By Nicole Wakim (Oregon Health & Science University)
Information
This Beyond session demonstrates a trauma-informed teaching approach tailored for statistics and data science. Trauma-informed pedagogy (TIP) acknowledges how individual and systemic trauma can affect student learning (Stromberg, 2023; Venet, 2023). In graduate-level statistics courses, unique challenges arise, including math anxiety, emotionally sensitive datasets, and seemingly objective course material. This session maps general trauma-informed principles to strategies specific to the statistics classroom. Implementation occurs across two complementary domains. First, instructor work and modeling emphasizes cultural humility, recognition of trauma responses, and flexibility during life disruptions. Instructors model vulnerability and highlight mistakes to foster a safe and supportive learning environment. Second, course design and environment shift the curriculum from a deficit-based perspective to a resilience-oriented approach. Community-led, culturally relevant datasets connect calculations and code to meaningful real-world questions. Multiple participation modes, collaborative peer structures, and AI tools support inclusivity, accessibility, and engagement while reducing educator workload. Students in TIP-informed courses report appreciation for varied participation options and extensive resources, noting an increased sense of belonging. By intentionally structuring the course to reduce barriers, TIP also enables students to engage with complex data in a way that promotes curiosity, experimentation, and discovery. Through this approach, students can explore statistical questions with confidence and agency, creating moments of insight that foster joy in learning. AI is an integral part of the process, supporting educators in making course materials inclusive while providing students with scaffolds that enhance their exploratory experience. Attendees will gain practical guidelines for applying TIP in statistics and data science courses, see examples of implementation, and learn how these strategies foster both student engagement and critical thinking. The session is particularly relevant for instructors of graduate-level courses, where students face high cognitive demands and diverse experiences with data, and where sparking curiosity, confidence, and discovery is essential for deep learning.
Personal website: https://nwakim.github.io/nickywakim/