Using the SGP Data Set to Analyze Student Growth Trajectories

The SGP data set provides the foundation for analyses of student growth trajectories. It includes the historical growth trajectories for students who were screened during the Star examination window, along with projections of future performance based upon current growth patterns. Using the sgpdata, researchers can identify trends in student learning and determine what future progress is necessary for students to reach proficiency or maintain their achievement levels.

SGP provides the ability to examine the performance of groups of students based on their demographic characteristics, such as gender, race/ethnicity, or home language. The sgpdata enables researchers to examine the performance of these groups relative to all other students, including their own students. These comparisons can be used to evaluate the effectiveness of interventions for specific groups, or compared across groups over time to compare the performance of different programs.

sgpdata also provides information that can be utilized to help teachers and their districts make decisions about the effectiveness of their teaching methods, curriculum and instructional practices. For example, sgpdata includes the sgpData_INSTRUCTOR_NUMBER table, which is an anonymized lookup table that provides instructors associated with each student test record. This can be used to assign multiple instructors to one student for a single content area during an academic year, and allows teachers to monitor their instruction compared to that of other teachers.

A key part of the SGP analysis is determining student growth percentiles, which represent how far a student must progress on their current trajectories to reach a target proficiency level. This is done by identifying the minimum number of standard deviations (SD) in either a math or ELA score that must be attained for a given student group, and comparing these to the actual growth of the student groups as measured by their past SGPs.

As the table below demonstrates, on average over cohorts, students who are identified as FRL or as having an other home language have true SGPs that are 5.1 points lower than those of all other students for both math and ELA. These differences are larger than those for the two lowest performing groups, which are generally students with excessive absences or those from low socioeconomic families.

Several lower level SGP functions (studentGrowthPercentiles and studentGrowthProjections) utilize the WIDE format of sgpData, while higher level wrapper functions such as teacherGrowthPercentiles and teacherTestGroupPercentiles require the LONG data format. We recommend that users of these functions use the LONG format for all operational analyses, as management of long data is simpler than that of wide data sets. This is especially important if the SGP package will be used to run analyses operationally year after year, as the LONG format has numerous preparation and storage advantages over the WIDE data.

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