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Responding to Special Education Disproportionality

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2. Special Education Disproportionality. Looking for answers to complex questions. 4 ... Intervention Monitoring System (SIMS) and provided corresponding ... – PowerPoint PPT presentation

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Title: Responding to Special Education Disproportionality


1
Responding to Special Education
Disproportionality
  • A Work Session for Newly Identified School
    Districts
  • Presenter
  • Jack Jorgensen
  • 8-4-08

2
Special Education Disproportionality
  • Looking for answers to complex questions

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Presentation Topics
  • Special education disproportionality defined
  • Providing a context framing the problem
  • One districts 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 Disproportionalityframing the
problem
  • Is it about
  • keeping students 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
Special Education Disproportionality in the MMSD
One districts response
  • 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.

8
MMSDs 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
MMSDs Data
  • Understanding the scope and
  • nature of disproportionality in one
  • Wisconsin school district

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MMSDs 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.
LIAutism, 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
    schools data

27
Response to Concerns - Professional Development
  • MMSDs 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
Closing The Achievement Gap
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-cultu
    ral 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

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