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Data sgp is a software program that converts raw student assessment data into projected future growth trajectories based on official achievement targets and goals. By identifying students at risk of missing their target and providing clear projections, SGP helps educators articulate performance goals that are more easily understood and implemented.
To perform SGP analyses, schools and districts must have access to longitudinal (time dependent) student assessment data. Generally, this data is stored in WIDE format, where each case/row represents a student and columns represent variables associated with the student at different points in time. The SGP package includes exemplar wide and long format data sets (sgpData and sgpData_LONG) for this purpose.
The sgpData data set provides the student ID, content area, year and scale score associated with each student for the past 5 years. The sgpData_INSTRUCTOR_NUMBER lookup table contains instructor details associated with each student test record. This is an important feature of the sgpData data set because SGP analyses require that each student be paired with an instructor for each content area in which they are assessed.
SGP analyses convert these historical student assessment trajectories into percentiles, which are then compared to the average of the same percentiles from other students with similar prior test scores (their academic peers). This comparison allows teachers and administrators to see how each individual student is growing relative to their peers in terms that can be understood by all stakeholders.
The SGP package requires the free, open-source programming language R to perform all of its functions. The R project website offers a number of resources to help users become familiar with this tool, including the SGP data analysis vignette. It is recommended that users familiarize themselves with these resources before attempting any operational analyses. In addition, the SGP package features higher level functions that simplify source code associated with these types of operational analyses. This is a useful tool for users who are unfamiliar with the more complex functions of the R programming language.