Data SGP refers to the collection and analysis of student achievement and learning data over time, which educators use to gain a better understanding of students’ progression over time. Data sGP can serve as an invaluable resource in shaping classroom practices, evaluating school/district performance evaluation, supporting wider research initiatives as well as providing insight into learning needs identification and targeted intervention for struggling students.
Data SGP leverages longitudinal student assessment data to produce statistical growth plots (SGPs), which measure students’ relative progress relative to academic peers. This information comes from students’ prior test scores and covariates established through their standardized testing histories; however, creating SGPs from this source requires complex calculations with large estimation errors which make these plots virtually unusable for measurement purposes.
As such, many educational researchers have turned to SGPs generated through non-standardized assessments or contextualized measures in order to gain additional insight. Such SGPs combine multiple measures of student progress such as academic skills, covariates and contextual factors into one data set which is then compared with an average test score from their cohort to determine how much progress has been made by that student relative to his or her predecessors.
Access to accurate and up-to-date data is essential for understanding how well students are learning. Educators and parents alike can utilize this data in decision making about how best to support the education of their children; additionally, researchers use it as evidence against different educational interventions which affect student development.
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An effective SGP Package should include more than just an extensive sgp database; it must also contain higher level functions, like studentGrowthPercentiles and studentGrowthProjections that “wrap” lower level functions into one function call, making operational SGP analysis simpler for source code development. To this end, prepareSGP in the SGP Package accomplishes this by taking two lookup files (LONG_DATA and INSTRUCTOR_NUMBER), merging them together to form Demonstration_SGP).
Future expansion of SGP data collection will include more states and additional standardized tests, which will create a comprehensive set of SGP data that is useful for conducting operational analyses as well as helping educators gain greater insights into student learning and development. We invite you to join us in this important endeavor and will keep you posted as new SGP data becomes available – thank you again for supporting the SGP project and we welcome any queries; we always enjoy hearing from our supporters!