Chapter 6: Life and Computational Thinking

6.1 Thinking from Personal Experiences

 As described in detail in Chapter 2, the Computational Thinking Colloquium was held six times at Future University Hakodate as a first step toward creating a book. The title of my second talk was “Thinking about Computational Thinking from Personal Experience” on May 12, 2017. In this chapter, I’d like to start with that talk and introduce some of the things I’ve been thinking about since then. Let’s do computational thinking, which computer scientists and a few others consciously enjoy, for all.

 Two experiences represent my idea of computational thinking: the programming language LOGO, which appeared in Chapter 3, and the other is cooking. First, Eiichi Osawa, a colleague of mine at Future University, introduced computational thinking in a column on the website of the Information Processing Society of Japan (IPSJ) in January 2014 as “Encouragement of ‘Computational Thinking,'” and then Hideyuki Nakashima, the editor of the book and former president of Future University, translated Jeannette Wing’s essay and introduced it in the IPSJ Journal in June 2015. Then, in June 2015, the book’s editor and former president of Future University, Hideyuki Nakajima, translated Janet Wing’s essay and introduced it in the Journal of the Information Processing Society of Japan.

 Wing’s ideas have impacted a wide range of educational circles. For example, in the United States and Europe, computational thinking has been discussed in K-12 education, and many books, textbooks, and curricula have been developed and implemented. On the other hand, the term “programmatic thinking” has become widespread in Japan, and it has been confused with computational thinking.

 Future University is home to researchers in computer science and a variety of other fields such as cognitive science, mathematical science, artificial intelligence, cognitive psychology, educational technology, design, and communication, and our educational and research activities have an interdisciplinary orientation. Some people think they understand and practice computational thinking, people who think they do but do not, and people who think it is irrelevant. In the “Computational Thinking Colloquium,” we will not only introduce the computational thinking discussed in Europe and the United States but also reexamine what computational thinking is in the first place. We thought that the process of thinking and creating a book together, to publish it from the Future University Press, would be meaningful in itself.

 To make this publishing project a success, we thought it was necessary to do it with the concept of Sampo Yoshi. The colloquium presenters could organize their thoughts. The audience can learn new things, be inspired to think and participate in discussions. And most importantly, people who have read the book will be glad to know about it.

 First, here is the definition that Wing gave at a lecture I attended at the University of Tokyo in 2017. As the discussion has expanded, she, the proponent of computational thinking, has been gradually changing the definition.

Computational thinking is the thought process involved in formulating a problem and expressing its solution in a way that can be effectively executed by a computer (human or machine).

Wynn emphasizes four things in computational thinking.

 First, computer science is different from mathematics and other forms of engineering. First, computer science is different from mathematics or engineering in that there is software, and software can do many things.

 Second, the characteristic ideas of computational thinking are algorithms and data structures, models and simulations, and abstraction.

 Third, computational thinking is practical beyond computer science in economics, law, life sciences, archaeology, journalism, humanities, and social sciences.

 Fourth, the weakness of computational thinking is that it is difficult for non-computer science majors to understand.

 The fourth one is particularly interesting. Wing herself is aware that it is difficult for people in other fields to understand. I wonder if this is because she has seen many examples of computational thinking being misunderstood in the education of computational thinking that is being provided in many places. In the previous lecture, she said she only advocates and promotes computational thinking but does not participate in education. Instead, she noted that it was something that educators should think about and not interfere.

 As I have repeatedly explained in this book, the problem-solving process using computational thinking has the following characteristics.

 Organizing data analytically and logically. Modeling, abstracting and simulating the data. Formalizing the problem so that a computer can handle it. Identify, test, and implement possible solutions. Automate the solution using algorithmic thinking. Then generalize and apply this process to other problems.

6.2 Originated in My Junior High School Days

 Computational thinking is something that I am very familiar with. This is not only because I majored in computer science at university, but also because it started before that.

 I came from an integrated junior high and high school for girls, and was a member of the mathematics club. Despite the fact that both junior high and high school students were involved in the club, there were only a few members at that time, and I remember that I became the head of the club in my third year of junior high school. I remember that I became the head of the club when I was in the third year of junior high school. I was recruiting juniors during the school festival because I knew that if no new members joined the club in the future, it might be closed. When I asked my former teacher at the alumni reunion the other day, I was very disappointed to hear that the math club was closed shortly thereafter.

