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Improving Computational Thinking with Machine Learning and AI with Robotics

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Colorado Convention Center, Bluebird Ballroom Lobby, Table 32

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Presenters

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Education and Workforce Development
FIRST
Lori became a teacher in 2000 as a science teacher in southern Illinois. She taught Career and Technical Education and Science for 15 years in Colorado with a variety of courses in STEM and Agriculture. As a teacher, Lori shared her passion for learning STEM with students, coaching FIRST Robotics teams, and mentoring students in Science Fair projects. Helping students connect their learning with careers has been a lifelong focus. Today, she shares this same passion with the FIRST organization on its Education team's secondary content and workforce development efforts at the national level.
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Senior Manager, Professional Develop
FIRST
Dana Aucoin is the Senior Manager, Professional Development at FIRST and is dedicated to preparing teachers to bring engaging hands-on robotics programs to their classrooms and students. She spent over 20 years in the classroom and ran STEM Camps for elementary students while coaching FIRST teams. She co-founded a STEM non-profit that brought robotic programs to schools in DC, Baltimore and Annapolis before joining the FIRST Education team.

Session description

Participants will explore how to utilize low-cost robots to teach the fundamentals of machine learning that can be transferred to larger robotics systems. Participants will experiment with a classroom implementation of image recognition through robotics, improving students' skills for high-demand careers.

Purpose & objective

- Bring robotics technologies to the classroom or afterschool program to help improve computational thinking skills that can be transferred to the workforce.
- Learn about implementing technologies such as vision processing used in artificial intelligence.
- Learn about how image recognition and machine learning are combined in automation and manufacturing.
- Learn about how robotic competitions provide an opportunity for students to gain work-based learning skills transferrable to the workforce.

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Outline

If selected, we would have 5 experimentation tables for different levels of engagement. Participants will be able to experiment and code an XRP robot to complete tasks to improve skills in algorithmic thinking.

Three substations will have three different tasks (drive forward and turn 5-10 min, 5-10 min line following, and 5-10 min distance detection). Participants will be guided on how the robot hardware receives data often abstracted in programming environments.

Participants will then be able to transition to the next station, where they can use a FIRST Tech Challenge robot to use image detection of April tags and a pre-built 3D image detection database (15 min). Participants will learn how databases and algorithms can have abstracted details that should be considered for modifying and utilizing algorithms.

In the last station, participants will be able to experiment with a machine learning toolchain kit where they can build their image detection models for a 3D printed object (30 min). Participants will learn the importance of the values that go into a data set to get an accurate recognition that robots can use to make decisions based on their environment.

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Supporting research

https://ftc-docs.firstinspires.org/en/latest/apriltag/vision_portal/visionportal_overview/visionportal-overview.html

https://experientialrobotics.org/

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Session specifications

Topic:
Emergent technologies
Grade level:
6-12
Skill level:
Beginner
Audience:
Chief technology officers/superintendents/school board members, Curriculum/district specialists, Teachers
Attendee devices:
Devices not needed
Subject area:
Career and technical education, STEM/STEAM
ISTE Standards:
For Students:
Computational Thinker
  • Students break problems into component parts, extract key information, and develop descriptive models to understand complex systems or facilitate problem-solving.
  • Students understand how automation works and use algorithmic thinking to develop a sequence of steps to create and test automated solutions.
  • Students formulate problem definitions suited for technology-assisted methods such as data analysis, abstract models and algorithmic thinking in exploring and finding solutions.
Disclosure:
The submitter of this session has been supported by a company whose product is being included in the session
Related exhibitors:
Copyleaks