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Using Data from Schools and Child Welfare Agencies to Predict Near-Term Academic Risks. REL 2020-027
Bruch, Julie; Gellar, Jonathan; Cattell, Lindsay; Hotchkiss, John; Killewald, Phil – Regional Educational Laboratory Mid-Atlantic, 2020
This report provides information for administrators, researchers, and student support staff in local education agencies who are interested in identifying students who are likely to have near-term academic problems such as absenteeism, suspensions, poor grades, and low performance on state tests. The report describes an approach for developing a…
Descriptors: At Risk Students, Data Use, Child Welfare, Predictor Variables
Johnson, Matthew; Lipscomb, Stephen; Gill, Brian; Booker, Kevin; Bruch, Julie – Mathematica Policy Research, Inc., 2012
At the request of Pittsburgh Public Schools (PPS) and the Pittsburgh Federation of Teachers (PFT), Mathematica has developed value-added models (VAMs) that aim to estimate the contributions of individual teachers, teams of teachers, and schools to the achievement growth of their students. The authors' work in estimating value-added in Pittsburgh…
Descriptors: Public Schools, Outcomes of Education, Academic Achievement, Teacher Effectiveness