TIES Inclusive Education Roadmap

Data-Based Decision-Making

Data collection is an important part of all implementation efforts. If baseline data related to the SMARTIE goals has not been collected, it is important to begin that as soon as possible. In order to measure progress in the implementation of new processes and practices, the EILTwill need to compare data collected during the initial implementation phase with the baseline data.

Once data is collected, the EILT needs to analyze and interpret the data. The usability of this data is a key consideration. Data collection on inclusive practices and processes only becomes usable information when decision-makers and stakeholders can understand what it represents (Wilcox et al., 2021). Is the data clear and not overly complicated? Is it presented in a way that is clear and concise? In order to present data in ways that are useful to others, the EILT will want to:

  1. Collect data according to the frequency decided in the Action Plan and organize the data related to each SMARTIE goal
  2. Create a concise summary of the most relevant findings
  3. Prioritize presentation of data that is most relevant to current questions and concerns

Role of the EILT

Reviewing data should routinely be on the agenda for EILT meetings. With the data organized in usable formats, the EILT will be able to use data-based decision-making to determine the effectiveness and fidelity of implementation of new inclusive processes and practices. Not every aspect of the Action Plan needs to be discussed at every meeting, but all parts should be revisited with implementation (process) and outcome data (if available) at least every two months. The presentation of clear and concise data summaries is followed by a discussion that includes both celebrations of successes and identification where the process is “stuck.” It is not uncommon during systems change efforts to have spots where implementation is lagging. As part of the data review process, the EILT may decide to address these area by allowing additional time for implementation to improve or to adapt the Action Plan in some way to jumpstart the work, perhaps by increasing the use of a greater variety of implementation drivers.

Sharing Data with Stakeholders

As the EILT reviews data, they will need to make decisions about when to share data, what data to share, and with whom to share data. For example, there may be too few data points to really tell if a trend is moving in a positive direction or not. When the decision is made to share data more broadly, consider what are the "talking points" to share with the data. What does the data tell you? Are you continuing the work as is or tweaking what you do? This level of transparency helps build trust and can help develop a culture of data-based decision-making at all levels of the system.

Teams will benefit from using the following types of data to create and sustain inclusive education systems:

  • outcome data for students and the system;

  • process data about the extent and quality of program implementation, including feedback from practitioners, parents and all students;

  • fidelity data to assure the continued use of inclusive systems and evidence-based practices; and

  • financial data to understand how resources are being used.

There will be situations where important process or outcomes questions about inclusive practices will be asked, but the data to inform those questions are not being collected or not available in an accessible format for a team to use. This is often the situation regarding implementation data or student data that have not been disaggregated for some sub-groups of students, such as the cluster of students with significant cognitive disabilities.

Challenges may also arise related to data for students with significant cognitive disabilities because there has been insufficient focus on collecting data related to the effectiveness of their instruction and outcomes. For example, systems may struggle to understand to what extent students with significant cognitive disabilities are engaged in grade-level, standards-based instruction or are successfully using their communication systems to participate in academic and other classroom activities. Unanswered questions identify systemic and specific gaps in data-based decision-making that must be solved so progress can be accurately monitored for each and every student.

It is important to identify data sources that can provide capacity and feedback data regarding the practices that are being implemented. For example, a teacher confidence/efficacy survey around inclusive practices could identify professional development that is needed as well as ways to utilize expertise in the building in different ways.

Below are examples of types of data used that can be gathered at the state, district, school, and team levels to help answer implementation and outcome questions.

For all data sources

Across All System Levels

To the greatest extent possible, consider each data point disaggregated by:

  • Disability, including students with significant cognitive disabilities

  • Race

  • Gender

  • Age and grade

  • Socio-economic status

  • English Learner/ Emerging Bilingual designation

  • Home language

Outcome Data Sources (system and student)

Across All System Levels

Graduation rates

State Accountability tests (literacy, math, science)

  • State achievement tests

  • Alternate achievement tests

Post-secondary survey outcome data

Attendance (absences and tardies)

Special Education

  • Child count

  • Overall special education rate

  • Initial referral count and eligibility decision

  • Least Restrictive Environment

  • Actual time in general education listed in the IEP

Behavior Interventions

  • Suspensions, Dismissals, Expulsions

  • Use of restrictive procedures for students with disabilities (Restraint or Seclusion)

