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New Directions in Education Research: Using Data Mining Techniques to Explore Predictors of Grade Retention

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dc.contributor.advisor Sutton, Clifton D.
dc.contributor.author Kelly-Winstead, Deanna
dc.creator Kelly-Winstead, Deanna
dc.date 2010-02-16
dc.date.accessioned 2010-05-19T13:55:47Z
dc.date.available NO_RESTRICTION en_US
dc.date.available 2010-05-19T13:55:47Z
dc.date.issued 2010-05-19T13:55:47Z
dc.identifier.uri https://hdl.handle.net/1920/5818
dc.description.abstract The purpose of this study was to use classification trees and logistic regression to identify subgroups of students more likely to be retained. The National Educational Longitudinal Study of 1988 (NELS:88) was used to identify the sociodemographic, family background and school related factors associated with grade retention. The sample size for this study consisted of 10,140 students, 1,570 of which had been held back. The NELS data were obtained from student questionnaires and surveys with the students’ parents, teachers, and school administrators. In order to identify the predictors of students more likely to be held back, models were built using classification trees and logistic regression. Overall, the current study identified the predictive factors of grade retention. Moreover, this study demonstrates the effectiveness of using classification trees in conjunction with stepwise logistic regression in educational research.
dc.language.iso en_US en_US
dc.subject classification trees en_US
dc.subject grade retention en_US
dc.subject logistic regression en_US
dc.subject held back en_US
dc.subject risk factors en_US
dc.title New Directions in Education Research: Using Data Mining Techniques to Explore Predictors of Grade Retention en_US
dc.type Dissertation en
thesis.degree.name Doctor of Philosoy in Education en_US
thesis.degree.level Doctoral en
thesis.degree.discipline Education en
thesis.degree.grantor George Mason University en


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