 In those days, we used to solve mathematical puzzles, make our own puzzles, write scripts that included trigonometry and infinity, make puppets and put on puppet shows, read programming texts written in English on the small Olivetti computers in the science and mathematics teachers’ rooms, and make little games. I also read programming texts in English on the small Olivetti computers that were in the rooms of the science and mathematics teachers, and made little games. At that time, I liked to read about mathematical puzzles and games written by Martin Gardner, a mathematician and author, which was serialized in “Nikkei Science” (Japanese edition of Scientific American magazine), “Bessatsu aha! and Charles L. Dodgson, the mathematician and author of Alice in Wonderland; mathematical puzzle books such as The Pillow Book of Problems by Charles L. Dodgson, the mathematician and author of Alice in Wonderland; and logical puzzle books such as Sophistical Logic by Akihiro Nozaki, the Japanese mathematician. What emerges is a picture of junior high and high school students who enjoy mathematical and logical thinking.

 When I was a freshman in high school, my club advisor took us on a tour of IBM Japan’s headquarters, which was located near the school, and it was the biggest turning point in my life, leading me from mathematics to computer science. I still have the admission ticket from that time. I still have the admission ticket from that time, with the date “Feb. 08, 1977” engraved on it.

 After that, I entered the University of Electro-Communications, the only university in Japan with a computer science department at that time. The club activity was contract bridge, which is a favorite game among computer scientists. From first-year students to graduate students and even assistants and professors, we would gather at lunchtime and after school to play bridge while making small talk.

 When I was in my third year of college, I was offered a part-time job as a programmer. The job was to port LOGO, an educational programming language developed at the Massachusetts Institute of Technology (MIT), to run on Japanese computers. At the time, LOGO was running on an Apple IIe 8-bit machine. LOGO was a list-processing language similar to LISP, unlike Pascal and FORTRAN, which I had used before. When I met LOGO, I realized that computers could be useful in education, and I researched and developed new educational methods and content.

 Seymour Papert, the inventor of LOGO, demonstrated through observation that LOGO could be a powerful thinking tool for developing new thinking skills in children. This idea is the root of Wing’s computational thinking. It is also discussed in Chapter 3, Section 3.2.

 One of the features of LOGO is the programming method in which a “turtle” cursor appears on the screen, and the user commands it. It is called “turtle graphics” because the picture is drawn as the trajectory of the turtle’s movement. The turtle exists not only in the monitor but also as a hemispherical robot with a diameter of about 20 centimeters, drawing pictures on the floor (Figure 6.1). Another feature of the robot is that it can easily handle words such as strings. A simple command can run expressions similar to the “natural language” we use in our daily lives (its synonym is an artificially created language, including programming languages). These two features help us to understand computational thinking.

(fig. 6.1) A vintage LOGO turtle robot in the author’s lab

6.3 LOGO, a Tool for Thinking

 Papert was a mathematician who began collaborating with Jean Piaget, a Swiss developmental psychologist trying to incorporate mathematical analysis into his work. He then moved to MIT, where he started collaborating with Marvin Minsky, the father of artificial intelligence. He developed LOGO, a programming language for children that combines artificial intelligence, mathematics, and developmental psychology. In his book 3, Papert talks about his interest in gears as a child. In his book 3, Papert talks about his interest in gears as a child: “I was fascinated by the movement of gears, and as I watched them intently for a long time, I became aware of the variety of movements that their size and combinations could create. He used gears to think about multiplication by 9-9. When a two-variable linear equation arose at school, he immediately thought of differential gears (a device that combines gears with different speeds to produce different rotations). The gears likely became a “model” for his thinking, a tool to bring abstract concepts into his mind and help him understand them. Although gears happen to be a mathematically excellent tool for thought, I wanted to give a good tool for thinking to children who have not encountered such a tool, which led me to LOGO.

 Teaching a turtle how to move, or having a turtle draw a picture, is programming in LOGO. For example, to teach a turtle how to move, you need to know

forward distance
back distance
right angle
left angle

to teach a turtle how to move. For a better understanding, let’s look at some problems while solving them.  

