Data SGP is an informational database that offers various types of assessment data to students and teachers alike. The primary aim is to give both individuals as well as educators information on their academic progress relative to other peers; using this knowledge, Data sgp aims to help improve performance while simultaneously aiding informed decisions regarding education for both parents and children alike.
This database offers information on Student Growth Percentiles (SGP), which are measures of relative student achievement. They’re calculated by comparing each student’s current test score with those who share similar prior achievements; so for instance a student testing in the 70th percentile would have an SGP of 70%. SGPs are widely utilized by educators because they enable them to communicate student achievement more easily to peers.
Utilising SGP data in schools requires considerable technical expertise; however, the SGP package and tools simplify this process by taking advantage of R – an open source software platform available across Windows, OSX and Linux operating systems – free download via CRAN. Running analyses using SGP requires some experience with programming but no more than any other statistical software package.
One key advantage of using SGP data is its manageability on computer systems – even for complex calculations with many observations. This makes SGP data relatively manageable in comparison with analysis of Facebook interactions or similar activities.
Individual SGPs can be useful, but aggregated to report growth across subgroups, classes, schools and districts. Median SGPs have traditionally been the go-to statistic when reporting aggregated growth; however, median SGPs tend to overestimate average school-level growth; mean SGPs provide more accurate depictions of school-level growth.
In this paper, we detail the rationale and results behind switching from median SGPs to means. We also present an alternative method for calculating mean SGPs which performs favorably when compared with median ones; this is particularly relevant given DESE plans to introduce new, more challenging MCAS assessments starting in 2022 that may necessitate higher growth rates than what was seen with old tests; hence moving toward means is an intelligent strategic move.