Paderborn Colloquium on Data Science and Artificial Intelligence in School

Virtual Conference
October 27, 2021
Conference Website

Data science, artificial intelligence, machine learning, data literacy, and statistical literacy concerning secondary education are currently discussed in the communities of scientists and educators in statistics, mathematics, computer science, social and natural sciences, and media education. Our colloquium intends to bring together these perspectives and communities to create an interdisciplinary community for scientific exchange.

Wednesday, October 27

Data Detective Clubs in the Time of COVID-19

Jan Mokros, Bill Finzer

4:00 PM – 6:30 PM (CEST, UTC+2)

The COVID-19 pandemic presents an opportunity to engage young people in exploring how data
can be used to understand a public health crisis, make decisions, and save lives. In this session we will describe a multifaceted project involving an adventure story about COVID-19 that is connected to data challenges in which CODAP (Common Online Data Analysis Platform) is used to explore time series of pandemic data. This work takes place in out-of-school clubs around the U.S., comprising 20 hours over two to three months with students who are 10-14 years old.

We will focus on two aspects of this work: First, we’ll demonstrate and discuss the affordances of CODAP and accompanying datasets in understanding how a dynamic pandemic unfolds. For example, CODAP interacts well with NetLogo, which means students (and those attending our session) can set parameters for infectivity and run multiple simulations to see how many people get sick, how many recover, and how long the outbreak lasts. In addition, CODAP’s “Story Builder” feature enables youth to combine graphs, photos, and text to tell the story of what happens over time with COVID under different circumstances and in different places.

Second, we’ll discuss the challenges and opportunities of working with real-time, highly relevant data that transcend the boundaries of school curricula. Most students do not study epidemiology in secondary school, though the subject area is an ideal vehicle for learning about data. Students’ work with data from pandemics also integrates social science, public policy, and the science of viruses.

The session will conclude with a discussion of the social-emotional aspects of using sensitive data. COVID data, like most data that truly matter, elicit a range of social and emotional issues, and we believe it is part of our role as data science educators to address these concerns.

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