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Review Article
Computer, Computer Science, and Computational Thinking:
Relationship between the Three Concepts
Pinaki Chakraborty
Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi 110078, India
Received 28 November 2023; Revised 27 February 2024; Accepted 9 March 2024; Published 28 March 2024
Academic Editor: Mirko Duradoni
Copyright © 2024 Pinaki Chakraborty. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
Digital computers were invented in the 1940s. They are sophisticated and versatile machines whose functioning is grounded in
elaborate theory. Advances in theory and the availability of computers helped computer science to develop as an academic
discipline, and university departments for the same started coming up in the 1960s. Computer science covers all phenomenon
related to computers and consists primarily of man-made laws governing building, programming, and using computers.
Computational thinking is a way of thinking influenced by computers and computer science. There are two schools of thought
on computational thinking. The first school sees computational thinking as the use of computers to explore the world, while
the other sees computational thinking as the application of concepts from computer science to solve real-world problems.
Scholars typically agree that computational thinking has four essential components, viz., abstraction, decomposition, algorithm
design, and generalization. Computational thinking is often feted by computer scientists as a useful skill that can be used by
anybody anywhere. However, it is necessary to find out ways for successfully using computational thinking in domains other
than computer science before it can be declared a universal skill.
1. Introduction
Computers distinguish themselves from other man-made
devices by the level of sophistication in their design, their
wide range of uses, and the theoretical foundation of their
working. Computers have been evolving steadily for the last
eight decades. Advances in computers have been comple-
mented by developments in computer science which is per-
haps the only academic discipline focusing on a single
man-made artefact. Computers [1] and computer science
[2] can influence how people associated with them think
and work. However, not many scholars have commented
upon the interplay of computers, computer science, and
computational thinking. This paper analyzes the relation-
ship between computers and computer science and how they
together influence the human thought process today. The
paper is a narrative review on the influence of computers
and computer science on human thought processes written
with an emphasis on the most important developments in
the field in the last eight decades. Care has been taken to
showcase the rich history of the topic in this paper.
2. Digital Computer
A digital computer is a machine that can execute programs.
A program is a sequence of instructions presented in a pre-
determined binary format. A program can be used to find
the solution to a problem which is difficult to solve manu-
ally. Such programs can be written to solve problems in dif-
ferent fields of human endeavor. These make computers
powerful and perhaps the most versatile machines in the
world. (The term “computer”is now typically used to denote
a digital computer. However, the term was used in the past
for a person with expertise in performing mathematical cal-
culations, i.e., a “human computer.”A digital computer is
also different from an “analog computer”which is a device
Hindawi
Human Behavior and Emerging Technologies
Volume 2024, Article ID 5044787, 6 pages
https://doi.org/10.1155/2024/5044787
performing specific types of calculations using analog signals
representing various physical quantities.) The first com-
puters were built in the 1940s, e.g., Z3 and Z4 computers
in Germany and ENIAC computer in the USA.
The computer is the Proteus of machines. (Seymour A.
Papert [1] (p. viii))
(In Greek mythology, Proteus is the god of the sea and
characterizes its constantly changing nature.)
Since the late 1940s, almost all computers follow a model
popularized by von Neumann [3]. According to this model,
known as the von Neumann architecture, a computer has a
memory unit which stores programs and data. The process-
ing unit of the computer fetches one instruction from the
memory, decodes it, and executes it. Executing an instruc-
tion may require reading some data from the memory and/
or writing some data in the memory. After executing an
instruction, the processing unit repeats these steps with the
next instruction in the sequence or an instruction elsewhere
in the program as dictated by the logic of the program. Since
the programs are stored within the computer, such a computer
is known as a stored-program computer. This technique of exe-
cuting programs in which instructions are fetched, decoded,
and executed one by one is known as the instruction cycle.
Instruction cycle is a characteristic feature of computers. Hence,
any machine capable of implementing instruction cycle is a
computer. Commonly used computers include desktop
computers, laptops, tablets, smartphones, and smartwatches.
