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Direct linkSorensen, 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
Download full textFuller, Sarah Crittenden; Davis, Cassandra R. – Regional Educational Laboratory Southeast, 2016
In the 2012/13 school year American Indian students accounted for 1.1 percent of K-12 students nationwide and 1.4 percent of K-12 students in North Carolina (U.S. Department of Education, n.d.). Research has identified substantial achievement gaps between American Indian and other students on national tests, in graduation rates, and in…
Descriptors: American Indian Students, American Indian Education, Achievement Gap, White Students
