Ben Shapiro, Ph.D., assistant professor in Learning Sciences, has an article published for the Journal of Education, Technology & Society.
The article is titled “”Bettering Data”: The Role of Everyday Language and Visualization in Critical Novice Data Work”. This work was supported by the National Science Foundation (Award #1951818). We asked Shapiro some questions about his work.
How does this publication help with your research goals and/or interests?
This work furthers efforts to design data science education programs for historically underrepresented populations that leverage community datasets to support more personally and culturally relevant data science experiences. It also shares findings from a multi-year project to support police-community relations in Atlanta through integrating data visualization and the arts.
Summarize your topic:
This paper shares findings from a multi-year research project at DataWorks, an organization we founded that trains and employs historically underrepresented populations in Atlanta to work with community data sets. We use qualitative methods to show the generative power of everyday language and visualization to support people who are beginning to learn — or aspire to learn — how to perform data work, but do not have formal training or education in computing, visualization, or statistics. We also detail how workers at DataWorks integrated visualization and the arts to support police-community relations in Atlanta in ways that highlight challenges and opportunities to foster critical data literacy with novice data workers in the workplace.
Are there other people to be credited?
DataWorks is an organization founded at Georgia Tech and co-authors include Betsy DiSalvo, Carl DiSalvo, Amanda Meng, Annabel Rothschild, Cicely Garrett, Sierra Gilliam, and Lara Schenck.