SECTION 6. OOI Education: Using Real-World Data from the Ocean Observatories Initiative in Teaching

Janice McDonnell1, Sage Lichtenwalner1 , Cheryl Greengrove2, Anna Pfeiffer-Herbert3 and Leslie M. Smith4 1Rutgers University, New Brunswick, NJ, USA 2University of Washington Tacoma, WA, USA 3Stockton University, Galloway, NJ, USA 4Your Ocean Consulting LLC, Knoxville, TN, USA

Engaging students in active learning by modeling the scientific process using real-world data is a high-impact educational practice (O’Reilly et al., 2017; Deslauriers et al., 2019). Working with real data allows students to conduct inquiries that model the actual process of science, facilitating knowledge retention and development of more sophisticated cognitive skills, such as the higher skill levels of Bloom’s taxonomy (Bloom et al., 1956; Krathwohl, 2002). Analyzing data and identifying patterns have become core skills for the 21st century workforce (Oceans of Data Institute, 2015; Partnerships for 21st Century Learning, 2016) and are required for almost all career paths (National Research Council, 2010a; Hubwieser et al. 2015). Expanded access to online data provides educators with a myriad of opportunities to engage learners through the use of real-world data sets, models, and simulations of oceanographic processes.

Since conception of the first OOI Science Plan, the OOI was designed as a research and education platform (ORION Executive Steering Committee, 2005). The same OOI technology, real-time data, and high-speed communication that promised to fundamentally change how ocean science research is conducted can also invigorate science education in the United States. The wealth of freely-accessible data provided by OOI platforms, provides an opportunity to bring these data into classrooms (Hunter-Thomson et al., 2017; McDonnell et al., 2018) and facilitates the connection between research and education.

These opportunities, however, can be challenging to implement in the classroom. Students often struggle to work with data and visualizations due to their limited experience with different data types, analysis tools, and complex lines of reasoning (Kastens, 2011). Cognitive studies reveal that students often fail to see patterns emerging across scientific experiments and they often ignore anomalous data or distort them to match their personal beliefs (Chinn and Brewer, 1998). By directly manipulating and analyzing data, students are challenged to develop a deeper understanding of a topic or phenomenon. Working with real data helps students develop practical science skills (Hays et al., 2000; Adams and Matsumoto, 2009) as well as an interest in, motivation for, and identity with respect to science (National Research Council, 2015).

In addition, there are technical challenges associated with integrating OOI data into educational applications, due to the large volume of raw data and the inherent complexities of working with real-world data from dynamic environments (McDonnell et al., 2015). For example, the initial effort required to retrieve and manipulate data