Question 1: What kind of program should I write to draw a square with a side of 50?

The answer is (Figure 6.2). 

forward 50
right 90
forward 50
right 90
forward 50
right 90
forward 50
right 90

This can be written in one line using the “repeat” command.

repeat 4 [forward 50 right 90].

If you program it further, you can write a complex diagram like Figure 6.3.

You can also teach the turtle “language” by creating a function called SHIKAKU. In the Japanese sense, to SHIKAKU means to square. The definition is as follows: to SHIKAKU

to SHIKAKU
repeat 4 [forward 50 right 90]

(fig. 6.2) Let the turtle draw a square

(fig. 6.3) An example of a diagram you could draw with LOGO

Question 2: How should I write a program to draw a circle?

 It is a little different from the usual drawing of a circle, which is to specify the radius. Let’s start with drawing a square and notice that the circle drawn in LOGO is a pentagon, a hexagon, a dodecagon, and so on, with the variable N being the number of sides of the square (4). As N increases, the N-gon becomes closer to a circle.

 LOGO allows you to draw complex movements and pictures by combining simple commands. You can draw regular polygons, spirals, and other fractals that are naturally recursive (Chapter 2, Section 2.5) or iterative. The famous Sierpinski gasket (Figure 6.4), which is a type of fractal (see Column 11) and consists of numerous self-similar triangles, can also be easily drawn with LOGO. In addition, moving the turtle robot involves a physical sensation, as you try to move like a turtle. The process of synchronizing one’s own body movements with those of the tortoise promotes the acquisition and understanding of geometric concepts. Papert calls this “body-syntonic reasoning”. We believe that the physicality of LOGO is one of the essential features in teaching and learning.

(fig. 6.4) The Sierpiński gasket

6.4 Poet Tanikawa Shuntaro’s Computational Thinking

Do you know the poem “Kappa” by Shuntaro Tanikawa?

Kappa

Kappa, kappa, ratta.
Kappa, rappa, kappa,ratta,totte chitte ta.
Kappa, nappa, katta.
Kappa, nappa, ippa, katta, katte, kitte, kutta.                  

              Shuntaro Tanikawa (Author) “Kotoba Asobi Uta” Fukuinkan Shoten Publishers

 I’d like to think about the rearrangement of letters, another feature of LOGO, in this poem “Kappa.” In this poem, the word “kappa” appears a lot. How many Japanese words have the character “ppa” in them? This is where LOGO comes in. Let’s try adding the letter “ppa” to each letter in order from “a” to “n”, like “appa”, “ippa”, “uppa”, and so on. It’s like a round-robin word game, and LOGO makes it easy to write.

Here is the policy.

  1. remove the first letter of the word
  2. the first letter of “letter” is taken from “a”
  3. put the second letter at the beginning of the first letter and write it out
  4. repeat steps 1 to 3 until the letter “n” is written

to Kotobalist :word :moji
   if :moji > ‘n’ [stop] print word char
   print word char :moji butfirst :word
   word list :word :moji+ 1
end

Here, the list of words is the function

where :word is a variable that takes a word

The function :word is a variable that receives the word’s first letter

So run it!

Kotobalist “kappa ‘a’

The result is something like this. The result is a list with “kappa” in it.

appa, ippa, uppa, eppa, oppa
kappa, kippa, kuppa, keppa, koppa
sappa, sippa, suppa, seppa, soppa
tappa, tippa, tuppa, teppa, toppa
nappa, nippa, nuppa, neppa, noppa
happa, hippa, huppa, heppa, hoppa
mappa, mippa, muppa, meppa, moppa
yappa, yuppa, yoppa
rappa,rippa, ruppa, reppa, roppa
wappa, woppa, nppa

 When Dr. Naomi Miyake, a cognitive psychologist, learned about LOGO, she introduced it to Mr. Shuntaro Tanikawa, a poet, as a programming language that could be used for exciting word games. To her surprise, Mr. Tanigawa told her that he does the same thing when writing poetry—deciding on and automating the procedure (algorithm) for creating a word with “pa” is connected to computational thinking. We tend to think that computational thinking has nothing to do with creativity. But, because it is a computer, the act of mechanically producing results according to specific rules can effectively stimulate and expand ideas in this way.

