Community
of Trust

A Community of Trust is a network of trusted relationships marked by data-sharing agreements that facilitate the exchange of student-centered information among consenting parents, families, schools, community partners, and related service providers.

A Community of Trust represents a network of relationships and organizations that are highly invested in, and passionate about student success and well-being. On the data side of these relationships are student education data records that can be safeguarded and shared with trusted others who are committed to improving student outcomes. 

When a Community of Trust approach is applied to student data, the processes and methods used to implement legal, technical, and human agreements create trust frameworks that reflect the community’s shared values and goals. 

 
 

GOALS

  • Make data actionable and meaningful for communities, schools, families, and students
  • Develop an open education data infrastructure to increase flexibility and equity for students
  • Create trusted and transparent governance and accountability structures for student data
 

VALUES

Equity. Narrow achievement and opportunity gaps by making equity a priority at all stages of design

Transparency. Make data practices sufficiently transparent and open to public scrutiny

Engagement. Engage stakeholders throughout each stage of design

Trust. Ensure that there are processes to build and safeguard trust at all stages of design

 

DESIGN PRINCIPLES

Define shared values. Community and school partners become empowered when they define shared values, especially as data practices and new technologies continue to shape education in unexpected ways. 

Assess needs and assets. Each community has unique needs and assets that can be mapped, visualized, and shared in secure ways. Knowing what data other community organizations gather creates a more detailed picture of existing services and resources, as well as gaps in knowledge. 

Create transparent processes. Develop transparent data protocols upfront, including algorithms, agreements, and processes. Data quality can vary widely; knowing how and when it was collected, warehoused, shared, distributed, and analyzed affects decision-making. Therefore, data practices should be transparent, including any formulas or algorithms used. 

Give data back. When collecting data, always return it in some form to those who provided it. Too often, data is collected from people who may never see how it was used, or toward what purpose. Sharing this data with interested stakeholders can build trust, whether the data is used for descriptive purposes or to make decisions. 

Test the accuracy and veracity of data. Stakeholders deserve to have access to measurement and evaluation practices so that they can assess the quality of that data, and to make sure it can be used to ask the right questions. 

Stress-test processes, protocols, and practices to ensure they increase equity and access. Where possible, co-design with families and parents to ensure that new data practices do not inadvertently worsen access or decrease equity. 

Develop open-source infrastructures. Where possible, replicate open infrastructure practices, models, and approaches that have demonstrated increased interoperability, utility, and prosperity meaningful gains for individuals and communities in other sectors. 

Document repeatable processes for traditionally underserved populations. Underserved populations can learn from one another when there are documented cases about successful real-world applications and processes. Traditionally underserved populations include individuals with disabilities, first-generation college attendees, English language learners, minorities, students from military families, and others who may have unique needs, or those who come from communities with high rates of intergenerational poverty and less access to resources.