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Various aspects of computational thinking, which builds on the power and limits of computing processes, whether they are executed by a human or by a machine, are discussed. Computational methods and models are helping to solve problems, design systems, and understand human behavior, by drawing on concepts fundamental to computer science (CS). Computational thinking (CT) is using abstraction and decomposition when attacking a large complex task or designing a large complex systems. CT is the way of thinking in terms of prevention, protection, and recovery from worst-case scenarios through redundancy, damage containment, and error correction. CT is using heuristic reasoning to discover a solution and using massive amount of data to speed up computation. CT is a futuristic vision to guide computer science educators, researchers, and practitioners to change society's image of the computer science field.
COMMUNICATIONS OF THE ACM March 2006/Vol. 49, No. 3 33
omputational thinking
builds on the power and
limits of computing
processes, whether they are exe-
cuted by a human or by a
machine. Computational
methods and models give us
the courage to solve prob-
lems and design systems that no one of us would
be capable of tackling alone. Computational think-
ing confronts the riddle of machine intelligence:
What can humans do better than computers? and
What can computers do better than humans? Most
fundamentally it addresses the question: What is
computable? Today, we know only parts of the
answers to such questions.
Computational thinking is a fundamental skill for
everyone, not just for computer scientists. To read-
ing, writing, and arithmetic, we should add compu-
tational thinking to every childs analytical ability.
Just as the printing press facilitated the spread of the
three Rs, what is appropriately incestuous about this
vision is that computing and computers facilitate the
spread of computational thinking.
Computational thinking involves solving prob-
lems, designing systems, and understanding human
behavior, by drawing on the concepts fundamental
to computer science. Computational thinking
includes a range of mental tools that reflect the
breadth of the field of computer science.
Having to solve a particular problem, we might
ask: How difficult is it to solve? and What’s the best
way to solve it? Computer science rests on solid the-
oretical underpinnings to answer such questions pre-
cisely. Stating the difficulty of a problem accounts
for the underlying power of the machine—the com-
puting device that will run the solution. We must
consider the machine’s instruction set, its resource
constraints, and its operating environment.
In solving a problem efficiently,
,we might further
ask whether an approximate solution is good
enough, whether we can use randomization to our
advantage, and whether false positives or false nega-
tives are allowed. Computational thinking is refor-
mulating a seemingly difficult problem into one we
know how to solve, perhaps by reduction, embed-
ding, transformation, or simulation.
Computational thinking is thinking recursively. It
is parallel processing. It is interpreting code as data
and data as code. It is type checking as the general-
ization of dimensional analysis. It is recognizing
both the virtues and the dangers of aliasing, or giv-
ing someone or something more than one name. It
is recognizing both the cost and power of indirect
addressing and procedure call. It is judging a pro-
gram not just for correctness and efficiency but for
aesthetics, and a system’s design for simplicity and
Computational thinking is using abstraction and
decomposition when attacking a large complex task
or designing a large complex system. It is separation
of concerns. It is choosing an appropriate representa-
tion for a problem or modeling the relevant aspects
of a problem to make it tractable. It is using invari-
ants to describe a system’s behavior succinctly and
declaratively. It is having the confidence we can
safely use, modify, and influence a large complex
system without understanding its every detail. It is
Viewpoint Jeannette M. Wing
Computational Thinking
It represents a universally applicable attitude and skill set everyone, not just
computer scientists, would be eager to learn and use.
34 March 2006/Vol. 49, No. 3 COMMUNICATIONS OF THE ACM
modularizing something in anticipation of multiple
users or prefetching and caching in anticipation of
future use.
Computational thinking is thinking in terms of
prevention, protection, and recovery from worst-case
scenarios through redundancy, damage containment,
and error correction. It is calling gridlock deadlock
and contracts interfaces. It is learning to avoid race
conditions when synchronizing meetings with one
Computational thinking is using heuristic reason-
ing to discover a solution. It is planning, learning,
and scheduling in the presence of uncertainty. It is
search, search, and more search, resulting in a list of
Web pages, a strategy for winning a game, or a coun-
terexample. Computational thinking is using massive
amounts of data to speed up computation. It is mak-
ing trade-offs between time and space and between
processing power and storage capacity.
Consider these everyday examples: When your
daughter goes to school in the morning, she puts in
her backpack the things she needs for the day; that’s
prefetching and caching. When your son loses his
mittens, you suggest he retrace his steps; thats back-
tracking. At what point do you stop renting skis and
buy yourself a pair?; that’s online algorithms. Which
line do you stand in at the supermarket?; that’s per-
formance modeling for multi-server systems. Why
does your telephone still work during a power out-
age?; that’s independence of failure and redundancy
in design. How do Completely Automated Public
uring Test(s) to Tell Computers and Humans
Apart, or CAPTCHAs, authenticate humans?; that’s
exploiting the difficulty of solving hard AI problems
to foil computing agents.
