2023 ASEE Annual Conference & Exposition

Problem/background: The broadening participation in computing (BPC) movement has evolved from a focus on increasing the number and diversity of students who participate in computing education to addressing the systemic barriers that prevent equity in computing education. Data has historically played a significant role in the BPC movement, however it is not neutral and can be used to both mask and perpetuate the systemic inequities in computing education. One strategy for ensuring equitable computing experiences is to develop a data infrastructure that democratizes data.

This research paper presents the early results of how two cohorts of multi-state teams focused on BPC in K-14 through equity-explicit changes in policy, pathways and practices have worked to develop state-specific data infrastructure that can help safeguard against these potential harms.

Research Questions: What are the components of a data infrastructure that supports equity driven computer science education efforts? And, how feasible is it to create cross-state data systems in support of the national BPC movement?

Methodology: Eleven states participated in a four-phase, facilitated process over 9 months to build a multi-state data infrastructure, uncovering state-specific needs as they worked the project. Teams learned from their neighbors, held each other accountable and uncovered the opportunities and barriers within their unique data context. The process involved framing of the BPC goals, assessing current data systems for points of vulnerability and opportunity, a data request, data visualization, utilization and reflection.

Data was drawn from reflective team journals; notes, collaborative materials and observations made during collaborative meetings and; the technical assistance requests made during the project.

Findings:
When developing data infrastructure in support of BPC, diverse teams matter. Teams needed representation from people who can access data, understand the practical context of the data to support interpretation, and help tie data to broader advocacy efforts. Ongoing team engagement, both within and across states, allowed the space to consider the complexity of issues and maintain sustained engagement and commitment to the topic in an environment with competing educational priorities.

Teams uncovered assumptions many states had about the access and quality of their data. Issues with course alignment across and within states about how courses are identified and entered into the data systems; the software systems data storage; and access in a politically charged climate all emerged. Data suppression rules and the implications for invisibilizing populations were apparent. Finally, common enough definitions for computer science seem possible and desirable but also challenging based on state policy contexts.

Implications:
This project leads to clear implications for creating data infrastructure; most importantly data needs to be clearly tied to equity driven questions and purpose; data cannot be examined in a void and data systems should be revised according to equity needs. States are often using data systems outside of the intended design and face limitations when trying to surface inequity for populations based on gender, disability, ethnicity and race, which are exacerbated when looking intersectionally. Across states, teams are now asking deeper and more complex questions about pathways, policy, and purpose.