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Format: Toolkits
Success Gaps Toolkit: Addressing Equity, Inclusion, and OpportunityThe Success Gaps Toolkit presents a process for using data and the Success Gap Rubric to identify root causes of gaps between groups of children in districts or schools. These success gaps occur when the education system is not meeting the needs of all groups of children and outcomes for some groups are different than outcomes for most groups. The toolkit, with its process and materials, provides a manageable and defined way for districts or schools to identify success gaps that are present and their root causes and then make a plan for addressing the gaps. The success gaps may be the graduation rate of students who are English learners compared to the rate of all other children, the out-of-school suspension rate of children who are Black compared to the rate of all other children, the identification of children who are Hispanic as children with specific learning disabilities compared to the identification of all other children, and other gaps.
Format: Recordings
Equity in IDEA: Comparing the Current IDEA Equity Requirements and Understanding the Proposed Changes for Significant Disproportionality WebinarThis webinar described the various equity requirements in IDEA and also provided information on the NPRM released by OSEP and implications for states if the NPRM moves forward.
Format: Toolkits
SEA Data Processes ToolkitUsing the SEA Data Processes Toolkit to document data processes for all 616 and 618 data collections will establish a well-managed process for data collection, validation, and submission. In collaboration with IDC State Liaisons, states can use the toolkit to create and maintain a culture of high-quality data and establish and support consistent practices that produce valid and reliable data, while building the capacity of state staff.
Format: Presentations
Collecting Quality Data: Why It Matters and Guidance to Improve Our ProcessThis presentation provided information that would allow participants to increase their knowledge of what constitutes high-quality data, how to improve processes for collecting high-quality data, and the use of high-quality data for measuring program effectiveness.
Format: Toolkits and Templates
Part C IDEA Data Processes ToolkitUsing the Part C IDEA Data Processes Toolkit to document data processes for all 616 and 618 data collections will establish a well-managed process for data collection, validation, and submission. In collaboration with IDC State Liaisons, states can use the toolkit to create and maintain a culture of high-quality data and establish and support consistent practices that produce valid and reliable data, while building the capacity of state staff. The toolkit contains an overview of the toolkit, Data Collection Protocols, SPP/APR Indicator Protocols, a State Landscape Protocol, a Local EI Program Determinations Protocol, a Data Collections Calendar, and additional resources that provide a structure for documenting data processes. The Data Collection Protocols are in Word, and states can tailor them meet their states' specific documentation needs.
Format: Presentations
Equity, Inclusion, and Opportunity: Creating Educational Systems That Meet the Needs of All Groups of StudentsMany schools and districts have been identified as low performing or disproportionate because of disparities between subgroups on a variety of success measures. Other schools and districts are proactively trying to address identified success gaps. Presenters from IDC demonstrated IDC's Success Gaps Toolkit that can help schools and districts 1) prepare all of their students for success in college and careers by addressing success gaps, 2) collect and use quantitative and qualitative data for the purpose of root-cause analysis of those success gaps, and 3) focus attention on those root causes for the benefit of children and students in the lowest performing subgroups.
Format: Applications and Spreadsheets
Equity Requirements in IDEAThis resource compares the three equity requirements in IDEA (disproportionate representation, significant discrepancy, and significant disproportionality) across various elements to explain the similarities and the differences among the requirements.