1 / 46

“Why Data?”

“Why Data?”. Putting Data to Work for School Improvement. Welcome. Presenter Vicki DeWitt Director Area 5 LTC Deb Greaney Area 5 LTC Ground Rules.. Please turn off/vibrate cell phone Be fully present Please ask questions/offer comments.

elpida
Download Presentation

“Why Data?”

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. “Why Data?” Putting Data to Work for School Improvement

  2. Welcome • Presenter • Vicki DeWitt Director Area 5 LTC • Deb Greaney Area 5 LTC • Ground Rules.. • Please turn off/vibrate cell phone • Be fully present • Please ask questions/offer comments

  3. Until you have data as a backup, you’re just another person with an opinion. Dr. Perry Gluckman

  4. Data helps make the invisible, visible.

  5. Get your facts first, then you can distort them as you please. Mark Twain

  6. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts. Sir Arthur Conan Doyle

  7. Errors using inadequate data are much less than those using no data at all. Charles Babbage

  8. Everyone is entitled to his own opinion, but not his own facts. Daniel Moynihan

  9. Data is a lot like humans: It is born. Matures. Gets married to other data, divorced. Gets old. One thing that it doesn't do is die. It has to be killed. Arthur Miller

  10. In God we trust. The rest of you bring your data….

  11. NCLB • Stronger accountability for results • More freedom for states & communities • Encouraging proven education methods • More choices for parents • Making AYP

  12. Our Story-ITS REAL • NCLB grant • Data gathered • SkillsCheck technology pre and post tests • Teacher Technology Proficiency • Implementation logs • Classroom observations • Collection and examination of • Pre and post vocabulary tests • Final Team Products • Writing samples • Unit templates • Standardized test data

  13. Results- Writing Samples

  14. Results- Writing Samples

  15. Results- Writing Samples

  16. Results- Writing Samples

  17. Results –Reading Scores

  18. A Few Questions…. • How are your students doing in reading and math? • Are all your students “meeting”? • Why are some of your kids not meeting? • Why are some of your kids exceeding?

  19. Data Everywhere • Technology’s influence • Students that are “over tested” but “under assessed” • Testing results are used to compare rather than improve performance • “Tons” of reports generated BUT………

  20. What Usually Happens…. • Data Collected is … • Not regular/periodic • Irrelevant – not useful • Not used • Cumbersome • Not clear • or • Data is not collected at all.

  21. Does Your School… • Deliberately set time aside for reflection on actual student work? • Have a process to ensure that teacher reflections and insights are used to modify current practice? • Take action as a result of patterns and trends that emerge from the data?

  22. What data do we currently have? Is it timely/meaningful? What can we really learn from this data? Do we look at it, and if so what’s the process? Process- how, how often, who? What do we do once we’ve looked at it? Do we need to collect different data? What is the most simple and most effective way to collect meaningful data? (Do you need a 25 item multiple choice or one open ended question?) Reflect

  23. Ready, Shoot, Aim Factors Effecting Student Achievement

  24. Barriers to Meaningful Data Use • Lack of … • Authentic training • Meaningful data • Perceived lack of time • Understanding the value • Accountability systems that narrow the focus

  25. Data-Driven Mania • A high school in an affluent suburb and its principal are recognized and financially rewarded by the state because their tenth graders scored 11 percent higher on the state assessment than tenth graders the year before. But the school had done nothing to improve its programs; staff readily acknowledged that a particularly academically strong group of tenth graders came along that year. • A high-poverty school is labeled as low-performing despite the fact the staff worked hard to implement a new standards-based mathematics program. The staff are deeply demoralized.

  26. and Trivial Pursuits • A teacher begins to question where she can continue to use an inquiry-based approach to science instruction when the state assessment emphasizes science facts, not big concepts or inquiry skills. • A school improvement team decides to provide more test preparation in mathematics and to tutor a small number of students whose scores on the state assessment fall just below the needs-improvement proficiency level. There is no discussion of tracking policy; the rigor, focus, and coherence of curriculum; or the effectiveness of instruction.