Staffing allocation and student/staff ratios





Data reported to U.S. Dept of Education EdFacts

Funding data

Data reported to U.S. Dept of Education EdFacts

Progress towards graduation (number of credit hours; number of credit hours in required courses for graduation)

Student grades

Funding Data

Progress towards graduation (number of credit hours; number of credit hours in required courses for graduation)

Student grades

School-wide Positive Behavior System data

Office Discipline Referrals (ODR)

IEP Goal Achievement

Achievement of grade-level, general education standards

Collaborative team lesson plans

Formative Assessments

Summative Assessments

Teachers' observations of student(s) learning

Team School-wide Positive Behavior

Progress towards graduation (credit hours)

IEP Goal Achievement

Process Data Sources (system and student)





Reflections on Inclusive Systems for Education (RISE) findings and changes over time

TIES Inclusive Education Initiative Inventory

State and Regional Professional Development data and feedback, including from coaching systems

Coaching data

Results-based Program Reviews of District Programs (Special education, English Learner, Title 1)

RISE findings and changes over time

TIES Inclusive Education Initiative Inventory

Professional Development data

Coaching data

Results-based Program Compliance Reviews

Financial Data

RISE findings and changes over time

TIES Inclusive Education Initiative Inventory

Master Schedule

Number of co-taught core and elective classes, including enrollment of students with disabilities

Student grades in regular classes and co-taught classes

Classroom Observation of Inclusive Evidence-based Practices

Inclusive Education “Help Desk” logs of usage and questions asked

Survey perception data or feedback from staff, parents and students

School Climate Data

Professional Learning Community (PLC) schedule, notes, examples of student work, and evidence of students learning the content

Coaching feedback

Learning Walk Observations

Peer Observation Feedback of Evidence-based Practices (EPB)

Students’ social networks

Type of Data

Collection Methods

Current data systems

These data can be at the state, district, and school level. You may need permission to obtain some types of data. District administrators are aware of the types of data that these report to the state and that the state reports to the federal government and can provide access to some levels of data.

Observational data

This type of data can be collected through the use of principal or administrator walkthroughs and/or classroom observation during learning walks by peer teachers, instructional specialists, teacher coaches, and others.

Photographs/ Video

Before and after photographs of environmental accommodations and alterations, signage, and classroom organization can be used to document change. With parent permission, photos and videos of students involved in classroom activities can be used.

Permanent products

This data can be collected, such as student work products, modified curriculum materials, lesson plans, and teacher/student/paraprofessional schedules. Each item should be dated and notated regarding the change type it demonstrates.

Questionnaires and Surveys

This type of data can be collected using an online portal such as SurveyMonkey or using paper/pencil surveys.

Anecdotal data

This type of data can be collected through conversations with others. Notes are taken and then shared with those involved in the conversation, checked for accuracy, and then finalized.

Identifying the Lead Data Support Person(s)

Whether at the state, district or school level, a robust data system means having someone identified who is responsible for the data collection and distribution. The role includes:

  • Being able to take a “balcony-view” of all of the data that are being collected to assure that the information fits together into a cohesive whole and provides meaningful information to teams.

  • Helping create systems where data collection is valid and reliable to the greatest extent possible.

  • Ensuring adequate measures are taken to protect data security, including protecting confidentiality and/or anonymity, as applicable.

  • Providing timely and easily understood data in usable formats to support decision makers at each level of the system.

  • Supporting teams to develop the capacity to use data in decision-making.

The data support person collaborates with leadership and teams to consider ways to use the data to answer an array of questions that will emerge while building inclusive systems at multiple levels of the system. For example at the instructional level, teachers may ask “Who are the students who might benefit from pre-teaching/ re-teaching the content?” or “To what extent do all students feel confident in expressing their needs across the school day?” Whereas at the school level, the leadership team could ask “Is there a relationship between attendance/tardiness and literacy achievement?"

Plus, Collecting and Using Data is a Collaborative Effort

While one person is often identified to be responsible for data, in truth, no one person can be totally responsible for all data collection. Data-supported decision making is a collaborative effort and the responsibility of everyone in the system. Consider how individual teams develop the capacity to pull reports from their online system to provide them with just-in-time information related to the focus of their work. For example, teachers on a grade-level team can track student progress monitoring data using an approved shared document platform to determine which students need additional support in key areas. Or, a principal can gather data on the fidelity of use of evidence-based practices in inclusive classes by looking at aggregated data from collaborative Classroom Learning Walks where observations are entered into an online form.