Trust is often invoked when talking about student data. What exactly does trust mean when it comes to education data practices, and who in the community should be involved in creating this culture of trust and support?
The Community Success Institute developed a “trust framework,” what we call a Community of Trust, to address this question. Our Community of Trust framework emphasizes the significance of human relationships, and is anchored by a set of unifying goals, values, and design principles.
A Community of Trust framework includes tools, resources, and processes to facilitate trust in:
networks of human relationships
technical systems and software
A Community of Trust framework promotes the exchange of student-centered information among consenting parents, families, schools, community partners, and related service providers in a safe, secure, transparent, and auditable manner.
Make data actionable and meaningful for students, families, schools, and communities
Develop an open, secure education data infrastructure to improve access and equity for students
Create trusted and transparent governance and public accountability structures for student data
Equity. Narrow achievement and opportunity gaps by making equity a priority at all stages in the design, implementation, and evaluation of data practices
Transparency. Create data practices that are transparent and open to public scrutiny
Engagement. Engage all stakeholders throughout each stage of design to ensure many voices are represented in data practices
Trust. Ensure that there are processes to build and safeguard trust at all stages of design and implementation
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.