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David Hellam
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Google for Education Certified Innovator: GTAUK 12, Raspberry Pi Certified Educator
Google for Education Certified Innovator: GTAUK 12, Raspberry Pi Certified Educator

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OK - here's my final project. I've listed three sets of activities I might use in this year's Global Virtual Classroom project with two other partner schools to encourage computational thinking through creating their own visualisations of data.

#grade9   #visualisations   #computerscience   #scratch   #gafe   #crosscurricular   #project   

To me, it isn't a case of which topics in a curriculum can be used as a context to teach computational thinking. Any topic could be used. However, some topics are more appropriate than others.

To me, the question is, which topics would benefit most from being taught in a way that encourages computational thinking?

I have been doing some thinking about how my computer science teaching builds computational thinking, so here are a couple of examples from my courses to show the types of thinking I try to encourage:

Interactive Animations (sixth grade, 11 year olds)

Decomposition: recognising the different elements which will make up the overall animated image.

Pattern recognition: experimentation with delays, loops and movement of sprites to simulate natural-looking motion on screen.

Abstraction/generalisation: use of variables to give an impression of depth and scale, keep track of interactions between sprites and simulating physical processes.

Algorithm design: planning which set of actions will be triggered by different events on screen.

GUI Controls (eleventh grade ,16 year olds)

Decomposition: recognition of different elements on screen which will act as buttons, sliders, checkboxes etc.

Pattern recognition: determining how the appearance of a control will change as the end-user interacts with them.

Abstraction/generalisation: designing controls to make it easier to re-use them in different contexts.

Algorithm design: adding code to be executed when a control is selected.

From the lesson on exploring algorithms:

1) I am passionate about equipping people with the skills they need to become independent problem-solvers. Computational thinking skills are an important set of tools to help us solve a wider range of problems than we could otherwise tackle. Obviously, thinking algorithmically and recognising patterns are useful in themselves, but when combined with the speed and capacity for automation that a computer allows, the possibilities are endless, bounded by the limits of our own creativity and imaginations.

2) I suspect the obvious answer to the question on application would be big data. Huge datasets, released by governments and large organisations to allow individuals to reprocess and display in different ways to reveal new information, but instead I would suggest that crowd sourced data exchange projects would be a much more fruitful development. I have conducted more conventional data exchanges between my students and partner classes round the world since the 1990s, but have usually used more conventional tools to represent the information they have discovered. However, there is nothing to stop a class from creating their own tools to make more interesting visualisations...

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Hi, I teach computer science at an international school in Prague, Czech Republic. My main reason for joining this course is to see whether it would be suitable for some of our core curriculum teachers.

We try to encourage problem-solving by allowing students to make an informed choice of which tools they would use for certain types of task. So, for a presentation; if a student is working collaboratively, Google Slides would be the ideal solution; but if their presentation involves working with random data to model a real-life  process,  or giving intelligent feedback to audience choices, then a tool like Scratch would be better. 

I'm looking for resources that would help my colleagues see the bigger picture of what computational thinking can add to enhance our students' problem-solving skills.
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