Daily Archives: September 18, 2017

Design Thinking vs Computational Thinking

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IDEO defines design thinking as the application of empathy and experimentation to arrive at innovation solutions through making decisions based on stakeholder input and evidence based research. Design thinking attempts to understand the intent or problem before looking at any solution . It emphasize the importance to identify why the problem exists in the first place before solving it.

Using the HOTS for KSSR Level 1, a design thinker would ask, what is the intent of provide HOTS for KSSR Level 1 instead at the first place?

Based on quicksense, (15 August 2017 – https://blog.quicksense.org/design-thinking-vs-computational-thinking-in-education-2dcf5b23aa12), Vivek Kumar clarified the difference between design thinking and computational thinking using simple  a thought experiment: You need to move 10 boxes from one side of town to the other. How would you do it?

As a computational thinker, a set of instructions would be drafted, tested, and the most efficient route would be attained. Questions that would be asked by a computational thinker could include ‘what are the sizes of the boxes, how heavy are they, and is anything fragile’ to best cater for the most effective action.

In design thinking, the primary question would be ‘why do you want to move the box in the first place?’.

To him, the question ‘why do you want to move the box in the first place’ is the most important question. This frames the problem in a whole new light. An interesting finding could include that you specifically do not need to move the box yourself or that there is something inside the box that needs to be moved, and not the box itself. I think that design thinking shapes computational thinking and it is design thinking that needs to be given the highest priority in our education system.

Computational Thinking

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Computational thinking is the thought processes involved in formulating a problem and expressing its solution(s) in such a way that a computer—human or machine—can effectively carry out. A few characteristics of CT (this points are especially for K12):

  • Using abstractions and pattern recognition to represent the problem in new and different ways
  • Logically organizing and analyzing data
  • Breaking the problem down into smaller parts
  • Approaching the problem using programmatic thinking techniques such as iteration, symbolic representation, and logical operations
  • Reformulating the problem into a series of ordered steps (algorithmic thinking)
  • Identifying, analyzing, and implementing possible solutions with the goal of achieving the most efficient and effective combination of steps and resources
  • Generalizing this problem-solving process to a wide variety of problems

Computational thinking is made up of four parts:

i. Decomposition.

ii. Pattern recognition.

iii. Pattern generalization

iv.  Abstraction.

The criticism

https://www.wired.com/2016/09/how-to-teach-computational-thinking/ define “computational thinking.”  Its intellectual core is about formulating things with enough clarity, and in a systematic enough way, that one can tell a computer how to do them. Mathematical thinking is about formulating things so that one can handle them mathematically, when that’s possible. Computational thinking is a much bigger and broader story, because there are just a lot more things that can be handled computationally.

But how about when we are in a position when the situation which doesn’t have enough clarity and ill-structured? where the situation is always uncertain and complex? When we try to handle the complex situation using complicated approach – clearly mismatched.

Furthermore, [18] Others worry that the emphasis on Computational Thinking encourages computer scientists to think too narrowly about the problems they can solve, thus avoiding the social, ethical and environmental implications of the technology they create.[19][2]

To handle the complex challenge and the narrow mindset – it is important to incorporate CT with design thinking and systemic thinking.

This is because, design thinking attempts to understand the intent or problem before looking at any solution . It emphasize the importance to identify why the problem exists in the first place before solving it. Incorporating CT with design thinking will help to clarify and frames the problem in a whole new light. By broaden the horizons, an innovative solutions might arise without thinkers terikat dengan specific solution that might be wrong and not effective.

However, being creative without systematic approach might bring the thinkers to nowhere. applying CT, we can bridge ideation into realization.

Phase 1: Identify the Problem

  • Using abstractions and pattern recognition to represent the problem in new and different ways (visualize – sketch using structure).
  • Logically organizing and analyzing data
  • Breaking the problem down into smaller parts

 

Phase 2: Solution

  • Reformulating the problem into a series of ordered steps (algorithmic thinking)

 

Phase 3: Evaluation

  • Approaching the problem using programmatic thinking techniques such as iteration, symbolic representation, and logical operations
  • Identifying, analyzing, and implementing possible solutions with the goal of achieving the most efficient and effective combination of steps and resources.
  • Generalizing this problem-solving process to a wide variety of problems? Can it be generalize or not?