Data SGP refers to an aggregated set of student performance measures collected over time that teachers and administrators use to gain a better understanding of students’ learning. It includes individual-level measures like test scores and growth percentiles as well as aggregate measures at school/district levels like class size, attendance rates and graduation rates. Data sgp allows teachers to identify areas for improvement as well as inform classroom practices, evaluate schools/districts, support broader research initiatives as well as inform decisions made about funding sources for research initiatives.
The SGP package contains classes, functions and data for calculating student growth percentiles and projected/trajectory projections/trajectories using large scale, longitudinal education assessment data. Calculations utilize quantile regression to estimate conditional density associated with each student’s achievement history before using coefficient matrices to display needed percentile growth towards future achievement targets.
Student growth percentiles provide an alternative assessment method that compares students from similar academic histories; unlike more traditional measures which compare pupils against each other on similar measures of academic ability, these percentiles estimate how much improvement a student needs to make before being proficient by the end of grade or course. This enables educators to focus on improving students’ abilities rather than searching for low scoring students who need improvement; though student growth percentiles still risk spurious correlations due to differences between teacher/student characteristics or baseline cohort designs.
As such, educators must be cognizant of the limitations and pitfalls associated with this methodology when using it in their classrooms and districts. Beyond understanding its basics, educators should also be cognizant of how different factors could impede results, including students’ abilities to improve with practice or whether a high score on one exam section indicates successful learning.
SGP includes both higher level studentGrowthPercentiles and studentGrowthProjections functions, as well as lower-level functions that require WIDE formatted data (studentgrowthpercentiles and studentgrowthprojections), with higher-level wrappers (studentgrowthprojectionsWide and studentgrowthpercentilesLong). The SGP Data Vignette offers comprehensive documentation of how to utilize these features with this package.
While big data has become an industry buzzword, SGP analyses are relatively modest when compared to other forms of analytical work. This is beneficial as any errors that arise in SGP analyses often stem from improper data preparation issues; thus highlighting why educators must take time to ensure their data is properly prepared prior to performing SGP analyses – once this has been accomplished, analyses can generally run without needing additional user input.