51.011 - PDC24: The Stanford Education Data Archive: Using Big Data to Study Academic Performance

Session Type: Professional Development Course

Abstract

The Stanford Education Data Archive (SEDA) is a new publicly-available dataset based on roughly 300 million standardized test scores taken by students in U.S. public schools from 2009 to 2015. SEDA currently contains average test scores by grade (grades 3-8), year (2009-2015), test subject (math and ELA), and subgroup (gender, race/ethnicity, and free lunch eligibility) for all school districts in the U.S. The test scores from different states, grades, and years are aligned to a common national scale, allowing comparisons of student performance across place and time. This course will provide a detailed description of what SEDA contains and how the dataset was constructed. It will include a description of how the test scores are linked to a common scale, a discussion of the sources and magnitude of uncertainty in the estimates, and a guide for interpretation of the estimates and how they can be appropriately used in both descriptive and causal research.

Sub Unit

• Professional Development and Training Committee

Instructors

• Sean F. Reardon, Stanford University

• Andrew Ho, Harvard University

• Benjamin R. Shear, University of Colorado Boulder

• Erin Michelle Fahle, Stanford University