Computational thinking will have become
ingrained in everyone’s lives when words like algo-
rithm and precondition are part of everyone’s vocab-
ulary; when nondeterminism and garbage collection
take on the meanings used by computer scientists;
and when trees are drawn upside down.
We have witnessed the influence of computa-
tional thinking on other disciplines. For example,
machine learning has transformed statistics. Statisti-
cal learning is being used for problems on a scale, in
terms of both data size and dimension, unimagin-
able only a few years ago. Statistics departments in
all kinds of organizations are hiring computer scien-
tists. Schools of computer science are embracing
existing or starting up new statistics departments.
Computer scientists’ recent interest in biology is
driven by their belief that biologists can benefit
from computational thinking. Computer science’s
contribution to biology goes beyond the ability to
search through vast amounts of sequence data look-
ing for patterns. The hope is that data structures
and algorithms—our computational abstractions
and methods—can represent the structure of pro-
teins in ways that elucidate their function. Compu-
tational biology is changing the way biologists
think. Similarly, computational game theory is
changing the way economists think; nanocomput-
ing, the way chemists think; and quantum comput-
ing, the way physicists think.
This kind of thinking will be part of the skill set
of not only other scientists but of everyone else.
Ubiquitous computing is to today as computational
thinking is to tomorrow. Ubiquitous computing was
yesterday’s dream that became today’s reality; com-
putational thinking is tomorrow’s reality.
Computer science is the study of computation—
what can be computed and how to compute it.
Computational thinking thus has the following
Thinking like a computer scientist means more than being able to
program a computer. It requires thinking at multiple levels of abstraction.
COMMUNICATIONS OF THE ACM March 2006/Vol. 49, No. 3 35
Conceptualizing, not programming.
Computer sci-
ence is not computer programming. Thinking
like a computer scientist means more than being
able to program a computer. It requires thinking
at multiple levels of abstraction;
Fundamental, not rote skill.
A fundamental skill is
something every human being must know to
function in modern society. Rote means a
mechanical routine. Ironically, not until computer
science solves the AI Grand Challenge of making
computers think like humans will thinking be
A way that humans, not computers, think.
tional thinking is a way humans solve problems;
it is not trying to get humans to think like com-
puters. Computers are dull and boring; humans
are clever and imaginative. We humans make
computers exciting. Equipped with computing
devices, we use our cleverness to tackle problems
we would not dare take on before the age of com-
puting and build systems with functionality lim-
ited only by our imaginations;
Complements and combines mathematical and engi-
neering thinking.
Computer science inherently
draws on mathematical thinking, given that, like
all sciences, its formal foundations rest on mathe-
matics. Computer science inherently draws on
engineering thinking, given that we build systems
that interact with the real world. The constraints
of the underlying computing device force com-
puter scientists to think computationally, not just
mathematically. Being free to build virtual worlds
enables us to engineer systems beyond the physi-
cal world;
Ideas, not artifacts.
It’s not just the software and
hardware artifacts we produce that will be physi-
cally present everywhere and touch our lives all
the time, it will be the computational concepts
we use to approach and solve problems, manage
our daily lives, and communicate and interact
with other people; and
For everyone, everywhere.
Computational thinking
will be a reality when it is so integral to human
endeavors it disappears as an explicit philosophy.
Many people equate computer science with com-
puter programming. Some parents see only a narrow
range of job opportunities for their children who
major in computer science. Many people think the
fundamental research in computer science is done
and that only the engineering remains. Computa-
tional thinking is a grand vision to guide computer
science educators, researchers, and practitioners as we
act to change society’s image of the field. We espe-
cially need to reach the pre-college audience, includ-
ing teachers, parents, and students, sending them
two main messages:
Intellectually challenging and engaging scientific prob-
lems remain to be understood and solved.
The prob-
lem domain and solution domain are limited only
by our own curiosity and creativity; and
One can major in computer science and do anything
One can major in English or mathematics and go
on to a multitude of different careers. Ditto com-
puter science. One can major in computer science
and go on to a career in medicine, law, business,
politics, any type of science or engineering, and
even the arts.
Professors of computer science should teach a
course called “Ways to Think Like a Computer Sci-
entist” to college freshmen, making it available to
non-majors, not just to computer science majors. We
should expose pre-college students to computational
methods and models. Rather than bemoan the
decline of interest in computer science or the decline
in funding for research in computer science, we
should look to inspire the public’s interest in the
intellectual adventure of the field. We’ll thus spread
the joy, awe, and power of computer science, aiming
to make computational thinking commonplace.
Jeannette M. Wing ( is the President’s
Professor of Computer Science in and head of the Computer Science
Department at Carnegie Mellon University, Pittsburgh, PA.
© 2006 ACM 0001-0782/06/0300 $5.00
... In the last decade, the concept of Computational Thinking (hereinafter, CT) has emerged as one of the competences that can help any citizen to manage the complex situations in which the new Knowledge Society (KS) is immersed (Acevedo Borrega, 2016). And it is in this context that Wing's (2006Wing's ( , 2010 reflection begins, when he stresses that CT will can be a basic tool to learn in an abstract, algorithmic and logical way and, therefore, will prepare students to solve complex and open problems. ...
