For this activity, I prepared t2 related lesson plans (one being “plugged” and using AI tools and other being “unplugged”) that are about teaching AI concepts in the classroom.
Computer Vision: feature extraction
In this unplugged activity, students will explore ways of sorting and classifying animals into groups on their features. Then, they extract data from the features and represent their data. In this way, they can learn how an AI sort and organise data by features.
- Teacher read a storybook about animal that emphasise features of animals.
- Students work in pairs to find as many ways as they can to group images according to their features.
- Students decide on one particular grouping to present to the class.
- What reason led students to group images the way they did?
- What patterns, similarities and differences in animal features did students notice?
- Can students use algorithmic language that incorporate branching (decisions) to explain their decision process?
Why is this relevant?
Feature extraction is an image process technique of AI area named Computer Vision. Ina AI machine learning, this process of looking patterns and identifying features that emerge is what is know as supervised learning technique that the machine would use.
Examples of a real world AI computer vision technology are phone and tablet applications used to identify plant and wildlife species such as “iNaturalist”.
Students will also build their skills and knowledge in Digital Technologies through exploring different types of data and sorting data to discover patterns. In particular it addresses the following content descriptors relating to data for F-4:
Recognise and explore patterns in data and represent data as pictures, symbols and diagrams (ACTDIK002, F-2)
Recognise different types of data and explore how the same data can be represented in different ways (ACTDIK008, Years 3-4)
Additionally, if extending the sorting and classification of image data to include algorithms (students describing algorithmic processes or creating algorithmic representations), the lesson can also provide opportunities for students to:
Follow, describe and represent a sequence of steps and decisions (algorithms) needed to solve simple problems (ACTDIP004, F-2)
Define simple problems, and describe and follow a sequence of steps and decisions (algorithms) needed to solve them (ACTDIP010, Years 3-4)
Computer Vision: image classification
In this plugged activity, students can learn about AI area of computer vision and in particular the technique of image classification.
- Explore the use of vision apps (e.g. AI Poly) to detect objects in their environment.
- As a whole class, students discuss which objects the app correctly guessed and which objects the app had more trouble with.
- Students train AI to see cats or dogs with “Cognimates”.
- What are the similarities and differences with the incorrect guesses? (consider things such as features and attributes: shape, size, distinguish features, brightness, whole or part object)
- Why is this project considered as a supervised learning approach?
- How can we improve the AI model if it was unable to recognise new images?
Why is this relevant?
Concepts of computer vision, image classification and supervised learning are fundamental for learning AI.
In this activity, students will work with sourcing, sorting and classifying digital data and developing their understanding of how AI systems analyse digital data in the form of images. In doing so, students will address some of the following content descriptors in the Australian Curriculum: Digital Technologies relating to their knowledge and understanding of data as well as the use of data with simple software (Cognimates):
Students recognise different types of data (images, numbers, videos) and explore how the same data can be represented in different ways (ACTDIK008, Years 3-4)
Students collect, access and present different types of data using simple software to create information and solve problems (ACTDIP009, Years 3-4)
Students acquire, store and validate different types of data, and use a range of software to interpret and visualise data to create information (ACTDIP016, Years 5-6)
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