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Data Science Academy

Data Science and AI Education Share Fair

This innovative and engaging fair was designed for university and high school data science instructors, researchers, and interest groups passionate about elevating data science education.

About

This Data Science and AI Education Share Fair revolves around the NC State University Data Science Academy ADAPT course design model (ADAPT stands for All-campus Data science through Accessible Project-based Teaching and learning), a cutting-edge framework for developing and teaching data science. Our goal is to foster a comprehensive understanding and effective implementation of this model across various educational settings.

Event Highlights:

  • Presentations from Data Science Instructors and Researchers: Delve into the essence of the ADAPT model, highlighting its significance in project-based learning.
  • Interactive Sessions: Engage in sessions focusing on project-based learning experiences, the ten common learning elements of data science, and fostering identity-conscious choices in classrooms.
  • Networking Opportunities: Connect with peers and experts during our virtual lunch break and interactive discussion segments.
  • Actionable Takeaways: Leave with a wealth of knowledge and practical steps to implement in your teaching practice.
  • Post-Event Engagement: Follow-up email containing valuable resources and session recordings to further help with our data science teaching journey.

Event Recordings

Welcome & Keynote Speaker

Introduction for the event and Keynote Speaker, Sunghwan Byun and David Stokes.

WATCH VIA VIMEO 

Rapid Fire Presentations

Breakout rooms focused on the 3 parts of the ADAPT Model: Project-Based Learning, the 10 Learning Elements, and Identity-Conscious Choices.

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Student Presentations

Two students provide their perspective about their experience learning via the ADAPT Model.

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Presentations and Guided Activities

Three breakout rooms focused on ways to implement the ADAPT model in the classroom.

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Please cite this work using CC-BY-NC-ND 2022