Author(s):
To meet the increasing demand for data literacy and acumen, broadening participation in data science education for learners with a wide range of disciplinary and sociopolitical identities is necessary (NASEM, 2018). Current participation in data science learning and workforce pathways is not as diverse as the US population but largely reflects the demographics of traditions that have been foundational or prerequisite to data science, such as statistics, mathematics, and computer science (Canner et al., 2017).Our 2-year project is guided by the question: How can a data science education program create data science experiences that will attract, retain, support, and empower diverse learners who will bring data skills and practices to their daily lives and fields of interest? The project team is working to implement and refine the ADAPT (All-campus Data science through Accessible Project-based Teaching and learning) model. This model is designed to encourage students with diverse disciplinary and sociopolitical identities to participate in data science education through project-based learning with purposeful choices that reflect students’ various identities and interests.In Year 1, we designed and implemented an initial version of the ADAPT workshop with 6 instructors from the NC State Data Science Academy and 2 instructors from Wake Tech Community College. The instructors selected a focus population (an identity-based sub-group of their class that they aimed to support, e.g., students with rural backgrounds) and designed and implemented instructional strategies that aimed to support students’ participation in data science learning. We also selected focus groups of students in three courses and conducted careful observations on their participation in data science courses. Our preliminary results show the interconnectedness of implementing project-based learning and designing identity-conscious choices. Instructors’ consideration of students’ diverse disciplinary and sociopolitical identities enriched students’ experiences with project-based learning opportunities. And vice versa, students were able to develop and present data science projects that connect with their identities. We also found evidence for students shifting views on data science, from a standalone computing-based field to a more integrated component of their diverse disciplinary studies (physics, humanities). The project team is planning to conduct a more comprehensive analysis of the gathered data to inform the next interaction of the ADAPT instructor workshop and dissemination.Our project aims to make broader impacts by addressing: (1) a lack of replicable models to support effective broader implementation of data science education programs and (2) research-based evidence that demonstrates how the models can support data science identity development while attending to students’ diverse disciplinary and sociopolitical identities. Year 1 development and research effort focused on a local instructor group from NC State Data Science Academy and Wake Tech Community College. These efforts are presented at the AERA annual conference and the ASA SDSS conference. The workshop in Year 2 will be conducted on an online platform to reach broader audiences. The project is poised to support data science instructors from seven additional partnering institutions across the nation.
Coauthors
Rachel Levy, Jeanne McClure, Shiyan Jiang, & Ela Castellanos-Reyes, North Carolina State University, Raleigh, NC
