In this dissertation, I engage in three analytic cuts to think about/with a relational ontological orientation to data and data literacies/science education. The analysis focuses on the following question: What possibilities for teaching and learning about data are made possible when we attune to the relational, noisy, liminal, and material dimensions of data and the connections between data and broader issues of power and ethics? This study is situated in an interdisciplinary course on data and data storytelling at a large public university in the U.S. Midwest that I and another colleague designed and taught in the 2022-2023 academic school year. I organize my findings and discussions along three chapters in this dissertation. Chapter 2 is a theoretical examination of norms and values about data suggested in certain data science education reform efforts in the U.S. and a reconsideration of new possibilities for teaching and learning about data informed by relational ontologies and philosophical theories about signal and noise. Chapter 3 is an empirical piece that shares vignettes of two students’ engagements with data physicalizations as part of a data postcard activity and year-long survey-based research project. Chapter 4 examines how opportunities for critical, creative, and interdisciplinary engagements with data throughout the data storytelling course shaped how students made sense of data and processes of data generation, analysis, and communication. I co-authored the piece alongside my co-instructor and five of the students from the data storytelling course. Overall, this dissertation offers a unique approach of attuning to and elevating the concept of noise as a potentially generative concept for data literacies/science education. It raises important questions about the role of material agency, ethics and response-ability, ambiguity and improvisation, storytelling, and interdisciplinarity in connection with efforts to teach and learn about data in critical, creative, and relational ways.
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