Enhancing Data Literacy in Undergraduate Oceanography: A Scaffolded Python Exercise for Exploring Primary Production Variability Using OOI Data

The development of data literacy skills to analyze and interpret large, complex datasets is essential for addressing how and at what pace the oceans are changing. Authentic data from sources like the Ocean Observatories Initiative (OOI) offer a valuable resource for cultivating these skills in undergraduate education. We have developed a new set of Python exercises complementing the existing OOI Data Lab on primary production (Lab 7). These activities aim to deepen students' understanding of oceanographic concepts while simultaneously developing data literacy skills. Students explore how the timing and magnitude of primary production vary with latitude, forming and testing hypotheses using real-world OOI data. Multiple Python activities are structured in a way to guide students through progressively complex data analysis tasks beginning with exposure to simple Python code. Students begin by recreating and customizing basic time series plots, adding a series from a second location, and building up to exposure to more advanced data visualization techniques. This approach introduces Python programming skills while reinforcing and deepening spatial variability in oceanographic processes, and is meant to be instructor-friendly, even for those without extensive Python expertise. This data lab activity gives students the opportunity to independently explore variables and create plots to investigate relationships between parameters, building students’ confidence in applying the scientific method to test hypotheses and draw evidence-based conclusions. These activities  may serve as a template adaptable for other teaching approaches in undergraduate education, offering a framework for integrating data literacy across various oceanographic topics and course levels. This framework allows for flexibility in coding abilities for both students and instructors and ensures consistency across existing and future activities. By combining conceptual learning with hands-on data analysis methods, these exercises help to prepare students to synthesize complex datasets and interpret broad ecosystem processes. Specific implementation strategies and potential impacts on undergraduate oceanography education will be discussed.

Presentation Preference: Either

Primary Presenter: Mikelle Nuwer, University of Washington Seattle (mrasmuss@uw.edu)

Authors:

Mikelle Nuwer, University of Washington, Seattle  (mrasmuss@uw.edu)

Katherine Qi, University of Washington  (klqi@uw.edu)

Amanda Kaltenberg, Savannah State University  (kaltenberga@savannahstate.edu)