 2.2% 2.8% 1.9% 1.6% 1.1% 0% 20% 40% 60% 80% 100% Satisfaction Confidence Relevance Attention Students n=220
Strongly agree Mostly agree Somewhat agree
Do not really agree Mostly disagree Completely disagree
Fig.8 Results for the four ARCS model factors 5.2
Results of evaluation questionnaires
Fig.7 shows the questionnaire tally results for the evaluation of this system according to the ARCS motivation model, and Fig.8 the results according to the four ARCS factors. In total, 29 (43.3%) instructors answered “Strongly agree” in response to question Q9 (“Goal was clear”), and 49 (73.1%) answered “Mostly agree” or less for Q9 (“Goal was clear”) and Q4 (“Looked interesting”). In contrast, only 9 (13.4%) instructors answered “Strongly agree” in response to Q12 (“Had confidence”), and 30 (44.8%) answered “Mostly agree” or less to Q12 (“Had confidence”). In addition, hardly any instructors (0 or 1) “Completely disagreed,” although 12 (17.9%) answered “Do not really agree” or less in response to Q12 (“Had confidence”). Then, 80% or more gave positive answers of “Strongly agree,” “Mostly agree,” or “Somewhat agree” in response to all question items.
Regarding students, 87 (39.5%) answered “Strongly agree” in response to Q4 (“Looked interesting”), and 136 (61.8%) answered “Mostly agree” in response to Q4 (“Looked interesting”) and Q8 (“Worthwhile”). In contrast, only 30 (13.6%) students answered “Strongly agree” in response to Q6 (“Voluntary”).
Furthermore, 88 students (40.0%) answered “Mostly agree” or less to Q6 (“Voluntary”), and 11 (5.0%) students gave the negative answer “Completely disagree” in response to Q15
(“Mastered”). In addition, the positive answers “Strong agree,” “Mostly agree,” and “Somewhat agree” constituted 75% or more of responses to all question items.
Summarized according to the four ARCS model factors, for instructors, the most frequent response for “Relevance” was “Strongly agree” at 29.9%. Looking at positive answers including “Mostly agree” and “Somewhat agree,” “Satisfaction” got the highest percentage at 91.1%, followed by “Relevance” at 90.7%. For students, the most frequent response for “Satisfaction” was “Strongly agree” at 31.9%. However, for positive answers including “Mostly agree” and “Somewhat agree,” “Attention” got the highest percentage at 87.4%, followed by “Satisfaction” at 84.2%. The most frequent response of instructors to “Goal was clear,” “Piqued interest,” and “Looked interesting” was “Strongly agree.” Positive answers constituted many responses to “Satisfaction” and “Relevance” in the ARCS model. Most students answered “Strongly agree” in response to “Looked interesting” and “Had fun.” Using the ARCS model, positive answers comprised most responses for all factors, and were evident for “Attention” and “Satisfaction.” Based on these results, in this system, the learning motivation factors were “Relevance” and “Satisfaction” for instructors, and “Attention” and “Satisfaction” for students.
