1. Using Data Process

2. Assumption #1 http://www.youtube.com/watch?v=_llFlj6FeQ8 Making significant progress in improving student learning and closing achievement gaps is a moral responsibility and a real possibility in a relatively short amount of time—two to five years. It is not children’s poverty or race or ethnic background that stands in the way of achievement; it is school practices and policies and the beliefs that underlie them that pose the biggest obstacles.

3. Assumption #2 http://www.youtube.com/watch?v=w6NR0edrgWE Data have no meaning. Meaning is imposed through interpretation. Conversely, data themselves can also be a catalyst to questioning assumptions and changing practices based on new ways of thinking.

4. Assumption #3 http://www.youtube.com/watch?v=pW8TdqF7Pao Collaborative inquiry—a process where teachers construct their understanding of student-learning problems and invent and test out solutions together through rigorous and frequent use of data and reflective dialogue—unleashes the resourcefulness and creativity to continuously improve instruction and student learning.

5. Assumption #4 http://www.youtube.com/watch?v=9dMzFNQwaqg A school culture characterized by collective responsibility for student learning, commitment to equity, and trust is the foundation for collaborative inquiry.

6. Assumption #5 http://www.youtube.com/watch?v=HbYiXyUynRk Using data itself does not improve teaching. Improved teaching comes about when teachers implement sound teaching practices grounded in cultural proficiency—understanding and respect for their students’ cultures—and a thorough understanding of the subject matter and how to teach it.

7. Assumption #6 http://www.youtube.com/watch?v=6d5IWsaDlkE Every member of a collaborative school community can act as a leader, dramatically impacting the quality of relationships, the school culture, and student learning.

8. Summative Formative State Assessments Demographics Benchmarks Common Assessments Classroom Assessments

11. Task 1: Using Data Project Overview

12. A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved. The Using Data Process

13. S 1.13 A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved. The Data Divide

14. S 1.14 A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved. Core Value Significant improvement in student learning and closing achievement gaps is a moral responsibility and a real possibility in a relatively short amount of time—two to five years.

15. S 1.15 A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved. Poverty, Race/Ethnicity, and Achievement Poverty and Race/ Ethnicity Enrollment % of Students Proficient or Above Source: Adapted from Accountability in Action (2 nd ed.) by Douglas Reeves, 2004, Denver, CO: Advanced Learning Press. Possibility X Schools with a majority of African American, Latino/a, and/or Native American students and/or students living in poverty are achieving at this level.

16. S 1.16 A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved. Core Value Collaborative inquiry—school teams constructing meaning of student-learning problems and testing out solutions together through rigorous use of data and reflective dialogue—unleashes the resourcefulness of educators to continuously improve instruction and student learning.

17. S 1.17 A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved. Anne Lieberman Dan Lorti Robert Marzano Milbrey McLaughlin Jay McTighe Fred Newmann Allan Odden Doug Reeves Mike Schmoker Deborah Shifter Dennis Sparks James Stigler Gary Wehlage Grant Wiggins and more… Deborah Ball Roland Barth Carol Belcher Louis Castenell Jim Collins Tom Corcoran Linda Darling-Hammond Lisa Delpit Rick DuFour Karen Eastwood Richard Elmore Susan Fuhrman Carl Glickman Asa Hilliard Virtually All Education Researchers Agree: Collaboration is Key

18. S 1.18 A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved. Building the Bridge Between Data and Results Instructional Improvement Data UseCollaboration Leadership & Capacity Culture/Equity/Trust

19. S 1.19 A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved. Culture, Equity, Trust Instructional Improvement Collaboration Data Use Leadership & Capacity Shifts That Are Evident in Using Data Schools Less EmphasisMore Emphasis External accountability, cultural blindness, little trust Internal and collective responsibility,cultural proficiency, trust Data to sort, learning left to chance Data to serve, expanding opportunities for all Top-down, data-driven decision making Ongoing Data-Driven Dialogue and collaborative inquiry Punishment/reward, avoidance Feedback for continuous improvement, frequent and in-depth use by teachers and students Individual charismatic leaders as change agents Learning communities with many change agents

20. A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved. Unique Features of the Using Data Process Builds everyone’s knowledge and skills Focuses on cultural proficiency, equity, and school culture Focuses on data culture and collaboration Includes both long-term and short-cycle improvement Is a structured and adaptable process: –identifies the student-learning problem –verifies its causes –uses research and a logic model to design interventions –monitors results Provides tools for collaboration and sense making

21. A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved. Using Data Process is the Intersection

22. New Baseline Year for Mathematics and Reading Purpose for Grades 3 – 8 Standard Setting Increase rigor by raising standards for Grades 3 – 8 student achievement on the OCCT as a means to be more competitive at the national and international levels. Align student expectations on the OCCT more closely with student expectations for the National Assessment of Educational Progress (NAEP). Because tests were implemented over multiple years, there were outliers. Vertically aligning performance standards for students on the OCCT tests for Grades 3 – 8 allows for consistent expectations. 22 August 27, 2009 State Board of Education Meeting

23. A scaled score is derived from the number correct and is used to place a student in one of the given performance levels for each content area. Score ranges vary across content areas. A scaled score of 700 is Satisfactory or Proficient across all content areas. Oklahoma Performance Index (OPI) 23

24. Standard Setting is the process that allows experts to make judgments about the content that a student should know and be able to do in order to be classified in a specific performance level. Cut scores are necessary for the categorization of student test scores into the four performance levels utilized in Oklahoma: Advanced, Proficient, Limited Knowledge, and Unsatisfactory. Standard Setting and Cut Scores 24

25. Standard Setting and Cut Scores The experts included on the standard setting committees were educators across Grades 3-8 with significant experience in reading or mathematics instruction, as well as representatives from higher education and business. One panel of experts conducted standard setting for reading and another panel conducted standard setting for mathematics. 25

26. Oklahoma Core Curriculum Tests Performance Benchmarks or "Cut Scores” The standard setting panelists determined cut score recommendations based on test content, performance standards, and test item difficulty, NOT based on number of test items answered correctly. –Raw scores were mapped to scale scores as is standard procedure with large-scale assessments such as the NAEP, ACT, or SAT. –Scale scores provide a consistent interpretation of assessments across years. 26

27. No blaming students. No blaming teachers. Data is just information. Use data for instructional purposes. “De-emotionalize” data. 27 Ground Rules

28. The median is the middle score in a set of ordered scores. The median is a better measure of central tendency than the mean (average) because it is not affected by extreme scores. Remember: Comparing Median % Correct does not take into account difficulty of items across years, yet it will show general trends in strengths and weaknesses. Median 28

29. 1. What does the data show? (Factual Information) 2. Why might this be? (Hypotheses) 3. How should we respond? (Plan for action) 4. How will you measure success? 29 Key Points When Analyzing Data

30. QUESTIONS COMMENTS CONCERNS