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Koon, Sharon; Petscher, Yaacov – Regional Educational Laboratory Southeast, 2015
The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…
Descriptors: Classification, Regression (Statistics), Models, At Risk Students
Koon, Sharon; Petscher, Yaacov; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
This study examines whether the classification and regression tree (CART) model improves the early identification of students at risk for reading comprehension difficulties compared with the more difficult to interpret logistic regression model. CART is a type of predictive modeling that relies on nonparametric techniques. It presents results in…
Descriptors: At Risk Students, Reading Difficulties, Identification, Reading Comprehension
Harris, Douglas N.; Sass, Tim R. – National Center for Analysis of Longitudinal Data in Education Research, 2009
Mounting pressure in the policy arena to improve teacher productivity either by improving signals that predict teacher performance or through creating incentive contracts based on performance--has spurred two related questions: Are there important determinants of teacher productivity that are not captured by teacher credentials but that can be…
Descriptors: Credentials, Teacher Effectiveness, Teaching Skills, Principals

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