Table 2 Results of the factor analysis Instructors n=67 Students n=220 Items 1st Factor (Confidence) Items 2nd Factor (Attention) Items 1st Factor (Satisfaction) Items 2nd Factor (Relevance) Q11 C 0.9179 Q2 A 0.9459 Q14 S 0.8743 Q6 R 0.8181 Q6 R 0.7172 Q14 S 0.7876 Q13 S 0.7992 Q5 R 0.7820 Q10 C 0.7075 Q4 A 0.7452 Q15 S 0.6671 Q7 R 0.6177 Q12 C 0.6947 Q3 A 0.7339 Q12 C 0.6584 Q10 C 0.6113 Q9 C 0.6698 Q1 A 0.5167 Q4 A 0.6139 Q3 A 0.5923 Q5 R 0.5574 Q8 R 0.4810 Q8 R 0.5956 Q2 A 0.5232 Q15 S 0.5274 Q7 R 0.4333 Q9 C 0.4886 Q1 A 0.4910 Q13 S 0.4947 Q13 S 0.3485 Q2 A 0.3741 Q11 C 0.4548 Q7 R 0.3783 Q15 S 0.3233 Q1 A 0.2742 Q4 A 0.2702 Q8 R 0.3222 Q5 R 0.2902 Q11 C 0.2281 Q9 C 0.2399 Q1 A 0.1634 Q6 R 0.1067 Q7 R 0.2177 Q8 R 0.1846 Q4 A 0.1097 Q10 C 0.0926 Q3 A 0.2147 Q12 C 0.1531 Q14 S -0.0024 Q9 C 0.0275 Q10 C 0.2036 Q13 S 0.0776 Q3 A -0.0557 Q12 C -0.0137 Q5 R 0.0260 Q15 S 0.0667 Q2 A -0.0568 Q11 C -0.1362 Q6 R -0.0280 Q14 S 0.0021
A: Attention, R: Relevance, C: Confidence, S: Satisfaction
viewpoints of the system differed, and confirmed that factors serving as learning motivation for teaching materials are intrinsic to this system.
5.3
Results of the factor analysis
We performed a factor analysis based on the responses to each question item on the teaching materials evaluation sheet to identify instructors and students’ different learning motivation factors as indicated in the questionnaire results provided in section 5.2. The factor analysis involved a maximum likelihood estimation and Promax rotation, and enabled maximized factor loading on each axis. In searching for factors, we used an eigenvalue 1.0 or more as a reference for instructors and 0.6 or more for students and assumed two factors from the value of eigenvalue differences for the cumulative contribution ratio and number of factors. The cumulative contribution ratio of these two factors was 58.06% for instructors and 59.75% for students. Here, we focused on items with a factor loading of 0.6 or more to identify factors. Table 2 provides the results. The factor loading for Q11, Q6, Q10, Q12, and Q9 was large for the first factor for instructors. This comprises all subordinate categories of “Confidence” other than Q6. Q6 (“Voluntary”) is a subordinate category of “Relevance,” but the implication of Q11 (“Controllable”), which is a subordinate category of “Confidence,” is close, so it is considered possible that it was selected. Accordingly, we assumed “Confidence” for this factor. For the second factor for instructors, the factor loading for Q2, Q14, Q4, and Q3 was large. This comprises all subordinate categories of “Satisfaction” other than Q14. It was considered that Q14 (“Had fun”), similar to Q2 (“Piqued interest”) and Q4 (“Looked interesting”), may have been selected as “Attention.” Thus, the second factor for instructors was “Attention.” Next, for the first factor for students, the factor loading for Q14, Q13, Q15, Q12, and Q4 was large. This comprises all subcategories of “Satisfaction” other than Q12 and Q4. It was considered that
Q12 (“Had confidence”) and Q4 (“Looked interesting”) were selected as reasons for “Satisfaction,” so we assumed this factor to be “Satisfaction.” For the second factor for students, the factor loading for Q6, Q5, Q7, and Q10 was large. This comprises all subordinate categories of “Relevance” other than Q10. Q10 (“Steady progress”) is a subordinate category of “Confidence,” but based on the fact that “Relevance” satisfies learners’ need to achieve objectives and that understanding Q10 (“Steady progress”) is a means to achieve objectives, it is considered to be selected as “Relevance.” Thus, we assumed the second factor for students to be “Relevance.”
Based on the above, when using this system, we found that instructors focused on the factors of “Confidence” and “Attention” in the ARCS model, and students focused on the factors of “Satisfaction” and “Relevance.”
