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Data driven ppt_presentation[1]


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Data driven ppt_presentation[1]

  1. 1. Data – Driven Instruction Presented by: Elyse Pernick Patricia Hutton
  2. 2. <ul><li>Educators learned how to teach through </li></ul><ul><li>Methodologies </li></ul><ul><li>Strategies </li></ul><ul><li>Textbooks </li></ul><ul><li>Programs </li></ul><ul><li>Professional Development </li></ul><ul><li>Mainly sound educational approaches were utilized to ensure that teachers adhered to required methods and materials. </li></ul><ul><li>Success of sound educational approaches were measured through annual summative test scores. </li></ul><ul><li>School staff evaluated test results to modify practices once a year that did not meet their expectations. </li></ul><ul><li>Annual summative test scores much change to drive instruction. </li></ul>
  3. 3. It used to be that schools talked first about children. Now they talk about test scores and data. “ Data Driven” is the latest buzzword sweeping the educational industry. “ One strategy for all” does not work in Data-Driven environments. The teacher must be able to multitask within the classroom environment and still keep discipline.
  4. 4. <ul><li>What are the challenges to using data to effectively make decisions? </li></ul><ul><li>According to a survey conducted by Grunwald & Associates in 2004: </li></ul><ul><li>· Lack of training: 50% </li></ul><ul><li>· Systems that are unable to share or exchange data: 42% </li></ul><ul><li>· Lack of understanding of what to do with the data: 39% </li></ul><ul><li>· Lack of clear priorities for data: 36% </li></ul><ul><li>· Failure to consistently collect data: 35% </li></ul><ul><li>· Outdated technology: 31% </li></ul><ul><li>· Inaccurate or incomplete data: 24% </li></ul><ul><li>· Timing of collection: 24% </li></ul>
  5. 5. What happens if the Data takes control of the teacher? Teaching to the test… How can we avoid these concerns turning into problems? If we can avoid these dangers, there are solid advantages to Data Driven Instruction.
  6. 6. <ul><li>Data Allows us to” </li></ul><ul><li>Provide feedback for teachers and administrators </li></ul><ul><li>Prevent over-reliance on standardized tests </li></ul><ul><li>Allow schools to address accountability questions </li></ul><ul><li>Better information allows educators to identify needs </li></ul><ul><li>Measure outcomes </li></ul><ul><li>Provide more individualized instruction to students </li></ul><ul><li>Track professional development resources </li></ul><ul><li>Identify effective instructional strategies </li></ul><ul><li>Better allocate resources </li></ul><ul><li>Communicate with parents and the community </li></ul>
  7. 7. <ul><li>What are some of the ways data reports can be structured? </li></ul><ul><li>Reports need to be: </li></ul><ul><li>timely </li></ul><ul><li>tied to objectives </li></ul><ul><li>show data in different ways such as tables, charts, graphs </li></ul><ul><li>longitudinal data to compare results over time </li></ul>
  8. 8. <ul><li>How can teachers use data? </li></ul><ul><li>formative assessments used to determine instructional interventions </li></ul><ul><li>reemphasizing skills </li></ul><ul><li>using additional diagnostic measures </li></ul><ul><li>changing instructional materials </li></ul><ul><li>creating groups of students with a similar achievement gap or pattern </li></ul><ul><li>benchmark assessments to determine progress </li></ul>
  9. 9. Data-driven decision-making is about getting better information into the hands of classroom instructors. Data-driven teachers understand the importance of utilizing multiple measures, and multiple indicators within measures, when assessing school and student success (Bernhardt, 2004). Once classroom teachers have good baseline information, they should select key indicators of success for their classrooms that are Specific, Measurable, & Attainable. Data analysis is meaningless if it does not result in meaningful instructional change. Data-driven educators are able to use summative and formative assessment data together to implement strategic, targeted, focused instructional interventions to improve student learning.
