Learning data science through civic engagement with open data - Graham Dove (USA)
Abstract
In this talk I will discuss work undertaken for the project “Learning Data Science Through Civic Engagement With Open Data”. This project, which is funded through the National Science Foundation’s Advancing Informal STEM Leaning (AISL) program, studies the informal data science learning that takes place within workshops, and other events and activities, that have been developed to support community engagement with civic open data in New York City (NYC).
NYC is a leader in Open Data initiatives, which are centered around the NYC Open Data portal, and which have become enshrined in the City Charter. It also has a large and highly diverse population, including many traditionally underserved communities. As government service provision becomes increasingly digital, large amounts of data are generated and subsequently used to assess need, drive service delivery decisions, and evaluate effectiveness. Services producing these data include education, transport, and 311 service requests (a non-emergency municipal service available in many cities for reporting problems such as noise or public safety concerns), and the data they produce can be probed to ask many important questions such as: “How do City agencies respond to noise in my neighborhood?”, “How do waste and recycling services in my neighborhood compare with others?”, and “Are there more construction permits issued for my neighborhood than similar areas?”. To better understand how diverse communities might access and analyze these data to answer questions, share narratives about issues of concern, and respond to data driven policy and resource allocation, we are studying programs offered by the Mayor’s Office of Data Analytics (MODA) and BetaNYC. MODA is the NYC agency with overall responsibility for the City’s Open Data programs, while BetaNYC is a leading nonprofit organization working to improve the lives of NYC residents through civic design, technology, and engagement with government open data. We study the ecosystem that has emerged around the programs these organizations offer as a possible model for identifying, validating, and evaluating best practices; including questions of participation and potential barriers to entry.
Bio Graham Dove
Dr. Graham Dove is a human-computer interaction researcher, with experience in participatory approaches to design and citizen science.
Based in NYU Tandon’s Dept. of Technology Management and Innovation (TMI), and the Center for Urban Science and Progress (CUSP), Graham investigates ways that people who are not experts in data science can use quantitative data and artificial intelligence to inform decision making, advocacy, and creativity in design. Current projects include investigating the informal learning that takes place around NYC Open Data, designing data rich interfaces to support future healthcare work practices. SONYC (Sounds of New York City), which investigates approaches to monitoring and mitigating noise pollution. He has previously worked in Denmark and the UK.
Recording and Slides
Why should students take a data science course? - Rob Gould (USA)
Abstract
The Mobilize Intro to Data Science (IDS, www.introdatascience.org) course was first offered in 2017 and was, at that time, the only data science course designed for secondary students in the U.S., and possibly anywhere. IDS was developed through a partnership between the Los Angeles Unified School District (the second-larger district in the U.S.), the UCLA Department of Computer Science, the UCLA Graduate School of Education and Information Sciences, and the UCLA Department of Statistics.
The IDS curriculum has several special features. First, it relies on a data collection paradigm, Participatory Sensing (Burke, et al 2006), in which students use mobile devices to collect multivariate data together. Second, the curriculum teaches students to use R, via the interface Rstudio and the mobilizR package, to organize, prepare, and analyze data. Finally, it relies on a student-centered, activity-based pedagogy. The primary goal of IDS is to develop in students the ability to synthesize statistical and computational thinking (DeVeaux, et al. 2017; Gould 2021) in order to work, and live, ethically and productively in a data-driven world.
Since 2017, interest in preK-12 data science and “data literacy” has blossomed in the U.S (and elsewhere), and varying visions and purposes for data science education have emerged. Data science education is many things to many people; it can be seen as an approach to developing “data literacy”, improving programming skills, increasing “college readiness”, improving equity in mathematics (Burdman,2015), and developing students’ appreciation for and skill in for mathematics. While we agree that many of these purposes are welcome consequences of a well-designed data science course, in our vision of data science education these are secondary to the primary goal, which is to teach students to analyze complex, multivariate data and to develop what has been called “data acumen”. Many in the U.S. see secondary-level Data Science as a sub-discipline of mathematics (Levitt 2019), which naturally affects the scope and intent of a course.
In this presentation, we’ll describe the IDS vision of data science and IDS’s role in developing data literacy for secondary students. We’ll also discuss some of the challenges that remain in implementing this vision, including teacher preparation and university acceptance.
References:
- P. Burdman, Degrees of freedom: Probing math placement policies at California colleges and universities (Report 3 of a 3-part series), Report issued by PACE Foundation and Learning Works, 2015, available at https://edpolicyinca.org/sites/default/files/PACE%20Report%203%2005-2015%20v7.pdf.
- J. Burke et al., Participatory sensing, Center for Embedded Network Sensing, 2006, available at https://escholarship.org/uc/item/19h777qd
- R. D. De Veaux et al., Curriculum Guidelines for Undergraduate Programs in Data Science, Annual Review of Statistics and Its Application, March 2017, 4:15-30. https://doi.org/10.1146/annurev-statistics-060116-053930
- R. Gould, Toward Data-Scientific Thinking, Teaching Statistics, May 2021. DOI:10.1111/test.12267
- S. D. Levitt, America’s math curriculum doesn’t add up (Ep. 391). Freakonomics podcast, 2019, available at https://freakonomics.com/podcast/math-curriculum
Bio Rob Gould
Dr. Rob Gould is a teaching professor and vice-chair of undergraduate studies in the Department of Statistics at UCLA, active in statistics and data science education since 1994. He is the founder of DataFest, a 48-hour undergraduate data analysis competition sponsored by the American Statistical Association and held at over 40 sites around the world.
He is co-author with Colleen Ryan and Rebecca Wong of an introductory statistics book. He was the lead Principal Investigator of the NSF-funded Mobilize project, which produced the Introduction to Data Science curriculum, the first high school data science curriculum in the U.S. Rob was elected Fellow of the American Statistical Association in 2012; in 2019 was awarded the CAUSE Lifetime Achievement Award for Statistics Education and the ASA Waller Distinguished Teaching Career Award. He received his B.S. from Harvey Mudd College in 1987 and PhD in Mathematics (concentration on Statistics) from the University of California, San Diego, in 1994. He is Vice-president of International Association of Statistics Education (IASE).
Recording and Slides
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