Data SGP is a set of aggregated student achievement and learning information collected over time, used to help educators and parents better understand student progress over time. It includes student-level measures like test scores and growth percentiles, as well as aggregated district and school-level information such as class size attendance rates and graduation rates. The data is also broken down by subgroups like gender ethnicity socioeconomic status to support broader research efforts.
The student-level data is used to create percentile growth projections/trajectories which are displayed in Star Growth Report as Window Specific SGP when a prior or current year is selected during the report customization process. This allows teachers/parents to estimate future performance of their students and develop instructional plans to address any areas of concern. These projections/trajectories are updated regularly based on the most recent achievement data available to ensure that they reflect a students actual progress.
Percentile growth trajectories are calculated by comparing a students current achievement to their past performance in the same content area. The higher the score, the more progress a student has made. Typical percentile growth trajectories are between about 36 and 64. Students or groups outside of this range may have higher or lower than average growth, but these differences are unlikely to be educationally meaningful.
While the sgpData tables provide invaluable data, they don’t contain all of the information required to understand how a students performance affects their learning. To fully grasp this, more details about the context in which the student is performing are needed. For example, determining whether a student has made adequate progress requires knowing the percentage of their grade level peers that have achieved the same or higher achievement levels.
Another factor to consider is that teacher or student characteristics may have an effect on a students progress, and it’s not possible to control for these factors when creating baseline-referenced Student Growth Profiles. For this reason, it’s important to carefully analyze the results of SGP analyses, ensuring that spurious correlations have not been caused by other factors.
SGP analysis is a complex and multi-step process that can be run in many different ways. The SGP package includes wrapper functions abcSGP and prepareSGP that simplify these steps into a single function call, which is helpful for operational analyses. Alternatively, these steps can be conducted directly using the lower level functions studentGrowthPercentiles and studentGrowthProjections. To illustrate these approaches, the SGP package includes exemplar WIDE and LONG format data sets (sgpData_LONG and sgpData_WIDE) and a student-instructor lookup file, sgpData_INSTRUCTOR_NUMBER, to assist with setting up longitudinal analyses in Star. The Demonstration_SGP object in the SGP package includes both of these exemplar files.