Week One - Why Data?


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Week One - Why Data?

  1. 1. Putting Your Data to Work for Student Success<br />Week One:<br />Why Does Data Matter?<br />09.08.10 – 09.14.10<br />
  2. 2. Learning Objectives<br />1) Understand the different types of data points that are used in the education system for measuring student achievement and district performance. 2) Know how to: find, access, present and utilize specific data sources.<br />
  3. 3. Why?<br /> “Until you have data as a backup, <br /> you are just another person <br /> with an opinion.”<br />Dr. Perry Gluckman<br />
  4. 4. What Has Changed?<br />Standards-based reform and the accountability movement –<br />A Nation at Risk Report<br />No Child Left Behind<br /> In education, the stakes have changed and the paradigm has shifted... There is a common agreement with all the stakeholders that all children can learn and must achieve academic success.<br />
  5. 5. External and Internal Forces and Conditions Influencing Data-based Decision Making<br />Public demand for evidence-base to demonstrate the effectiveness of student learning and educational programs.<br />Rapid increase in sophisticated technologies for handling information.<br />
  6. 6. External and Internal Forces and Conditions Influencing Data-based Decision Making<br />Historically, data has been utilized within the K-12 system to inform decision makers at many different levels.<br />However, districts have frequently used a multitude of data, with very little information on which to base decisions. Most decisions were based on experience, intuition, and political acumen rather than being systematic, complete, and research-based.<br />Data provided by the State Education Agency (SEA) was not: informative for strategic decisions, delivered in a timely manner, or interoperable in the data exchange. <br />
  7. 7. External and Internal Forces and Conditions Influencing Data-based Decision Making:<br />Data is neutral. When data is placed into context and analyzed it becomes information. When we act on the information it becomes knowledge.<br />Data alone is/are not evidence of anything, until users of the data bring concepts, criteria, theories of action, and interpretive frames of reference to the task of making sense of the data.<br />Intelligent Data... intelligence: what people do in terms of abstract reasoning and deduction when observing facts or phenomena.<br />
  8. 8. Intelligent Data<br />The goal is to use data in thoughtful and strategic ways to inform all decisions at the district level - policy, management and instructional - from the boardroom to the classroom.<br />
  9. 9. What is the Effect of Using Intelligent Data?<br />This paradigm shift of using Intelligent Data to inform learning and teaching requires considerations at every level of the entire system. As school board directors and superintendents, you want to ensure the resources are aligned to provide tools that support a culture of inquiry and shift the conversation from a monologue to a dialogue about the role of data in decision making.<br />
  10. 10. Ways Leaders Use Data<br />Diagnosing or clarifying instructional or organizational problems.<br />Weighing alternative courses of action.<br />Justifying chosen courses of action.<br />Complying with external requests for information.<br />Informing daily practice.<br />Managing meaning, culture, and motivation.<br />
  11. 11. The Four Locations of Data Use <br />State Level - to set policies related to public school system improvement.<br />Local Level - to develop policies, strategies and initiatives focused on school district improvement.<br />Building Level - to create action plans to improve instructional programs.<br />Classroom Level - to improve instructional practice to increase student achievement.<br />
  12. 12. Characteristics of Data-Informed Leadership<br />ClarifyingFocus - Vision, Values, Theories of Action<br />Changing the Culture - Cycles of Inquiry<br />Creating Conditions - The Policy Environment<br />
  13. 13. Understanding Data and Its Sources<br />Data-Literacy<br /> A growing competence with the interpretation of data, and a familiarity with data sources and creativity in assembling relevant data quickly and efficiently.<br />
  14. 14. Two Types of Data<br /> “In the context of schools, the essence of holistic accountability is that we must consider not only the effect variable—test scores—but also the cause variables—the indicators in teaching, curriculum, parental involvement, leadership decisions, and a host of other factors that influence student achievement.”<br />(D. Reeves, Accountability for Learning, 2004)<br />
  15. 15. A Three Tier Framework<br />Accountability Data<br />Professional Data<br />School Narrative Data<br />(Reeves 2005)<br />
  16. 16. Data Categories<br />Student Learning<br />Demographic<br />Perceptual<br />School Process Data<br />Teacher Characteristics <br />(Bernhardt 1998, Knapp et. al 2006)<br />
  17. 17. Types of Student Data<br />Demographic<br />Data<br />Enrollment<br />Free and Reduced Lunch<br />Ethnicity<br />Gender<br />ELL<br />Special Education   <br />Graduation Rates<br />Dropout Rate<br />Attendance<br />
  18. 18. Types of Student Data<br />Student Learning Assessments<br />PISA - Program for International Student Assessment<br />TIMSS - Trends in International Mathematics and Science Study<br />NAEP - The National Assessment of Educational Progress<br />SAT/ACT - Scholastic Aptitude Test / American College Testing <br />AP – Advanced Placement<br />ITBS/ITED - Iowa Test of Basic Skills/ Iowa Tests of Education Development<br />HSPE - High School Proficiency Team<br />MSP - Measures of Student Progress<br />
  19. 19. Longitudinal Data Systems<br />Data Representation<br /><ul><li>Tabular
  20. 20. Trend
  21. 21. Segmented</li></li></ul><li>Some Important Data Terminology<br />Norm-Referenced and Criterion-Referenced Tests<br />Trend Analysis and Cohort Data<br />Qualitative and Quantitative<br />Validity and Reliability<br />
  22. 22. Data Sources<br />Data Quality Campaign<br />http://www.dataqualitycampaign.org/<br />DQC Annual Survey Presentation<br />http://tinyurl.com/dqc-annual-survey<br />IES WA State SLDS Grant<br />http://tinyurl.com/wa-slds<br />
  23. 23. OSPI<br />OSPI - Data and Reports<br />http://k12.wa.us/dataadmin/<br />OSPI - State Report Card<br />http://tinyurl.com/wa-reportcard<br />
  24. 24. KidsCount<br /> The Casey Foundation provides funding and technical assistance for a nationwide network of KIDS COUNT grantee projects. They collect data about and advocate for the well-being of children at the state and local levels.  <br />http://datacenter.kidscount.org/<br />
  25. 25. US Department of Education and NSBA<br />Institute of Educational Sciences<br />http://ies.ed.gov/<br />http://nces.ed.gov/fastfacts/<br />Center for Public Education<br />http://tinyurl.com/28qkcmc<br />
  26. 26. Next Steps. . .<br />This week’s conversation<br /><ul><li>How are we currently using data in our districts?
  27. 27. What are some of driving forces to use data more effectively in your district?
  28. 28. What is the story behind the numbers? How can we find out?
  29. 29. Assignment
  30. 30. Getting to Know Your District’s Data
  31. 31. Gather Your District’s Vision-Mission Statements, Strategic Plan and Annual Goals</li>