What is Data SGP?

Data sgp is the aggregated student performance data that teachers and administrators use to better understand students’ growth over time. It includes individual-level measures like test scores and growth percentiles as well as aggregated data at the school and district levels such as class size, attendance rates and graduation rates. This data can be used to identify areas for improvement, inform classroom practices, evaluate schools/districts and support broader research initiatives.

The sgptData_LONG data set is an anonymized panel data set with 8 windows (3 windows annually) of assessment data in LONG format for 3 content areas (Early Literacy, Mathematics and Reading). It contains student assessment data with a variety of variables that can be used to create SGP analyses such as student level growth plots. The sgptData_LONG dataset also includes state specific meta-data in the embedded SGPstateData metadata. All higher level functions in the SGP package are designed to work with this data set.

SGP analysis uses the sgptData_LONG file and an iterative process to create individual level growth plots for each student. The individual-level plots are based on the student’s average SGP score compared to all other students in the sample who have taken the same assessments at the same point in time. These plots allow educators to observe a student’s growth relative to other students in their classroom.

An average SGP score of 50 indicates that a student is growing at an above average rate compared to their peers. Conversely, a SGP score below 50 indicates that a student is growing below average compared to their peers. SGP analysis can also be used to observe a student’s average growth over a period of time by calculating a trend line for the individual’s progress from one assessment to the next.

A key feature of SGP is the ability to compare student performance between districts, schools and classes. This capability allows educators to evaluate their student groups against the same student performance measures used by other teachers across the country. This is especially useful for comparing teacher effectiveness and evaluating the impact of professional development.

The iterative SGP analysis process is a powerful way to analyze large amounts of data and make informed decisions about classroom practice, student groupings and student needs. The iterative process also helps to improve the reliability of the results, so that teachers can be confident in their interpretations and conclusions.

Using the iterative process, educators can analyze the SGP data to identify patterns in student performance and make changes to their classroom practice that will result in positive outcomes for all students. The iterative process also enables them to identify the effects of interventions on student learning and achievement. This information can then be shared with other educators to encourage best practices and drive educational reform. Ultimately, the iterative process is an important tool for ensuring that all students are on track to graduate high school. A new iteration of the SGP process was launched in 2015 to further refine the methodology and provide a more robust set of tools for interpreting student growth.