Data sgp is the collective of aggregated student performance data collected over time that teachers and administrators use to make decisions about instruction and assessment. The information can be used to identify areas for improvement, inform classroom practice, support teacher evaluation systems and support research initiatives at the school and district levels.
Data SGP utilizes longitudinal standardized test data to produce statistical growth percentiles, which measure a student’s relative progress compared to their academic peers. Unlike traditional test score averages, which often report results that are difficult to interpret, growth percentiles provide a clear picture of a student’s progress over time, making them easier to understand and more useful for educational decision-making.
These percentiles are also a powerful tool to communicate with stakeholders that a student is on track to achieve proficiency within a specified timeframe. This can be particularly important for Michigan educators who incorporate student growth percentiles into their educator evaluation system.
Growth percentiles are generated by analyzing a student’s history of standardized test scores and comparing them against the results of all students who have taken the same assessment in the same grade. Using this method, a statistician determines the probability that a given student will reach a specific achievement level, such as the 75% of their academic peers. This method is significantly more robust than standard growth models and can be used to compare the performance of individual students, schools/districts and teacher groups.
Currently, SGP analyses are available only to those with access to the software environment called R. This free-of-charge, open source programming language is available for Windows, OSX and Linux computers. Since running SGP analysis requires a certain amount of familiarity with the program, numerous resources on the CRAN website help users get started.
In order to run SGP analyses, the sgptData_LONG data set must be downloaded and inserted into an R session. This dataset provides an example of a longitudinal panel data set consisting of 5 years of annual, vertically scaled, assessment data. It includes the sgpData meta-data and embedded state specific metadata to make it easy to analyze a variety of content areas in a single session. The sgpData_LONG data set also demonstrates the format required for use with lower level SGP functions such as studentGrowthPercentiles and studentGrowthProjections.
SGP leverages the power of the open source R software environment to automate the creation and analysis of student growth percentiles from longitudinal standardized test data. These percentiles can then be compared across students, schools and districts to allow for more accurate and efficient determination of students’ progress towards their academic goals.
While the term ‘big data’ is increasingly being used to describe enormous datasets that are too large for traditional data management tools, a similar challenge exists with the aggregation and analysis of student assessment data. In this article, we describe how the SGP tool addresses these challenges to make it more feasible to use student growth percentiles to improve teaching and learning in the real world.