Comparing self-regulatory and early academic skills as predictors of later math, reading, and science elementary school achivement
Murrah, William M, Curry School of Education, University of Virginia
Grissmer, David W., Curry School of Education, University of Virginia
Richards, Herbert, Curry School of Education, University of Virginia
Ferree, Ruth, Curry School of Education, University of Virginia
Whaley, Diane, Curry School of Education, University of Virginia
The achievement score gaps between advantaged and disadvantaged children at school entry is a major problem in education today. Identifying the skills critical for school readiness is an important step in developing interventions aimed at addressing these score gaps. The purpose of this study is to compare a number of school readiness skills with an eye toward finding out which are the best predictors of later academic achievement in math, reading, and science. The predictors were early reading, math, general knowledge, socioemotional skills, and motor skills. Data were obtained from the Early Childhood Longitudinal Study of 1998 (NCES, 1998) database. While controlling for an extensive set of family characteristics, predictions were made across five years - from the end of kindergarten to the end of fifth grade. Consistent with current findings, reading and math skills predicted later achievement. Interestingly, general knowledge, attention, and fine motor skills also proved to be important predictors of later academic achievement, but socioemotional skills were not. The findings were interpreted from a neurobiological perspective involving the development of self-regulation. These school entry skills are used to predict later achievement in reading, math, and science. I argued that in addition to acquiring early academic knowledge, children need to regulate the use of this knowledge to meet academic goals.
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PHD (Doctor of Philosophy)
Digitization of this thesis was made possible by a generous grant from the Jefferson Trust, 2015.
Thesis originally deposited on 2016-02-18 in version 1.28 of Libra. This thesis was migrated to Libra2 on 2017-03-23 16:33:45.
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