OOI Ocean Data Labs feature web-based interactive "widgets" that allow students to interact with pre-selected data from the OOI (Ocean Observatory Initiative) and are freely available online at the OOI Ocean Data Labs website (https://datalab.marine.rutgers.edu/) for anyone interested in using them in their courses. In this teaching demonstration, I will highlight one of these data labs, focused on primary productivity, and describe how it was incorporated into the laboratory section of an introductory oceanography class at a community college. The topic was chosen because many students have a preconceived misconception that temperature is the most important factor controlling primary productivity in the ocean. Student learning was assessed using pre-lab and post-lab quizzes and students shared their experience in working through the data labs in anonymous surveys. By working through this data lab, students were able to discover for themselves how patterns of primary productivity correlate with nutrient levels and light levels and not with temperature. Arguably more importantly, students also developed skills at graph interpretation and scientific reasoning as they worked through the data lab and reported that they enjoyed the interactive nature of the widgets and the ability to work with authentic, current oceanographic data.

This activity will demonstrate a lesson created to increase student understanding of primary production trends and driving forces in coastal areas. This lesson was designed using the Ocean Observatory Initiative (OOI) Ocean Data Lab "Chlorophyll-a in Temperate Zones of the Ocean" – an online data widget that allows students to interact with and explore real oceanography data. This lesson walks students through data exploration, identifying trends, and creating hypotheses, with an additional focus on increasing data literacy. By using a real-life data set student practice interpreting authentic data, which includes unidealized data trends and outliers. Additionally, to increase student interest and understanding in how oceanography data is used to make real-world decisions, this lesson demonstrates how primary productivity data can be used to make decisions about fisheries management, including salmon hatchery production. This presentation will provide an overview of the lesson plan, including the associated student handout, potential answers, and areas where students may have questions and/or need clarification. Participants will have the opportunity to explore the data widget themselves, as well as work through the worksheet and discuss possible student answers. Additionally, this activity will include a discussion on how to adapt this lesson to different teaching modalities.

Teaching with real data is fun and engages students more thoroughly as it really illustrates the true nature of scientific inquiry, that data is not always straight-forward, as it can be messy, but still useful. The Ocean Observatory Initiative (OOI) Ocean Data Explorations provide an avenue to teach using various data collected by remote instrumentation, incorporated into meaningful datasets by professors, which have also created shareable activities that can be modified for different academic levels.The "Dynamic Air-Sea Interaction" dataset uses atmospheric and oceanographic data to show the 2018 "Bomb Cyclone" that hit the northeastern U.S. The authors of this dataset have created a stepwise exploration of the atmospheric and oceanographic conditions observed during this event. The online graphs are interactive and allow students to zoom into certain hours of data collection, add data sets, and even create predictive rainfall curves. I will work with you through the standard activity that is used in my introductory oceanography laboratory class along with tips that will include ideas for using this activity remotely, both asynchronously and synchronously. Using this dataset, students will become more comfortable with analyzing data, recognizing patterns and trends between datasets, and developing and testing hypotheses.

In this activity, students use online widgets to build connections across oceanographic disciplines – physics (upwelling, wind patterns), chemistry (dissolved gases, pH), and biology (photosynthesis, acidification impacts, fisheries). Each component of the learning cycle is explicitly considered. Students are invited to the topic using a podcast episode and an engaging visualization of annual CO2 patterns. They explore how atmospheric CO2 levels vary over long time scales (Mauna Loa Trends) and compare atmospheric CO2 levels to ocean CO2 levels off the coast of Oregon (OOI Ocean Data Labs). During the concept invention phase, students examine wind patterns throughout the year at this location (NANOOS Mapper). Students apply this new knowledge to describe the drivers of the seasonal CO2 and pH patterns off of Oregon (OOI Ocean Data Labs). To reflect on what students have learned, these concepts can be linked to fisheries and marine conservation in a class discussion. For example, though upwelling can lead to highly productive forage fish fisheries, it can also lead to reduced productivity of shellfish farms in the Pacific Northwest. Through this activity, students develop familiarity with various types of data visualizations, including regressions, time series data, and spatial data.

