Data Driven Instruction

Data driven instruction is essential in not only supporting our school's vision, mission and goals, but in creating the goals as well. Data should be analyzed and used to make instructional decisions rather than just collected and never used. Data collecting and analyzing should be an on-going process and decisions should be flexible and fluid. As the school looks at performance data, they should be willing to make changes as needed during the year to ensure the students' academic needs are being met. For example, kids placed in an on grade level class whose assessment data show they are exceeding the standards should have the chance to move to a more challenging group and have their performance continue to be monitored. Using the data to keep groupings and placements flexible and fluid in this manner supports the school's mission of providing rigorous and appropriate instruction for all students and supports the school's AMO targets and SIP goals.
Data drives instructional decisions at the more individualized level also. Classroom teachers should be using formative and summative assessment data to make decisions about reteaching, acceleration, grouping, assignments etc. Collecting and analyzing performance data with students helps them set goals, strive to meet them, monitor their progress along the way, and increase their motivation through stakeholder buy-in. Using data to drive instruction decisions school wide and classroom based, gives parents and the community an objective rationale for decisions they may wonder about and keeps them informed on the school's performance across the board.
Some of the data collection tools we use at my school include: MyMCPS, rubrics, MCPS Grading and Reporting collection sheets, MClass, Dibels, Progress Monitoring, MAP R, attendance records, excel, running records, math unit assessments, county formatives for math, BCR graphs, quizzes, tests, Survey Monkey, ENCORE, teacher surveys, anecdotal records and ActivVotes.
These tools could be better utilized if our data chats included more of the tools. Many of these tools are used by classroom teachers to collect performace data in different ways, but often school based data chats and instructional assumptions are made based on a handful of tools such as MCPS math unit assessments, MAP R, and MSA prep tools like CARS and STARS. While these are indicators on how the school's MSA data might look, they do not always represent the academic abilities of the child.
While I think it is important for the school to look at data and make predictions of high stakes testing and I certainly know as an administrator, I will have to be concerned with this, it is also important to make grouping choices based on other data collected in the classroom as well. For example, guided reading placements can not be made based on MClass scores alone, formative running records taken over time and looked at for trends and patterns should be used as well.