Collaborative Data Engineering: Strategies to Support Macro-level Exploration of Youth Learning Ecosystems

Abstract

The time has come for the learning sciences to expand design and research methods in order to understand access and participation to learning opportunities at a macro level. Advances in technology allow networked social systems (such as out-of-school providers) to crowdsource and combine the data they collect and interpret collectively through data representations. Learning sciences can leverage networked data as a way to stress test theories based on ethnographic research with individuals or small groups and offer learning science perspectives to how we understand and address power-related infrastructure and historic racism. In this paper, we share a process for designing technical systems, refining data models, visualizing learning landscapes, and working with decision makers to impact change using an investigation of the 2019 Chicago summer learning landscape as a case example.