Personal Informatics in Undergraduate Data Science: Learning by Analyzing the Self

Personal informatics refers to the data-driven, iterative process of collecting personal data, reflecting on it, and using the resulting insights to inform positive behavioral change. This practice, which grows in popularity with the widespread availability of wearable devices, can also be viewed as a form of data-driven scientific inquiry into personal behavior. When embedded in educational contexts, personal informatics offers college students an authentic and active learning experience that cultivates both data science skills and self-regulation, which are critical competencies for learners whose executive functions are still developing. In this work, we describe a personal informatics project piloted in an introductory data science course. The project began with scaffolded supports to help students identify areas of their lives they wished to improve and to formulate testable hypotheses informed by existing literature. Students then collected personal data continuously over a five-week period, and met with their accountability partner weekly to discuss and reflect on the data based on initial analysis. In the second half of the semester, students engaged in hands-on labs designed to help them generate deeper insights into their own behaviors while building key data analytics skills. These included data wrangling and visualization, making sense of data artifacts, and applying statistical inference techniques. Students wraped up the projects by crafting creative products (e.g. Infographics or Memes) that can be shared with peer students. We present an overview of analysis of students' final reflection and creative artifacts. Our findings offer insights into how this multi-purpose learning activity can be improved to support students' development as data science learners, while empowering them to engage in meaningful, evidence-based inquiry grounded in their lived experiences.