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|REL 2022133||Branching Out: Using Decision Trees to Inform Education Decisions
Classification and Regression Tree (CART) analysis is a statistical modeling approach that uses quantitative data to predict future outcomes by generating decision trees. CART analysis can be useful for educators to inform their decisionmaking. For example, educators can use a decision tree from a CART analysis to identify students who are most likely to benefit from additional support early—in the months and years before problems fully materialize. This guide introduces CART analysis as an approach that allows data analysts to generate actionable analytic results that can inform educators’ decisions about the allocation of extra supports for students. Data analysts with intermediate statistical software programming experience can use the guide to learn how to conduct a CART analysis and support research directors in local and state education agencies and other educators in applying the results. Research directors can use the guide to learn how results of CART analyses can inform education decisions.
|REL 2021074||Steps to Develop a Model to Estimate School- and District-Level Postsecondary Success
This tool is intended to support state and local education agencies in developing a statistical model for estimating student postsecondary success at the school or district level. The tool guides education agency researchers, analysts, and decisionmakers through options to consider when developing their own model. The resulting model generates an indicator of a school's or district's contribution to the postsecondary success of its students after contextual factors are accounted for that might be outside a school's or district's control, such as student demographic characteristics and community characteristics. State and local education agencies could use the information generated by the models they develop to help meet federal and state reporting requirements and to inform their own efforts to improve their students’ postsecondary success.
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