Building a Data Culture. Data Guru Roles, Responsibilities & Expectations

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1 Building a Data Culture Data Guru Roles, Responsibilities & Expectations

2 Eight Steps for Success Establish and communicate a clear vision Research and learn from others successes Examine infrastructure for effective data use Ensure buy-in, commitment, and trust Foster professional development Lead by example and encourage data utilization Establish data meetings Remove or modify barriers to effective data use Source: Creating a Data-Driven Culture: Leadership Matters Dr. Lane B. Mills, a SAS white paper

3 Our Objectives school stakeholders (district personnel, school administrators, curriculum and instructional facilitators, and teachers) to make based on An analysis of trends and relationships Across different types of data From a variety of data resources Over time

4 Our Objectives the,, and of available to the schools and different of of to and

5 Our Objectives a for greater of of and to make through the that is at all levels

6 Data Guru Workshops Build capacity At least one data guru from each school (Principal, Assistant Principal, Instructional Facilitator, or Teacher) Hard copy of Principals Playbook Data Notebook Jump Drive Establish a common language Encourage collaboration and administrative support Provide leadership (walk the walk and talk the talk)

7 What is your purpose? Data worth collecting should have a purpose How do you use data to inform instruction and improve student achievement? How do you decide which data are the most important to use, analyze, or review? How do you apply the right data at the right time to the right issue? In the absence of data, what is used as a basis for instructional decisions?

8 Are you using a Data Process? Collect and chart data Analyze strengths and obstacles Establish goals: set, review, revise Select instructional strategies Determine results indicators Evaluate effectiveness Sustain or refine, and use the data to make instructional improvements

9 Best Practices Structure organization and practice to support data-driven decision making Collaborate and share ideas regarding data inquiry and analysis issues Examine student data to determine which practices work best for which students Use assessment data to identify gaps in student achievement Adapt instructional activities to meet students individual needs Communicate with parents about student progress

10 Asking Questions is Key! How do you accelerate learning for all students? Which programs best prepare students for future success? Who are the students that need remediation before moving to the next level? What early warning indicators are the most predictive of possible future failure or dropping out? How successful are students that attend our schools?

11 Roles & Responsibilities Act as a role model for data use Gather, report, analyze, and monitor data for your school Provide timely access to information Share data with stakeholder groups through data notebooks, presentations, data walls or data boards Provide reinforcement and support for others as we move toward being part of a data-driven culture

12 Expectations Assist staff in the analysis and interpretation of data to guide instruction and student achievement Cultivate a data culture by training teachers to use and analyze data Coordinate PLC meetings through scheduling and creation of agendas, minutes, and necessary data Provide support and instructional coaching that builds teacher capacity Promote and coordinate professional development and the implementation of research-based instructional strategies

13 Continuous Journey Data should invite action Data that is collected should be analyzed and used to make improvements (or analyzed to affirm current practices and stay the course). S. White, Beyond the Numbers, 2005, p. 13 Education thrives on an environment of continuous learning and improvement. Once users have access to information they become data consumers. As such, they tend to become more sophisticated in their questions. G. Mariani, Data-Driven Decision Making, a SAS white paper

14 Moving from Reactive to Proactive Source: Data-Driven Decision Making, a SAS White Paper

15 Ready to answer these questions? Which students are at risk? Which intervention strategies work best to help at-risk students stay in school? Which students are not on track to graduate? How can we predict likely student test results early in the year and take steps to improve student success on an individual basis? Which programs are working to improve student achievement and which are not? How do we optimize resources and funds by forecasting student enrollment, population patterns, and student performance?