Data sgp is the collective of aggregated student achievement and learning data collected over time, used to help educators and parents better understand the progress students make over time. This information can be utilized for shaping classroom practices, evaluating school/district performance and supporting wider research initiatives. This data includes individual measures like test scores and growth percentiles; as well as aggregated metrics such as class size, attendance rates and graduation rates; and a wide range of demographic variables including grade levels, gender, ethnicity and socioeconomic status that are aggregated at a group level for broader studies and research efforts.

A central part of data sgp is statistical growth plots (SGP), which display a student’s relative progress against a standard set by their academic peers. These are calculated by comparing a student’s current test score to their previous ones. This allows for a very simple and straightforward assessment of whether or not a student has met an agreed upon growth target. Unfortunately, creating SGPs from a students’ standardized test score histories involves complex calculations and large estimation errors.

SGPs can be generated using longitudinal data, such as a district’s student records system or an institutional database for teacher evaluation. However, it is often challenging to provide these data in formats compatible with operational SGP analyses. The Macomb and Clare-Gladwin ISDs have made their SGP data available for districts in formats compatible with these analyses. The SGPdata package offers 4 example data sets that demonstrate how to prepare and use these data for SGP analyses.

Each of these data sets contains SGP analysis functions that can be used to generate SGPs for a given cohort of students. All of these functions can be run from within the R Studio environment, or they can be executed as part of a script. This enables SGP analysis to be integrated into the daily routines of teachers and administrators, rather than being a separate event that requires additional resources for planning, execution and reporting.

One of the key features of this new data is that it can be analyzed using percentile terms, which are familiar to most teachers and parents. This allows for the SGP to be viewed in terms that are more understandable to teachers and parents, as opposed to a raw score which can often appear intimidating. The data sets also include the ability to run multiple analyses at once, which provides more flexibility for those who need to compare groups or subgroups.

Another feature of the SGPdata is the ability to connect instructors with their students, as it enables districts to assign a student to a particular instructor for a given content area. This is accomplished through the sgpData_INSTRUCTOR_NUMBER field, which is a lookup table that allows districts to associate a student’s test record with its associated instructor. This field is crucial for ensuring that districts are able to accurately produce and analyze SGPs. This is a significant improvement over the legacy SGPTDATA package, which only provided for instructor-student lookups.