  10. 10. <ul><li>If you're not using data to make decisions, you're flying </li></ul><ul><li>blind. </li></ul><ul><li>This is all about a process, not a specific technology. </li></ul><ul><li>Get ready to feel threatened. people much more accountable </li></ul><ul><li>a culture shift, a new learning paradigm </li></ul><ul><li>educators need to come up with the right questions </li></ul><ul><li>Data-driven decision making does not save time. </li></ul><ul><li>both administrators and teachers have to take ownership in the action-research process—and that means more work, not less. </li></ul><ul><li>Figure out exactly what questions you want the data to answer. </li></ul><ul><li>NLCB is just the beginning of your journey. </li></ul><ul><li>Get information to the people who need it—and in a form that they can use presented in a simple list, table, or drill-down form with good built-in explanations and definitions. </li></ul><ul><li> </li></ul> 
  11. 11. Confucius noted that a journey of a thousand miles starts with a single step. While teachers may not be able to address the often-overwhelming problem of low student achievement all at once, they can take small steps that together add up to big improvements over time. Once classroom teachers have access to good baseline information, they should select key indicators of success for their classrooms. With Data Driven decisions we are able to accurately point out problems; identify students needing interventions and find solutions. We are also able to make decisions in mid-course to continually improve the academic success of our students.
  12. 12. <ul><li>content area input from teachers </li></ul><ul><li>district sets benchmarks </li></ul><ul><li>principals set goals </li></ul><ul><li>schools create a strategic plan </li></ul><ul><li>a school planning team assembled </li></ul><ul><li>the school team meets with experts </li></ul><ul><li>determine short-term goals </li></ul>Sample Plan for Implementing Data Driven Instruction
  13. 13. <ul><li>Collect relevant demographic and achievement data – examine past raw data to identify issues </li></ul><ul><li>Conduct an item analysis of students' results on NJ ASK to identify areas where the whole staff can act </li></ul><ul><li>Devise a school improvement plan with measurable goals and a timetable </li></ul><ul><li>Monitor goals at faculty meetings </li></ul><ul><li>Grade-level meetings to identify trends across grade levels and classrooms and individual students' strengths and weaknesses </li></ul><ul><li>Compile lists of students slightly above or just below the passing line </li></ul><ul><li>Analyze instruction and resources for students on the list </li></ul><ul><li>Determine which data to use when developing a baseline for the upcoming year </li></ul>
  14. 14. <ul><li>Data at the Classroom Level </li></ul><ul><li>Teacher-made tests </li></ul><ul><li>Assignments </li></ul><ul><li>Portfolios </li></ul><ul><li>Evidence of student learning </li></ul><ul><li>Discussing assignments, the link between the work and content standards, teacher expectations for student learning, and using rubrics improves teaching and student learning. </li></ul>
  15. 15. Grade: 6 The Assignment: The assignment was an end-of-the chapter test on decimals. Maximum score on the test was 50. Students were required to: Write numbers as decimals Estimate answers Solve problems and equations Graph solutions and equations Evaluate equations Construct tables Scoring Guide: Raw Score Percentage Letter Grade 48-50 96 A+ 46-47 92 A 45 90 A- 43-44 86 B+ 41-42 82 B 40 80 B- 38-39 76 C+ 36-37 72 C 35 70 C- 33-34 66 D+ 31-32 62 D 30 60 D- 0-29 58 F Example Classroom Data Analysis
  16. 16. Raw % Letter Name Gender Score Correct Grade 1. Andres M 47 94% A 2. Arlene F 38 76 C+ 3. Bryan M 44 88 B+ 4. Carlos M 44 88 B+ 5. Cesar M 45 90 A- 6. Daniel M 48 96 A+ 7. Dustin M 48 96 A+ 8. Edith F 44 88 B+ 9. Eunice F 31 62 D- 10. Gustavo M 42 84 B 11. Jessica F 34 68 D+ 12. Joshua M 45 90 A- 13. Juliana F 42 84 B 14. Laau F 41 82 B 15. Linh F 40 80 B- 16. Lucera F 44 88 B+ 17. Marisol F 46 92 A 18. Mikaele M 40 80 B- 19. Nayeli F 47 94 A 20. Neil M 40 80 B- How can these data be organized to inform instructional decision-making?
  17. 17. Implications for Instructional Decision-Making What did you learn about the students’ performance on this test from organizing these data in a user-friendly format? 1. 2. 3. 4. 5. How might these data be used for instructional decision-making? 1. 2. 3. 4. 5.
  18. 22. Three Ways to Think About Organizing Data To promote the skillful use of classroom data for instructional decision-making, teachers, and administrators can organize data by the: 1. Distribution of Scores ... ...which answers the questions related to “how many?” 2. Distribution of Students ... ...which answers the questions related to “who?” 3. Patterns in Student Work... ...which answer the questions related to “what?”
  19. 23. What Do I Do for the Students Who Don’t Get It? 1. Re-do? 2. Review? 3. Re-teach?