Session #01

Jan Mokros and Bill Finzer (USA) & Matti Tedre and Henriikka Vartiainen (Finland)

Data Detective Clubs in the Time of COVID-19 - Jan Mokros and Bill Finzer (USA)

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 US, 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.

Dr. Jan Mokros is a developmental psychologist who is currently directing National Science Foundation-funded projects focused on how youth learn about data in out-of-school clubs.  Jan’s work with data science education introduces youth to topics including Lyme disease, teens’ use of time, sports injuries, and COVID-19. The COVID project centers on using a combination of CODAP, data activities, and a young adult adventure book, “The Case of the COVID Crisis”, by Pendred Noyce, to explore infectious disease epidemics. Afterschool programs around the US are using this program with youth who are underserved with respect to STEM. Jan is a Senior Research Scientist at Science Education Solutions. In prior positions, she has designed curriculum and conducted research at TERC and at the Maine Mathematics and Science Alliance. She has authored three books, including one for museum educators on incorporating math into exhibits and programs, and one for parents on exploring math in everyday life. She has been involved as a writer and researcher for the math curriculum Investigations in Number, Data and Space.

Bill Finzer’s work has long centered on getting students using data in every subject they study. He led the Fathom Dynamic Data Software development team at KCP Technologies before joining the Concord Consortium in 2014 where he leads development of the Common Online Data Analysis Platform (CODAP). He has been a classroom teacher, curriculum developer, teacher professional development course designer and leader, and educational software developer. Bill works with staff of many projects both inside and outside Concord Consortium to help them make use of CODAP. He loves nothing better than fixing bugs and implementing new features.

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Teaching machine learning in school: Some emerging research trajectories - Matti Tedre and Henriikka Vartiainen (Finland)

A major technological shift has recently triggered discussions about the need to amend computing education at all education levels. Traditional, rule-based automation has been joined by machine learning (ML), which, when provided with enough computing power and data, has enabled new classes of jobs to be automated, and thus expedited automation in the society, workplace, and in people’s everyday lives.

Although ML has become an integral part of our lives, communities, and societies, it has gained very little attention in K–12 (school) computing education which mainly focuses on rule-based programming and computational thinking. This talk will map the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. It will situate that research in the broader context of computing education and describe what changes ML necessitates in the classroom. The talk will outline the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula.

Dr. Matti Tedre is a professor of computer science, especially computing education and the philosophy of computer science, at the University of Eastern Finland. His 2019 book “Computational Thinking” (The MIT Press, with P.J. Denning) presented a rich picture of computing’s disciplinary ways of thinking and practicing, and his 2014 book “Science of Computing” (Taylor & Francis / CRC Press) portrayed the conceptual and technical history of computing as a discipline.

Dr. Henriikka Vartiainen is a senior researcher at the University of Eastern Finland, School of Applied Educational Science and Teacher Education. Currently, her research focuses especially on learning Machine Learning through co-design as well as on the ways to support children’s data agency.

Recordings and Slides

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