Fraction Genius

Slide 1

Lesson Introduction Slide.

Slide 2

Apprise learners of the objectives for the lesson.

Slide 3, 4, 5

Slide 6

Using the example of a pizza, introduce learners to fractions by sharing that fractions are a way to describe how much of a whole something is.

• Ask learners to imagine that they have a whole pizza (6 slices in one whole). If they ate two slices of the pizza, what fraction of the whole pizza did they eat? (A: ⅓)
• Build on this example to help learners visualize fractions (e.g., what if they ate three slices? 4?)

Slide 7

Ask learners how they arrived at the correct fraction. (A: The total number of pizza slices forms the denominator, and the number of slices eaten forms the numerator.)

Revise definitions of numerator and denominator.

Slide 8

Revise the concept of equivalent fractions with the help of the following example:

• There are six learners in a class, and three are wearing blue shirts. What fraction of the class is wearing blue shirts? (Expect answers like: 3/6)
• Would saying that half of the class is wearing blue shirts also be correct?
  • Expect answers to range from Yes and No. Address student misconceptions amongst those who say No.
• Consolidate by sharing that equivalent fractions have the same value. Use other examples as appropriate.

Slide 9

Slide 10, 11

Introduce learners to the rules of the game, listed on slide 10

NOTE: As a less logistics-intensive alternative to Fraction Bingo, teachers may check out these alternative activities.

Slide 12

Slide 13

Share that one way to help a machine recognize fractions is through machine learning.

Introduce the concept of machine learning.

• Machine learning is a computer program that can help computers learn and make data-based decisions. 
• It's like teaching a computer how to think, learn, and improve independently.

Slide 14

Ask them to state instances where they have witnessed machine learning outcomes. Apart from those stated on the slide, other examples of machine learning include:

• Instagram/Tiktok filters
• Music recommendations on Spotify or YouTube

Slide 15

Ask learners if they think machines recognize different types of fruits.

• If learners respond with a “YES,” clarify that untrained machines cannot recognize fruits, but we can train them to do so.

Explain how machine learning works by using a worked example. At the start, a machine cannot recognize fruits or anything else, but we can train it by showing examples of what fruits look like.

• At first, you might show the robot a picture of an apple and tell it that this is an apple. Then you provide images of different kinds of apples from different angles for the machine to understand and identify patterns. 
• Then, you might show the robot a picture of a banana and tell it that this is a banana. Following a similar process for training the machine on many images of bananas would help the machine identify patterns.
• The machine would look at the pictures and learn to recognize their differences.

Slide 16

Now, ask learners how they would train their machine to recognize fractions. 

• Expect responses like providing the machine with fraction images to help it learn fractions.
• To further probe learners, ask: what features or characteristics of fraction shapes are important for the machine to recognize?
• How would you collect and label data to train the machine to recognize fraction shapes?

Slide 17

Slide 18, 19, 20, 21, 22, 23, 24

Introduction to Machine Learning for Kids 

• Get students to navigate on their devices to Machine Learning for Kids. 
• Guide your students through creating a project and training the model.

If it’s your first time using Machine Learning for Kids, please refer to the tech tutorial here.

You can also refer to this video tutorial.

Slide 25, 26, 27, 28, 29

Slide 30

Slide 31, 32, 33, 34

Slide 35

Invite learners to test their model and complete page 2 on this worksheet in a group. Sample answers for two of the questions are given below:

Q. How would you improve the accuracy of your model even further? 

• Add more training data (more images)
• Use a more comprehensive data set (e.g., clearer images, blurry images, bright or dim lighting).
• Supervised learning - use models which allow for feedback to be given to improve the accuracy of the model

Q. How would you use machine learning to improve your classroom or school? Give an example.

• Detect if students are wearing a mask or not to prevent the spread of disease.
• Face recognition - attendance taking
• Convert speech to text for note-taking

Slide 36, 37