Data SGP (Student Growth Progress) is a metric designed to track student achievement progress over time using longitudinal test score data. It can provide useful insight for informing instruction, assessing student/teacher performance and supporting educator evaluation systems. Using standardised assessment data and student covariates as predictor variables, SGP employs latent achievement trait models estimated by using teacher evaluation criteria alongside historical test score histories as growth standards; this allows us to compare latent achievement trait models against growth standards established as growth benchmarks to reduce estimation error while increasing validity when making comparisons across individuals or groups of students.
Contrary to most assessment metrics, SGP metrics do not depend on an absolute value or fixed scale; rather they are reported as relative percentiles. A student’s growth percentile indicates how much their raw score on a test section has grown relative to that of their academic peers; educators and administrators can use growth percentages as tools for evaluating student/teacher performance, informing instructional practice, supporting classroom research initiatives, as well as evaluating schools/districts.
Growth percentages provide educators with more accurate measures of student achievement over time than traditional metrics like mean, median and mode scores do. They enable educators to more easily identify struggling students who require additional support while differentiating classroom instruction for high-performing ones and monitor progress of those performing well over time. Furthermore, when used alongside longitudinal data SGP allows educators to make more accurate predictions regarding future student performance.
SGP analyses require longitudinal student assessment data in either WIDE or LONG format; most errors associated with SGP analyses result from issues in its preparation. The SGP Package offers both WIDE and LONG data sets along with higher level wrapper functions called studentGrowthPercentiles and studentGrowthProjections to assist with creating appropriate student assessment data suitable for SGP analyses. When conducting SGP analyses on an ongoing basis, LONG format student assessment data tends to offer more advantages due to preparation, storage and retrieval benefits than WIDE data sets.
Prior to applying SGP methodology on your data, it is essential that you gain a full understanding of its underlying mathematical models and assumptions. To aid beginners, the SGP package includes several tutorials and examples designed to introduce these fundamental concepts of SGP analysis.
SGP stands out from standard growth models or other methods in that it allows schools and districts to measure student/teacher performance against official state achievement targets/goals. This allows schools and districts to communicate to stakeholders that proficiency must be reached within a specified timeframe while also serving as a tool to motivate teachers by linking performance against measurable goals – this cannot be accomplished via standard growth models alone! Michigan specifically utilizes student test score progression data in educator evaluation systems; therefore being able to effectively meet this objective is of critical importance.
