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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 textBonsu, Pam; Goertzen, Heidi; Howard-Brown, Beth; Kaase, Kris; LaTurner, Jason; Times, Chris – Southeast Comprehensive Center, 2016
The Southeast Comprehensive Center (SECC) at SEDL, an affiliate of American Institutes for Research (AIR), partnered with the Alabama State Department of Education (ALSDE) in 2014 to assist in evaluating Alabama Plan 2020, mainly focusing on learners and the graduation rate. SECC provided professional development and analytic technical assistance…
Descriptors: Data Analysis, Graduation Rate, Strategic Planning, Faculty Development
Montana Office of Public Instruction, 2012
Schools and communities across Montana are working hard to ensure that students receive the best education and graduate ready for college and careers. One of the most important components in this effort involves understanding where students are and how schools and communities can best help them achieve at the highest levels. Data is an invaluable…
Descriptors: Academic Achievement, Achievement Gap, American Indians, American Indian Students
