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University of Pittsburgh School of Education Partnership for School District Improvement (PSDI)

University of Pittsburgh School of Education Partnership for School District Improvement (PSDI). Data and Data Teams Intermediate Unit IV QCC Meeting. Senior Academic Leaders: Steve Biancaniello Stan Herman Dan Miller. Senior Data Administrators : Debbie Raubenstrauch Jim Turner.

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University of Pittsburgh School of Education Partnership for School District Improvement (PSDI)

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  1. University of PittsburghSchool of EducationPartnership for School District Improvement (PSDI) Data and Data Teams Intermediate Unit IV QCC Meeting

  2. Senior Academic Leaders: Steve Biancaniello Stan Herman Dan Miller Senior Data Administrators: Debbie Raubenstrauch Jim Turner IntroductionsPSDI (Pitt) Trainers

  3. This Session’s Objectives To reinforce the reasons to focus on data To jointly develop reasons for data teams To share some best practices around the use of data and data teams To consider the development of a data culture and a data calendar To demonstrate some reading and math instructional strategies based on data

  4. Two Points to Remember • In God We Trust… • Data rarely answers questions but it almost always raises questions • Circus Trainer

  5. Core Beliefs Are Critical Smart is not what you are---Smart is what you become All kids can learn----with effort and good instruction Students rise, or fall, to the level of the expectations of those around them, especially their parents and teachers Lauren Resnick Steve Biancaniello Jaime Escalante

  6. Jim Turner’s Belief It’s All About Teams of TeachersUsing Data to Develop Improved Instructional Strategies on a student-by-student basis focused on the assessment anchors

  7. Ingredients for Jim’s Belief “Stew” • Time • Tools • Transitions • Temperament • Training • Teams

  8. Time—How Do We Find More to Focus on Data • Some strategies used: • Common planning/use of duty time • Early dismissals/late starts • Substitute teachers • Reduced class load • Shave minutes from each class • Extended days • More act 80 days

  9. Tools to Build Achievement • SAS • Assessment Anchors • e-Metric-PSSA Data Interaction • 4Sight/Ongoing Formative Assessments • PVAAS—Progress vs. Achievement • Data Systems—EdInsight Data Window • Good news and bad news

  10. The Rhyme of the School Administrator(Borrowed from the Rhyme of the Ancient Mariner) • Data, data everywhere— so much it’s hard to think; • Data, data everywhere— if only it would link!

  11. Transitions—the Old 3R’s • The Old 3R’s—still important • Reading • ‘riting • ‘rithmetic

  12. Transitions—the New 3R’s • Rigor (and Remediation) • Rigor—Dan Miller-questioning strategies • Relevancy • Jaime Escalante—Stand and Deliver • Relationships—Engaging Students with data • Ligonier HS • Wilkinsburg MS

  13. Temperament (all about Leadership) • Paul O’Neil—criteria for good organizations: • Was I treated with dignity and respect? • Was I given the tools and training that I needed to do the job? • Did I make a difference? • Did anyone notice? • Directly tied to data teams

  14. Training • It’s impossible to have too much good training—especially around data and instructional strategies • Focus on Data • Data Days—around common planning time • Quad’s—quarterly data reviews • Data Retreats—annual review of data

  15. Teams • The last (but not least) ingredient for Jim’s belief!! • Much more on this later

  16. Cognition • Tell me, I forget • Show me, I remember • Involve me, I understand Lao Tse (604 BC)

  17. Cognition Today (2010) • Listen • Listen and reflect • Apply it to something • Teach others

  18. A QuestionWho is W. Edwards Deming?

  19. A Continuous Improvement Process Is Used (PDCA) Assess the Students Have Teams of Teachers Analyze Data Apply the Strategies to the Students Develop Instructional Strategies Based on the Data

  20. One of Victoria’s Secrets! • Identify the problem • Describe hunches and hypothesis • Identify questions and data • Analyze multiple measures • Analyze political realities • Develop action plan resolution • Implement action plan • Evaluate implementation • Improve the process—then start over Data Data Everywhere—Victoria Bernhardt (2009)

  21. Assignment #1 • Select a recorder and team leader at each table

  22. Assignment #2 • List some good reasons for each school to create one or more data teams • Take two minutes for each person to list at least three reasons • Then take three minutes to reach a table consensus and list three reasons • Table leader breaks ties (or legs if necessary) • Recorder lists the results

  23. Some Good Reasonsto Have Data Teams • “Beating the odds” schools focus on data • Teams are better problem solvers • Teams reduce the isolation factor • Teams can divide up the work • Teams help spread the best practices • Working in teams can be fun!

  24. Some Things Data Teams Can Do • Expand data control past administrators • Develop and model data analysis skills • Work with staff using essential questions • Disseminate data to individuals/selected groups • Schedule dissemination and analysis • Help staff analyze and interpret data • Engage staff in setting targets for improvement • Respond to data requests from staff Making Sense of All Your Data (2006)

  25. Assignment #3 • List some poor reasons to have data teams (or reasons not to have data teams) • Take two minutes to individually list two or three poor reasons • Take three minutes to reach a table consensus on several poor reasons • Table leader breaks ties (or legs) • Recorder lists the results

  26. Some Poor Reasons to Have Data Teams • To reduce the administrative burden • So that I don’t have to understand the data • To keep the data limited to a “trusted few” • To fill up PD days • Because other districts have them • So we can figure out who to blame!!

