Copyright © 2011 Pearson Education, In. or its afliate(s). All rights reserved. The nation is showing an unprecedented focus on increasing the rigor in education and preparing students for college. The college readiness trend is driving changes in the ways in which the nation uses student test data. Educational data are no longer limited to static data snapshots showing the status of a student performance at one point in time. Instead data are linked grade-to-grade and course-to-course, to create a longitudinal measure of student performance. Inferences about student progress are now made using status as well as growth models. As an illustration of the national focus on longitudinal data, the United States Department of Education (2010) publication, A Blueprint for Reform:The Reauthorization of the Elementary and Secondary Education Act noted, “Instead of a single snapshot, we will recognize progress and growth” (p. 2). States have previously focused on snapshots of student performance and have drawn inferences about progress from those snapshots, assuming that passing in one grade/ course meant that students were on track to passing in the next grade/course. The problem is that data supporting those assumptions were not typically provided. In some instances when states did analyze longitudinal data from a system built for static interpretations, the results proved surprising. For example, states transitioning from a horizontal to vertical scale have found that when passing standards are put on a vertical scale and comparisons are made across grades, passing standards for grade level can be lower than the passing standards for the prior grade level. The new national trend is to enhance our ability to draw inferences about student growth by collecting more direct evidence from longitudinal student data. The use of longitudinal data expands beyond informing about student progress to evaluating teachers and educational leaders. President Obama has repeatedly highlighted the need for teacher efectiveness measures and ofered incentives for Making Sense of the Metrics: Student Growth, Value-added Models, and Teacher Efectiveness Kimberly O’Malley, Ph.D., Katie McClarty, Ph.D., Tracey Magda, Ph.D., and Kelly Burling, Ph.D. those who are willing to implement them. The Department of Education awarded billions of dollars from the Race to the Top fund to 11 states and the District of Columbia in 2010. In granting the awards, the Department evaluated state applications for which 28% of the points were dedicated to a section entitled “Great Teachers and Leaders.” As part of the application requirements, states had to develop and describe a system for assessing teacher efectiveness that included student achievement data and provided annual efectiveness ratings for all teachers. States awarded the Race to the Top funds are currently working to implement their plans for teacher efectiveness systems, with most relying on student growth measures as essential measures in their systems. The college readiness trend is driving changes in the ways in which the nation uses student test data. The use of student score changes in diferent applications has led to confusion in the use of terms and concepts. Terms such as student growth, value-added models, and teacher efectiveness are often used interchangeably. The diferences in these three measures are signifcant. Using one when another is intended has impeded the nation’s ability to develop these measures well and to use the information in optimal ways. The goal of this paper is to defne student growth, value- added models, and teacher efectiveness, the three terms that are often confused. Furthermore, the paper will compare and contrast features of these three measures and identify next steps needed for advancing the use of these measures for educational reform. Student growth measures focus on performance of individual students, addressing questions about how much a student progressed and if the student is on track, where on track Bulletin April 2011 | Issue 19 www.pearsonassessments.com 1