Dijkstra remarked that computers are “usually electronic”
machines which can store large volumes of data and process
them at a high speed with a high reliability [4]. (Although most
computers built so far use electronic components, it is possible
to build computers without any electronic part.)
On the theory front, Turing proposed a theoretical
model of computation which could be used to solve a large
class of computational problems [5]. The model later
became known as Turing machine. (Turing [5] used the
terms “automatic machine”and “universal computing
machine”to denote the Turing machine and universal
Turing machine, respectively. The term “Turing machine”
was first used in a review of Turing’s work by his doctoral
advisor Alonzo Church [6].) Turing also defined a universal
Turing machine which is a theoretical model of computation
that can emulate an arbitrary Turing machine [5]. In other
words, a Turing machine can solve a particular computa-
tional problem while a universal Turing machine can solve
all computational problems solvable by the Turing
machines. A device is called Turing complete if it can emu-
late all Turing machines; i.e., it is as powerful as a universal
Turing machine. Computers are Turing complete. Further,
no theoretical model or physical device is known to be able
to solve any computational problem that cannot be solved
by a Turing machine. Therefore, computers can solve all
computational problems which can be solved mechanically.
3. Computer Science
Scholars have provided several interesting definitions of
computer science. However, the definition by Newell et al.
[7] quoted below is certainly the most succinct one. Newell
et al. [7] viewed computer science as the study of phenom-
ena surrounding computers. Following the same line of
thought, Denning defined computer science as the science
of information processes and their interaction with the
world [8]. There is an emphasis on the role played by com-
putation in the real world in the definitions of Newell et al.
[7] and Denning [8]. Alternatively, definitions by Knuth
[9] and Wing [2] are more procedure oriented. Knuth
defined computer science as the study of algorithms [9],
while Wing saw computer science as the study of computa-
tions which primarily dealt with determining what can be
computed and how they can be computed [2].
...computer science is the study of computers. (Newell
et al. [7])
Among all phenomena related to computers, algorithms
are perhaps the most important [9]. An algorithm is a finite
sequence of steps that can be followed to transform the given
input information to the desired output information. A pro-
grammer designs algorithms intended for mechanical execu-
tion [4] and writes a program according to the algorithm in
a programming language. A program is actually a particular
way of representing an algorithm [9]. Theoretically, an algo-
rithm for a function fis equivalent to a Turing machine that
computes f[10]. Programming languages are models of
computation equivalent to a universal Turing machine and
hence can be used to implement various algorithms. Pro-
grammers however prefer writing algorithms intuitively
rather than as Turing machine functions. Dijkstra felt that
programming is a type of mathematical activity and such
activities are characterized by high precision, wide generaliz-
ability, and high confidence level [4]. Mathematics and com-
puter science have man-made laws which can be proved,
instead of natural laws which have to be discovered but
could never be known with certainty [9]. Computer science
involves systematic study of algorithmic processes that
describe and transform information [11]. (Denning et al.
[11] used the term “computing.”Knuth [9] discussed the
terms used to denote computer science in several (Western)
languages and countries and tacitly accepted their equiva-
lence.) Appropriate algorithms are necessary for mechanical
realization of information processes, and according to Den-
ning et al. [11], the fundamental question underlying all com-
putational tasks is “what can be (effectively) automated?”
Computer science was born as an academic discipline
with the joining together of mathematical logic and algorith-
mic theory and the invention of stored-program computers
in the mid-1940s [11]. By the early 1960s, there was a suffi-
cient body of knowledge to justify starting academic pro-
grams and even establishing university departments for
computer science [12]. (The Diploma in Numerical Analysis
and Automatic Computing of the University of Cambridge
started in 1953 was perhaps the first academic program in
computer science, while Purdue University established a
computer science department in 1962 and was perhaps the
first university to do so.) Once computer science was estab-
lished as an academic discipline, universities played a crucial
role in expanding its body of knowledge and disseminating
the same among people. Computers are novel and complex
equipment [7] which can act as universal computing
2 Human Behavior and Emerging Technologies
machines [12]. In other words, computers are tools to imple-
ment, study, and predict information processes [8]. As a
result, computers lie at the center of computer science, and
much of the body of knowledge in the discipline is con-
cerned about computers and phenomena surrounding them
[12]. Programmers design algorithms and write programs
for existing and conceivable computers [4]. (Lovelace [13]
and Shor [14] designed algorithms for a mechanical computer
and a quantum computer, respectively, which were conceiv-
able but did not exist at their respective time.) Computer sci-
ence is however more than just programming [2]. Computer
science covers the underlying principles of computation,
development of working computing systems with hardware
and software components,and how people interact with them.
