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1. The Oregon DATA Project: “Direct Access to Achievement” Technical Training Component 1: “The Need for Data Quality” Run time: 11:00 A project of the Oregon Department of Education, with support from the EESC www.oregondataproject.org

2. Educational Data • Various types of data are available to inform the decision making process. • Student learning data • State and local assessments, teacher observations, etc. • Perception data • Data that tells how people feel about the district/school • School processes data • Programs, instructional strategies, classroom practices, etc. • Demographic data • Enrollment, attendance, ethnicity, gender, etc.

3. Demand for Data • Instructionally: • The Oregon DATA Project’s Instructional Training encourages more use of data to drive student achievement • The advent of regional data warehouses provides new ways of combining data elements for decision making • The KIDS Project allows data to flow across the state providing new access to more data • Accuracy of the data is essential • Accountability: • State Collections gather required data • The collections have multiple uses including funding and accountability measures • Accuracy of the data is essential

4. State Collections - Data Challenge • Locating data sources • Local databases • Local spreadsheets • Student Information System • Insuring data accuracy • Sometimes updated in one but not all locations • How to insure the data you pull is the updated data • Data Certification

5. Data Certification Examples • District Budget • Identifies how much money is available to spend • Decisions are made based upon that amount • Monthly statements certify the accuracy of the available balance • Educational data (multiple types) • Can be used for instructional decisions • Can determine funding and accountability • How often is its accuracy certified? • Many times, it is just when a collection file is being submitted

6. A Real Case Example • IDEA Census Collection • Legacy system v eSIS system • Legacy: 1267 special ed students • eSIS: 1236 special ed students • Which number should be submitted?

8. Case in point – IDEA Census • Legacy system v eSIS system • Legacy: 1267 special ed students • eSIS: 1236 special ed students • Which number should be submitted? • After nearly 8 weeks of data clean-up • 1333 special ed students • $135,000 - $200,000 in funding • High stake outcome for data accuracy

9. Improving district data • National Forum on Education Statistics • The Forum Curriculum for Improving Education Data: A Resource for Local Education Agencies • National Center for Education Statistics • Forum Guide to Building a Culture of Data Quality: A School and District Resource • Provides a model for districts • “Creating a Culture of Data Quality”

10. Defining a Culture of Data Quality • It is the belief that good data is an integral part of the educational enterprise. • Everyone in the enterprise demonstrates that shared belief by using appropriate data to achieve the enterprise’s goal of student achievement. • The enterprise is willing to invest resources to obtain quality data • Everyone in the enterprise shows respect for the effort taken to produce quality data

11. Factors Affecting Components Quality DataCulture Importance Roles Technology Policies & Regulations Hardware Timeliness Security Standards & Guidelines Software Network Utility Data Entry Environment Timelines & Calendars Accuracy Training & Professional Development More Effective Decision-Making Data Steward Superintendent Other Board Member Teacher Achieving AYP (Adequate Yearly Progress) Office Staff Program Funding Principal Technology Support Staff

12. Quality DataCulture Roles Culture of Data Quality • A district commitment • A purposeful assignment of responsibility/oversight Data Steward Superintendent Board Member Teacher Office Staff Principal Technology Support Staff

13. Data Steward Responsibilities • Coordinate data collection process • Provide training • Resolve errors • Establish audit procedures (data certification) • Create Data Collection Calendar • Develop and disseminate data entry standards • Interpret data entry manuals for end-users

14. Data Steward Responsibilities • Collaborate with others (directors of curriculum, special education, assessment, etc.) • Collaborate with the IT Director to enhance computer software for data entry, correction, and auditing • Be the driving force for maintaining data accuracy, utility, timeliness, and security • Be aware of the complexity and changing nature of the data required for state and federal accountability (State Collections, NCLB, etc.)

15. A Culture of Data Quality • How does a district create a Culture of Data Quality? • How can a district address the factors that impact data quality? • How can the responsibilities of a Data Steward be met in a district that cannot hire such a person?

16. DATA Project Technical Training • Component 2: Building a Culture of Data Quality • Create a Data Quality Oversight Committee • Assign the responsibilities of a Data Steward • Complete a self-assessment of current data practices • Develop a district data calendar • Coordinate for State Collections (decrease error rate)

17. What’s in it for you? • Opportunity for in-district discussion that will customize the model to fit the unique needs of your district. • Internalizes and deepens the implementation • District and school profile that can act as a road map toward improvement. • High level of confidence in your data.

18. For additional information/materials or to contact someone with your training questionsplease visitwww.oregondataproject.orgThe Oregon data project Technical Training ComponentsComponent 1: The need for data qualityComponent 2: Building a Culture of Data QualityComponent 3: Tools and documents for data quality Direct Access To Achievement www.oregondataproject.org