Ten Big Data, AI, and Machine Learning Projects for the K12 Classroom
Explore and create : BYOD
Monday, June 24, 8:30–9:30 am
Location: Room 117
Dr. Scott Garrigan
Machine learning relies on analyzing masses of data in ways that have been inaccessible to kids and teacher. Let’s change that with 10 interactive explorations for kids from first to 12th grade. Ten activities on common devices will demystify artificial intelligence and build computational thinking skills.
|Audience:||Coaches, Teachers, Principals/head teachers|
|Attendee devices:||Devices required|
|Attendee device specification:||Laptop: Chromebook, Mac, PC
Tablet: Android, iOS, Windows
|Participant accounts, software and other materials:||No advance accounts are needed.
Prior to session, I will e-mail registered participants of specific software they may wish to download in advance.
|Focus:||Digital age teaching & learning|
|Topic:||Computer science and computational thinking|
|Subject area:||STEM/STEAM, Computer science|
|ISTE Standards:||For Educators:
Purpose: This session addresses the huge gap between current curriculum and the skills students will need in the near future for school, work, and life. The activities use tools that are commonly available and can be implemented and replicated anywhere. The presenter will suggest ways teachers can integrate these activities into different subjects and levels. The insights through computational thinking for both teacher and students are priceless.
Objective #1: Teachers will learn practical ways to integrate powerful emerging technologies through interactive classroom activities.
Objective #2: Teachers will learn ways to develop power computational thinking skills for themselves and their students that will be needed to thrive in the coming age of machine learning.
Note: The specific activities are described in the detailed outline below.
These activities have been successful in classrooms whose teachers were trained through Dr. Garrigan’s graduate courses and professional development workshops. The newest activities will have been refined in a graduate “Artificial Intelligence and Machine Learning for K-12 Educators” course to be taught spring 2019. Several of the activities have been shared with ISTE developers of their first AI course cohort last winter. Dr. Garrigan was invited by ISTE to share these ideas directly to the teachers in the AI course.
Participants will be able to try the activities or visit the resources during the BYOD session. They will focus on activities most suitable for their classroom subjects and grade levels. The ten specific activities are listed below.
5-min – introduction and setting of context and expectations
50 min – roughly 5-minutes per each of the following activities:
- evaluate recommender systems in Amazon or Netflix (1st through 7th grade)
- evaluate voice recognition and synthesis systems (Siri, Cortana, Alexa) for English language proficiency and limits (such as English Language Learners)
- evaluate facial identification in photos: take student photos and label them. See how well a classroom photo identifies each kid. Try the same on Halloween with masks and makeup. (1st through 5th grade)
- explore big data and multivariate analysis with Gapminder (4th –to- 9th grades)
- explore emergent behavior “agent modeling” with NetLogo (4th –to- 10th grades)
- evaluate information systems: what can they do and what are their limits? (Siri, Alexa, Wolfram Alpha, Cortana – 4th –to- 9th)
- explore genetic “evolutionary” algorithms (Breve – 7th-10th, Microsoft runner – 9th-12th)
- build a “self-driving car” (path-follower 3rd to 6th, maze-runner 7th to 10th)
- build a “classifier” machine learning system (most common kind of ML – 10th –to- 12th grades)
- build a “recommender” ML system (like Amazon and Netflix – 10th –to- 12th)
5-min wrapup and Q&A (questions welcomed and addressed throughout session)
Each activity varies in the level of practical participant engagement. With some, group discussion will be used. With others, visit to website will be used. With Gapminder, NetLogo, and Breve, participants can interact hands-on as their students would.
There has been no research done on these topics in K-12 related to AI and Machine Learning. Some familiar tools are used in new ways such as Gapminder for big data (http://gapminder.org) and line-following robots using sensors (MANY sources). Others use less familiar tools such as the AI and Machine Learning resources from Microsoft, Google, and courses/tools from U.C. Berkeley and Harvard. The R and Python websites also have a wealth of information for high school applications.
All of the activities have been (or will have been tested in my Spring course) with real teachers and their students. The presenter is experienced with using all of these tools.