  27. Different Ways to Look at Data

  28. Looking at Data from Different Heights--Data Teams Can Help • The Airplane View • From 5,000 feet—for administrators/boards • The Helicopter View • From 500 feet—for principals/coaches • The View from the Ground • Primarily for teachers

  29. The Airplane View(from 5,000 feet) • Where do we stand with respect to proficiency? • How are our AYP subgroups doing? • How many students have to improve to reach safe harbor?

  30. The Helicopter View(from 500 feet) • Reporting category data showing strengths/weaknesses • Are we making progress over time? • How do our subgroups compare to our students as a whole? • Do we have weak areas across all schools? • How did we do in the heaviest weighted reporting category?

  31. The View from the Ground(the most important view) • Will Joey be proficient in math? • Does Katie have a shot at proficiency? • Is Todd improving in the eligible content in “numbers and operations”? • Is our tutoring program working with “bubble” students? • Do the 11th grade kids understand quadratic equations?

  32. Now Let’s Hear From Dan Miller

  33. A Variety of Best Practices

  34. What are some best practices forusing data? • Get the right data (valid/reliable) • Get the data right (disaggregated accurately) • Get the data right away (timely) • Get the data the right way (by class/school) • Get the right data management (one stop) Source: Starkman, Neal (2006). Building A Better Student. T.H.E. Journal, September, 46.

  35. Ensuring That Students Learn3 Critical Questions • What do we want students to learn? • How can we be certain all students have learned it? • How can we assist those who are not mastering the intended outcomes? Rick DuFour 2002

  36. The Perfect Grading System for the Future • The student is advanced (TSIA) in a subject or • The student is proficient (TSIP) or • TSADY!! • So what could TSADY possibly stand for? Steve Biancaniello

  37. A Question to Consider • Is it better to tap a few individuals to become “data experts” or to build a culture where the whole school participates in analyzing data and figuring out the implications for improving instruction? Data Wise (2005)

  38. Types of Data Teams • Horizontal –e.g. all the 4th grade teachers • Vertical—e.g. K-3 teachers • Specialty Teams—e.g 9th grade transition • Technology Linked Teams—connecting with other schools/partners Dufour et al (2006)

  39. Effective Use of Data Depends Upon: • Strong leadership—most important • Up-front planning for data collection/use • Time is a key item • Human capacity for data use • Technology capacity • Reading and formulating results • Developing and using classroom assessments Bernhardt et al

  40. Data Culture Supports • Creating and guiding a data team • Create a data inventory of internal and external assessments and other student info • Enabling collaborative work among faculty • Every teacher on a team with time to meet • Planning productive mtgs—lesson planning • Agendas/recorders/leaders/notes/follow-up • Norms/protocols—Compass Points Protocol Data Wise (2005)

  41. Compass Points Protocol • Interesting Way to start with a new team • Every team member selects one characterization • North—just get it done • West—pay attention to details • East—look at the big picture • South—take everyone’s feelings into account Data Wise 2005

  42. Establishing Trust and Norms • Creating team norms that all agree on • Rules regarding meeting times • How is listening fostered and interruptions discouraged • Rules regarding confidentiality • Decision-making rules • Attendance and participation expectations DuFour et al 2006—ERS Informed Educator (2008)

  43. Practices That Support Data Use • Provide timely easily accessible data • Data disaggregation is a key item • Establish structures that support data use • Encourage a culture of questioning • Ensure adequate teacher PD time • Demonstrate leadership Practices that Support Data Use in Urban High Schools

  44. Leadership Structures That Support Data Use • Supportive principal • Instructional coaches • Data teams • Data coach (Turner predicts!) • Procedural assistance in getting better data • Modeling and skill building assistance Practices That Support Data Use in Urban High Schools

  45. Data Driven Dialogue • Step 1—Predict what the data will say • Step 2—Factually analyze the data • 25% of the students correctly answered this item—versus wow our kids did poorly • Step 3—Interpret the data Taking Data to New Depths (2004)

  46. Data Team Needs • Assigned clear responsibility for data process • Resources—Time and $ • Professional development • Possible external resources • Technological capabilities Ruth Johnson—Using Data to Close The Achievement Gap

  47. Data Team Meeting Steps and Structure • Step 1—Collect and chart data • Step 2—Analyze strengths & obstacles • Step 3—Establish goals—set/review/revise—data are to goals what signposts are to travelers! • Step 4—Select Instructional Strategies • Step 5—Determine results indicators Leadership and Learning Center 2007

  48. “Smart” Goals • Example: Percentage of (student group) scoring proficient or higher in (content area) will increase from x% to (goal %) by the end of (month) as measured by (assessment tool) administered on (date). • Specific • Measurable • Achievable • Relevant • Time bound

  49. Reducing the Threat from Setting Goals and Using Data • Do not use data primarily to identify or eliminate poor teachers • Collect and analyze data collaboratively • Allow teachers substantial autonomy in choosing a variety of data to examine • Inundate staff with success stories that include data Results by Mike Schmoker

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