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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
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Williamson, Gary L. – Cogent Education, 2018
Individual growth curves yield insights about growth that are not available from any other methodology; and developmental scales based on conjoint measurement models provide unique interpretive advantages for investigations of academic growth. The advantages are apparent when: (1) 15 consecutive statewide reading growth curves are annotated with…
Descriptors: Reading Achievement, Mathematics Achievement, Psychometrics, Educational Innovation
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Herrera, Sarah; Zhou, Chengfu; Petscher, Yaacov – Regional Educational Laboratory Southeast, 2017
The 2001 authorization of the No Child Left Behind Act and its standards and accountability requirements generated interest among state education agencies in Florida, Mississippi, and North Carolina, which are served by the Regional Educational Laboratory Southeast, in monitoring changes in student reading and math proficiency at the school level.…
Descriptors: Reading Achievement, Mathematics Achievement, Trend Analysis, Achievement Gap
Northwest Evaluation Association, 2016
Northwest Evaluation Association™ (NWEA™) is committed to providing partners with useful tools to help make inferences from the Measures of Academic Progress® (MAP®) interim assessment scores. Recently, NWEA completed a concordance study to connect the scales of the North Carolina End-of-Grade (EOG) English language arts (ELA) and math with those…
Descriptors: Scores, Inferences, Academic Achievement, Achievement Tests