6.5 Thinking with Food

 Next, let’s think about computational thinking through another experience, cooking. A recipe for a dish can be thought of as a kind of algorithm. I have always used cooking as an example to explain things. This may be because cooking is a very familiar activity to me.

 After graduating from university, I worked for an international computer maker, where I was involved in artificial intelligence education and teaching materials developments. In the textbook, I also used it to explain backward reasoning and forward reasoning. Backward reasoning is when you decide what you want to cook or eat and then determine what you need. Forward reasoning is to recall what is in the refrigerator and think about what kind of dish can be made.

 The next time the relationship between cooking and computers appeared in my life was in the January 8, 1990 issue of the magazine Nikkei PC, in a special feature entitled “PCs at home are Interesting.” This was probably because there were few people using computers in everyday life. I was interviewed. There, I was introduced to structuring my recipes using Hypercards. It’s not so much programming. On the top page, there is a matrix with pictures of animals. The rows contain the main proteins: pork, beef, chicken, and fish, and the columns contain the words Japanese, Western, and Chinese. Each cell has a total of 12 buttons, multiplied by 4. Clicking on one of them will bring up, for example, a Chinese menu with fish.

 If there is another Chinese menu with fish, an arrow will appear to go to the next menu. Since it is a hyperlink, it is possible to go from a Chinese fish menu to a Western one. The main protein, vegetables, and other ingredients are linked to different menus. This is the idea of a “housewife” who wants to use leftover ingredients without wasting them. Of course, you can also search by dish name. There are both backward and forward links, as mentioned above. It doesn’t include the cooking instructions, which should be included in every recipe. I am posting my repertoire, and all I need are the necessary ingredients, seasonings, and tools. It is an auxiliary storage device to ensure I don’t forget to shop for ingredients.

You could say it’s an application that helps you think about what to make. It’s a system for me.

 The other things that impressed the reporter in the interview were a household account note and a basal body temperature chart using simple spreadsheet software. I started keeping a household account note when I was a university student, and I still keep it today, which means I have been keeping it for 40 years. The original purpose of a household account book is to look back and use it for the next time. But it also is helpful when I want to know how much I have spent in each category.

 The basal body temperature chart is probably not very familiar to men. So I recommend that women in their late teens start taking it every morning. Nowadays, there are thermometers and wearable ones that record data directly to a smartphone or PC, making it hassle-free.

 There were only analog “gynecological thermometers” at this interview with a fine-scale with two digits after the decimal point. I had to rub my eyes sleepily every morning to read the scale, write down the values, and write them on a small graph paper with a small scale, which was specially designed for recording basal body temperature. After a few days, I gave it up and input the data into a spreadsheet. When I went to see the gynecologist at the university hospital, I printed out a graph of the data and brought it with me. My doctor was so surprised that he showed the printed chart to the nurses and doctors around him, saying, “There is an amazing patient who made this. It was not a big deal at all for me, and I just wanted to make things easier.

 All of these uses have in common: they turn household chores into simple ones. The key is to formulate it so that a computer can do it. The interview headline that appeared in the magazine was, “PCs can be so much fun at home.” In today’s parlance, this could be called a life hack. Today’s parlance is a life hack, a clever way to make your daily life more comfortable, more fun, and more efficient.

 The next time cooking and computers came into my life, and I decided to research learning in everyday life. I thought that metacognition was essential for proficiency in cooking. Metacognition means looking at one’s intellectual activities (cognition) from a higher level. To devise intellectual activities, it is necessary to take a broader perspective. Metacognition is the ability to view intellectual activities objectively and adjust one’s behavior.

 In general, cooking is not learned at school but is often learned at home as an apprentice or by imitating others. We wanted to use computers to support this process of mastery. To develop a system to support proficiency in cooking, we began by observing the differences between novices and experts, preparing recipes for two dishes simultaneously, and cooking them using the same kitchen system. The difference between the novice and expert cooks was seen in the difference in cooking time. The average cooking time for the novice was 69 minutes, and the average for the experts was 37 minutes. The average cleanup time for the novices was 16 minutes, and surprisingly, for the experts, it was only 2 minutes. The expert cooks were almost done cleaning up as soon as they finished cooking.

