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MOY Data Analysis Increasing Achievement and Growth Grant NW BOCES

MOY Data Analysis Increasing Achievement and Growth Grant NW BOCES. February 6, 2014. Objectives for today. The RLC will understand DIBELS Next growth data (BOY to MOY) for the entire BOCES population of K-3 students with disabilities

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MOY Data Analysis Increasing Achievement and Growth Grant NW BOCES

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  1. MOY Data AnalysisIncreasing Achievement and Growth GrantNW BOCES February 6, 2014

  2. Objectives for today • The RLC will understand DIBELS Next growth data (BOY to MOY) for the entire BOCES population of K-3 students with disabilities • Each literacy lead will understand how to do a similar evaluation of the data for their school/district • Literacy leads will utilize the data analysis methods presented in this webinar to help develop next steps for students with disabilities in their school/districts

  3. Analysis of growth • Purpose: determine whether the instructional programming for a district, school, class, program and/or individual student has changed or maintained the trajectory of growth • DIBELS is a “stoplight” assessment – lets us know whether a student is at risk of “reading failure” based on research

  4. Percentiles • A percentile or percentile rank in DIBELS is a description of how a student scored compared to a national sample of about 30,000 students • For DIBELS, percentiles were last published for the 2011-2012 school year. • Amplify percentiles are local (i.e. compared only to your school)

  5. Percentile rank example • If a kindergartener at the beginning of the year has a composite score of 10, they are in the 25th percentile. That means that 25% of kindergarteners in DIBELS sample group scored at or below where this student scored • DIBELS places their benchmark (green) score at or near the 40th percentile

  6. Percentile growth • If a student is making typical growth, they will stay at the same percentile ranking • If a student is making less than typical growth, their percentile ranking will drop • If a student is making more than typical growth, their percentile ranking will increase • If a student’s percentile ranking changes enough, then their category of risk will change

  7. Questions for our data • Are the changes we are implementing through the grant making a difference for students? • Which students are making adequate growth? • Which students need changes to their systems of support in order to make adequate growth?

  8. Our Data - Process • Created cohort group • Pulled BOY and MOY data from Amplify for cohort group • Analyzed change from BOY to MOY based on risk category • Analyzed change from BOY to MOY based on percentile ranking • Disaggregated by disability

  9. Our Data - BOY

  10. Our Data - MOY

  11. Our Data – BOY to MOY

  12. Our Data - BOY

  13. Our Data - MOY

  14. Our Data - BOY

  15. Our Data - MOY

  16. Next Steps/Considerations • What does your school’s data tell you? • What are the big picture patterns? • Which students need to be looked at individually? • What other patterns could you look for in the data? • What does it mean when a student is not making typical growth? (hint: think about their core instruction!) • When a student is making accelerated growth, how can you replicate that success?

  17. Reflect • What did you learn about analyzing student data today? • What will be valuable to use with your staff? • What is the best presentation method so that staff understand and have buy-in to what you share with them? • How will you use what you learned today in your school/district? • What questions do you want to answer with your data? • What are you wondering about your data?

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