{"id":"AIBD-1157","title":"The What Works Clearinghouse finds that predictive placement at seven SUNY colleges raised first-semester completion of college English and math","summary":"The What Works Clearinghouse found that 34.30% of SUNY students placed by algorithms completed college-level English in their first semester, against 28.00%. Effects on degrees were uncertain.","date_observed":"2017-01-01","date_basis":"event","date_precision":"year","benefit_status":"reported","source_collection":null,"source_entry":null,"account":"Seven State University of New York community colleges built predictive placement algorithms with the Center for the Analysis of Postsecondary Readiness. Each college weighted placement test scores, high school GPAs and other factors by how well they had predicted success in college-level courses. The algorithms placed entering students in remedial or college-level math and English. In a randomized study of 12,971 students who entered from fall 2016 to fall 2017, the comparison group was placed mostly by ACCUPLACER cut points. The What Works Clearinghouse reviewed the study in August 2021 and found that it meets its standards without reservations. In the first semester, 34.30% of students placed by the algorithms completed a college-level English course, against 28.00%. In math the rates were 17.00% and 15.00%, and the math gain faded after the first semester. Effects on degree attainment were indeterminate. The study's authors report that students did better when allowed to start in college-level courses, whether or not they were predicted to succeed. In July 2026 the clearinghouse reviewed a 2025 study that followed 12,796 of the students for nine semesters. It found strong evidence that the algorithms raised first-semester enrollment in college-level English, 58.20% against 44.70%, and in college-level math, 29.80% against 27.10%, and that more students completed these courses. Effects on degrees and certificates were again uncertain, and the difference in credits earned after nine semesters was not statistically significant. 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The algorithms weight a few factors fitted to each college's historical data. The authors of the 2025 follow-up describe them as regression models chosen by their fit to past data, and neither review describes machine learning. The study's authors report that students gained when placed in college-level courses, whatever the algorithms predicted."},"evidence":[{"kind":"assessment report","citation":"U.S. Department of Education, Institute of Education Sciences, What Works Clearinghouse. Single study review of Barnett, Kopko, Cullinan and Belfield (2020), Who Should Take College-Level Courses? Impact Findings from an Evaluation of a Multiple Measures Assessment Strategy. Reviewed August 2021. The Institute of Education Sciences funded the study (grant R305C140007).","title":"WWC review of this study: Who Should Take College-Level Courses? 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The Institute of Education Sciences funded CAPR, which built the formulas.","title":"WWC review of this study: The long-term effectiveness of multiple measures assessment: Evidence from a randomized controlled trial","url":"https://ies.ed.gov/ncee/wwc/Study/95266","date":null,"written_by":"evaluator","quote":"College Readiness outcomes-Tier 1 (strong evidence) found for the domain"},{"kind":"report","citation":"Kopko E, Daniels H, Cullinan D. The Long-Term Effectiveness of Multiple Measures Assessment: Evidence From a Randomized Controlled Trial. A CAPR Working Paper. Center for the Analysis of Postsecondary Readiness, October 2023, revised March 2024, Appendix B. Supported by the Institute of Education Sciences through Grant R305C140007.","title":"The Long-Term Effectiveness of Multiple Measures Assessment: Evidence From a Randomized Controlled Trial","url":"https://ccrc.tc.columbia.edu/wp-content/uploads/2023/10/long-term-effectiveness-multiple-measures-assessment.pdf","date":null,"written_by":"makers","quote":"Historical high school and placement test data were needed to create predictive algorithms at each college.","supports_benefit":false}],"replication":{"produced_by":["Each college fitted its algorithm to historical data, weighting placement test scores, high school GPAs and time since high school.","Each college set its cut points from calculated misplacement rates."],"must_hold":["Colleges need historical records of their own students' test scores, grades and course outcomes.","Placement must let more students start in college-level courses, which is where the study's authors found the gains."],"echoes":[]},"paired_incident":null,"headline":"*Data analytics placement* lifts college English completion at SUNY","metric":"34.30% of students placed by the algorithms completed a college-level English course","comparator":{"against":"Students placed by the colleges' standard procedures, mostly ACCUPLACER cut points","before":"28.00% completed a college-level English course in the first semester","after":"34.30%","basis":"measured","quote":"Completed college-level English course Multiple measures and assessment and placement vs. Business as usual 1 Semester Full sample for students that received an English placement; 10,719 students 34.30 28.00 Yes","url":"https://ies.ed.gov/ncee/wwc/Study/90140"},"later":null,"value":null,"developer_country":"United States","deployer_country":"United States","unrated_because":null,"brief_notes":null,"brief":null,"revisions":[{"date":"2026-11-01","change":"Added the record from the finding of the What Works Clearinghouse.","source":"https://ies.ed.gov/ncee/wwc/Study/90140","editor":"AIBD"}],"confidence":"claimed","shade":"low","claim_status":"The makers' claim","claim_basis":"Sources: two reports from the makers and two assessment reports from the evaluators. 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