 If you watch the video of the cooking process carefully, you will see that the expert cooks start by arranging the seasonings and utensils in a convenient place and setting up the work area. They also wash the colander and bowl after each use and keep the vegetable peels and other debris from the cooking process in one place (Figure 6.5). On the other hand, the novice cooks left the bowls and seasonings after using them and looked for where they were every time they needed them. This led to differences in cooking time and cleanup time. The expert cooks could plan, or set up, the spatial and temporal homogeneity of their tasks.

 In the cooking table of the novice in Fig. 6.5, the cooking oil is placed horizontally on top of the miso. On the other hand, the tools and seasonings are placed in an organized manner in the expert’s kitchen. In the sink of the novice, the used items are placed haphazardly, while in the sink of the expert, the vegetable trash is placed in a group on the right side.

 This is not the only ability needed while cooking. For example, if you want to cook three dishes using two stoves, you want each dish to be at the right temperature and on the table at about the same time. We want to minimize the cooking time while minimizing the number of dishes to be washed. Trying to solve a problem under such conditions leads to computational thinking.

 Nowadays, there are many recipes on the Internet, so the way of learning has changed. However, since these are single recipes, it is up to the individual to accumulate them as meta-knowledge. Meta-knowledge here refers to knowledge that can be used in other ways and is more versatile, such as “If you can make a good Oyako-don, you can make a good Tanin-don,” or “When stewing, it is better to add sugar first before adding salt to make the dish good seasoned. Metaknowledge can be developed by finding similarities, patterns, and modularization.

 Metacognition is also important during cooking. Metacognition allows us to compare the cooking process with the image of the finished product, taste it, and make adjustments such as turning down the heat on the stove. Furthermore, if we consider the cooking process, we can see three essential components of an algorithm: sequence, selection, and repetition.

 Many cooking recipe books exist for different readers, such as novice, intermediate, and advanced. Nowadays, many recipes are on the Internet besides books. Moreover, in the COVID-19 pandemic, more and more people are cooking for themselves, and videos of chefs from famous restaurants appear.

 Cooking and computational thinking are a perfect combination. It would be possible to develop a computational thinking version of a recipe that is different from conventional recipes and considers metaknowledge, pattern recognition, metacognition, algorithms, and optimization as “tricks” that can apply.

(fig. 6.5) The difference between a novice and an expert in cooking

6.6 New Learning and Computer Science

 What I’ve written so far is what I talked about in my 2017 colloquium that led to this book. Now, I would like to consider the new knowledge and skills arising.

 At the end of the 20th century, the knowledge and skills needed by children in the 21st century have changed dramatically. The Organisation for Economic Co-operation and Development (OECD) has published a new set of internationally agreed-upon competencies as key competencies, along with their assessment methods. Along with these key competencies, the importance of computer science as a learning content has also begun to be recognized. The field of educational technology has been classifying educational goals (cognitive, motor, emotional) since the 1950s, and what learners need to acquire has shifted from knowledge (what they know) to cognitive abilities (knowledge and skills), non-cognitive abilities (attitudes and values), and key competencies (what they can do).

 The OECD has identified the achievement of these Sustainable Development Goals for education as a critical challenge for all countries and has been working on the Education 2030 project since 2015.

In 2019, the OECD released a concept note as one of the final reports summarizing the project’s results so far (Phase 1).7 The concept note adds a new category of transformative competencies to the existing categories of key competencies. The new category of transformative competencies was added to the existing categories of key competencies. The transformative competencies consist of the following three elements:

  1. The ability to create new value: collaborating with others to develop new products, services, methodologies, ways of thinking, and new social models.
  2. The ability to reconcile tensions and dilemmas: mediate between diverse ideas and interests and balance various competing demands.
  3. Taking responsibilities: to consider the future consequences of one’s actions, evaluate risks and rewards, and take responsibility for the products of one’s work.

 The emphasis here is that educating young people is to prepare them for work and equip them with the skills to become responsible, engaged citizens. It also emphasizes the importance of learner agency and the need for learning through anticipation, action, and reflection concerning the real world.

