BK1A: Opportunities and Barriers to Implementing Universal Design for Instruction in Introductory Statistics and Data Science Classrooms

Abstract

As more students from diverse backgrounds enroll in statistics and data science courses, it is important to incorporate instructional approaches to effectively facilitate supportive learning environments. Universal Design for Instruction (UDI) is a framework that can be translated into introductory statistics and data science courses to support learning among diverse student populations. In the first part of the breakout session, we will present the initial findings of our study conducted with instructors teaching introductory statistics and/or data science. We’ll share a range of teaching experiences, perceived opportunities. and potential barriers which were later mapped to UDI principles. We will also share findings about instructors’ motivations for implementing such teaching practices based on the Expectancy-Value Theory framework for motivation. In the second part of the session, participants will have the opportunity to share their own experiences through a collaborative platform and group discussions. Through these interactions, we aim to spark conversation about opportunities and challenges related to applying UDI principles in introductory statistics and data science classrooms to better support diverse learners.