Teachable Machine, Samantha Norris

I think a lot of teachers who want to integrate AI into their classroom struggle with where to start and how to build capacity in students understanding it from a foundational level. Samantha Norris gives you a clear outline on how to do just that. In this video, Samantha provides a trainer-of-trainers style of framework for introducing the concept of machine learning as a subset of artificial intelligence to students, then moves into common applications of machine learning and then how to create machine learning models using the online tool Teachable Machine. What I appreciate about Samantha’s presentation is that she aligns the bigger picture of metacognition and how we learn to reasons beyond content knowledge acquisition for creating machine learning models. She points out the teachable moments inherent in identifying a mistake a computer is making and the process of figuring out why the mistake is happening. The correlation to a student understanding that the machine is not “stupid” but just did not have the information yet it needed to understand how this applies to our learning is paramount. Samantha takes what could be a rote training and embeds best practices and concepts of social-emotional learning that you might not normally associate with a training on machine learning. Furthermore, with exposure to the inner workings of how machine learning works, student alignment to goals based on learning analytics derived from machine learning means a more coherent usage of data outputs and increased alignment of personal learning goals to self-regulated learning.

References

AI Teachable Machine.mp4. (n.d.). Google Docs. https://drive.google.com/file/d/1AFWbV2NyPv30oueoWWUOraWJ5C-mBF9U/view