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COMPUTATIONAL THINKING

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Siyabonga Mathebula
Siyabonga Mathebula

Making use of computational thinking reasoning to address issues arising in the current evolving world.

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COMPUTA TIONAL THINKING
TABLE OF CONTENT CONTENT PAGE NUMBER 1. COMPUTATIONAL THINKING 3RD SLIDE 2. THE FOUR PILLARS OF COMPUTATIONAL THINKING 4TH SLIDE 3. BENEFITS OF TEACHING COMPUTATIONAL THINKING AT SCHOOLS 5TH SLIDE 4. REFERENCES 6TH SLIDE
CONCEPT OF COMPUTATIONAL THINKING THE GENERAL DEFINITION WOULD BE TO THINK LIKE A COMPUTER, WHEREBY AN INDIVIDUAL IS EQUPPED WITH SKILLS AND PRACTICES OF BEING ABLE TO SOLVE PROBLEMS USING REASONING ACQUIRED FROM COMPUTING. IT IS MADE POSSIBLE BY FOLLOWING FOUR STEPS WHICH ARE: DECOMPOSITION PATTERN RECOGNITION ABSTRACTION ALGORITHM DESIGN
FOUR PILLARS OF COMPUTATIONAL THINKING 1.DECOMPOSITION THIS IS THE WAY OF ANALYSING A PROBLEM AND IN ORDER TO BE ABLE TO BREAK THE PROBLEM DOWN BY ITS STRUCTURE, FUNCTION, SEQUENCE AND DEPENDANCE (RICH ET AL, 2019) 2. PATTERN RECOGNITION THE PROCESS WHEREBY ONE INTERPRETES AND ANLYSES THE INFORMATION AT SUCH AS IMAGES AND TEXTS TO CREATE CONNECTIONS ON HOW THEY COMPLEMENT EACH OTHER. 3. ABSTRACTION THE PROCESS OF DISCARDING EVERYTHING THAT WILL NOT HELP YOU IN SOLVING THE PROBLEM AND FOCUS ON THOSE ASPECTS THAT WILL GIVE US A SOLUTION AT THE END. 4. ALGORITHM DESIGN ALMOST THE SAME AS CODING WHEREBY, WE INTRODUCE APPROPRIATE STEPS WHICH WILL PROVIDE GUIDANCE IN SOLVING THE PROBLEM CORRECTLY
BENEFITS OF TEACHING CT IN SCHOOLS EQUIP LEARNERS WITH TECHNOLOGICAL SKILLS ENABLES REAL-WORLD PROBLEM SOLVING PREPARES LEARNERS TO ADDRESS FUTURE CHALLENGES HELP LEARNERS TO GAIN A DEEPER UNDERSTANDING OF FOUNDATIONAL SCIENTIFIC PRINCIPLES ENHANCES LEARNERS MATHEMATICAL SKILLS WATCH THE FOLLOWING VIDEO FOR MORE INFORMATION
REFERENCES LIST Calderon, A. C., Crick, T., & Tryfona, C. (2015, July). Developing computational thinking through pattern recognition in early years education. In Proceedings of the 2015 British HCI conference (pp. 259-260). Cetin, I., & Dubinsky, E. (2017). Reflective abstraction in computational thinking. The Journal of Mathematical Behavior, 47, 70-80. Navlakha, S., & BarJoseph, Z. (2011). Algorithms in nature: the convergence of systems biology and computational thinking. Molecular systems biology, 7(1), 546. Rich, P. J., Egan, G., & Ellsworth, J. (2019, July). A framework for decomposition in computational thinking. In Proceedings of the 2019 ACM conference on innovation and technology in computer science education (pp. 416-421).

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COMPUTATIONAL THINKING

  • 2. TABLE OF CONTENT CONTENT PAGE NUMBER 1. COMPUTATIONAL THINKING 3RD SLIDE 2. THE FOUR PILLARS OF COMPUTATIONAL THINKING 4TH SLIDE 3. BENEFITS OF TEACHING COMPUTATIONAL THINKING AT SCHOOLS 5TH SLIDE 4. REFERENCES 6TH SLIDE
  • 3. CONCEPT OF COMPUTATIONAL THINKING THE GENERAL DEFINITION WOULD BE TO THINK LIKE A COMPUTER, WHEREBY AN INDIVIDUAL IS EQUPPED WITH SKILLS AND PRACTICES OF BEING ABLE TO SOLVE PROBLEMS USING REASONING ACQUIRED FROM COMPUTING. IT IS MADE POSSIBLE BY FOLLOWING FOUR STEPS WHICH ARE: DECOMPOSITION PATTERN RECOGNITION ABSTRACTION ALGORITHM DESIGN
  • 4. FOUR PILLARS OF COMPUTATIONAL THINKING 1.DECOMPOSITION THIS IS THE WAY OF ANALYSING A PROBLEM AND IN ORDER TO BE ABLE TO BREAK THE PROBLEM DOWN BY ITS STRUCTURE, FUNCTION, SEQUENCE AND DEPENDANCE (RICH ET AL, 2019) 2. PATTERN RECOGNITION THE PROCESS WHEREBY ONE INTERPRETES AND ANLYSES THE INFORMATION AT SUCH AS IMAGES AND TEXTS TO CREATE CONNECTIONS ON HOW THEY COMPLEMENT EACH OTHER. 3. ABSTRACTION THE PROCESS OF DISCARDING EVERYTHING THAT WILL NOT HELP YOU IN SOLVING THE PROBLEM AND FOCUS ON THOSE ASPECTS THAT WILL GIVE US A SOLUTION AT THE END. 4. ALGORITHM DESIGN ALMOST THE SAME AS CODING WHEREBY, WE INTRODUCE APPROPRIATE STEPS WHICH WILL PROVIDE GUIDANCE IN SOLVING THE PROBLEM CORRECTLY
  • 5. BENEFITS OF TEACHING CT IN SCHOOLS EQUIP LEARNERS WITH TECHNOLOGICAL SKILLS ENABLES REAL-WORLD PROBLEM SOLVING PREPARES LEARNERS TO ADDRESS FUTURE CHALLENGES HELP LEARNERS TO GAIN A DEEPER UNDERSTANDING OF FOUNDATIONAL SCIENTIFIC PRINCIPLES ENHANCES LEARNERS MATHEMATICAL SKILLS WATCH THE FOLLOWING VIDEO FOR MORE INFORMATION
  • 6. REFERENCES LIST Calderon, A. C., Crick, T., & Tryfona, C. (2015, July). Developing computational thinking through pattern recognition in early years education. In Proceedings of the 2015 British HCI conference (pp. 259-260). Cetin, I., & Dubinsky, E. (2017). Reflective abstraction in computational thinking. The Journal of Mathematical Behavior, 47, 70-80. Navlakha, S., & BarJoseph, Z. (2011). Algorithms in nature: the convergence of systems biology and computational thinking. Molecular systems biology, 7(1), 546. Rich, P. J., Egan, G., & Ellsworth, J. (2019, July). A framework for decomposition in computational thinking. In Proceedings of the 2019 ACM conference on innovation and technology in computer science education (pp. 416-421).