SGP measures relative student growth by comparing it with their academic peers who began at similar points in time. If Simon has achieved an SGP rating of 63 this year, that indicates he surpassed more than 63% of students who started out where he did in terms of development.
SGP results are commonly used by educators to measure student growth in academic subjects like mathematics and reading; however, educators also take other measures into consideration to fully comprehend a child’s development (such as social-emotional learning or creativity).
SGP analyses can be particularly helpful to teachers and administrators when presented on a scale from 1 (lowest growth) to 99 (highest growth), making it simple for educators to gauge how much a student has grown compared with his or her academic peers.
Educators use SGP data as part of their communication plan with parents and other stakeholders about the achievements and progress at their school. By sharing SGP alongside other measures of student success, educators can give context for a student’s development while showing them which areas may need further improvement.
The data sgp tool offers users an intuitive interface for accessing core functions and creating SGP analyses. The home page lists all applications currently available within an account, making it easy to find and navigate all functions and functionality provided by data sgp. Users can create new applications by clicking “Add Application”, while existing ones can be reviewed by clicking their name from within their list of apps on the home page.
Data SGP requires users to have a computer with R installed; R is free and open source software available for Windows, OSX and Linux operating systems.
SGP analyses are performed in R using the data set sgpData_LONG, which contains anonymized student assessment data in LONG format for 8 windows (3 windows annually of assessment data in three content areas). There are seven required variables in sgpData_LONG that must be present to conduct SGP calculations: ID, VALID_CASE, YEAR and the additional variable “SS_2016”. Only users who wish to create student growth projections need this additional variable.
Once information has been saved in sgpData_LONG format, users can perform analyses using R’s SGP package for analysis. This package offers both low-level functions that perform SGP calculations directly as well as higher level functions that serve as wrappers for those calculations.
Window Specific SGP and Current SGP analyses are the two primary SGP analyses conducted. The former allows comparison between two testing windows; while the latter focuses on one test using its prior assessments to calculate it. Both analyses aim to establish an objective baseline from at least two prior test administrations so that students who already performed well on state assessments don’t receive falsely inflated SGP growth scores simply due to being at the top of their cohort.