Numerous countries and regions undertaking curriculum redesign within recent years have embraced computational thinking as an essential mindset for students and teachers of the digital age in which live.

    Although Computational Thinking sounds a little nerdy and daunting, the reality is quite the opposite.  Computational Thinking is an easy concept to grasp and a fun concept to teach and learn.

    Let’s look at why Computational Thinking has grown so popular in classrooms worldwide and learn more about it.

    What is Computational Thinking?

    Computational thinking is a problem-solving approach rooted in the principles of computer science. It involves breaking down complex problems into manageable components, devising systematic strategies (algorithms) to solve them, and applying logical and analytical reasoning to reach solutions. Computational thinking goes beyond programming; it’s a versatile cognitive framework that is used across various domains.

    At its core, computational thinking is characterized by four key components:

    1. Decomposition: This involves breaking down intricate problems into smaller, more understandable parts. By dividing a problem into manageable components, individuals can approach it more systematically and address each element independently.
    2. Pattern Recognition: Computational thinking emphasizes the ability to recognize recurring structures or trends within data or information. Identifying patterns enables individuals to make informed decisions and predictions based on existing evidence.
    3. Abstraction: Abstraction involves simplifying complex systems by focusing on essential details while disregarding irrelevant information. This simplification makes it easier to conceptualize and work with complex concepts or systems.
    4. Algorithmic Thinking: Algorithmic thinking entails designing step-by-step procedures or algorithms to solve problems. It encourages individuals to think logically and consider the sequence of actions required to reach a solution.

    Where is Computational Thinking Used?

    • Computer Science and Programming: Computational thinking is foundational to computer science and programming. It underpins the creation of software, algorithms, and coding practices.
    • Science and Research: Researchers use computational thinking to analyze and model complex scientific phenomena, from climate patterns to genetic sequences.
    • Mathematics: It aids in solving mathematical problems, including optimization and data analysis.
    • Engineering: Engineers apply computational thinking to design systems, analyze structural integrity, and optimize processes.
    • Business and Data Analysis: In the business world, computational thinking is employed for data analysis, market research, and process optimization.
    • Healthcare: It plays a role in medical research, disease modelling, and healthcare process improvement.
    • Education: Computational thinking is integrated into educational curricula to enhance problem-solving skills across subjects.
    • Everyday Life: Individuals use computational thinking when planning daily routines, solving puzzles, making decisions, and troubleshooting problems, even if they are unaware of it.

    1:  Computational thinkers are Problem Solvers

    Computational thinking is a structured and proven method to identify problems regardless of age or computer literacy level.  It is made up of four parts.

    • decomposition – breaking down a complex problem or system into more petite, more manageable parts
    • pattern recognition – looking for similarities among and within problems
    • abstraction – focusing on the crucial information only, ignoring irrelevant detail
    • algorithms – developing a step-by-step solution to the problem, or the rules to follow to solve the problem

    This process can be used by students and teachers in an English class to reinforce spelling rules through pattern recognition, and planning.  Create different styles of writing using algorithms and enhance research skills through abstraction.

    2:  Computational Thinkers are Innovators

    An inventor creates something new, but an innovator takes a great idea and enhances it or applies it to a new purpose.

    The process of Abstraction within computational thinking is unique compared to other popular thinking strategies such as De Bono’s Six Thinking Hats.

    When students can determine what to extract from a system or problem in order to create a solution they are forced to think differently about the most essential elements of what they are working with and remove irrelevant factors.

    By doing this, we can laser focus on the tools, resources and skills available to us to create new innovative solutions and directions.

    Students can use abstraction in the classroom to design graphics that communicate a message or emotion and write efficient instruction sequences for others to follow.

    3:  Computational Thinking is research-based and tested.

