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Paderborn Colloquium on Data Science and Artificial Intelligence in School - Session #09 - Part 1: Nick Horton - Part 2: Francine Berman

Part 1: Teaching reproducibility and responsible workflows – Nick Horton (USA)
Modern statistics and data science utilizes an iterative data analysis process to solve problems and extract meaning from data in a reproducible manner. […]
The National Academies of Science, Engineering, and Medicine’s (NASEM) 2018 „Data Science for Undergraduates“ consensus study identified the importance of workflow and reproducibility as a component of data acumen. But data science is increasingly important in primary and secondary education. How can we help students scaffold their analyses and foster responsible workflows as they begin to develop data fluency? In this talk, I will explore data tools and approaches that are intended to help students develop these important capacities.

Part 2: Teaching Social Responsibility for a Tech-Powered World – Francine Berman (USA)
Today’s world is complex and tech-driven. How do we use the tools of information technology to solve problems in a socially responsible way, i.e. in a way that both empowers us and promotes the well-being of the communities in which we live? 

Detailed information can be found at https://www.prodabi.de/colloquium/.