TIES Inclusive Education Roadmap RETIRED

Data to Support Decision-Making

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Similar to a compass, data let organizations know if they are moving in the right direction toward achieving and sustaining their goals. In inclusive education systems, all levels of the system use data that describe both the implementation of inclusive practices and policies and student outcomes. Data supported decision-making is essential to building equitable, inclusive education systems for students with disabilities, including those with significant cognitive disabilities. It is important to identify who is responsible for data access, collection, and review at each level of the system and to develop a plan for both sharing the data and supporting its use as part of continuous improvement of inclusive education systems.

  • Educational leaders and teams use data for multiple purposes, including diagnosing or clarifying instructional or organizational problems and successes; considering and then justifying various alternatives regarding a direction for action; informing daily practices; complying with external requests for information; and guiding the development of culture and climate in an inclusive organization (Knapp et al., 2006).

  • One component of educational leadership that leads to meaningful change is insisting on the use of data-based decision making (Louis et al., 2010). 

  • Inclusive education for any historically excluded or potentially marginalized group of students demands deep reflection on long-held false beliefs about the value of inclusive education for all students, and the benefits each and every student receives as their worth and potential are recognized by the system. Data that are disaggregated by sub-groups of students, when used consistently and with fidelity, provide accurate information that counters false-beliefs and drives equity-based instruction for all students. 

Guiding Questions

  1. What questions do we have about inclusive education? For example:
    1. Do we have schools and teachers already using inclusive practices? Who and where?
    2. How many students do we send to out-of-district placement?
    3. How many students are served in self-contained classrooms for the entirety of the school day?
    4. Are any racial or cultural groups of students disproportionately placed in self-contained settings?
  2. What types of data does the team need to answer the questions regarding implementation and student outcomes in building an inclusive education system?
    1. What data are already being collected to inform these key questions?
    2. Are they being disaggregated by student sub-groups, including students with significant cognitive disabilities?
    3. What other types of data may be useful?
  3. What are different data collection strategies that can be used to collect data?
  4. Who in the organization/team will be responsible for coordinating the collection of the data that teams use?
  5. How will the team use data-based decision making for building inclusive schools? 

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 by some sub-groups of students, such as the cluster of students with significant cognitive disabilities. 

Challenges may also arise related to data regarding 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

State

District

School

Grade/Team

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)

State

District

School

Grade/Team

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” log

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 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 as: student work products, modified curriculum materials, lesson plans, 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, check for accuracy, and then finalized.

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?" 

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 can develop the capacity to pull reports that 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 a 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.

What is data-based decision making?

Data-based decision-making is the process by which questions are answered, implementation is reviewed and improved, and student progress is monitored in a systematic and objective manner. This is ensured through:

  • data collection that is standardized and consistent across an organization,

  • use of a transparent data-review process that is designed so the individual and organizational capacity can grow, and

  • clear communication regarding how the data will be used and by whom. 

Why is data-based decision-making vitally important in creating equitable and inclusive school communities for all students, including those with significant cognitive disabilities?

Inclusive education for any historically excluded or disenfranchised group of students demands deep reflection on long-held false beliefs about the value of inclusive education for all students and the benefits each student receives as their worth and potential are recognized by the system. Data that are disaggregated by sub-groups of students, when used consistently and with fidelity, provides accurate information that counters false-beliefs and drives equity-based instruction for all students.

How do we know which data are the best to use for decision-making?

It is often said that systems are data rich and information poor! Choosing what data to collect and use starts with formulating the questions you want to answer. From here, choose the specific types of data that will answer these questions. Think both about the components of implementation that you want to evaluate and the student outcomes you want to monitor.

What processes should we have in place to use the data?

A process for using data for decision-making should include: 

  • Systems data being disaggregated, analyzed, and summarized at least quarterly;  

  • Data summaries that are communicated clearly to relevant staff;

  • Action plans being developed, regularly revisited and revised, if needed, to improve implementation supports and outcomes; and 

  • Data summaries and action plans that are shared with key stakeholders.