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D istrict Leadership Team Webinar #1: Data Based Decision Making

D istrict Leadership Team Webinar #1: Data Based Decision Making. Center for Education and Lifelong Learning. The Equity Project at Indiana University. Culturally Responsive Positive Behavioral Interventions and Supports www.indiana.edu/~pbisin. Focus of Webinar.

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D istrict Leadership Team Webinar #1: Data Based Decision Making

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  1. District Leadership Team Webinar #1:Data Based Decision Making Center for Education and Lifelong Learning The Equity Project at Indiana University Culturally Responsive Positive Behavioral Interventions and Supports www.indiana.edu/~pbisin

  2. Focus of Webinar • Review of the Role of the DLT • Assesses district strengths and needs related to using Data for Decision Making in CR-PBIS • Discuss Next Steps for the DLT in your District • Continued Work with PBIS-IN

  3. The Blueprint

  4. Using Data for Decision Making

  5. Data! Data! Data! • How do we collect data? • How do we analyze data? • How often? • Who? • When? • How can the data and information be used to improve our discipline system? • Applying to all students equally • Use of demographic data • Behaviors and locations • Classroom managed vs. Office managed

  6. Defining the Problem • Review your data to determine current strengths and areas of need: • Compare district enrollment numbers to current outcomes • Disaggregate data by ethnicity, gender, SES, grade level

  7. District A Composition

  8. Data-Based Decision making using ODRs • Examine ODRs( number of office referrals): • Per day per month • Based on location • Based on type of behavior • By student • By time of day • By subgroup (i.e. ethnicity, gender, special education status) • Examine consequences of referrals • Suspension and expulsion data • Disaggregated suspension and expulsion data

  9. Analyzing ODR data • Do we have a problem? • Avg./per day per month • Elem. .22/100 students per day. • MS .50/100 per day • HS .68/100 per day • Trends and Peaks • What kind of problem? • Behaviors of concern • Where? • Hot spots, cool spots

  10. Analyzing ODR data • When? • Time of Day • By Whom? • Lots of students or few students? What percentage of students have been to office? • Subgroups? • Do you have an disproportionate representation problem?

  11. District A Data Summary

  12. Next Steps for your DLT

  13. Your Next Steps

  14. Contact Us • www.indiana.edu/~pbisin • Contact us with questions: Renae Azziz • razziz@virtuosoed.com Shana Ritter • rritter@indiana.edu

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