Ask students to screen capture one of their images and write a story about it.Have students summarize one of their illustrations, describing what they just drew through a voluntary share aloud or a Timed RoundRobin.Some ideas for classroom application include: I can easily see Quick, Draw! being used with English Language Learners, especially those who are at the low-beginning to mid-beginning levels in writing English as their second language. The notion is for students to capture as many details as they can during the time period. Students are encouraged to quickly draw as many simple sketches as possible. So, I asked myself, how can this be used in the classroom? I was quickly reminded of the Quick Draw strategy, where students are asked to respond to a piece of text (literary or expository) through illustrations. Give it a try and see how many of your doodles can be recognized. Scroll down and you’ll see the images that the neural network thinks a bee looks like.
First, you see what kind of object the computer thought you were going to draw. Let’s use my pathetic attempt at a bee as an example (try not to laugh). After you complete a game, just select one of the drawings and you’ll see two sets of results. My iPhone try went a bit better, but that’s probably because the objects were fairly simple.Īn interesting feature of Quick, Draw! is the insight players can gain about how the network works. My first time playing on the laptop, I only got two out of six. However, I have to admit, I’m not very good at it. I gave it a try on my laptop and my iPhone. While the player doodles, the neural network throws out its best guesses of the subject, stopping mid-sketch if it guesses correctly. Players are prompted to sketch an object on a 20-second clock using the mouse or touchscreen. Quick, Draw!, which launched last November, is a game built with machine learning. At the recent TCEA Convention and Exposition, I learned about an experiment from Google called Quick, Draw!.
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