SGP analyses transform raw student assessment scores into scaled scores that can be compared with an average for their grade and subject area. SGP programs then determine if an individual student’s scale score falls above, below or at the same level as this average while also projecting what their future scores might look like.
Unfortunately, many districts find SGP difficult to implement due to the lengthy time required to develop an accurate predictor model and collect reliable data on student achievement growth. Furthermore, correlations between baseline SGP results and prior year assessment scale scores likely won’t be exactly zero, potentially creating substantial bias into interpretation of results from SGP analyses.
The SGP data package seeks to address these challenges, making it simpler for teachers to use SGP to inform their practice. With its current release, this package contains sample longitudinal data sets in WIDE and LONG formats (sgpData_LONG and sgpData_WIDE respectively) that district users can use for practicing SGP analyses as well as an instructor-student lookup table (sgpData_INSTRUCTOR_NUMBER), allowing instructors to be associated with students via unique test record identifiers within test records.
Finally, the SGP data package offers several functions to perform and combine SGP analyses. These functions include prepareSGP, analyzeSGP and combineSGP. prepareSGP uses an exemplar LONG data set such as sgpData_LONG to create Demonstration_SGP@Data while analyzeSGP conducts SGP analyses on student growth percentiles and projections for all content areas and years studied; finally combineSGP incorporates these results back into Demonstration_SGP@Data for final steps of analysis before merging it all back together again to form one master longitudinal record: Demonstration_SGP@Data
As more states adopt Common Core, SGP analysis will become increasingly necessary. The authors of the SGP data package hope it can assist educators and their districts to use student growth measures for improving educational outcomes for all children.
If you are interested in contributing or using the SGP data package, please read through our documentation and consider opening an issue on GitHub. We welcome any and all feedback and suggestions for further development! We look forward to hearing your voices!
Before recently, educators were left without an effective means of using students’ standardized test scores as a means of comparison across schools, districts and state governments. This led to an uneven playing field between them. SGP data can help level the playing field by offering more accurate, meaningful, and timely measures of individual student achievement. This information can then be utilized by educators in differentiating instruction and supporting students as needed. SGP data can also help districts measure the effect of different teaching methods on student achievement. Policymakers can utilize SGP information to understand how effective their education policies are and allocate resources based on student needs. Information gathered through SGP data collection, processing and analysis is essential to making informed decisions regarding education. By collecting, processing and analyzing this data policymakers can create equitable school systems that benefit all children equally – thus unlocking its full potential as a global leader for innovation and economic prosperity.