The data sgp package provides tools for conducting Student Growth Percentile (SGP) analyses of longitudinal student assessment data. SGP is a measure of relative student growth that compares students’ test scores to those of their academic peers. Using statistical models, SGP can predict whether students’ performance in future assessments is likely to fall within a given range. Consequently, it can be used to make predictions about student outcomes such as grades and graduation rates.
A SGP score is calculated by comparing a student’s current test score with his or her prior test score, with each assessment weighted equally. During the SGP process, student-level information is combined with teacher-level information, which enables comparisons of student progress with that of their academic peers. SGP calculations are complex, but results can be reported in simple percentile terms that teachers and parents can easily understand.
There are two common formats for representing longitudinal student assessment data: WIDE and LONG. In a WIDE format, each case/row represents a unique student and the variables associated with that student at different times are stored across multiple rows in the data set. The data sgp package includes exemplar WIDE and LONG formatted datasets (sgpData_WIDE and sgpData_LONG) to assist with setting up your SGP analysis.
SGP is commonly calculated over a single testing window, which may not correspond to a school or district’s “school year”. However, many teachers and administrators find it useful to view SGP scores for a student over a longer time period, and therefore, the data sgp package includes SGP calculation tools that produce both Window Specific and Current SGP.
A key feature of the data sgp package is that it incorporates Gaussian Process regression (GPR) model-based predictive models, which are more efficient than conventional regression methods when compared to their complexity. This is especially true for the case where the number of data points is large. However, the computational complexity of GPR models, which is O(N3), limits their application to large datasets.
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