1st we simulate the amount of solar energy striking the surface of the Earth (insolation) using artificial light source, light meter, and protractor. Light striking meter directly (perpendicularly)=equator, meter 90degrees from light source=poles. Next, students research average annual temperature from several locations (near sea level) at equator and every 5degrees (usually 2 places N and 1 S of equator or more). Students graph all 3 paired sets of data: Latitude v. Insolation, Latitude vs. Temperature, Insolation vs. Temperature on linear and log scales and can use curve-fitting algorithm to determine best-fit equation. Can do regression analysis (with college students). The outcomes are: 1. Students understand through direct measurement and research the primary cause of atmospheric circulation patterns and distribution of biomes; 2. Students experience how a global phenomenon can be simulated in the lab; 3. Students explore geography with Google Earth and how to find climate data; 4. Students gain experience making and interpreting bivariate graphs and discuss correlation vs. causation.

We used a combination of an OER textbook (https://opengeology.org/textbook/), a collaborative e-reading program (https://perusall.com/), and weekly homework assignments (Learning Journals) that students could work on in small groups to promote the development of small learning communities within a larger, online physical geology class of 170 students. For all collaborative assignments (homework, readings and labs), students work in groups based on their lab section, each of which is capped at 24. The reading program generates a report that allows instructors to create just-in-time teaching opportunities based on the topics of greatest interest to students. These range from fairly typical for this audience (How can rocks behave elastically?) to more creative or unusual (Can we harness the kinetic force of an earthquake and use it in a productive way?).Student feedback has been overwhelmingly positive, including high attendance at synchronous sessions despite no participation requirement. Lab instructors report that students appear to have a greater degree of fluency with the material than in past semesters. Student questions are asked and answered quickly, and more engaging questions can be addressed with the full class.

The most fundamental problem in teaching observational astronomy is image saturation - a student looks into a telescope at the Moon and says "Yeah, the Moon - I've seen it." An image in the telescope becomes just on of hundreds, perhaps thousands of images students see each day. By having students construct a model of lunar surface features in clay, adding maria, lava flows, craters, ray systems, mountains, and more; students become intimately familiar with lunar surface features. Students can then map and explore the surface mathematically as well. After modeling the lunar surface, the observation time at the telescope is enhanced. Students who bring knowledge to the eyepiece take much more information away from it. Students search for, and recognize features that they have modeled. The geology of the lunar surface becomes more familiar and exploration through a telescope becomes more exciting and engaging, allowing the instructor to conduct more advanced exploration of the structure and evolution of the lunar surface.

Armed with previous knowledge about basic plate tectonic theory, students hypothesize about motions across specific plate boundaries. They then test their hypotheses by using a Google Earth platform to calculate relative plate velocities (speed and direction) across these specific boundaries using three independent methods: 1) long-term average rates from sea-floor age, 2) long-term average rates from presumed hot spot tracks and 3) near real-time rates from high-precision GPS data. Focusing on relative rates across boundaries allows students to get around reference frame issues when comparing the different data sets, and they also gain experience in quantitative skills using real data. In doing so, students confirm the basic tenets of plate tectonic theory, but also discover its complexities and limitations: plate boundaries are not simply those narrow lines depicted in plate boundary models, plate velocities change over time and space, internal plate deformation does occur and "hot spots" don't work like hypodermic needles providing a constant, steady pipeline of magma. That is, students reinforce their knowledge of a fundamental theory while also grappling with areas of current research.

The strike and dip tool is currently a web/desktop based virtual reality teaching instrument designed to practice taking strike and dip measurements and using those measurements to compile a geologic map. We have 5 different mapping environments and 4 different settings that adjust the level of challenge within the mapping environment. The tool is designed to be used in conjunction with a blank map so that a student working within the virtual mapping environment must still transcribe data to their own map and make their own geological interpretations. The activity we have currently designed for use within the virtual environments is to make a geological map and accompanying cross section. The tool has been pilot-tested at three different institutions with encouraging first results!