4. Computational Thinking
Papert is believed to have coined the term “computational
thinking,”and he used it in his book [1]. (Papert [1] used
the word “computational”as an adjective to qualify things
which were related to computers in some way. The word
was used 51 times in the book including the index. The term
“computational thinking”appeared only once in the book
(p. 182) and was not listed in the index. Nevertheless, con-
cepts pertaining to computational thinking were discussed
at various places in the book.) Papert recognized the poten-
tial of computers in the field of education and, in the 1960s,
started developing computer-based tools and techniques for
school-aged children. He shared his experience on this work
in his book. Papert [1] appreciated computers for their util-
ity and versatility. He predicted that the world will be
“computer-rich”in the future (p. 3). He believed that com-
puters are powerful tools which can enable children to
explore a wide range of topics. His team developed a device
which they named “turtle.”(Papert [1] went on to call the
turtle a “computer-controlled cybernetic animal”(p. 11).)
The turtle was connected to a computer and could plot lines
and curves on a flat surface. The turtle’s movement and plot-
ting activities were controlled by programs written in a
purpose-designed programming language called LOGO.
LOGO was a simple language, and children could write pro-
grams in it intuitively. Papert described how children
explored concepts of geometry and physics by writing
LOGO programs [1]. Papert felt writing programs helped
children in two ways; viz., they attained mastery over com-
puters, and they could connect with various concepts of sci-
ence, mathematics, and arts [1]. (Babbage [15] predicted that
computers will influence the course of science in the future
(p. 137).) Papert [1] believed that using “computer as pencil”
will help children explore and learn new concepts (p. 210).
Years later, Papert reiterated that digital technology is a lib-
erator and can help in implementing new concepts in educa-
tion [16]. He felt that computers can help children to think
of new ideas and even realize some of them [17]. Papert’s
philosophy was that children can learn computer program-
ming and learning to program influences how they learn
other things [17]. It can be inferred that for Papert, compu-
tational thinking was thinking influenced by computers,
thinking in terms of computers, and thinking to use com-
puters as tools in various activities.
Papert’s work influenced scholars from many disciplines
for years, but computational thinking did not receive
extraordinary attention. Things changed suddenly when
Wing wrote about her views on computational thinking
and the perceived benefits of the same in an essay [2]. (It
is difficult to say if Wing was influenced by Papert’s view
on computational thinking. Wing [2] did not cite any litera-
ture because the format of the article did not allow her to do
so. Wing [17] cited some papers but none by Papert.) Wing
described computational thinking as a universally applicable
attitude and skill set that builds upon the concepts of com-
puter science [2]. She later elaborated her point by stating
that computational thinking is taking an approach for solv-
ing problems, designing systems, and understanding human
behavior using concepts that are fundamental to computer
science [18]. According to Wing [2], computational thinking
allows finding approximate solutions problems, reducing a
problem into another, and solving problems using recursion
and parallel processing. Computational thinking is a way of
thinking for human beings and not computers and can be
used by everyone everywhere. Computational thinking is
about conceptualization and not programming, and it is an
idea and not an artefact. Wing claimed that computational
thinking is a fundamental skill for everyone and not just
for computer scientists [2]. She went on to say that compu-
tational thinking should be taught to every child along with
reading, writing, and arithmetic. Further, Wing remarked
that computational thinking will influence everyone in every
field of endeavor [18]. Wing’s work brought computational
thinking in the spotlight, and researchers from multiple dis-
ciplines started investigating its utility. Computational
thinking lessons were introduced in some schools as well.
