The data SGP tool enables educators to conduct in-depth analyses and projections on all students in their class or school. Students’ projected proficiency dates are based on how other similar students performed in previous state assessments with similar growth trajectories. Furthermore, this tool also detects those students not growing adequately while providing guidance about what steps can be taken to close gaps between their actual growth trajectory and predicted one.
The tool utilizes up to two years of historical test results for each student in order to create projections and student growth percentiles (SGP), which can then be used to identify projected proficiency trajectories including likely timeframes in which proficiency will be reached or exceeded.
Quantile regression, a statistical method applied to historical test scores of each student, allows us to calculate SGP. This translates each student’s previous year score into a percentile ranking based on normal distribution; furthermore, number of students at each percentile are modelled and averaged before comparing current assessment with prior assessment to determine both actual score as well as how much progress has been made over time.
Students with higher initial scores do not typically progress as quickly, given that they are already nearing or exceeding the top of the scale. Nonetheless, the model strives to ensure all children meet the minimum standards in each grade and subject area.
SGPs in Washington State are calculated based on the performance of all students who take valid tests in their grade and subject over an extended testing year, thus providing students with both high and low SGP scores during this testing year. A student can receive either high or low scores during one testing year.
This example data set, called sgpData, shows a unique student identifier as ID in its first column; then in each of its five subsequent columns – GRADE_2013, GRADE_2014, GRADE_2015, GRADE_2016 and GRADE_2017 – there are each student’s assessment scores from 2013 – 2017 divided up according to grade level (2013 – 2017). It serves to show teachers how the sgpData function works as well as provide them with a reference when conducting analyses themselves.
At Student Growth Planning (SGP) Analytics, our tools and processes have been tailored to be user-friendly. In our experience, most errors associated with SGP analyses result from issues related to data preparation; thus we advise educators to take their time in carefully preparing SGP data before beginning calculations. Once this step has been taken successfully, most analyses should be straightforward to execute.