A brief overview of what computational thinking is and its different components.

1. COMPUTATIONAL THINKING
THE
PROCESS
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Email:
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2. Contents
What is Computational Thinking........................4
The 4 pillars of computational thinking…………5
Decomposition …………………………………..6
Pattern recognition………………………………7
Abstraction………………………………………..8
Algorithm …………………………………………9
computational thinking in the classroom………10
Summarizing video………………………………11
Assessment Activity……………………………..11
Reference list ……………………………………12
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3. What is Computational
Thinking
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4. Computational Thinking
The 21st century is marked by the ever-changing technology
advances which makes life a bit easier, a learner can simply put
a complex sum in an online calculator and get the desired
answer. The computer or mobile application is designed to
analyse the problem and show the calculated steps, does this
really guarantees that effective learning took place? The answer
is no, the computer did all the work. This is where the concept
of computational thinking arises, learners should be encouraged
to be computational thinkers as this will assist them to be able
to solve problems they come across in class and in the real
world. Computational thinking is a methodology of instructing
that utilizes a various scope of procedures derived from
computers for the goal of problem solving joined with
competencies such as critical thinking and collaboration
(Brackmann, Barone, Casali, Boucinha & Muñoz-Hernandez,
2019). Introduction of computational to learners will broaden
their understanding as it will allow them to deeply to interact
with a problem and come up with solutions using guided step by
step process just like computers do. The notion of
computational thinking can be applied in every day to day
problem solving, just as computers, human beings can also be
able to solve a complex problem by breaking it down into
smaller tasks, recognize trends, abstract information and design
a set of rules to come up with a solution. Computational thinking
utilizes four step by step techniques in coming up with a
solution to a problem. The four pillars of computational thinking
are: Decomposition, Abstraction, Pattern recognition and
Algorithms which will be further discussed later in this book.
These pillars play their independent roles in coming up with
effective solutions to problems.
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5. Pillars of Computational
Thinking
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6. The first step involves breaking a problem down into smaller
and manageable part. In this step the problem is assessed and
broken down in parts which will make it easier to recognise all
the steps to be followed to solve a problem. Lui, Gu and Zhang
(2013) views decomposition as a simplified way of problem
solving as it allows problem solvers to divide a great task into a
series of smaller tasks which can be solved one task at a
moment to achieve a bigger goal. In mathematics, learners can
be given a task to calculate the total surface area of a
rectangular prism. At first learners can panic and try to look for a
formula to calculate this complex problem, this problem can be
solved by finding the areas of the squares and rectangles of a
net rectangular prism and at the end sum up the individual
areas to get the total surface area of the rectangular prism.
This approach encourages critical thinking as it allows problem
solvers to critically engage with the problem in search for the
best possible solution.
Decomposition
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7. Abstraction
According to Brackman, Barone, Casali et al (2016) abstraction
comes after pattern recognition and it is a process where focus
is just on the information that is significant or relevant to a
specific problem while unessential information is disregarded.
This process allows problem solvers to identify crucial
information about solving the problem at hand. Abstraction is
convenient because it moves away from all general approaches
which usually throws away details and mainly focuses desirable
approaches and integrate the old patterns into the new problem
which can then be solved easily by relevant information.
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8. Pattern Recognition
Recognizing similarities and trends in a problem is very important in
coming up to a solution in problem solving. Pattern recognition is
concerned with identifying similarities and differences in the broken
down groups of the problem (Karyakarte & Savant, 2019). This step is
important as it extract trends and patterns involved in that particular
problem. The utilization of pattern recognition is applicable to most
strategies of problem solving. For an example, in a chemistry
laboratory there is a trend in which elements or compounds behave
when exposed to high or low temperatures. In order to be able to
group elements and compounds based on their classifications one
should first look for similarities in chemical or physical changes then
from there they can be grouped according to their behaviour.
Another way of recognizing or coming up with a pattern to solve a
problem is by reflecting back and look at how the similar problem has
been solved previously, incorporate those ways with new ones and
become a step closer in completing the pattern of solving a certain
problem.