Papert mentioned computational thinking in context of
an alternative and computer-assisted approach for teaching
mathematics and other disciplines to children [19]. Papert
recommended the use of computer as a tool for learners to
explore various topics. For example, writing simple pro-
grams can be an effective way to understand Euclidean
geometry. Papert’s version of computational thinking is con-
cerned about forging ideas and implementing and explain-
ing them. Alternatively, Wing saw computational thinking
as an effective way of solving problems in the daily life. For
example, one can use the concept of topological sorting to
schedule the activities for a busy day. Wing’s version of com-
putational thinking focuses on abstraction, analysis, and
automation. What Papert and Wing had in common was
their emphasis on reasoning, and both of them indicated
that computational thinking is more than just using com-
puters to solve particular problems [20].
Computational thinking is a way of thinking [20] and
acting [21] in which problems are formulated in such a
way that their solutions can be represented as a series of
computational steps [10]. The solutions may be imple-
mented with or without the assistance of computers and
should be reusable in different contexts [21]. Alan J. Perlis
used to call the quantitative analysis of the way one does
things “algorithmizing”and recommended that computers
3Human Behavior and Emerging Technologies
be treated as general tools to facilitate reasoning rather than
devices to solve specific problems [22]. Computational
thinking is a type of analytical thinking and has similarities
with mathematical, scientific, and engineering thinking
[18]. Analytical thinking is not unique to any discipline
[23], and computational thinking cannot lay claim on very
broad ways of thinking [24]. Computational thinking is an
important element of computer science [23]. However, it
does not represent the entire discipline of computer science
[23] and is certainly not an independent discipline [25].
Since Wing [2] published her essay, researchers started
investigating what all consist computational thinking and
several scholars like Selby and Wollard [26], Shute et al.
[21], Lodi and Martini [25], and Yadav and Chakraborty
[27] came up with their lists of components that comprise
computational thinking. There is consensus among scholars
about four components which are considered to be funda-
mental to computational thinking as listed below.
(i) Abstraction. A problem should be modeled in a way
that highlights the computational aspects and
obfuscates the rudimentary aspects of the problem
space. Such an abstraction is a way of understanding
the problem [28] and is not bound by algebraic
properties or dimensions of the physical world [18]
(ii) Decomposition. A complex problem is divided into
simpler subproblems. The subproblems are solved
separately, and their solutions are combined to
obtain the solution of the initial problem. Decom-
position is often realized using divide-and-conquer,
i.e., a problem is divided into subproblems that are
similar in nature, or recursion, i.e., a problem is
reformulated in terms of smaller instances of the
same problem
(iii) Algorithm Design. A basic skill in computer science
is to formulate a sequence of computational steps
which if followed generates a solution to a given
problem [12]. The sequence of steps thus formu-
lated applies mathematical logic to obtain efficient,
fair, and secure solutions [28]
(iv) Generalization. Computational thinking is con-
cerned about the application of concepts drawn
from computer science to solve various real-world
problems. Computational thinking will be more
effective if the procedure followed to solve a prob-
lem can be reused to solve another problem in some
other domain [21]
Computer programming is a part of the standard prac-
tices in computer science [11], and writing computer pro-
grams influences the way one thinks. (Computer
programming began in earnest when six young female
mathematics majors, viz., Betty Jennings, Betty Snyder, Fran
Bilas, Kay McNulty, Marlyn Meltzer, and Ruth Lichterman,
were hired to operate ENIAC. Their work, although
remained unnoticed for years, proved that programming a
computer is not same as using an arbitrary apparatus but
needs training, practice, and thinking.) Computer program-
ming helps people to acquire skills necessary for thinking
rigorously and effectively expressing the thought process
[29]. Lewis [30] argued that it is reasonable to assume that
engaging in intellectually demanding tasks facilitates cogni-
tive development in people, and if this argument is correct,
then computer programming is an activity which can cer-
tainly help people to enhance their ability to think and rea-
son. However, the computer science community, including
accomplished computer scientists like Seymour A. Papert
and Mitchel Resnick, is understandably biased when it
comes to reflect on the utility of computer programming
and computational thinking in the real world. (It may be
noted that Papert and Resnick developed influential pro-
