Journal article
Combining academic, noncognitive, and college knowledge measures to identify students not on track for college: A data-driven approach
2017
Research and Practice in Assessment, 12, 5–19.
Abstract
Research shows college readiness can be predicted using a variety of measures, including test scores, grades, course-taking patterns, noncognitive instruments, and surveys of how well students understand the college admissions process. However, few studies provide guidance on how educators can prioritize predictors of college readiness across instruments, constructs, and frameworks to optimally identify students not on track for college. Using a nationally representative dataset with thousands of measures, I employ data reduction techniques to identify a handful of variables that are the strongest predictors of college readiness and understand what they measure. Based on my models, enrolling in college and persisting for a semester can be predicted with almost 90 percent accuracy using a small set of predictors. Evidence suggests these predictors measure academic preparation, postsecondary aspirations, teacher perceptions of readiness, and socioeconomic status. Educators can use results to help identify appropriate supports for students not on track for college.
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