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Dr. Michael E. Hickey mehickey@towson Dr. Ronald S. Thomas rathomas@towson

RE-THINKING HOW SCHOOLS IMPROVE: A Team Dialogue Model for Data-Based Instructional Decision Making. Dr. Michael E. Hickey mehickey@towson.edu Dr. Ronald S. Thomas rathomas@towson.edu Center for Leadership in Education at Towson University CCSSO Education Leaders Conference

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Dr. Michael E. Hickey mehickey@towson Dr. Ronald S. Thomas rathomas@towson

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  1. RE-THINKING HOWSCHOOLS IMPROVE:A Team Dialogue Model for Data-Based Instructional Decision Making Dr. Michael E. Hickey mehickey@towson.edu Dr. Ronald S. Thomas rathomas@towson.edu Center for Leadership in Education at Towson University CCSSO Education Leaders Conference September 12, 2007

  2. The Big Picture • In today’s session, we are going to: • Re-think our understanding of how schools improve—moving from the dysfunction of the old model to the requirements for what a “new model” might look like. • Focus on a “new model” for improving performance that enables content, vertical, or departmental teams to use data more effectively for classroom instructional improvement and increased student learning

  3. Part 1: What are we trying to do and why?

  4. “Every organization is perfectly designed to get the results it achieves.” --W. Edwards Deming

  5. Think about how long you have been engaged in the school improvement process. Has the school gotten better each year? Has the performance of each student improved as a result of each year he/she spends in the school? If your answer to one or both questions is no, what will it take to change it to yes?

  6. What are data? Data are observations, facts, or numbers which, when collected, organized and analyzed, become information and, when used productively in context, become knowledge.

  7. The DRIP Syndrome DATA RICH INFORMATION POOR

  8. Being Data Rich Your school may suffer from DATA OVERLOAD You may need ways to organize the data.

  9. Sources of Student Achievement Data • External assessment data • Benchmark or course-wide assessment data • Individual teacher assessment data --Supovitz and Klein (2003)

  10. Data-driven schools and school districts use data for two major purposes: • Accountability (to prove) • Instructional decision making (to improve)

  11. The Hierarchy of Data for Accountability Purposes External (State & National) Assessments System Benchmark Assessments Common School or Course Assessments Classroom Assessments of Student Work

  12. The Hierarchy of Data for Instructional Decision Making Classroom Assessments of Student Work Common School or Course Assessments System Benchmark Assessments External (State & National Assessments)

  13. Think about it . . . Do you have a school improvement plan? Or a school accountability plan? Or a SAP? A SIP ? Have a three minute conversation with someone sitting near you about what you think most schools currently have.

  14. The OldModel The School Improvement Team, a Data Committee, or one person analyzes data, using primarily state test data. These data are mined for every possible nuance.

  15. The Old Model The data are presented at a faculty, SIT, or department meeting, and faculty members brainstorm ideas for what to do to increase student performance.

  16. The Old Model Faculty or team members “average opinions” and put forth the solutions that are acceptable to the largest majority of people.

  17. The Old Model This results in school-wide or department-wide initiatives that may or may not be implemented. Data expert Mike Schmoker has estimated that about 10% of what is planned in SIPs actually is implemented at a high level of quality.

  18. Results of the Old Model

  19. Why is the old model not working anymore?

  20. Why? Wrong Data • We have been using the wrong data. State test data are: • Way too general • Instructionally insensitive – not designed for instructional improvement

  21. Why? Wrong Time • The data come at the wrong time. State test data are: • Out of date when they arrive • For students we no longer have The results of the changes that are implemented will not be known for a year.

  22. Why? Wrong Team • The SIT, a full department, or a Data Committee is the wrong team to do the analysis. • Membership is too diverse (often including parents) • Meets too infrequently • Not connected to immediate classroom needs

  23. Why? Wrong Plan • The initiatives that are put in place are: • Too global to address the diversity of students • Aimed at performance increases of groups on average • Looking for the “silver bullet” that will have a schoolwide impact

  24. We need a new model. • Real time • Specific to each grade and subject • Addresses individual students’ needs • Results in instructional improvements that will actually occur at a high level of quality • Can be re-directed frequently • Has meaning for teachers (seen by teachers as a worthwhile use of their time) • THREE MINUTE CONVERSATION: How do the data conversations in schools that you know of • rate against these criteria?