gramming languages. Papert and his team developed LOGO
as a programming language to facilitate children to explore
mathematics and science. Alternatively, Resnick and his
team developed Scratch as a language to introduce computer
programming to learners. A comparison of these two types
of programming languages has been provided by Papert
[1] using the examples of LOGO and BASIC programming
languages (p. 29).) For example, Papert [1] tried to show
how engaging in computer programming can help children
to think computationally and explore the world (pp. 28,
98, 105, 114, 154, 176). Similarly, Resnick et al. [31] claimed
that computer programming is necessary to design, create,
and invent using digital technologies, and Brennan and
Resnick [32] claimed that indulging in computer program-
ming improves computational thinking skills. Unfortu-
nately, there is little evidence to prove that proficiency in
computer programming can help a person in nonprogram-
ming endeavors [33] and the claim that computational
thinking can easily be transferred to other disciplines is
largely unsubstantiated [25]. Every academic discipline has
its own set of concepts and techniques. Nevertheless, there
are some skills, such as fundamental mathematics and basic
language skills, which find application across academic disci-
plines and professions. There is a dearth of evidence to show
that computational thinking can be considered in that cate-
gory. Consequently, discussions on computational thinking
should be based on the principles of computer science rather
than skills of computer programming [34]. One of the key
aspects of computational thinking is explainability, and it
can be helpful in upholding ethical considerations in many
domains. Nevertheless, algorithm design bias needs to be
taken care of while researching computational thinking. In
computational thinking, one is expected to be able to unam-
biguously explain the way a problem is analyzed and a solution
is designed. This facilitates communication and collaboration.
5. Conclusion
Computers have now become omnipresent. A vast majority
of the world population today uses computers of some type
for performing some of their daily life tasks. Scientists, engi-
neers, and professionals in other fields use computers to per-
form highly specialized work. Computers also provide
employment to many people in developed as well as devel-
oping countries. In the last eighty years, computer science
4 Human Behavior and Emerging Technologies
has evolved a lot, perhaps more than any other academic
discipline. Computer science has helped in developing com-
puters with increasing sophistication and extending their use
for the benefit of a growing number of people.
Every academic discipline deals with a large body of
knowledge which is continuously refined by scholars.
Scholars from all disciplines engage in critical thinking,
and thinking cannot be considered a monopoly of any par-
ticular discipline. Hemmendinger [24] felt that computer
scientists often present computational thinking in terms
which can be interpreted as arrogant or overstatement by
others. Computational thinking can be considered a univer-
sally valuable skill only if people other than computer scien-
tists use it and find it beneficial. This paper only discusses
how thinking can be influenced by computers and computer
science. The influence of other academic disciplines like
mathematics and philosophy on thinking is also important
and needs to be researched.
As of now, there are three important reasons to teach
computational thinking to learners of various ages. First,
the nature of science and mathematics is changing rapidly
[35], and they are becoming increasingly computational as
Babbage and Papert envisioned long ago. Possessing compu-
tational thinking skills may help people in understanding
science and mathematics better. Second, concrete thinking
is present in five-year-old children, while children develop
formal thinking skills by twelve years of age. Using com-
puters and engaging in computational thinking can help
children to develop formal thinking skills at a younger age
[25]. Third, children who receive lessons in computational
thinking may choose to study computer science when they
grow up not only for career opportunities but also for its
intellectual content [25]. Yadav and Chakraborty [27]
hypothesized that incorporating computational thinking les-
sons at the K-12 level can encourage children to enroll in
engineering programs in the future. Although Wing’s essay
[2] and later research on the topic led to the introduction
of lessons in computational thinking in several schools
around the world, there are many challenges in teaching
computational thinking such as lack of suitable textbooks,
tools and techniques, assessment methods, and trained
teachers.
Data Availability
No primary data was used in this study.
Conflicts of Interest
The author declares that he has no conflicts of interest.
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6 Human Behavior and Emerging Technologies
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