Data SGP is a powerful database of student achievement information. Analyzing this information can be a time consuming process, but it can help educators pinpoint areas of improvement in student learning. It can also be a helpful tool for informing instructional practice and supporting classroom research initiatives. However, understanding how to use this information can be difficult for novices.
SGP stands out from standard growth models and other methods in that it allows schools/districts to link student/teacher performance against official state achievement targets/goals – something that is not possible using standard growth models alone. This can be a powerful tool to communicate with teachers/staff that they must meet specific proficiency goals within a given timeframe, while simultaneously providing motivation for student and teacher growth.
In order to generate SGPs, a student’s assessment data is normalized to a common scale, and then compared against other students with similar test score histories. This information is then reported as the student’s relative percentile ranking on a given content area, such as math or reading. The relative percentile ranking can then be used to identify struggling students or celebrate the successes of high-performing students.
For example, Simon’s sixth grade SGP is based on his assessment performance in math compared to other students with comparable score histories. This information is reported as Simon’s relative percentile ranking on a sixth grade math test, and the growth percentage calculated for him will indicate how much he improved from his fifth grade assessment performance. This information can then be used by teachers to compare Simon’s progress with their other students in a particular class, and also as a measure of his overall academic progression.
The sgptData_LONG dataset contains the student assessment records in LONG format for 3 content areas (Early Literacy, Mathematics and Reading) over 8 windows (3 windows annually). In addition, the sgptData_INSTRUCTOR_NUMBER dataset includes the anonymized instructor lookup table that associates an instructor with each student’s test record. In the case of Simon, his test record was associated with one instructor for each content area over the years.
This data is useful for many different stakeholders, including administrators and educators, as it can be used to identify and target areas for improvement, support instructional decisions, monitor teacher/student growth and help guide the development of student learning goals. Data SGP can be a key component of educator evaluation systems, and it can also be used to identify the highest-performing teachers for possible recognition or compensation.
As an added benefit, this dataset is a great resource for researchers interested in developing and testing new SGP methodologies. By releasing this data into the public domain, researchers can help ensure that SGP methodologies are validated and tested in a variety of educational contexts. By contributing to the creation and distribution of this data, we hope to facilitate the growth of this valuable resource. This will in turn lead to a more accurate and robust model of student performance over time, which is important for measuring achievement and determining instructional implications.