 On the other hand, school education in Europe and the U.S. introduces computer science and computational thinking into the curriculum from elementary school on, as the 21st century is the age of computation. In the U.K., the national curriculum was revised in 2012 to include computer science at all levels, from 5 years old to elementary, middle, and high school, starting the following year. In the U.S., in a 2016 Executive Order, President Obama stated that all children from kindergarten to high school should learn computer science and that everyone should be able to think computationally. In response to this, computer science and computational thinking, along with STEAM (Science, Technology, Engineering, Art, and Mathematics) education, have been developed and implemented in curriculums from elementary school to high school. For more information on this, please refer to Chapter 3.

 On the other hand, “programmatic thinking,” widespread in the Japanese educational community, is different from computational thinking. In programmatic thinking, the metaphor of “thinking like a computer” is often used. On the other hand, computational thinking is “thinking like a computer scientist,” as I have explained so far. Computational thinking is a highly versatile approach to problem-solving that can solve problems facing society, such as climate change, genome analysis, and fake news detection.

 Educators often translate competencies as “qualities and abilities” in Japanese. Dr. Yutaka Sayeki, an educationalist and cognitive psychologist, points out that competency summarizes what it means to be “competent,” not the motive or “power” that causes it. In other words, competency is not a quality or an ability but a capability that emerges in activities. If you read the original English text where the concept of competency was introduced, you will find the following.

The ability to use language, symbols, and text interactively.

 If we translate this as “the ability to use language, symbols, and text interactively,” we are left with the question of how to develop such an ability (educationally speaking). However, Sayeki argues that if we translate it as “the ability to use language, symbols, and text interactively in various situations,” then there would be no objection to it being one of the indicators of “competency.

 Sayeki cites philosopher Gilbert Ryle’s “Ghost in the Machine” dogma in his book “The Concept of Mind.” This dogma assumes a person’s “mind” is capable of “intelligent behavior. This misunderstanding is called the “ghost in the machine” dogma, which assumes that there is a “mind” inside the “intelligent machine” that causes it to behave intelligently. Rather, the “mind” is the sum of the possibilities (what Ryle calls “dispositions”) for people’s behavior to exhibit certain aspects.

 Dr. Minoru Murai, a philosopher of education, explains the symptomatic principle of education in his book, “An Encouragement of New Pedagogy,” using the parable of the quack who catches a cold. When Sayeki told him about this parable, he thought it was an interesting thing to say. The story is as follows.

 There was a country where a good cold catcher was considered a good person. All the parents wanted their children to be good cold catchers. At a meeting of professional doctors, a scientific study was conducted on the characteristics of ministers, presidents, rich people, and other people who were considered to have a good cold. The results showed that a person must have 1) a fever of 37 degrees or higher, 2) a headache, and 3) a feeling of dullness. To induce a fever of 37 degrees Celsius or higher, curry powder and horseradish must be kneaded together and rubbed all over the body. To cause a headache, give a blow to the head. To make them feel dull, have them carry a bale of rice around the playground three times. All over the country, this method was used on children. To see the effects of the treatment, tests were conducted by taking their body temperature. The result was a child with all three symptoms of a “full-blown cold.

 Murai pointed out that what is being done in schools today is the same as this strange cold catching. What is so funny about this story? Where did the people of this country go wrong?

 The story of the “quack who let people catch colds” is similar to the story of competency as a quality or ability and having people acquire it. The idea of goal-based evaluation comes from the fact that the goal is to develop the competencies and then to “evaluate” whether the goal has been achieved or not. Competency in education was initially discussed by the OECD in the Definition and Selection of Competencies (DeSeCo) project and appeared in the final report issued in 2003. In defining (clarifying) and extracting the competencies, elements of people who have shown competence in various industries were collected and organized. It is a symptom of “catching a cold.

 In other words, competency is a point of view, an indicator, that we look at when we carefully observe and find the competencies that emerge. It is a shift to discovering the diversity of competencies that appear in daily life and work, finding previously unrecognized “goodness,” recognizing it, paying renewed attention to its aspects, recognizing it in oneself and others, and developing it.