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9. Algorithm
This is the process of establishing an order in which a problem
can be solved. Lockwood, Asay, DeJarrette, Thomas (2016,
p.1591) explained algorithms as a logical, organized way of
thinking used to separate a confounded objective into a
progression of ordered steps utilizing accessible instruments.
For example, when drawing graphs one will first need an
equation in relation with the graph to be drawn, calculate the x
and y components of the graph, plot the points on the set of
axis then finally connect the points to form the graph required.
In determining the rules to be followed in solving a problem one
should stop and think if this is the only approach to the solution
or are there any other ways to reach the desired solution, the
chosen approach can from person to person depending on
which way is more convenient for them. Algorithms is a step by
step problem-solving process which makes solving basic or
complex problems easier.
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10. Using Computational Thinking
in my discipline: Mathematics
The 21st century classroom is very evolving with the technology
advances, learning is slowly moving online and this mean that
learners are beginning to depend on online information to learn.
Online tools at the learners disposals really come in handy to
aid learning. This way of solving problems in learning comes
with a bit of a disadvantage as learners become too reliable on
these tools and end up not focusing on learning the manual way
of calculating and face challenges in tests when such tools are
not allowed in the test room. Learners need to be taught in a
way in which they will be tested on and this means that the only
calculators allowed are the standard pocket scientific
calculators which provide answers only therefore learners
should be able to decompose a problem, recognise patterns,
abstract relevant information and follow the step by step method
preferred to come to a solution. I will encourage learners in my
class to try by all means to apply the computational thinking
process to work on problems and then refer to online
calculators to verify their work when studying or completing
homeworks, this will allow them the opportunity to check if they
understood what was taught in class by making use of online
tools instead of waiting for the next class to consult with the
teacher.
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11. Assessment Activity
The concept of computational thinking is a broad one and it can
take a while before one is fully familiar with it. The following
video provides a summary of what computational thinking is and
it also shed a light into the four pillars of the computational
thinking process. Click here to watch the video.
”All work and no play makes Jack a dull boy”, now that we have
read the book and watched the summary video on The
Computational Thinking Process it is time to put our
understanding to test. The following quiz will help you review
your understanding of The Computational Thinking process.
Click here to access the quiz, Room name is BHEMBE.
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12. References
Brackmann, C., Barone, D., Casali, A., Boucinha, R. and Munoz-Hernandez, S.,
2016, September. Computational thinking: Panorama of the Americas. In 2016
international symposium on computers in Education (SIIE) (pp. 1-6). IEEE.
Available from:
https://www.researchgate.net/profile/Christian_Brackmann2/publication/310807767_Computational_think
ing_Panorama_of_the_Americas/links/5aefbba2458515f599845b90/Computational-thinking-Panorama-
of-the-Americas.pdf
Karyakarte, S. and Savant, I., 2019. Pattern Recognition Process, Methods and
Applications in Artificial Intelligence. Pattern Recognition, 6(11).
Liu, H.L., Gu, F. and Zhang, Q., 2013. Decomposition of a multiobjective
optimization problem into a number of simple multiobjective sub problems. IEEE
transactions on evolutionary computation, 18(3), pp.450-455. Available from:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.719.9353&rep=rep1&type=pdf
Lockwood, E., DeJarnette, A.F., Asay, A. and Thomas, M., 2016. Algorithmic
Thinking: An Initial Characterization of Computational Thinking in
Mathematics. North American Chapter of the International Group for the
Psychology of Mathematics Education. Available from:
https://files.eric.ed.gov/fulltext/ED583797.pdf
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13. Additional information
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Who Date of
birth
Place of
birth
Currently
residing in
which town
Palesa Bhembe 1997-11-21 Kathlehong Johannesbu
rg
Salome Bhembe Mother 1970-07-22 Kathlehong Malelane
Naledi Mtetwa Cousin 2002-04-26 Germiston Centurion