  25. What should that new model look like? “School improvement is most surely and thoroughly achieved when teachers engage in frequent, continuous, and increasingly concrete and precise talk about teaching practice . . . adequate to the complexities of teaching, capable of distinguishing one practice and its virtue from another.” --Judith Warren Little

  26. In other words . . . A Classroom-Focused Improvement Process (CFIP)

  27. Education After Standards

  28. The Classroom-Focused Improvement Process is the work that professional learning communities do. A professional learning community is not an organizational structure. It is a WAY OF DOING BUSINESS.

  29. Focus on teaching Emphasis on what was taught Coverage of content Curriculum planned in isolation Infrequent summative assessments Focus on average scores Focus on learning Fixation on what students learned Demonstration of proficiency Shared knowledge of essential curriculum Frequent common formative assessments Monitoring individual proficiency on every essential skill CFIP: A WAY TO MOVE SCHOOLSFromTo

  30. Remediation One opportunity to demonstrate learning Isolation Each teacher assigning priority to different learning standards Privatization of practice Focus on inputs Intervention Multiple opportunities Collaboration Teams determining priority of learning standards Sharing of practice Focus on results CFIP: A WAY TO MOVE SCHOOLSFrom To

  31. Fundamental Concepts of Collaborative Learning Communities • Teachers establish a common, concise set of essential curricular standards and teach to them on a roughly common schedule. • Teachers meet regularly as a team for purposes of talking in “. . . concrete and precise terms” about instruction with a concentration on “thoughtful, explicit examination of practices and their consequences.” • Teachers make frequent use of common assessments. Continued on next slide

  32. “These elements, so rarely emphasized in school . . . improvement plans, deserve our attention more than anything else we do in the name of school improvement.” --Mike Schmoker (2006)

  33. Our Goal in the Data Dialogues: Frequent, continuous, and increasingly concrete and precise dialogue by school teams, informed by data

  34. IS IT WORTH THE EFFORT? Take a look at the following results. Then you tell us.

  35. Grasonville Elementary School Maryland School Assessment - Reading

  36. Grasonville Elementary School Maryland School Assessment - Mathematics

  37. Part 2: Components of THE NEW MODEL THE CLASSROOM-FOCUSED IMPROVEMENT PROCESS (CFIP): A Team Data Dialogue Protocol

  38. What are the right teams to conduct data dialogues? • Grade-level • Vertical • Content

  39. When is the right time to conduct data dialogues? • At a minimum, devote at least one hour to data dialogues every two weeks. • According to several studies, schools that realized the greatest results from a shift to a data culture scheduled data dialogues at least once a week.

  40. Frequency of Data Dialogues • Source: Stanford University, Stanford Research Institute, Education Week, January 24, 2004

  41. What are the right data to use in the data dialogues? • Triangulate three types of data: • External Assessment Data • Course-wide Benchmark Assessment Data • Classroom Assessment Data • --Supovitz & Klein (2003)

  42. THE GPS ANALOGY

  43. What is the right plan where the results of the data dialogues should be used? • Conclusions are specific to students in the class. • Conclusions are used to plan upcoming daily instruction. • The plans are implemented.

  44. What is the right way to use the results of the data dialogues? • Conclusions are used to identify enrichments and interventions for the students in the class. • Conclusions are used to plan upcoming daily instruction.

  45. The new process needs to be built on: 1. Dialogue 2. Protocols 3. Triangulation of Data

  46. Why True Dialogue? “In dialogue, a group accesses a larger ‘pool of common meaning,’ which cannot be accessed individually. People are no longer primarily in opposition, rather they are participating in generating this pool of common meaning…. We are not trying to win in a dialogue. We all win if we are doing it right.” - Senge, The Fifth Discipline (2006)

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