Data Doulas: Supporting Educators to Cultivate Data-Rich Instruction in STEM Classrooms

• In Event: Supporting Teachers to Engage in Meaningful Data Literacy and Data Science Practices Across Disciplines

Abstract

Objectives. Integrating data into STEM education can be challenging due to the lack of available classroom data and the complexity of aligning datasets with diverse classroom needs (Authors, 2024). STEM teachers aiming to create data-rich opportunities often find the need to create or adapt lessons to fit their curricular contexts. This study introduces the "Data Doula" role to bridge this gap. During our professional learning (PL) initiative aimed at enhancing teachers' content and pedagogical content knowledge for data-rich instruction, we found that teachers needed specialized support beyond typical PL facilitation — they needed a Data Doula. Data Doulas help craft data-rich learning experiences by aligning curricular goals, pedagogical strategies, and disciplinary content with effective datasets. This work is guided by the questions, “What specific challenges do teachers face when designing and implementing data-rich instruction in STEM? How does the Data Doula role address these?”

Theoretical Framework. This research uses the Data Fluency Framework for Teaching (DFFT) (Authors, 2025), describing the technological, pedagogical, data, and STEM domain knowledge that educators need to plan and enact data-rich instruction (Figure 1.1).

Methods and Data Sources. The research emerged in the context of a mixed-methods study of data-focused PL involving 23 math and science teachers in grades 5–9. Data for this analysis included observations of data-focused PL, lesson-planning sessions, and community of practice meetings, guided by five PL specialists. Teacher interviews provided insights into the challenges, barriers, and supports encountered. Data were analyzed using thematic analysis (Braun & Clarke, 2023) to extract central themes.

Results and Conclusions. Data Doulas supported educators by both providing practical tools and supporting teachers’ pedagogical reasoning. They helped teachers address key challenges in bridging from PL to classroom instruction, like identifying essential STEM and data goals for students, sequencing and aligning activities with standards, finding datasets, considering students’ prior knowledge, evaluating technological tools, and considering formative assessment opportunities. When finding and modifying datasets, key activities included sourcing and evaluating data for their match with instructional goals, identifying key variables, reducing data, highlighting the pedagogical affordances, creating student-ready definitions, and integrating datasets into meaningful lessons.

The Data Doula role requires robust understanding across multiple domains, including deep data knowledge, STEM expertise, data-technology proficiency, and knowledge of effective pedagogy for adult learners and students. It also demands an understanding of classroom environments, familiarity with common technological barriers, strategies for integrating data into learning management systems, and an ability to navigate the ever-evolving landscape of data sources.

Significance and Future Directions. This work highlights the knowledge and strategies Data Doulas use to aid teachers in planning and implementing data-rich instruction, offering insights for developing professional learning and facilitator training. Future research will explore supporting and scaling Data Doula activities through online platforms and AI agents, and will examine the role's adaptability across different educational contexts, as well as its potential to promote interdisciplinary connections with fields like computer science and engineering.

Authors

• Nicole Wong, WestEd

• Leticia Perez, WestEd

• Kirsten Daehler, WestEd