A Data-Driven Decision-Making Model for Student Learning: Asking the Right Questions

• In Event: Rethinking School System Organization and Practice

Abstract

This research explores a critical need for today’s schools: how to use data meaningfully to improve instruction. A state-funded grant, data-driven differentiated instruction, explored emerging uses of data to inform instruction with a particular focus on middle schools within 18 districts. The evaluation report revealed that some districts had grown sophisticated in their use of data. This research project was born from the recognition that promising practices could be captured from an analysis of these districts. The research presented represents a study of those practices and the development of a data model framework to guide schools in their data-driven decision-making practices.

Authors

• Ellen B. Meier, Teachers College, Columbia University

• Dawn M. Horton, Teachers College, Columbia University

• Jessica Yusaitis Pike, Teachers College, Columbia University