Responding to Special Education Disproportionality

1. Responding to Special Education Disproportionality A Work Session for Newly Identified School Districts Presenter: JackJorgensen 8-4-08

2. Special Education Disproportionality Looking for answers to complex questions

4. Presentation Topics • Special education disproportionality defined • Providing a context: framing the problem • One district’s story – the Madison Metropolitan School District • Examining data to understand the scope and nature of the problem • Using data to inform our conversations and to help target areas of improvement

5. Disproportionality Discrepancy in the performance or representation of one group of students when compared to another group. Special Education Disproportionality The overrepresentation of racially, culturally and linguistically diverse students in special education has been well established in over 30 years of research and is emphasized as a critical issue in IDEA ’04.

6. A Context for Disproportionality“framing the problem” • Is it about: • keepingstudents of color out of special education? NO! • improving the evaluation procedures that are used to determine disability and need for special education? YES • more stringent application of the eligibility criteria, including exclusionary factors? YES • improving early intervening services and strengthening student interventions prior to a referral? YES!!!

7. Primary Goal: Eliminate race as a predictor of a child being labeled and placed in special education. Related Goal: Eliminate race as a predictor of a child with a disability being disproportionately excluded from gen. ed., suspended, expelled or not completing high school. Special Education Disproportionality in the MMSD: One district’s response

8. MMSD’s use of data to understand the scope and nature of the problem • Overall S.E. prevalence rate • Percentage of students in S.E. by ethnic group • Risk Ratios • S.E. referral and placement rates • Referral and placement data desegregated by ethnicity, primary disability, socio-economic status • New students to special education – where were they coming from? • Least Restrictive Environment • Suspension, expulsion, attendance and high school completion • Transfer students

9. MMSD’s Data Understanding the scope and nature of disproportionality in one Wisconsin school district

16. MMSD’s Risk Ratio2006-07 Year (Ages 6-21) Risk ratio compares the risk of one racial/ethnic group to the risk for all other racial/ethnic groups combined. *LI=Autism, Deaf-Blind, Hearing Impairment, Orthopedic Impairment, Significant Developmental Delay, Traumatic Brain Injury, Visual Impairment

17. Using “Risk Ratio” to further understand where the district is disproportionality represented What is “Risk Ratio”? Risk ratio compares the risk of one racial/ethnic group to the risk for all other racial/ethnic groups combined. Calculating Risk: AA students w/CD = 130 ALL AA students = 5,360 All other racial/ethnic students w/CD = 132 All other racial/ethnic students = 18,982 Calculating Risk Ratio: 2.43 0.70 Risk: AA are 3 ½ times as likely to be receiving SE services for CD than all other racial/ethnic groups combined. = 2.43% = 0.70% =3.49

18. Going Deeper Into the Data Revealing other faces of disproportionality in the MMSD: • District Enrollment & Percent in Special Education • LRE Comparison (06-07 and 07-08) • Risk Ratio • Suspension • Expulsion • Attendance • High School Completion • Transfer student data

19. MMSD District and Special Education Enrollment Comparison of 2002-03 and 2007-08

20. ExaminingK-12 Transfer Student Data Primary Disability Entering and leaving by grade Level Outcome of File Review Ethnicity Improvements

21. Using Data to Inform our Conversations and Target Areas of Improvement Risk Ratio Transfer Students School Data Packets Professional Development

22. Response to Concerns – Risk Ratio • Secured a WI Department of Public Instruction mini-grant to examine root causes as to why African American and American Indian students are at greater risk of being labeled EBD and SLD • Entered into multi-year partnership with National Institute for Urban School Improvement - NIUSI • Focused on greater adherence to the eligibility criteria, especially for overrepresented groups (e.g., African American students) Continued…

23. Response to Concerns – Risk Ratio • Examined more carefully transfer students to MMSD and the effect on our disproportionality. • Using DPI grant funds, employed university personnel to develop a process and tool to assist with addressing the exclusionary factors – Culturally Responsive Practices in Schools – The Checklist to Address Exclusionary Factors When Considering Special Education Eligibility Determination.

24. Response to Concerns - Transfer Students • Dedicated psychologist allocation to review all transfer student special education records to determine if an evaluation was warranted: • In-state transfers were referred to school as reevaluations • Out-of-state transfers were referred for initial evaluations • Improved version of exclusionary factor checklist tailored specifically for the review of transfer records • Eligibility criteria checklist developed for each disability area are now used in the transfer student record review process

25. Response to Concerns - School Data Packets Individual School, Level, & District Data Disaggregated by: • Referral & Placement Data • Ethnicity • Gender • Class • Socio-Economic Status • Least Restrictive Environment Data • Ethnicity • Gender • Socio-Economic Status • Risk Ratio Data • Achievement Gap Data • Behavior Data (e.g., expulsions, suspensions, etc.)

26. A Sample of Questions You Might Ask… • Examining your referral/placement data by ethnicity, is there a high rate of referrals coupled with a low rate of placements in any of the groups? • If so, what actions might you consider? • How does our data compare with district data and other schools? • What might account for differences in our school’s data

27. Response to Concerns - Professional Development • MMSD’s Educational Framework – where does disproportionality fit? EIS and RTI? • National Institute for Urban School Improvement (NIUSI) - a partnership to address disproportionality • Created a Student Intervention Monitoring System (SIMS) and provided corresponding professional development

28. Engagement • Classroom • School • Learning • Content • Instruction • Relationships Closing The Achievement Gap Student Student Student Staff Staff Staff Staff Families Core practices, services & programs Core practices, services & programs IF A STUDENT ISN’T SUCCESSFUL? Classroom specific supports School Wide Supports Time Limited Specialized Support Long Term Intensive Support Comprehensive System Supporting Positive Behaviors Early Intervening Services & Response to Interventions Race & Equity Kronenberg Training SIMS Mixed ability Classrooms & Inclusive Education Team Infrastructure Disproportionality Instructional Design Developing Inclusive High Performing Middle School; Leadership Institute LRE Data Risk Ratio w/EBD & SLD ExclusionaryFactor Checklist Parent/ Community Involvement Suspension Expulsion Data Non-biased Multicultural Assessments UW Grant Parent Survey Spec. Ed. Advisory Council

29. In closing…where do you begin to address disproportionality? • Critically examine your district data and provide your schools with their own data to discuss – understand the problem! • Race and Equity – are you ready to have courageous conversations? • Develop a multi-year, comprehensive improvement plan that addresses all facets of disproportionality (EIS, RTI, non-bias/multi-cultural evaluations, etc.) • Professional development – learn together!

30. “As Diversity Grows, So Must We” … a quote by author Gary Howard School leaders should also model for their colleagues inclusive and nonjudgmental discussion, reflection, and engagement strategies that teachers can use to establish positive learning communities in their classrooms

32. Questions/Comments