By Kylie Van Dyke (University of Minnesota Rochester) and Dr. Abraham Ayebo (University of Minnesota Rochester)
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This poster presents the design and outcomes of a flipped undergraduate biostatistics course for health science students that emphasizes authentic data analysis, engagement with scholarly literature, and the development of statistical reasoning skills. Many students encounter coding and real data analysis for the first time in this course. To support learning and reduce anxiety, the course incorporates scaffolded assignments, project-based assessment, and growth mindset principles (Dweck, 2006), encouraging students to view mistakes as opportunities for learning. Two core instructional components structure the course: 1. Final Project: Reading Research “Through a Statistical Lens” Students critically analyze two published research articles using the Four-Step statistical framework (State, Plan, Do, Conclude). The project is scaffolded across the semester into several stages: abstract analysis, statistical outlining using the Four-Step Method, a written statistical analysis of one article, a short oral presentation on a second article, and reflective responses on their learning experience. This structure allows students to gradually build confidence in interpreting statistical analyses and communicating statistical results. 2. Work with the Data (WWtD) Assignments Students analyze authentic datasets using R, including a colorectal cancer dataset and a gene expression dataset related to psoriasis-model mice. The first assignment includes scaffolded pseudocode guidance to support novice programmers, while the second requires more independent coding and interpretation. These activities encourage students to move beyond procedural computation toward authentic statistical reasoning and interpretation of real scientific research. The instructional approach also aligns with ongoing scholarship examining how engaging students with research literature can strengthen statistical literacy (Ayebo & Dingel, under review). The course is taught at University of Minnesota Rochester (UMR), an undergraduate institution focused on health sciences education. Students are typically preparing for careers in healthcare, biomedical research, or public health. Courses at UMR emphasize active learning and collaborative instruction, with flipped classroom structures commonly used. The biostatistics course typically enrolls 6–12 students, allowing for substantial discussion, collaboration, and individualized feedback. Pre-class videos introduce statistical concepts, while class time is used for coding demonstrations, collaborative data analysis, and guided interpretation of statistical results. This instructional approach aims to spark joy in a world of AI by fostering confidence, curiosity, and meaningful engagement with real data and research.