  27. Things to Avoid • “Bureaucratic Creep” • Districts adding additional requirements that overtax teachers and students but add little useful information • “Rearview Mirror Effect” or planning the future on the basis of past events • Doesn’t allow for responding to a rapidly changing reality • Waits for the road to reveal itself • Focuses on a single dimension of the road • Looking back when times were simpler

  28. Some Things to Do • Identify antecedents • “structures and conditions that precede, anticipate or predict excellence in performance” • Teacher, student, and system Understanding of the effect (results) requires understanding of the cause (antecedent)

  29. Learning Matrix Reflect: Where do you fit now? LUCKY High results, low understanding of antecedents Replication of success unlikely LEADING High results, high understanding of antecedents Replication of success likely Achievement of Results LOSING Low results, low understanding of antecedents Replication of mistakes likely LEARNING Low results, high understanding of antecedents Replication of mistakes unlikely Understanding of Antecedents of Excellence

  30. Antecedents of Excellence • Founded in research • Examples • Reinforcement of writing conventions • Flexibility for teacher management of curriculum • Assessments • Collaborative scoring of student products • Meaningful feedback

  31. Some Things to Do • Institute an accountability system • action follows analysis • roles & responsibilities identified • user-friendly timelines • power of subtraction Accountability includes authority to act and permission to subtract

  32. Some Things to Do • Build a professional, collaborative culture. • Deliberate effort to build “professional candor” • Every step of the process requires at least two sets of eyes, ears, two brains, and two hearts • True collaboration occurs only when systems are created that embed it in the routine processes and provides information that and support essential to improve practice

  33. Successful Collaboration • Collaborative cultures promote diversity, independence, and decentralization • Collaboration must be present from planning to execution in DDDM • Collaboration does not mean “one size fits all” professional development

  34. Collaboration Antecedents • Action planning and continuous improvement cycles • Collaborative improvements • Lesson logs • Common assessments • Instructional calendars • Data teams • Program evaluation

  35. Staff “Buy In” • Teachers must see the value in what they are asked to do • You must “subtract” procedures and practices that are not producing results Data that is collected should be analyzed to make improvements. If data is not being used, stop collecting it!

  36. Some Things to Do • Learn what you can from standardized tests • Use multiple measures, including common grade-level, subject area, or course-specific assessment

  37. Some Things to Do • Analyze data on multiple levels • Use/develop common classroom level assessments Using multiple measures to dig into student learning results

  38. Data Retreats • Three days in the summer to examine data • Work in teams • CESA 7 –Judy Sargent

  39. Prep Packet The Process -- 8 Steps • 1. Team Readiness • 2. Collect & Organize Data • 3. Observe and Analyze Patterns • 4. Pose Hypotheses • 5. Prioritize & Set Improvement Goals • 6. Design Study & Strategies • 7. Define Evaluation Criteria • 8. Make the Commitment & Plan the Roll Out Before the retreat During the retreat After the retreat

  40. Student Data Family & Community Data Professional Practices Data Program & Structures Data Four Lenses of Data • Broad to Specific • Specific to Patterns • Observing Patterns

  41. No Excuses INFLUENCENCE CAN’T INFLUENCE CAN’T COTROL CONTROL Forget about it!

  42. Some Things to Do • Curriculum mapping/alignment • Assessment calendar • Performance assessment checklist

  43. Assessment Calendar Template Beyond the Numbers Making Data Work for Teachers & School Leaders Stephen White, Ph.D.

  44. Things to Do • Identify antecedents • Institute an accountability system • Build a professional, collaborative culture • Use multiple measures, including common grade-level, subject area, or course-specific assessment • Reveal the operational curriculum

  45. In Summary • Expertise in data analysis is the ability to use information to solve problems and identify solutions consistently, efficiently, and effectively • Teachers and leaders who identify antecedents, engage in collaboration, and hold themselves accountable for results demonstrate expertise in data analysis Take time to stop and smell (analyze) the roses (data).

  46. Thank You! • Contact info • vdewitt@lth5.k12.il.us • dgreaney@lth5.k12.il.us • www.lth5.k12.il.us

More Related