Data Rich vs. Data Savvy: personalized learning and ai

How will the algorithm and AI influence personalized learning in K-12?

1. Data Rich vs.Data Savvy How will the algorithm and AI influence personalized learning in K-12? Monday, April 10th, 2017 Bob Tarle OCT / EAO Executive Director, Innovation & Technology TFS - Canada's International School [email protected]

2. CAIS Standard 3 Guiding Questions How does the school continually ensure that all students experience high quality online learning? Evaluate a recent training program for effective online teaching, including assessment practices. How could the PD for online learning be enhanced?

3. Shared Practice: Google Classroom

4. SAMR and Bloom

5. “ Differentiation ”

6. “Adaptive learning” and “AI” technology promise to help personalize learning

7. Mr. Pric e TFS Music Teacher

8. Mr. Price TFS Music Teacher

9. Mr. Price TFS Music Teacher

10. Mr. Price TFS Music Teacher

11. Mr. Price TFS Music Teacher

12. “Perceptual Noise Shaping” and the Algorithm There are certain sounds that the human ear cannot hear. There are certain sounds that the human ear hears much better than others. If there are two sounds playing simultaneously, we hear the louder one but cannot hear the softer one.

13. MP3 & the Internet Distribution : i.e., Napster

14. MP3 & the Internet Data Mining: I.e., Spotify’s “Discover” weekly

15. Google CS First

16. “Computational Thinking” Learning to program is ultimately about learning to think logically and to approach problems methodically. Decomposition: Breaking down data, processes, or problems into smaller, manageable parts Pattern Recognition: Observing patterns, trends, and regularities in data Abstraction: Identifying the general principles that generate these patterns Algorithm Design: Developing the step by step instructions for solving this and similar problems Link: https://www.cs-first.com/create

17. The data volumes are exploding, more data has been created in the past two years than in the entire previous history of the human race. We perform over 1 trillion Google searches per year 1 billion people have used Facebook in a single day. More than 1 billion smart phones shipped in 2016 - all packed with sensors capable of collecting all kinds of data, not to mention the data the users create themselves... At the moment less than 0.5% of all data is ever analysed and used… Data Sets

18. Data Sets In 1854 , a London physician named John Snow helped squelch a cholera outbreak that had killed 616 residents. Brushing aside the prevailing theory of the disease—deadly miasma—he surveyed relatives of the dead about their daily routines. A map he made connected the disease to drinking habits: tall stacks of black lines, each representing a death, grew around a water pump on Broad Street in Soho that happened to be near a leaking cesspool. His theory: The disease was in the water. Classic principles of computational thinking came into play here, including merging two datasets to reveal something new (locations of deaths plus locations of water pumps), running the same process over and over and testing the results, and pattern recognition . The pump was closed, and the outbreak subsided.

19. Where are your students Inputting data?

20. Artificial Intelligence Time-to-Adoption Horizon: Four to Five Years Neural networks, a significant area of AI research, is currently proving to be valuable for more natural user interfaces through voice recognition and natural language processing, allowing humans to interact with machines similarly to how they interact with each other. By design, neural networks model the biological function of animal brains to interpret and react to specific inputs such as words and tone of voice. As the underlying technologies continue to develop, AI has the potential to enhance online learning, adaptive learning software, and simulations in ways that more intuitively respond to and engage with students. http://cdn.nmc.org/media/2016-nmc-cosn-horizon-report-k12-EN.pdf

21. AI Acquisitions

22. AI based on Neural Networks...

23. Dr. Wilder Penfield

24. Geoffrey Hinton “ Neural Networks”

25. Yoshua Bengio “Neural Networks”

26. “ZPD” Jean Piaget & Lev Vygotsky

27. “Spacing Effect”

28. Voice “AI”

29. Will Voice “AI” decrease screen time?

30. “ Xiaoice” ChatBot Personal Assistant

31. Teddy Ruxpin

32. Describe a conversation that a parent might have with a student - and the student’s “AI” personal assistant - on the commute to school?

33. Prediction is key to AI Sam "Ace" Rothstein, gambling handicapper

34. Data Rich vs.Data Savvy How will the algorithm influence personalized learning in K-12? Monday, April 10th, 2017 Bob Tarle OCT / EAO Executive Director, Innovation & Technology TFS - Canada's International School [email protected]