Branching Out: Using Decision Trees to Inform Education Decisions

Title:	Branching Out: Using Decision Trees to Inform Education Decisions
Description:	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.
Online Availability:	Download, view and print the report as a pdf file. (4.5MB) Download, view and print the report infographic as a pdf file. (499KB)
Cover Date:	December 2021
Web Release:	December 27, 2021
Publication #:	REL 2022133
Center/Program:	REL
Associated Centers:	NCEE
Authors:	Neil Seftor, Lisa Shannon, Stephanie Wilkerson, and Mary Klute
Type of Product:	Tools
Keywords:	Evidence-Based Decisionmaking Multi-Level Modeling Statistical Standards and Methodology
Questions:	For questions about the content of this Tools, please contact: Amy Johnson.