Youth Data Wrangling and Modeling Family Migration

• In Event: 63.022 - Designing and Implementing Learning Technologies to Enhance Social Justice

Abstract

Big data technologies are powerful tools for telling evidence-based narratives about the world. In this paper, we examine the sociotechnical practices of data wrangling—strategies for managing and selecting datasets to produce a model and story in a big data interface—for youth assembling models and stories about family migration with big data using interactive visualization tools. Our initial analysis identified nine data wrangling practices: 1) filtering data; 2) data visual encoding; 3) interpreting data points 4) identifying data patterns 5) pursuing data surprises; 6) reasoning about data relationships; 7) countering data; 8) approximating data; and 9) making data predictions. These practices are important to understand for supporting future data science education opportunities that facilitate learning about scientific and socioeconomic issues.

Authors

• Jennifer Beth Kahn, University of Miami

• Shiyan Jiang, Carnegie Mellon University