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SGPA is an aggregate measure of student academic achievement that represents the sum of students’ growth in math and ELA. Since its introduction in 2008, median SGPs have been the primary summary tool used by districts and schools to summarize student growth. However, the use of medians may not be optimal for aggregating student data to identify high-achieving groups because it underestimates the amount of progress made by individual students in high-performing groups. This article argues that medians should be replaced by mean SGPs as the primary tool for aggregating student data for the purposes of identifying high-performing groups.
Mean SGPs better align with Department of Education policy that emphasizes student growth as the most important metric in assessing educational success. In addition, mean SGPs are more transparent than medians in that they reveal the distribution of student progress across a group rather than just the average.
A major challenge in analyzing SGPs is determining which student characteristics have the largest impact on student performance. This is difficult because of the complex nature of latent achievement attributes and the many ways they interact with one another. Fortunately, recent research has improved methods for estimating the structure of latent achievement attributes. These improvements enable us to construct more accurate models of student performance and to evaluate the distributional properties of these models.
This paper presents a new methodology for estimating the structure of latent achievement attribute models and evaluating their distributional properties. The key to the method is a simple, graphical representation of the model that allows users to see the relationship between a latent trait and its direct effect on student performance. This new approach to analyzing SGPs is easy to understand and can be applied to any data set containing aggregated student growth information.
SGPs can be aggregated to produce reports on the progress of subgroups, classes, schools, and districts. The results from these reports can be compared to those of other groups in order to make informed decisions about student learning and teaching. Traditionally, median SGPs have been the primary summaries used to represent these group-level trends, but this paper argues that mean SGPs are a more appropriate statistic for this purpose.
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