Data Science and AI Education Share Fair

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.

• Date: May 1, 2024
• Time: 12pm to 3:30pm EST

Why You Should Attend:

• Discover Innovative Teaching Strategies: Learn how to integrate the ADAPT framework effectively into your data science teaching practices.
• Focus on Project-Based Learning: Dive deep into the benefits and methodologies of project-based learning in data science courses.
• Explore Data Science Learning Elements: Gain insights into the essential elements of data science education and how to incorporate them effectively.
• Promote Identity-Conscious Education: Understand the importance of identity-conscious choices in your curriculum.

Interested in Presenting?

Are you an instructor or researcher who has implemented the ADAPT model or aspects of it, such as project-based learning, any of the ten common learning elements, or incorporated identity-conscious choices in your work? We encourage you to apply to present or speak at our event. Your experiences and insights could immensely benefit fellow educators. You will be asked if you want to present via the Registration link above.

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.

Agenda

Time	Session	Presenter
12pm - 12:15pm	Welcome & Introduction
	Opening Remarks & Workshop objectives	Rachel Levy, Ph.D. Zarifa Zakaria, Ph.D.
12:20pm - 1pm	Keynote Presentation: The Essence of ADAPT
	Overview of the ADAPT model: Significance of project-based learning, learning elements and identity-conscious choices	Sunghwan Byun, Ph.D. David Stokes, Ph.D.
1:05pm - 2pm	Rapid Fire Presentations
Breakout Room 1: Project-Based Learning & Learning Elements	~ Gemma Mojica, Ph.D.: Moderator ~ Deb Crawford, Ph.D.: Virginia ASCD Data Science Micro-credentials ~ Rishika Rishika, Ph.D.: Understanding the Impact of Incorporating project based learning in Analytics Courses ~ Shawn Cradit, Ph.D.: Data collection during course
Breakout Room 2: Identity-Conscious Choices	~ Chanel Carrell: Moderator ~ Priscila Neves, Ph.D.: Ways to encourage student participation through conscious choices ~ Ryan Urquhart, Ph.D.: Data with Dignity: Ensuring Accessibility in Data Science ~ Mahmoud Harding: ADAPT model implementation - Relevance to rural students
2pm - 2:10pm	BREAK
2:10pm - 2:35pm	Student Presentations & Breakout Room Discussions *After the student presentations, participants will be randomly assigned to one of 3 breakout rooms to have a roundtable discussion moderated by our staff.	Satya Mungoti (Senior Social Sciences) Omry Brewster (Institute for Advanced Analytics)
2:40pm - 3:10pm	Guided Activity
Breakout Room 1: Touching Data: ADAPTing Data Science and Critical Making to the Classroom		James Harr, Ph.D.
Breakout Room 2: Data Investigation Process and the ADAPT model		Gemma Mojica, Ph.D. Emily Thrasher, Ph.D.
Breakout Room 3: Real-time data collection of continuous and discrete attribute		Bucky Gates, Ph.D.
3:10pm - 3:30pm	Wrap-up and Conclusion	Zarifa Zakaria, Ph.D.