Co-designing for Data Equity
From the Learner Information Framework Data Team
Let’s talk about why designing for data equity is critical.
In today’s world, learning and employment data is an important resource for institutions and employers to understand problems and develop solutions to support learners and workers, and is also important for individuals to use in reaching their employment and education goals. Research on the lived experiences of learning and work, however, reveals that many individuals do not have access to their data. Nor do current technological systems make it easy for them to collect, learn from, or use their data and information to create a comprehensive picture of their learning and work journeys for themselves or to share with others. This is particularly true for learners and earners from marginalized, historically excluded, and underrepresented communities who comprise the New Majority Learner in postsecondary education and work-based learning.
Data systems that currently support learners have been designed under the assumption that they serve “all learners”, yet their data model designs do not take into consideration key differences among learners and workers. For example, people who did not pursue a four-year degree immediately after high school but who gained work experience and part-time education along the way often have a difficult time compiling their wealth of experiences. This omission may perpetuate inequities and further marginalize the needs and experiences of New Majority learners. It is critical that we do more to design data and technology solutions to improve access to, learn from, and enable easier sharing of data for people from marginalized, historically excluded, and underrepresented communities.
Co-designing for Data Equity Approach
The Learner Information Framework’s data team is working to address this by co-designing an approach grounded in data equity principles for assembling and evaluating learner data models, including data standards already in use, and use cases.
So, what do we mean by “grounded in data equity principles”? The Learner Information Framework data team, in collaboration with partners and the project management team, has integrated data equity into project goals, language, work practices, and deliverables. They are considering key questions including – “are we only including credentials granted at an institution, or are we also including credentials that are self-attested or from an employer?” The team is also exploring if the team includes different experiences and backgrounds that can contribute to decision making that will widen the approach. The data team is in continual conversation about the ways bias shows up in data and technology solutions, as well as the ways we may mitigate this bias in the project.
Dr. Kelly Page, from LWYL Studio, a consultant in Equity and Inclusive Experience Design who works on the project, shared, “In this project, we openly acknowledge as a team that bias is inherent in data and in our decisions, the data elements, solutions, and systems, and that we the designers, data scientists, researchers, and developers all bring our own experiences and biases to the co-design table. Our team is focused on making these biases and their potential impacts visible in each decision and stage, so we may co-design the solution differently with them in mind. Our hope is that this will help to develop a solution that supports a learner in their access to their data and information that overtime leads to equitable opportunities for marginalized learners.”
So, how are we doing this?
The team focused on 5 key collaborative ways to build data equity into the project to ensure it is prioritized and integrated throughout each stage and decision. These include:
- Inclusive Experience and Community Co-design: With the help of LWYL Studio and other partners, the team is working to prioritize the experience of marginalized learners and earners in the solution design and use case considerations. We also include all project, data and engineering team members, as well as input from our advisory council, at the data equity co-design table.
- Data Equity Literacy: They enlisted the support of leading experts in data equity literacy, We All Count (WAC), in collaboration with LWYL Studio to deliver hours of training and 1-1 consultation on data bias, data equity, and ways to illuminate and mitigate bias in the design and implementation of data and technology projects.
- Data Equity Tools and Resource Development: As a team they use our learnings to inform the co-design of tools and resources to support the data, engineering, and demonstration teams, as well as similar projects, on their data equity journeys.
- Data Equity Resource Prioritization: The team worked with the project funder, the Bill & Melinda Gates Foundation (BMGF), to ensure resources were available to support our data equity priorities and activities.
- Agile Project Management: The project management team at Unicon and Alvarez & Marsal is experimenting with an agile approach to manage the project and create space for activities and decisions that may emerge along the journey that may be unknown.
Chris Moffat, a mission-driven technologist and principal at Touchdown Consulting, LLC serving on the data team, shared, “The focus on data equity as a core tenet of the Learner Information Framework is one of the most fulfilling aspects of working on the project. There is a commitment to provide training and resources, as well as provide the data team the necessary time and space to consider and prioritize challenging questions. At its core the team is striving to ensure that our design choices amplify the voices of the marginalized and pave the way for fair, just, and impartial experiences.”
Contribute Your Thoughts
We are now in the process of co-designing a set of resources to support teams along their data equity journey while working on learner data and information projects like the Learner Information Framework.
We’d love to hear your thoughts on the following questions via Google Form:
- What resources have you found helpful in learning about increasing marginalized learners’ access to their data and information?
- What are valuable questions to ask to ensure data equity remains a priority in order to build data and technology solutions for today’s learners and the organizations that serve them?