    Whilst the concept and term of Computational Thinking was first coined and implemented by Seymour Papert in the eighties it was Jeanette Wing who ‘iSTEM eBook | Computer Science | Digital Technologies | Coding | Robotics | AI | Critical Thinking | ICTnnovated’  it to global attention with her research paper identifying the impact computer science, algorithmic design, and technology has upon our society in so many aspects from controlling traffic, finding a partner online and decomposing human DNA down to individual genetic elements.

    You can read Jeanette’s paper here. As a result of Wing’s research, world leaders such as Barack Obama and educational philosophers such as Ken Robinson identified Computational Thinking as an essential skill that opens our student’s minds to using data, technology, resources and people in a manner that shifts us from technology consumers to creators.  

    Companies such as Google, Apple and Microsoft actively recruit and train staff in Computational Thinking as an essential skill and competitive advantage in their marketplace.

    4:  Computational Thinkers leap from consumers to creators.

    Unlike humans, computers are incredible at doing tedious, repetitive tasks with flawless efficiency and accuracy.  But they can only do them when somebody can specifically instruct them what to do and how to do it.

    We call this process in Computational Thinking Algorithmic design, and an algorithm is nothing more than a set of instructions.

    When used in Cooking it is called a recipe.  When used in Mathematics, it is called an equation.  When used in a basketball game, we call it a play; when used in computer science, we call it coding.

    Algorithmic Design is a logical part of the computational thinking process allowing students to create computer instructions using languages such as Scratch and Python, which make computers and machines do things they could previously not.  

    5:  Computational Thinking is simple to teach and fun to learn.

    I am confident any teacher reading this can claim many of the teaching concepts mentioned in this article, even if they were undertaken in isolation or without any knowledge of Computational Thinking.

    Computational Thinking is a skill that can be applied to any area of the curriculum, from Kinder to University level in any walk of life.

    Here are three resources I can strongly recommend all teachers utilize to gain a solid understanding of Computational Thinking and how to apply it to their own teaching and learning roles.

    Google’s Computational Thinking Course for Educators:  This free course can be completed at your leisure within just a few hours using various interactive tools and activities you can then do with your students.  A great starting point.

    Computational Thinking, Coding and Robotics for Teachers:  This could be viewed as the 2016 ‘bible’ of S.T.E.M, Coding, and Computational Thinking.  It ties together hundreds of resources and explains complex concepts in very easy-to-understand language so any teacher can rapidly teach and learn within a modern curriculum.

    CS Unplugged:  Once you understand Computational Thinking and its place within the curriculum, this resource is an incredible collection of over 200 activities from Professor Tim Bell that teach computational thinking to students of all ages without needing a computer.  

    Top Tips for Teaching Computational Thinking

    Teaching computational thinking is a valuable skill that can benefit students in various aspects of their academic and professional lives. Here are five tips for effectively teaching computational thinking:

    1. Start with the Fundamentals: Introduce students to the core concepts of computational thinking. Emphasize the four key components: decomposition, pattern recognition, abstraction, and algorithmic thinking. Explain each concept using relatable examples to ensure students grasp the fundamental principles.
    2. Hands-On Activities and Coding: Engage students in hands-on activities and coding exercises that allow them to practice computational thinking. Coding platforms like Scratch, Python, or Blockly provide interactive environments where students can apply their learned concepts. Start with simple problems and gradually increase the complexity to build their problem-solving skills.
    3. Real-World Problem-Solving: Encourage students to apply computational thinking to real-world problems. Present challenges from various domains, such as mathematics, science, history, or even daily life. Guide them in breaking down these problems into smaller, solvable components and developing algorithms to address them. This practical application helps students see the relevance of computational thinking in their lives.
    4. Collaborative Learning: Foster a collaborative learning environment where students can work together to solve problems. Please encourage them to discuss their approaches, share insights, and provide feedback to one another. Collaboration enhances their problem-solving skills and promotes communication and teamwork, valuable professional skills.
    5. Embrace Failure as a Learning Opportunity: Teach students that failure is a natural part of problem-solving and that it provides valuable learning experiences. Please encourage them to persevere when facing challenges, iterate on their solutions, and learn from their mistakes. Emphasize the growth mindset, where setbacks are viewed as improvement opportunities rather than failures.

