Structuring Genre Performance for Future Data Scientists

• Main Authors: Laudenbach, Michael, Hutchison, Allison, Guo, Zhiyu, Xu, Danielle
• Format: Conference Proceeding
• Language: English
• Subjects: Computer-assisted analysis; Conferences; Data science; data science pedagogy; Education; Employment; Engineering profession; genre awareness; Surveys; technology-enhanced learning; Writing
• ISSN: 2158-1002
• DOI: 10.1109/ProComm57838.2023.00040

In our panel's presentations, we will discuss how our approaches to curriculum design in data science can help researchers and instructors name the types of writing skills they are asking students to display-and to perform-in varying instantiations throughout their academic careers, as well as later workplace contexts. This is especially relevant for data-driven writing in technical and professional settings, which we address in the teaching of our respective courses at two universities. The panelists will present two complementary studies that use Write & Audit, a text visualization tool that displays disciplinary genre choices for students. The presenters stress that Write & Audit is a non-evaluative revision tool designed for students to make more rhetorically informed choices in their technical writing. The course and workshops we've designed represent an " inter actionist" model, where writing and content knowledge are intertwined. Additionally, panelists will share survey results from their respective studies which capture students' sense of communicative self-efficacy and motivation. Overall, both studies show that our interventions positively affected students' learning in several areas. Therefore, we believe communication advances data analysis that is core to problem-solving efforts in the data science field.