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Different Teachers, Different Peers: The Magnitude of Student Sorting Within Schools

Issue/Topic: Equity; Instructional Approaches--Tracking/Ability Grouping
Author(s): Loeb, Susanna; Kalogrides, Demetra
Organization(s): Stanford University
Publication: Educational Researcher
Published On: 2013

Background:
Large urban school districts serve increasingly diverse student bodies. Although many studies have examined racial segregation among schools, far fewer have examined the extent to which students are sorted across classrooms within schools.

Purpose:
To examine the extent to which students are sorted across classrooms within schools by race and ethnicity or by family income or achievement.

Findings/Results:
  • Sorting occurs even in self-contained elementary school classrooms and is much larger than would be expected were students assigned to classrooms randomly.
  • Much of the racial and socioeconomic sorting is accounted for by differences in achievement, particularly at the high school level.
  • Classrooms with the most low-achieving, minority and poor students are more likely to have novice teachers.
  • Sorting students by achievement level exposes minority and poor students to lower quality teachers and less resourced classmates.

Policy Implications/Recommendations:

  • Documented patterns could have implications for inequality, but the evidence isn't clear.
  • Grouping students in classrooms by ability (or by race, ethnicity, or poverty status) might have significant impacts on student achievement, but this depends on the magnitude of peer influences and the ability of teachers to appropriately differentiate instruction within classrooms.
  • Assignment of novice teachers to disadvantaged and low-achieving students likely has negative consequences for equity.

Research Design:
Regression analyses

Population/Participants/Subjects:
All students in three large urban school districts

Year data is from:
Districts 1 and 2: 2003-2004 through 2009-2010; District 3: 2001-2002 through 2009-2010

Setting:
Multi-State

Data Collection and Analysis:
Administrative data was collected from three large urban school districts. Students are linked to each of their teachers and students' classmates identified.

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