For some students, reaching proficiency on an achievement scale may not be enough. Instead, they need to strive for something beyond proficiency to help them get on a more advanced track. This is the idea behind Student Growth Points (SGP), a metric that shows the amount of student learning required to progress from one level to another on an achievement scale.
SGP leverages longitudinal student assessment data to produce statistical growth plots that quantify relative progress compared to academic peers. The plots are calculated using student’s prior test score history and covariates to create a forecast/projective percentile growth trajectory that represents what they need to achieve in order to reach an agreed upon growth standard (e.g. 75% of their academic peers).
The percentile projections are then used to determine how much student learning is required for each student in a given year to achieve the target. The SGP method allows educators and parents to stipulate both what the student needs to achieve and how much the student has already achieved.
This information can be displayed at the student level and is useful in identifying strengths and weaknesses in student performance. Teachers can use these data to inform their student-level instructional plans and administrators can incorporate them into their system accountability structure. The SGP metric is different from value added, which is also a popular way of looking at student growth and is used in the state accountability system.
The SGP metric provides an alternative to the typical mean test score that is displayed on state report cards. Although both measures have their strengths and weaknesses, SGP is an effective tool for evaluating individual students’s academic growth over time. This data is especially useful for demonstrating student progress to families and educators.
Typically, it takes about 10 SGP points of progress to move from the proficiency level of an achievement scale to advance performance levels. SGPs can be a powerful tool for students at all academic levels, as low performing students can demonstrate their growth while high achieving students have something to strive for.
The sgptData_LONG data set contains longitudinal (time dependent) student assessment data in LONG format for 8 windows (3 windows annually). It is a sample of the student growth data that is used to run SGP analyses. This data set is anonymized and includes the following variables: VALID_CASE, CONTENT_AREA, YEAR, ID, SCALE_SCORE, GRADE, and ACHIEVEMENT_LEVEL.
To conduct SGP analyses, the sgptData_LONG file must be opened with the summarizeSGP function. This function converts the longitudinal data into a table that can be sorted by a variety of criteria and calculates SGPs at various points in time for each student. The SGPs can then be visualized in a variety of formats. SGPs are most often reported as percentages, but can be presented in a range of other formats. Mean and median SGPs have similar distributions, but means are more tightly distributed than medians. As a result, means tend to be a better indicator of school (or group) performance.