Data sgp is an R package that simplifies the process of converting student growth percentiles and projections from longitudinal educational assessment data into statistical growth plots. To use it effectively requires only basic knowledge of R programming language and data manipulation techniques; available for Windows, Mac OSX, and Linux download. sgpData set contains five years worth of assessments from each classroom in which teachers can assess how their own students perform relative to academic peers by ranking them against each other over various grade levels – information easily digestible by teachers themselves!
Baseline-referenced SGPs differ from their cohort-referenced counterparts in that they can be calculated with data from only two assessments – typically the most recent year’s assessment and scale scores from an earlier year’s test – rather than three consecutive years of stable assessments being available to calculate them. This method allows teacher evaluations to be completed rapidly and effortlessly, but has some significant drawbacks: First, correlations between baseline SGPs and prior year scale scores may not be perfectly zero; thus introducing significant bias into interpretation of results. Second, this evaluation assumes that every teacher in each classroom has the same growth target for all their students in that class. This may not always be true in every district and it is essential to account for differences in student population structure when evaluating individual teachers.
Although sgpData contains all of the data necessary for baseline-referenced SGP calculations, sgpPrepareSGP is an extension of sgpData that makes conducting studentGrowthPercentiles and studentGrowthProjections simpler. PrepareSGP can be particularly beneficial to districts with multiple content areas and instructors as it offers an efficient means of connecting students to instructors by tapping into its INSTRUCTOR_NUMBER lookup field.
sgpPrepareSGP not only supports standard SGP calculations, but it also accommodates custom teacher growth models created by district administrators. These customized teacher models may take into account student characteristics not captured by standard calculations such as special education status or language proficiency – customizations such as these can be applied across classrooms or school-wide and are frequently used to differentiate instruction.
Data SGP is an essential resource for educators, providing them with a more accurate view of students’ academic progress while making it simpler to recognize top teachers. Though not foolproof, its information can assist educators in improving student achievement and closing gaps among those from different socioeconomic backgrounds. With proper training, SGP can also assist educators in identifying potential barriers to student learning and devising plans to overcome them. This article was written by Darren Snaith, Chief Data Officer for Macomb and Clare-Gladwin ISDs in Michigan. Darren has been using SGP since its conception. Additionally, he serves on its Advisory Committee and advocates its use as part of educator evaluation processes.