2025 ASEE Annual Conference & Exposition

In this work-in-progress paper, we report findings from the first year of implementation of a data science workshop situated in water science, i.e., hydrology. This work is part of an NSF CyberTraining project focused on developing data science knowledge and skills in the context of water science, with a focus on a summative hackathon-style event at the end of each three-week training period. The participants are graduate students, postdoctoral fellows, and faculty members interested in professional development in data science who participate virtually, collaborating with a team of fellow participants on a final project for the hackathon. The participants received grant-funded financial support for participating in the full extent of the program and intend to continue during the Summer of 2025. We surveyed participants before and after the three-week event as part of ongoing design-based research embedded into the project timeline. We asked participants about their experiences, perspectives of the program, conceptual and procedural understanding, and self-efficacy related to their newly developed knowledge and skills. Concerning overall satisfaction with the program, most participants were moderately satisfied with it, though a few participants were extremely satisfied or dissatisfied. Considering the program’s effectiveness in meeting their learning and professional goals, most rated it as moderately effective. However, some participants found it very effective, while others felt it was only slightly helpful. Instruction and materials followed the same trend, with most participants finding them moderately beneficial and a small number finding them very effective. As part of this work-in-progress and to provide more context for and understanding of their survey responses, during the Fall 2024 semester, we are interviewing the WaterSoftHack Fellows to gain a more nuanced understanding of their survey feedback. The interview protocol continues the themes of program experience, knowledge and skills, and self-efficacy with open-ended questions designed to generate discussion. In this paper, we present our findings from interviews with project participants in synthesis with survey results to share a comprehensive analysis of participants’ experiences in a data science training program.