6. DISCUSSION
Based on the results of the analysis, in evaluating this system according to the ARCS motivation model, instructors positively evaluated the system for ARCS motivation factors and all elements including subordinate factors, particularly “Relevance” and “Satisfaction.” Students positively evaluated the system for ARCS motivation factors and all elements including subordinate factors, particularly “Attention” and “Satisfaction.” The results indicated that even if the factor of “Satisfaction” was shared, it differed from the factors instructors and students considered the most positive (“Relevance” and “Attention,” respectively), and there are clear differences in learning motivation regarding the system. Thus, instructors and students differed in the importance of each learning motivation factor, and we found that they both positively evaluated all four ARCS factors. This confirmed that the system is appealing as teaching material. In particular, instructors gave more positive answers than students, and since
the negative answers “Completely disagree” and “Mostly disagree” were hardly evident, we confirmed that the system was evaluated as a useful teaching material.
Based on the results of the factor analysis, for instructors, the first factor was “Confidence” and the second “Attention.” For students, the first factor was “Satisfaction” and the second “Relevance.” This clarified differences in learning motivation factors when instructors and students use this system.
Furthermore, for instructors, the first factor “Confidence” is a factor in learning requirements, and the degree of control and learning assistance from teaching materials. Therefore, we think that instructors judge their effects when introducing the system. The second factor “Attention” is used to arouse interest; thus, we think that instructors judge students’ aroused interest by using this system. We found that instructors focus on the learning effects on students and their aroused interest and evaluate the system from a teaching perspective. For students, the first factor “Satisfaction” provides a sense of achievement from learning, meaning students feel a sense of achievement from using this system. In addition, we found that the second factor “Relevance” satisfies learners’ personal reasons, and therefore, they judge the personal usefulness of this system. Furthermore, we found that students focus on the sense of satisfaction and personal usefulness of the system and evaluate it from a learning perspective.
Since this system is used as teaching material for self-study in which learning involves changing the environment from school to industrial settings, developers are required to design teaching materials that arouse learning motivation from the perspective of learners. Thus, based on the results of the analysis, we believe it is necessary to device the design based on the ARCS model factors of “Satisfaction” and “Relevance,” which students focus on. And when we observed the students using the system, they showed more interest in the practicality of the video than the functionality of the system. Therefore, we consider that it is important to design the video that make a commit of skill acquisition. As a method, we consider that the learner will get a more positive impression of the skill acquisition by presenting concrete images of the product, detailed procedures, and using friendly words. Also, by inserting an easy-to-understand explanation about the relationship between skills and qualifications/work into web pages and videos, we consider that students will be able to increase their career awareness. And, if it is used as a self-training teaching material at a place away from home or out of school such as a training ship, it will be useful for learning techniques faster. In addition, graduates can use this system in the workplace to review the techniques. In this way, this system can help students become a future technician.
One issue with this study was the wording for question items in the teaching materials evaluation sheet. Some student respondents reported that the “questions were difficult to understand,” and some students may have responded with a different meaning from desired meaning. Thus, when using the teaching material evaluation sheet, it is necessary to give concreteness to the wording so that the meaning does not
change significantly and it is not inductive question.
The followings are future research issues. Since this system is expected to be used in industrial sites, it is necessary to evaluate from the viewpoint of skill succession in the industrial field. In addition, since students will be successors in the industry, it will be also necessary to research the transformation of students' views of the profession by this system.
Reference
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Aknowlegment
We would like to offer our deepest thanks to all relevant personnel and students at the prefectural fisheries high schools who participated and cooperated in the survey
Tsukasa Kato: He is a teacher at Okinawa Prefectural
Fisheries High School and is also enrolled in the PhD program at the Faculty of Engineering, University of the Ryukyus. The Japan Society Fisheries Science member
Itaru Nagayama: He is an associate professor at the
Faculty of Engineering, University of the Ryukyus. Specialties are image information processing, evolutionary artificial intelligence, and intelligent control technology. The Institute of Electrical Engineers of Japan member.
Date received May 4, 2019 Date revised Nov. 11, 2020 Date accepted Dec. 12, 2020
