UCLA SEA Change Initiative: A collaborative approach using data for institutional change

The AAAS's Science, Technology, Engineering, Mathematics and Medicine (STEMM) Equity Achievement (SEA) Change initiative uses research-informed strategies including a self-assessment process to build capacity for sustainable transformation. The goal is to make diversity, equity, and inclusion in STEMM normative and tied to excellence. UCLA joined this initiative and convened a Change Leadership Team (CLT) to collaboratively design, plan and implement change efforts across the Divisions of Life Sciences and Physical Sciences, Samueli School of Engineering and David Geffen School of Medicine.

The SEA Change theory of change uses a collaborative organizational learning approach (Borden & Kezar 2012), based on the principle that institutional research (IR) and data are used as tools to promote learning, and knowledge is created through collaboration and communication. This iterative process occurs through guiding interactions using an equity minded sensemaking approach through three cyclical phases; 1) template guided self assessment 2) meaning making 3) implementation and evaluation.

The CLT began the work of Phase 1 by populating the self-assessment template using a dashboard with compositional diversity data developed by IR. The CLT decided to identify the status of equity gaps in five domains: 1) Undergraduate "achievement" 2) Undergraduate persistence 3) Graduate persistence 4) Faculty diversity, retention, and promotion 5) Climate. Sources of institutional data were identified for indicators across these domains. Undergraduate achievement became the focus because STEMM course performance is one determinant of persistence, particularly in lower division courses. Representatives from the divisions and IR collaborated to revise dashboards to provide functionality, and identify course-level equity gaps in grades and DFW rates by student characteristics. Next, the CLT works with divisions and departments to review local data on equity gaps, generate hypotheses that may explain observed equity gaps, and identify data "close to practice" to test these hypotheses, collect in-depth qualitative data and design interventions.