    By incorporating these tips into your teaching approach, you can help students develop strong computational thinking skills that will serve them well in their academic and professional journeys. These skills empower students to analyze complex problems, devise innovative solutions, and adapt to the evolving demands of the modern world.

    Computational Thinking classroom connections with the real world?

    Computational thinking in the classroom serves as a bridge between academic learning and real-world problem-solving. When students develop and apply computational thinking skills in educational settings, they acquire a set of versatile cognitive tools that can be directly translated to the challenges they’ll encounter in the real world. Here’s how computational thinking in the classroom translates to the real world:

    1. Problem Decomposition: In the classroom, students learn to break down complex problems into smaller, manageable components. This skill is invaluable in the real world, where individuals often face multifaceted challenges. Whether it’s troubleshooting a malfunctioning device or addressing a complex business issue, the ability to decompose problems helps individuals approach them systematically.
    2. Pattern Recognition: Students are trained to recognize patterns and regularities in data or information. In the real world, this skill aids in data analysis, market research, and even everyday decision-making. Recognizing trends and anomalies in data can inform critical decisions in various fields, from finance to healthcare.
    3. Abstraction: Abstraction involves simplifying complex systems by focusing on essential details while ignoring irrelevant information. This skill is applicable when designing systems, creating models, or understanding complex concepts. In engineering, for instance, abstraction is used to simplify electrical circuits for analysis, making the design process more efficient.
    4. Algorithmic Thinking: Students learn to develop step-by-step procedures (algorithms) to solve problems in the classroom. In the real world, this translates into creating efficient workflows, optimizing processes, and automating repetitive tasks. For example, algorithmic thinking is used in logistics to determine the most efficient routes for delivery trucks.
    5. Creative Problem-Solving: Computational thinking encourages creativity in problem-solving. In everyday life and professional environments, creativity is invaluable for innovation and finding novel solutions. Whether it’s designing a new product, devising a marketing strategy, or resolving interpersonal conflicts, creative problem-solving is a skill sought after in every industry.
    6. Interdisciplinary Applications: Computational thinking isn’t limited to a single discipline. It encourages students to draw from multiple areas of knowledge to solve complex problems. In the real world, this interdisciplinary approach is essential. For instance, a biologist may use computational techniques to analyze genetic data or simulate ecological systems.
    7. Adaptability and Resilience: In the classroom, students learn that encountering errors or setbacks is a natural part of problem-solving, and they develop the ability to iterate on their solutions. This adaptability and resilience are crucial in the real world, where unexpected challenges and changing circumstances are common.
    8. Technology Literacy: Classroom exposure to computational thinking often involves technology tools and platforms. This familiarity with technology prepares students for the technology-driven workplace of the real world. They are better equipped to learn and adapt to new software, tools, and systems in various professional settings.
    9. Data-Driven Decision-Making: Computational thinking encourages the use of data to inform decisions. In the real world, data-driven decision-making is a cornerstone of business strategy, healthcare, policy development, and scientific research. Students who are comfortable working with data are better equipped to make informed choices in their careers.
    10. Global Problem-Solving: Many real-world problems are global in nature, requiring individuals to collaborate across borders and cultures. Computational thinking, which provides a common problem-solving framework, can facilitate effective collaboration in solving complex, global challenges like climate change or public health crises.

    In summary, computational thinking in the classroom is not just an abstract concept; it’s a set of skills and problem-solving techniques that directly translate to the real world. These skills empower individuals to navigate, understand, and address the increasingly complex challenges they encounter in their personal and professional lives, making them more adaptable, creative, and effective problem solvers.

    We have a great 180-page ebook which goes into great detail about computational thinking and how to teach it in the classroom. It can be found here.

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