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Sorensen, Lucy C. – Educational Administration Quarterly, 2019
Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research…
Descriptors: At Risk Students, Dropouts, Data Collection, Data Analysis
Yamamoto, Yoko; Holloway, Susan D.; Suzuki, Sawako – School Community Journal, 2016
In spite of evidence indicating the benefits of parental engagement for children's achievement, little is known about the factors that contribute to parental engagement in countries outside the United States. In this study, we addressed this gap in the literature by examining teachers' outreach in addition to maternal psychological elements…
Descriptors: Parent Participation, Cross Cultural Studies, Cultural Differences, Parent Role
Minor, Elizabeth Covay; Desimone, Laura M.; Phillips, Kristie J. R.; Spencer, Kailey – American Journal of Education, 2015
In this study, we use the US nationally representative Early Childhood Longitudinal Study-Kindergarten Cohort to examine inequalities in opportunities to learn mathematics. Specifically, we examine the characteristics of US students' elementary school math teachers and the instruction they provide. While our findings are consistent with previous…
Descriptors: Early Childhood Education, Longitudinal Studies, Cohort Analysis, Mathematics Education