... Wing brings to the fore the Papert's expression "computational thinking" (Papert, 1980) with a viral article (Wing, 2006). ...
... As Wing says "thinking like a computer scientist means more than being able to program a computer" (Wing, 2006) and we think that giving to teachers a philosophical perspective to insert computational thinking can help to perceive the cultural aim of coding activities. This extended abstract focusses on teacher education in the North. ...
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The study examined students’ perceptions of participating in collaborative learning activities in ICTPED MOOC (Pedagogical Information and Communication Technology (ICTPED) Massive Open Online Course) offered by a University College in Norway aiming to develop professional digital competence in students. The study also provided an insight into what students' perceptions and experiences of taking part in collaborative learning practices suggest when it comes to promoting collaborative learning activities in MOOCs, and online learning environments. Analyses of the post-course survey data suggested that most of the students were satisfied with opportunities to learn collaboratively through discussion forums, peer reviews, and online video meetings. The asynchronous modes of collaboration (discussion forum and peer review) remained dominant modes of collaboration, compared to the synchronous ones (online meetings). However, data suggest many factors such as feeling interfering in others’ activities, being exposed to unknown peers, and unknown technology might hinder students' participation in online collaborative learning activities.
... GİRİŞ Dünya'da ve Türkiye'de bilgi işlemsel düşünme (BİD) becerisini kazandırmaya verilen önem çağın ihtiyaçları doğrultusunda önem kazanmıştır. (Wing, 2006). Avrupa Komisyonu tarafından yayınlanan raporda (Bocconi vd., 2016), dijital dünyaya tam katılım için gerekli olan 21. yüzyıl becerilerini geliştirmek ve özellikle istihdam olanaklarını güçlendirmek için bilgi işlemsel düşünmenin öğretim programlarıyla bütünleştirilmesi desteklenmiştir. ...
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Over the past decade, computational thinking has attracted increasing interest within K-12 education. The subject area is being implemented in curricula around the world, as our societies have become increasingly digitalized, thus it seems to be regarded as a fundamental general competence in students’ present and future lives. In continuation of this development, the purpose with this research project was to dig into the reasons why, including if, computational thinking is a central competence to develop in compulsory education (K-9), and what aspects of it that are important.
Humans and machines recognize or understand various 3D shapes through projections from different views. Descriptive Geometry (DG) constructs dimensional one-to-one map with unambiguity. The presentation and computing advantages of DG are presented from the perspective of modern computing, including the methods of dimension reduction with projection and dimension upgrade with geometric construction. The concept of graphic computational thinking (GCT) is then proposed, with an integration of graphic thinking and computational thinking. The characters and the hierarchical structure of GCT in DG are described, along with the potential educational values.
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The purpose of this study was to develop a program that incorporates computational thinking into technology education classrooms and to investigate its effect on students. Software (SW) education and physical computing education are frequently addressed topics in technology education, but education about computational thinking (CT) lacks interest and research. Therefore, it is necessary to further develop educational programs in technology. In this study, we developed a program integrating CT, which centered on technological problem-solving processes. The program comprised 12 total hours of hacking a remote control (RC) car using Micro:bit development tool. This study investigates the effects of the developed program with a single group pre- and post-test quasi-experimental design. Nineteen students participated in the study, completing survey instruments that measure CT competency and attitudes toward CT and technology, answering an open-ended questionnaire, and voluntarily took part in semi-structured interviews. The results showed that the technological problem-solving program positively affected participants’ CT-related competencies. Moreover, we observed improvement in participants’ attitudes toward technology due to the integration of CT into their technology education classes. This study provides a strong case for incorporating CT into technology education. It also suggests future research direction regarding the development of students’ CT competencies in various technological problem-solving contexts.
Die Beziehung von Mathematik und Informatik ist seit Langem in der didaktischen Diskussion. Ursprünglich war es eine zentrale Frage, ob es ein eigenständiges Fach Informatik in der Schule geben sollte oder ob entsprechende Anteile in den Mathematikunterricht integriert werden können. Diese Diskussion ist weitgehend mit der Etablierung eines eigenständigen Schulfachs Informatik entschieden.
Computational thinking (CT) is a critical skill needed for STEM professionals and educational interventions that emphasize CT are needed. In engineering, one potential pedagogical tool to build CT is modeling, an essential skill for engineering students where they apply their scientific knowledge to real-world problems involving planning, building, evaluating, and reflecting on created systems to simulate the real world. However, in-depth studies of how modeling is done in the class in relation to CT are limited. We used a case study methodology to evaluate a model-planning activity in a final-year undergraduate engineering classroom to elicit CT practices in students as they planned their modeling approach. Thematic analysis was used on student artifacts to triangulate and identify diverse ways that students used CT practices. We find that model-planning activities are useful for students to practice many aspects of CT, such as abstraction, algorithmic thinking, and generalization. We report implications for instructors wanting to implement model-planning activities into their classrooms.
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