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Scratch Creative Computing Guide

There is also a series of units comprising learning activities, paired with assessment activities and templates that can be used to support use of the Scratch (MIT) platform. The Scratch Creative Computing Guide supports assessment activities with visual programming environments.

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When I post something online how permanent is it?

Students engage in a photo rip up activity to emphasize the permanency of online information, they explore factor trees, doubling and line graphs through the lens of sharing information, and they collaboratively develop a set of protocols around sharing information online.

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Anti-bullying AI

Sometimes we write and post things on social media in a hurry. Such posts can hurt people and even make them feel bullied. Wouldn't it be great if an Artificial Intelligence application could check our posts as we write them, and warn us if they were potentially hurtful?

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Data bias in AI

Artificial intelligence can sometimes be biased to certain shapes or colours. When such AI systems are applied to situations that involve people, then this bias can manifest itself as bias against skin colour or gender. This lesson explores bias in AI, where it comes from and what can be done to prevent it.

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AI image recognition - exploring limitations and bias

A hands-on activity to practise training and testing an artificial intelligence (AI) model, using cartoon faces, including a discussion about sources of potential algorithmic bias and how to respond to these sources.

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Visual to text coding: Lesson 5

This is the fifth in a series of lessons to transition from visual coding to text-based coding with a general purpose programming language. This lesson may take two to three 45-minute periods. It introduces how to create and use arrays (also called lists).

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Visual to text coding: Lesson 9

This is the ninth in a series of lessons to transition from visual coding to text-based coding with a General Purpose Programming language. This lesson may take two to three 45-minute periods. It explores creating powerful programs for managing and analysing data, by combining the previous skills of using loops and working ...

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Note the music

We can program a computer to play music. Conventionally this is done by hard coding, which is the process of coding all possible expected behaviours. Alternatively, we can train an artificial intelligence (AI) computer about what notes go well with others, so it can play a duet with a human musician. Students can make their ...

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Recognising AI

Use the tasks in this lesson to introduce concepts that underpin artificial intelligence (AI). The majority of the tasks are unplugged (do not require a digital device). Use the downloadable AI cards with your students to explore what they know about AI.

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Turtles: exploring data tracking turtle movements

By gathering data on marine turtles, scientists have evidence that helps them work out where turtles migrate and the journeys they take. Scientists can then help to reduce the threats to the turtles’ survival. In this lesson we look at satellite tracking using real scientific data. Explore ways to model, interpret, represent ...

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Visual to text coding: Lesson 6

This is the sixth in a series of lessons to transition from visual coding to text-based coding with a General Purpose Programming language. This lesson may take two to three 45-minute periods. It builds on the previous introduction to arrays (also called lists) and brings in the length property.

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Visual to text coding: Lesson 11

This is the eleventh in a series of lessons to transition from visual coding to text-based coding with a General Purpose Programming language. It builds on the coding concept of functions. With the addition of parameters, functions allow the programmer to adapt their reusable code’s behaviour, tapping into the Computational ...

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Visual to text coding: Lesson 7

This is the seventh in a series of lessons to transition from visual coding to text-based coding with a General Purpose Programming language. This lesson may take two to three 45-minute periods. It introduces iteration (also called loops).

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Fun projects with language translation

Natural language processing is growing in importance. We often converse with automatic chatbots for customer service without even knowing. We also use online translation services or mobile apps. But how do these services work? Is there artificial intelligence (AI) in them? Three projects are offered to cater for student ...

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Visual to text coding: Lesson 3

This is the third in a series of lessons to transition from visual coding to text-based coding with a general-purpose programming language. This lesson may take two to three 45-minute periods. It introduces how to generate and use random numbers.

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AI quiz

This lesson provides an opportunity for students to draw on their ethical understanding when asked to respond to different scenarios. The scenarios feature information systems that incorporate an AI application. Students are given four options to select which one they believe to be the ‘right’ thing to do. Students develop ...

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Heads or tails

In this lesson we show how to transition from a visual based programming language to using a text-based programming language using the example of a heads or tails coin toss application.

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Protecting dugongs

The Western Australian Marine Science Institution has been collecting data about dugongs off the coast of the Kimberley in Northern WA. We have been provided with the raw data from its sightings trips. In this project students will learn how dugong sightings are conducted, and develop the data-science skills needed to make ...

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Automated soil moisture sensor

The soil moisture sensor project integrates science understandings and computational thinking to solve a problem about sustainable watering practices. This lesson was devised by Trudy Ward, Clarendon Vale Primary School, Tasmania.

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Visual to text coding: Lesson 2

This is the second in a series of lessons to transition from visual coding to text-based coding with a general-purpose programming language. This lesson may take two to three 45-minute periods. It introduces how to make decisions (branching) and identify data types.