By Jeanne McClure (North Carolina State), Hamid Sanei (North Carolina State University), Sunghwan Byun (North Carolina State University)
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The field of data science is dominated by men; less than 24% of all data scientists are female. It is crucial to increase diversity and representation in the data science community. However, more needs to be known about what motivates female students to pursue data science. Therefore we asked: What beliefs or motivations do females have when pursuing introductory data science courses? This study was conducted at North Carolina State University, which aims to widen pathways to data science education across the land-grant university. We interviewed focus group students from five 1-credit hour data science courses. One-on-one, semi-structured interviews were conducted with seven females (3 Black/African, 3 White, and 1 Asian) and analyzed based on the framework of expectancy value theory (Wigfield & Eccles, 2000). We found some instructional features that empowered these women, including congruent social identity between the student and instructor, scaffolding strategies, collaboration opportunities, and affinity groups. Our analysis suggests that the three domains (expectancy of success, personal values, and anticipated costs) offer insights into how female students make decisions and choices about participating in data science education and how the program can better support them.