Introduction to CT

1. INTRODUCTION TO
COMPUTATIONAL THINKING IN
SCIENCE CLASSROOM

2. MENU

3. INTRODUCTION
• Computational thinking (CT) is the process of formulating problems and
representing their solutions in a form that an information processing agent can
effectively carry out (Wing, 2011).
• CT also entails using fundamental computing principles to address issues, create
structures, and comprehend human behavior (Wing, 2006, 2008)

4. PILLARS OF COMPUTATIONAL THINKING

5. To think computationally
through a difficult problem,
task, or activity, you should be
familiar with the following five
pillars/principles of CT
PILLARS OF CT
DECOMPOSITION
ABSTRACTION
PATTERN RECOGNITION
ALGORITHM DESIGN
AUTOMATION

6. DECOMPOSITION
• This involves breaking down a complex task into smaller, and more
manageable components.
• This could be achieved through
• Analysing a problem: breaking a problem into parts
• Synthesising a problem: combining solutions to small subproblems
to solve the large problem.
• Parallelization: solving subproblems simultaneously
• Sequential: solving subproblems in specific order.
• Example can include asking students to research the different organs
in order to understand how the human body digests food.

7. ABSTRACTION
• This involves identification of particular similarities and differences
between comparable problems to work towards a solution.
• It aids in the development of problem-related models that can handle
large amounts and ranges of data.
• Examples include:
• Learning about physics using a ball and ramp
• Experimenting and graphing results in an acceleration lab.
• Developing laws and theorems by looking at similar formulas and
equations.

8. PATTERN RECOGNITION
• This involves identifying and defining trends or patterns within a
problem.
• Patterns make it easier to see the relationships between different
parts of a larger problem and to decide what actions can and must be
taken to address it.
• Example can include asking students to classify animal based on their
characteristics and articulate common characteristics for the
groupings.

9. ALGORITHM DESIGN
• This involves the development of step-by-step rules or instructions for
solving a problem or completing a task.
• The instruction can be used again to answer similar problems.
• The instructions must be precise and unambiguous.
• Examples include
• Following a recipe or direction
• Getting dressed.

10. AUTOMATION
• This involves the use of technological tools to mechanize problem
solutions/perform tasks with reduced human assistance.
The principle of automation can be applied to tasks that are:
• Repetitive: This include tedious tasks like copy and paste, data
entry, and switching between tabs
• Fragile: This are tasks or activities that involve a high level of
human error such as typos, forgotten checklists or coordinated
changes steps; and
• Timely: This include recurring tasks or activities such as reminders
and automatic/instant responses.

11. CT ATTITUDES AND DISPOSITIONS
• Ability to handle difficulty tasks with assurance
• Perseverance in tackling challenging issues
• Acceptance of ambiguity
• The capacity to handle unresolved (open) issues/problems
• The ability to cooperate and communicate with others in order to
reach a common objective or fix a problem.

12. COMPUTATIONAL THINKING
STEM PRACTICE

13. According to Weintrop et al. (2016), computational thinking can be
implemented in Science, Technology, Engineering and Mathematics
(STEM) classrooms using the following 4 CT-STEM Practices:
• Data Practices
• Modeling and Simulation Practices
• Computational Problem-Solving Practices
• System Thinking Practices
CT STEM PRACTICES

14. Data Practices
This involves ability to:
• Collect data using multiple computational tools
• Create or generate data for large and complex systems
• Manipulate or reorganize data in a meaningful way
• Use computational tools to analyze data and draw valid conclusions
• Communicate and present (visualiza) data in multiple way.

15. Modeling and Simulation Practices
This involves ability of learners to:
• Use computational models to understand a concept
• Use computational models to answer problems through hypothesis
testing
• Assess or evaluate the effectiveness of models
• Select essential elements for models (design)
• Implement or construct new models or extend existing models.

16. Computational Problem-Solving Practices
This involves ability to :
• decompose and reframe problems using suitable computational
tools for solutions
• possess basic programming knowledge and skills
• evaluate pros and cons of computational tools in order to select
effective ones
• assess pros and cons of possible approaches/ solutions to a
problem
• develop solutions that can be applied to a wide range of
problems
• distill the most relevant information from a problem

17. System Thinking Practices
This involves ability to :
• Investigate and understand system functions as a whole
• Understand the operation and interrelationship of elements in a
system
• Think from multiple perspectives and levels convey information
about a system effectively and efficiently
• Define scope of systems and manage complexity.

18. TEST
You have come to the assessment section. Please read the statements and provide your answers.
Remember to click on the submit button after filling in your answers.

19. TEST
1. Describe computational thinking?
2. Identify and discuss the fundamental tenets of computational
thinking.
Time: 40 minutes
3. Explain briefly how teachers can incorporate computational
thinking into their science instruction.

20. SUMMARY
• Computational thinking is a problem-solving process that is based
on computer science principles.
• The five key principles of computational thinking are
decomposition, abstraction, pattern recognition, algorithm
design and automation.
• Computational thinking can be integrated into science classrooms
through five practices: data practices, modeling and simulation
practices, computational problem-solving practices and system
thinking practices.

21. REFERENCES
• Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., &
Wilensky, U. (2016). Defining computational thinking for mathematics and
science classrooms. Journal of Science Education and Technology, 25(1),
127–147. https://doi.org/10.1007/s10956-015-9581-5
• Wing, J. M. (2006). Computational thinking. Communications of the ACM,
49(3), 33–35. https://doi. org/10.1145/1118178.1118215
• Wing, J. M. (2008). Computational thinking and thinking about
computing. Philosophical: Mathematical, Physical and Engineering
Sciences, 366(1881), 3717–3725. https://doi.org/10.1109/
ipdps.2008.4536091
• Wing, J.M. (2011), Research Notebook: Computational thinking -what and
why? Th Link Magazine, 20–23. https://www.cs.cmu.edu/link/research-
notebook-computational-thinking-what-and-why

22. Thank You
• Presenter name: Ogegbo Ayodele
• Email address:
• Website address