The Limitations of Data SGP
Data sgp is the collection of a student’s past and projected achievement trajectories. It includes a student’s current achievement as measured by the Star examinations and a projection of where a student is likely to be on the proficiency scale if they continue along their current trajectory. This projection is based on historical student performance in each of the prior testing windows. In addition to using current assessments, a student’s SGP is also calculated by comparing their current score to their score on an assessment taken during a prior testing window. Generally, a SGP will be similar to the prior year’s assessment for the same student.
SGPs are commonly used to evaluate students’ progress and teacher effectiveness. They are a form of student growth percentiles, which measure a student’s current achievement in terms of their rank against other students matched to them with respect to their prior achievement (Betebenner, 2009). The appeal of SGPs is twofold. First, they are defined on a percentile rank scale that is familiar and interpretable, even when test scores are not vertically or intervally scaled. Second, SGPs are a more appropriate measure of student progress than unadjusted levels of achievement because they take into account the difficulty of the task and the time needed to learn it.
Despite their popularity, SGPs are not without limitations. Several important issues need to be addressed when using them for evaluation purposes. These include:
One of the key issues is that, despite efforts to control for covariates, there are still differences in SGPs among students with different backgrounds. The most important of these is that students with greater background characteristics tend to be taught by more experienced teachers, which can lead to inflated SGPs.
Another issue is that SGPs are calculated from the average of all student assessments, which may not be representative of a student’s overall achievement. To address this, it is important to understand the distribution of students’ actual achievements and how this relates to their SGPs.
While SGPs are a valuable tool for evaluating student progress, it is also essential to recognize the limits of this approach and what needs to be done to improve it. Moreover, the use of SGPs must be balanced against other measures of student achievement that may be more informative in assessing school and teacher effectiveness.
Fortunately, the bulk of the work involved in analyzing SGPs is on data preparation rather than on running the analyses. We have designed our system to make this process as simple as possible and most analyses that we assist with follow a two step process. The system is a custom relational database that is capable of managing millions of analytical results. We often refer to it as a ‘medium data’ system, in contrast to the term ‘big data’ which is usually applied to datasets that are too large for traditional data management systems. We think that this is an appropriate distinction because SGP research does involve an unprecedented amount of analytical results but, in comparison to, for example, an analysis of global Facebook interactions, it is relatively modest in size.