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Data-Based Decision Making: School District Examples

Data-Based Decision Making: School District Examples. “As this (student learning) does not occur by serendipity or accident, then the excellent teacher must be vigilant to what is working and what is not working in the classroom.” John Hattie . Data-Based Decision Making (DBDM).

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Data-Based Decision Making: School District Examples

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  1. Data-Based Decision Making: School District Examples “As this (student learning) does not occur by serendipity or accident, then the excellent teacher must be vigilant to what is working and what is not working in the classroom.” John Hattie

  2. Data-Based Decision Making (DBDM)

  3. Data Based Decision Making and Problem Solving

  4. Data Based Decision Making and PLCs

  5. Comparing DBDM & other cycles/models DBDM

  6. DBDM & PLC Questions DBDM PLC • Both utilize data • Both require teachers to ask and answer questions, specifically why? • Both require reflection Both are better with collaboration • Not a one time thing • Collaboration drives process • Useful when building a team, common language is important • Data drives process • Assumes strong data source, people understand what the data source tells them

  7. Data Based Decision Making and Tiered Instruction Tier III: Intensive Tier II: Strategic Tier I: Core

  8. Data-Based Decision MakingCore Instruction – Guilford County Schools

  9. Data-Based Decision MakingDistrict-wide Core Instruction First Grade Middle of Year Comparison DIBELS Composite Score (assessed using mCLASS) 2008-09 2009-10 2010-11 2011-12 2012-13 DIBELS 6th Edition DIBELS Next At or Above Benchmark Below Benchmark Well Below Benchmark

  10. Data-Based Decision MakingDistrict-wide Core Instruction DuFourQuestions: (asked after each assessment period, district, school, and grade level) What is it we want our students to learn? First Grade – phonemic awareness, phonics, text fluency 2. How will we know if our students are learning? Analyze indicator performance over time. Set an initial goal of 85% of students reaching the benchmark category. Phonemic Awareness (PSF) Basic Phonics (NWF) 2008-09 2009-10 2010-11 2011-12 2008-09 2009-10 2010-11 2011-12

  11. Data-Based Decision MakingDistrict-wide Core Instruction DuFourQuestions: 3. How will we respond when students don’t learn? Give schools, teachers resources to teach phonemic awareness and phonics. 2008-11 – providedresources from mCLASS, FCRR, Reading Foundations 2012-13 – explored options for systematic word study in all K-2 classrooms. 2013-14 – begin Fundations K – 2 in 68 elementary schools 4. How will we enrich and extend the learning for students who have demonstrated proficiency? Shift instructional focus on the next building block skill (increase fluency and comprehension)

  12. Data-Based Decision MakingDistrict-wide Core Instruction • Implementation • Training and materials for all teachers • Strategic, district coaching • Close monitoring of data • Year 1 – Daily word study instruction kindergarten through second grade • Year 2 - Daily word study instruction kindergarten through third grade, supplemental word study kindergarten – second grade • Year 3 - Daily word study instruction kindergarten through third grade, supplemental word study kindergarten – third grade • Evaluate Action • Collect Data • Ask Questions • Analyze and Summarize • Modify Plan

  13. Data-Based Decision MakingSchool Level – Charlotte-Mecklenburg Schools

  14. Data-Based Decision MakingSchool-wide Instruction & Intervention Analyzing Tier I Instruction Five (5) key areas to consider: • Academic Standards • Instructional Strategies • Curricular Materials • Assessment • Systems of Support

  15. Data-Based Decision MakingSchool-wide Instruction & Intervention ABC School Overview -Students • 762 students • 18% LEP • 8% Students with Disabilities • 6% Identified as Gifted White 12%

  16. Data-Based Decision MakingSchool-wide Instruction & Intervention ABC School Data: Reading EOG 2011-2012

  17. Data-Based Decision MakingSchool-wide Instruction & Intervention ABC School Data: Reading

  18. Data-Based Decision MakingSchool-wide Instruction & Intervention ABC School Overview: Literacy Lab • 45 minutes of customized instruction offered to the 20 lowest performing students at each grade level • Station teaching delivery • Focus on foundational skills • Small group and 1:1 learning opportunities

  19. Data-Based Decision MakingSchool-wide Instruction & Intervention At or above expectancy Expected Below expectancy Served in Literacy Lab

  20. Data-Based Decision MakingSchool-wide Instruction & Intervention

  21. Data-Based Decision MakingSchool-wide Instruction & Intervention

  22. Data-Based Decision MakingSchool-wide Instruction & Intervention

  23. Data-Based Decision MakingSchool-wide Instruction & Intervention

  24. Data-Based Decision MakingSchool-wide Instruction & Intervention

  25. Data-Based Decision MakingSchool-wide Instruction & Intervention • Let’s review: • Is our Tier I effective? • No • Who needs support? • Approximately 80% of our learners • Who is receiving support? • Approximately 25% of our learners

  26. Data-Based Decision MakingSchool-wide Instruction & Intervention Additional Data: Teacher Surveys Teaching: • Lack of trust • Unwillingness to take risks • Lack of non-instructional time Professional Development Needs: • Differentiated Instruction • Struggling Learners • Reading Strategies

  27. Data-Based Decision MakingSchool-wide Instruction & Intervention Action Plan: • Provide Professional Development • Augment Tier I with strategies to improve fluency and comprehension • Provide opportunities for peer shadowing and job embedded coaching • Progress monitor students • Monitor and provide ongoing feedback to teachers

  28. Data-Based Decision Making Student Level –Alamance County Schools

  29. Data-Based Decision MakingIndividual Student Level • 6th Grade Universal Screening Data (AIMSweb): • Fall Math Computation: 7 points: between 10th and 25th percentile • 5th Grade EOG Math Scores • Level 2: 16th percentile • Level 1: 6th percentile (retest)

  30. Data-Based Decision MakingIndividual Student Level Looking for strengths and weaknesses across 4 domains

  31. Data-Based Decision MakingIndividual Student Level • What are possible resources for student’s instructional plan? • TransMath • FasttMath • IXL • Back to Basics • Resources from Quantile.com • Which resources best fit student’s need? • SMI Results • Fluent with addition but not multiplication facts • Weaknesses in place value and fraction concepts • Analysis of Math Computation Probe • Errors in math facts • Errors in reducing fractions/converting to decimals

  32. Data-Based Decision MakingIndividual Student Level • SMART Goal • Student will increase from 17 points correct to 53 points correct on a 4th grade math computation probe by June 10, 2013. • Student will increase from 7 points correct to 20 points correct on 6th grade math computation probe by June 10, 2013. • Plan Details • Who: • Interventionist • Math Teacher • What & Frequency: • TransMath: 5 days a week for 40 minutes during intervention block • FasttMath: 3 days a week for 10 minutes during math warm-ups in classroom

  33. Data-Based Decision MakingIndividual Student Level

  34. DBDM: One Process…

  35. Many Data Sources….

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