Explanatory and Predictive Modeling Within Improvement Science Projects

• In Event: 45.028 - Technologies to Construct Knowledge

Abstract

This paper explores the role of explanatory and predictive modeling within a large-scale, networked improvement community called the Carnegie Math Pathways. Drawing on multiple data sources from across multiple years, we demonstrate how both explanatory and predictive models helped in understanding what it means to be a successful student and learner in the Pathways.

Authors

• Andrew E. Krumm, Digital Promise

• David Yeager, The University of Texas at Austin

• Hiroyuki Yamada, Carnegie Foundation