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-  2019 

Improving Placement Accuracy in California’s Community Colleges Using Multiple Measures of High School Achievement

DOI: 10.1177/0091552119840705

Keywords: community college,high school,achievement,assessment,placement,multiple measures,developmental,remedial,decision tree,data mining

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Abstract:

Objective: Standardized placement tests remain the primary means by which new community college students are assessed and placed in the hierarchy of math and English coursework. A growing body of evidence indicates that placement tests tend to underestimate students’ likelihood of achieving passing grades in college-level courses, leading to students being misplaced in developmental coursework, slowing their academic progress, and increasing their likelihood of dropping out of college. This article discusses the results of a statewide research effort to improve the accuracy of student placement in math and English in the California Community College system. Method: Decision tree methods were applied to high school and college transcript data to identify key measures of high school achievement that predict performance in nine levels of community college math courses (three developmental and six college level) and four levels of community college English courses (three developmental and one college level). Results: Cumulative high school grade point average (GPA) is the most consistently useful predictor of performance across levels of math and English coursework, and a higher GPA is necessary to signal readiness for college-level coursework in math than is necessary to signal readiness for college-level coursework in English. In addition, cumulative GPA combined with specific indications of progress in the high school curriculum is frequently useful for predicting performance in math among direct matriculants and for predicting performance in both math and English among nondirect matriculants. Conclusion: Drawing on the findings, placement rules that colleges can apply directly in their placement processes are developed and validated

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