 There is a shift from the traditional view of learning, where knowledge is accumulated, and skills are acquired, to a new view of learning, where knowledge is created through dialogue and competence is recognized in the process. As the view of learning changes, it is a natural consequence that the view of assessment and assessment methods will also change. Based on the competency discussion above, it is necessary to shift from the conventional evaluation of “whether or not the student has mastered the skill” for a new assessment of “whether or not the student is aware of the good points and is trying to improve. When we consider this competency discussion in conjunction with computational thinking, many things become clear. If we think of the elements of computational thinking described in the previous chapters as “the ability to do XX,” then the question becomes, “How can we educate people to do XX?

 In general, experts have many useful knowledge and skills that they use without being aware of it. In most cases, they are difficult to define or explain in words. We know how to use many things in our field of expertise but cannot present in terms to non-specialists. This book boldly challenges that difficulty. Computational thinking is a skill that computer scientists display, and it is an approach to problem-solving that they use consciously and sometimes unconsciously. It is helpful for children, and when we try to teach it, we need to be very careful not to make the same arguments for competency that we have discussed so far.

A competent person has X and Y elements when looking at an activity.

To be competent, one needs to acquire elements X and Y.

If we stop here, we will fall into the symptomaticism of quackery. So, let me explain.

 A competent person has acquired the elements X and Y through a particular series of activities.

If we think of it as “a series of activities,” we can see a hint in performing and experiencing these activities. In addition, it is not just one person who conducts these activities, but others recognize them as “good,” the community. The self, others, and the community reflect on the action results. If we assume that the characteristics of computational thinking are strengthened in these activities, then to acquire computational thinking, we need to design an environment in which we can manifest, recognize, and develop our abilities by creating opportunities to interact with the world (environment).

6.7 Computational Thinking for All

 Finally, I would like to consider developing and using computational thinking.

In the discussion of competency, I mentioned that it is a matter of discovering the diversity of abilities that emerge in society, finding “goodness” that was not previously assumed, recognizing it, paying attention to that aspect again, identifying it in oneself, and others, and developing it. Consider computational thinking as one of those “goodness” perspectives.

 For example, if you want to make a lot of cookies with a heart pattern in the middle (Figure 6.6), make a lot of chocolate cookie dough, one by one, in the shape of a heart. Then, combine the butter cookie dough made separately with the heart-shaped dough, one by one, and bake them.

 Some of you may have thought of a more efficient way to do this. Make a heart-shaped bar of chocolate cookie dough, wrap the butter cookie dough around it, and combine them. This is how to make Kintaro candy or the “decorative sushi rolls” that have recently become a bit of a craze. The latter method requires less work and is also more consistent in size.

Then, say aloud.

“Isn’t this computational thinking? Yes, it is.”

“Isn’t this the same way you make a beautiful, artistic terrine?”

At some point, you may suddenly realize that this is computational thinking. You may also realize it when you see what others are doing. It’s okay if you’re wrong. First of all, let’s put it into words. Then, let’s say it out loud. After a few times of doing so, you may find yourself computational thinking, “Huh? You may find yourself thinking, “I’m learning computational thinking. Or someone may say to you, “You are a person who can do computational thinking.

 In the 1980s, many educational studies using LOGO showed that programming skills did not lead to the acquisition and development of logical thinking and problem-solving skills. Subsequent research in learning science has revealed that learning transfer (the influence of previous learning on later learning) does not occur automatically across contexts but is context-dependent and involves experience. This is one of the reasons why Project-Based Learning (PBL) has become an effective learning method in the world today. Therefore, computational thinking can also be practical if used in activities such as project-based learning. In programming and STEAM education activities, social activities, and co-creative design activities, we can design situations where computational thinking can emerge, find it, and develop it.

 By adding or limiting the characteristics of computational thinking to the competency “the ability to use language, symbols, and text concerning each other in various situations,” we may be able to create an index of “competency in computational thinking. However, this will require further discussion.

 Finally, I would like to emphasize that when we think about teaching and learning computational thinking, we should not be looking for “ghosts” or collecting “symptoms,” and we should integrate computational thinking into our daily lives so that many people can use computational thinking in their lives. Above all, I think it is essential for everyone to enjoy using computers.

 Let’s do computational thinking, which computer scientists and a few others enjoy, available to everyone. This is computational thinking for all.

(fig. 6.6) Heart Cookies

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