Data SGP, a database containing student academic achievement growth information, can provide educators and school administrators with invaluable insight. It enables them to quickly identify students who require additional support as well as identify which strategies would work most effectively – tutoring programs or mentoring could potentially benefit. In addition, data SGP allows them to predict future trends in students’ performances.
MCAS scores and growth percentile ranks provide an indication of how much progress has been made over time in comparison with students with similar MCAS performance histories. Percentile ranks are calculated based on an individual student’s MCAS scores from one or more assessment administrations and adjusted for differences in test complexity.
SGPs offer more in-depth and reliable measures of student progress than unadjusted standardized tests alone, making them valuable in evaluating both individual students as well as teacher effectiveness; however, some educators remain concerned with unadjusted student results when used solely as assessments of teachers’ quality.
Some variation in true SGPs is explained by individual student background characteristics influencing performance; however, another portion appears to be explained by teacher-level factors like teaching style and student learning abilities. This finding aligns with evidence showing that classroom measures such as instructional practice and student-teacher fit explain more of the variation in student achievement than do teacher-level factors such as professional certifications or education levels.
In addition, the Department’s analyses show that some of the variation in expected aggregated SGPs from year to year isn’t due to contextual effects or teacher sorting; when comparing school-level mean SGPs with median SGPs using 2013 4th grade math growth data, standard deviations of means were less than those for medians – suggesting more stable differences between averages and medians over time.
The SGPdata package installed with the software includes sample WIDE and LONG data sets (sgpData_WIDE and sgpData_LONG), respectively. When conducting most analyses, long-format data should be formatted as higher level functions like studentGrowthPercentiles and studentGrowthProjections can utilize its advantages for preparation and storage purposes.
SGP projections utilize historical student growth trajectories from Star examinees to project what their MCAS score and growth percentile will be during an upcoming testing window. They are updated frequently so as to reflect the latest growth data available. If you would like to generate one for any student specifically, simply select their prior or current testing window from the report customization menu to generate one.