Toward Critical Data-Scientific Literacy: An Intersectional Analysis of the Development of Student Identities in an Introduction to Data Science Course
The national imperative to increase the presence of women and people of color in science, technology, engineering, and mathematics (STEM) coupled with the growing presence of Latinos in the United States has led to the dramatic rise of programs and initiatives aimed at improving access to and equity in STEM careers and education for Latino youth. Through the use of critical social theory and critical theory of education as guiding frameworks, the dissertation examines an instantiation of STEM reform efforts to analyze the classroom participation structure that emerged in a piloted introduction to data science course at a local high school in one of the largest school districts in the country. The study is particularly concerned with identifying emergent classroom norms and practices, and understanding whether and how they came to support and/or hinder students’ opportunities to learn richly with data through an analysis of the development of student learning identities. This qualitative case study draws on audio-recorded student interviews, video-recorded classroom observations, and field notes collected during the second year of the curriculum’s implementation. To identify classroom norms and practices as they relate to the development of student identities as data science doers, the study examines the classroom participation structure (Cobb and Hodge, 2002) and employs Cobb, Gresalfi, and Hodge’s (2009) interpretive scheme for analyzing the development of mathematical student identity (also see Cobb & Hodge, 2010). While the multiperspectival approach of this study will provide innovatively insightful contributions to a number of fields including education, cultural studies, data and computer science, the study will also push how educators, learning science researchers, curriculum writers, and policymakers think about the pursuit of equity in STEM education in general and data science-oriented programs and initiatives in particular as they relate to STEM reform efforts.