Data Made Me Do It: edTPA and the Algorithmization of Teacher Preparation Practices and Policies

• In Event: Data Use Tools in Teacher Preparation and Practice

Abstract

The purpose of this study is to understand both why and how outcomes-driven teacher preparation and policy increasingly relies on data-driven algorithmic processes. Specifically, this study examines the ways in which edTPA reflects and perpetuates increasingly algorithmic processes in teacher preparation practices and policies. We use a discourse-driven, grounded theory method to generate a conceptual framework, which explains an increasingly algorithmic education policymaking cycle related to teacher preparation, certification, and professionalization. Evidence suggests that although edTPA pairs portfolio-based assessments with human scorers, it sufficiently increases the depth and opaqueness of data and that policymakers have a strong incentive to algorithmize teacher preparation practices and policies based on the increasing layers of data at both the individual and aggregate levels.

Authors

• Alexander W. Wiseman, Texas Tech University

• Levi Johnson, Texas Tech University