Data SGP (Student Growth and Progress) is the collective of aggregated student achievement and learning data collected over time, used to help educators and parents better understand the progress of individual students over time. It includes individual-level measures like test scores and growth percentiles as well as aggregated measures at school- and district-level such as class size, attendance rates and graduation rates; in addition to grade levels, gender ethnicity socioeconomic status that are aggregated at a group level for larger studies and research efforts. Such information can be utilized for shaping classroom practices evaluating school/district performance while supporting wider research initiatives and supporting broader research initiatives.
The data set contains numerous tables and files. One of the most helpful tables is sgpData_INSTRUCTOR_NUMBER table, an anonymized lookup table providing instructor details associated with each student test record. Teachers can utilize this table to assign multiple instructors to one student across an entire content area for one academic year.
sgpData_STUDENTS_PERCENTILE_TABLE is another vital sgpData table, providing teachers and parents a convenient way to compare each student’s performance against that of similar students in their school, region, state or country – helping them determine whether their grades and demographic groups have outliers or average performers.
These tables show the number of students from each grade who scored at each score point and how many were in each group. It can also be sorted by name, grade, gender, race/ethnicity status and socioeconomic status to help users easily identify trends within the data. Download and print copies can also be provided for further analysis.
Although the sgpData tables provide invaluable data for researchers and educators, they don’t contain all of the information required to make decisions regarding student learning and instruction. To fully grasp how a student’s performance has an effect, more details about its context need to be known – for instance determining whether adequate progress in reading has been achieved requires knowledge of which percentage of grade level peers are at or above this same level as well.
Research consortia and community databases such as Genbank or EarthChem both collect and make available vast amounts of data, but their goals and approaches differ considerably. Research consortia typically focus on questions that are of immediate interest to their members, while full community databases aim to store and make accessible all available data. This approach to science is crucial because it creates an incentive for researchers to contribute their data and metadata to a database, while simultaneously guaranteeing users access to high-quality, reliable sources of information. Furthermore, databases allow scientists to compare and contrast information from different sources – an essential step in scientific discovery process known as “Full Monty.”