City Digits: Brooklyn, NY

City Digits is a project to develop and pilot integrated curriculum resources and web-tools that support high school students' learning of mathematics. The project is a collaboration between CUNY's Brooklyn College and MIT's Civic Data Design Lab.

The team has designed two curricular modules to investigate social justice themes related to the local, urban context. The curricular modules are enhanced by the integration of geo-spatial technologies that enable students to explore their local urban landscape, collect field data, and organize and visualize patterns. Their first module, Local Lotto, is about state lotteries and was pilot tested in two rounds in 2013. Their second module, Cash City, is about pawn shops and alternative financial institutions and was pilot tested in two rounds in 2014.

City Digits uses a combination of mapping, data, and media technologies to bring learning and information into the classroom in fresh and exciting ways.

Integrating real-world data into the classroom brings an added level of authenticity and civic awareness, ultimately turning otherwise disconnected mathematical investigations into rich and connected learning experiences.

The primary way City Digits brings data into the classroom is by using mapping as a language to communicate a complicated issue. Students can explore familiar areas on a map, at various levels of scale, in terms of socioeconomic and other measures, toward the development of a data-informed understanding of the topic and its significance. For that reason, a mapping API is at the center of the City Digits tools. On top of this are a host of image, audio file, and text media-upload features that allow students to augment data presented to them with their own observations.

Ultimately the tool helps to teach students data literacy - the ability to work with, analyze, and make arguments with data, which is an essential skill in our data-driven society. This material is based upon work supported by the National Science Foundation under Grant No. DRL-1222430 to the City University of New York. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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