Four Computational Thinking Strategies to Develop Problem-Solving Skills Across the Curriculum

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Algorithmic design – establish steps and rules for solving problems

Most problems will require students to employ multiple strategies. Julie Evans, CEO of the non-profit organization Project Tomorrow, illustrated this point by asking participants in a session to draw a cat in less than 30 seconds. No two drawings looked exactly alike, but participating educators had to quickly break down their mental image of a cat into important parts, such as a tail and whiskers (decomposition). They discarded unnecessary data; for example, a cat can be conveyed by drawing its head and body or just its face (abstraction). And they imagined and executed steps to go from a blank page to a finished drawing (algorithmic design).

Bryan Cox, who works at the Georgia Department of Education to expand computer science education, offered practical and pedagogical reasons for integration. Not all schools offer computer science and even in schools that do, not all students take these courses. For elementary school teachers, stand-alone computer lessons can seem like just one more thing to add to an already packed curriculum. “Integration is less disruptive,” Cox said. He also said the integration mirrors how computational thinking happens in the real world in areas like medicine, automotive, law, and sports.

Over the past two years, Project Tomorrow has trained 120 New York City elementary school teachers to incorporate computational thinking into their classrooms. In an example of a second- and third-grade writing unit, students wrote a realistic fictional story and created a film to bring the story to life. It might seem like a fairly typical language arts project, but the difference was in the approach, according to Project Tomorrow’s instructional coach David Gomez. Rather than being told how to write a realistic fictional story, students developed an algorithm for the process, with steps such as creating a fictional character, naming the character, imagining decor, etc. In this example and others, Gomez said algorithms help students recognize the steps they take during a task and increase their awareness of their work processes.

Gomez works with teachers to help students recognize when they are also using other computational thinking strategies. A second-grade teacher, for example, used a poster with sticky notes to get students reflect on the strategies they had used in different topics throughout the day.

Evans said she loves hearing the kids identify strategies when talking to each other. She heard questions like “Have you tried abstraction? and “Why didn’t you pattern recognitionof students chatting with classmates. “These little sophomore kids are already developing their problem-solving muscles, and they have the vocabulary to make it a lasting skill for the future,” he said. she declared.

Elaboration of calculation problems

Not all questions or problems are IT related. Carolyn Sykora, Senior Director of ISTE Standards Programs, shared three characteristics teachers can use to identify a computer problem:

  • It is open with multiple potential solutions. “How can we design a car to get from point A to point B?” is an example that meets this criterion, while “How does a self-driving car work?” is a knowledge-based question.
  • This requires using or collecting data. Data doesn’t just mean numbers. These can be, for example, the lines of a poem or the notes of a musical composition.
  • It includes the possibility of creating a procedure or an algorithm. In some cases, such as an engineering challenge, it is easy to identify where this opportunity will arise. But often it is not so clear. “Sometimes you don’t understand where algorithm design comes in until you break down your problem,” Sykora said.

Using these features can help teachers redesign the curriculum, rather than trying to add something new. “We have our tried and true lessons and the things we want our kids to learn,” Sykora said. The next step is to look at these lessons and ask yourself, “How can we take something that is knowledge-based and turn it into a computational problem?”



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