AERA, the American Educational Research Association, is scheduled to honor two members of the Graduate School of Education faculty — David Labaree and Sean Reardon — at its annual meeting in San Francisco on April 27-May 1.
Labaree, professor of education, was selected to be a 2013 AERA Fellow for his exceptional scholarly contributions to education research. A sociologically oriented historian of education, Labaree explores in his studies the development of the American system of schooling and the role this system plays in American society. He is the author of a number of award-winning books, including Someone Has to Fail: The Zero-Sum Game of Public Schooling; The Trouble with Ed Schools; and How to Succeed in School Without Really Learning: The Credentials Race in American Education.
The John Dewey Society also asked Labaree to deliver its annual John Dewey Lecture in conjunction with the AERA conference. The lecture, “College — What Is It Good For?” will be at 4:15 p.m. on April 27 at the Hyatt Grand Cypress. Founded 78 years ago, the John Dewey Society exists to keep alive John Dewey's commitment to the use of critical and reflective intelligence in the search for solutions to crucial problems in education and culture. It has been sponsoring the lectures since 1958.
Reardon, professor of education, and Andrew Ho, who received his PhD from the GSE and is now assistant professor of education at Harvard, were chosen to receive AERA’s Palmer O. Johnson Memorial Award for their paper, “Estimating Achievement Gaps from Test Scores Reported in Ordinal Proficiency Categories” in the August 2012 issue of the Journal of Educational and Behavioral Statistics. The award is given annually to recognize the highest quality of scholarship published in JEBS and three other AERA publications.
In the paper, Reardon and Ho present novel statistical methods that enable researchers to better use readily available test score data to estimate achievement gaps among student groups. In particular, they describe ways to more accurately estimate achievement gaps when only incomplete data are available.
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