TIES Inclusive Education Roadmap

Data-based Decision Making

Data-based decision-making is essential to building equitable, inclusive education systems for students with significant cognitive disabilities. Inclusive education for any historically excluded or potentially marginalized group of students demands deep reflection on long-held false beliefs about the value of segregated education, including students with significant cognitive disabilities. Systems data disaggregated by subgroups of students can provide accurate information that can counter false beliefs and drive equity-based instruction for all students.

 A review of relevant state, district, school, and classroom data will allow the EILT to create SMARTIE (Specific, Measurable, Attainable, Relevant, Time-bound, Inclusive of students with significant cognitive disabilities, Equitable) goals and monitor progress towards creating an inclusive system. The first step is to determine what kinds of data you want to gather or collect. Next, identify who will be responsible for gathering and/or collecting the data. Lastly, determine how and when (and by whom) the data will be reviewed. A transparent data system allows for improving individual and organizational capacity while maintaining a focus on equity for historically disenfranchised student groups. 

Data Sources

The easiest way to determine the data you need to collect is to start with the things you want to change (i.e., your SMARTIE goals). The M (for measurable) in your SMARTIE goal identifies what you will measure. Identifying data sources for some SMARTIE goals may be easy, while it may be more complex for others. For example, suppose you want to increase the number of students with significant disabilities who are included. In that case, you might identify system data on the least restrictive environment (LRE) for these students as your primary data source. However, it may take a while to see a change in the LRE data, so you might also want to use the minutes in general education found in student IEPs. Calculating the actual percentage of time in general education for individual students can help you identify those students who are close to meeting the requirements for a change in LRE (i.e., a student currently included for 75% of the school day may only need one more general education class or activity per day to move them over the 80% requirement for LRE 1 or A).

Using preexisting data systems (data already being collected) is an excellent time-saving strategy. Collect additional types of data only when it is essential to answer specific questions related to implementation or outcomes. For example, to measure the implementation of new classroom practices, you may want to use a classroom walkthrough weekly or monthly. Student work products are another excellent data source. These can be used to measure improvement in modification practices, increased academic expectations for students with significant cognitive disabilities, and improved academic outcomes for students. The table below provides more examples of potential data sources.

Data Collection and Analysis

The first task is to gather and collect baseline data for each SMARTIE goal. Without this benchmark, you will not be able to demonstrate progress on your goals. As some administrative data systems may not be accessible to everyone, it will be essential to identify the specific individuals who have easy access to the data you will be using. Co-creating a data gathering schedule with these individuals will ensure that you have your data when needed. As you identify additional data sources (i.e., administrator walkthroughs), determine who will be taking this data and how frequently you will take it. Identify the necessary data collection tools and ensure the users understand the data collection process. Finally, identify who will analyze the data, how it will be presented (i.e., raw data, bar graph, line graph), and how frequently the data from each data source will be reviewed.

Examples: Applying the Data-Decision Making Driver to Inclusive Education System Change

What you want to measure?

Data Source Examples

Time students with significant cognitive disabilities spend in general education setting

  • System LRE data
  • Placement in neighborhood schools
  • Minutes in General Education on Student IEPs
  • Analysis of Student schedules

Instructional effectiveness

  • Curriculum-based tests and quizzes
  • Progress on IEP goals and objectives
  • Frequency of use of Alternative and Augmentative Communication Systems (AAC) 
  • Data from involvement in school-wide PBIS and frequency of challenging behavior

Engagement of students with significant cognitive disabilities in grade level general education curriculum activities

  • Classroom observation
  • Administrative walkthrough
  • Curriculum-based tests and quizzes
  • Student communication about academic topics (including family reports)

Engagement of students with significant cognitive disabilities with general education peers

  • Classroom observation
  • Frequency of use of AAC with peers
  • Participation in recess and district/school sports programs
  • Participation in after-school clubs
  • Activities with peers outside of school hours
  • Utilize the TIES Center Belonging  Reflection Tool