Bridging the gap in oceanographic data science curriculum: prototyping experiential learning materials in Python

Abstract

The data-oriented field of oceanography asks professionals within it to be confident and capable in working with various types of oceanographic data. To develop such a skillset, individuals must have sufficient self-efficacy to continue to challenge themselves while learning (Bandura, 1977). In an effort to help aspiring oceanography professionals develop self-efficacy in data science skills, two new classes were created at the University of Washington, School of Oceanography that focused on using experiential learning to teach oceanographic data science through computer programming. These courses aimed to help students understand the importance of this skillset and encouraged the use of collaboration with peers, instructors, and outside resources to solve course problems. Student self-efficacy was assessed through surveys created by the author (who also acted as the instructor of both courses), and end of course evaluations helped examine student experience in each course. Survey results indicate that all students increased their self-efficacy in course content and had generally positive experiences learning course material through experiential learning in a small classroom setting.