Science is the process of learning how the world works. Students come to class to learn about the world, but the world in the classroom often does not correspond to the world where they live their lives. Bridging the divide between these two worlds can be difficult and requires that students apply the same skills and thinking from the classroom to where they live.
Minerva’s mission to provide our students with practical and transferable knowledge is enabled by a dedicated effort to ensure students have opportunities to apply the concepts they are learning in the classroom to the world around them and to themselves. This is especially important in the Natural Sciences in which theoretical knowledge should be paired with hands-on experimentation and observation so that students can explore the full scope of the scientific method. At many traditional institutions, this is typically facilitated through laboratory courses. While these lab courses have the potential to offer valuable learning experiences, they tend to emphasize procedural information and skills, rather than engaging students in inquiry-based hypothesis development, experimental design, and analysis. At Minerva, rather than simulate real-world phenomena in a lab, students make a lab out of the real world.
Every course at Minerva has a “location-based” assignment, meaning that it includes certain elements that require students to go out and interact with their city of residence. This series will showcase examples of such assignments across different disciplines within the Natural Sciences, including physics, chemistry, and environmental sciences.
The assignments presented in this series can serve as examples to inspire educators to incorporate experiential learning in their Natural Science courses. All examples share a few common elements:
Empirical Analyses is a general sciences course for all first-year students. Students practice the iterative process of scientific inquiry and apply problem-solving tools to big societal issues. The Location-Based Assignment (LBA) challenges students to explore the potential consequences of climate-change-associated sea-level rise in their current city, using interactive web-based tools and site visits.
Students can choose one impact of the sea-level rise that is interesting and relevant to them. For instance, submissions have ranged from assessing the impact on real estate in Miami to rice production in Vietnam. Students analyze computational models available online and develop hypotheses and predictions from those results. For example, the Surging Seas Risk Zone Map allows students to create maps of flooding from different future sea-level rise scenarios. They then visit a site of their choosing and observe if adaptation measures have been implemented or would be feasible. A popular choice for students in San Francisco is the Embarcadero -- a historic district on the San Francisco Bay that is currently protected by a seawall that may prove insufficient to protect landmarks or commercial activity in the future.
The design of this assignment is targeted at specific learning objectives from the course:
Hypothesis Development: Students must examine data from different sources and develop a prediction about sea-level rise in a specific location as part of this assignment. This skill emphasizes the ability to develop plausible and testable potential mechanisms for how the world works, both now and in the future.
Modeling: Students must analyze modeling data and develop predictions from those results. To do this well involves understanding and describing the processes that are and are not included in the model(s) they choose.
Data Visualization: Students have to both interpret visualizations of model output as well as generate their own visualizations of quantitative information they collect from the models and site visits.
By using a combination of model output and in-person observations, students can better justify their predictions about future impacts and they get a better understanding of the importance of multiple types of evidence in scientific discovery. Since this course is required for all freshmen, the analysis here can be mostly qualitative and connected to issues that stretch beyond pure science.
Monitoring and Modeling the Earth’s Systems is an upper-level course that focuses on the fundamental processes that control the weather, air pollution, and climate change and how to study these processes using both models and observations. LBA design is very similar for upper-level courses, with a few key differences centered around the complexity of the data used and the potential rigor of analysis.
The objective of the LBA in this class is to compare different types of environmental observations and their suitability for addressing specific environmental problems. Students are tasked with designing an observing strategy for recording measurements of visibility along multiple sightlines in their city using cameras/phones. They then compare their visibility measurements to both local measures of pollution as well as observations of pollution from satellite instruments. This requires them to apply their understanding of atmospheric chemistry, dynamics, and remote sensing. An example of this is an examination of pollution evolution in a student’s hometown during Spring 2020 — in the figure below, the student has compared photographs they took with data visualization of air pollution data from the NASA MODIS satellite; this brings home what the abstracted data look like in terms of everyday life.
Comparison of local visibility to NASA satellite data and local pollution by a student in Morelia, Mexico. Photographs of the lines of sight (first two columns) over the days of observation, and remotely sensed measurements of aerosol optical depth (third column, from NASA MODIS). The locally measured NO2 concentration is inset. The satellite pixel containing Morelia is indicated by a red square in the remote sensing maps. (Figure from Minerva student Robin Ivan Villa Soto).
The specific learning objectives from the course emphasized here are:
Remote Sensing: Students have to explain how radiation moves through the atmosphere; this impacts both human-sensed visibility and measurements from satellites. They also have to analyze the remotely-sensed measurements and justify their comparison of these measurements to the in situ data.
Environmental Chemistry: Students have to identify what chemicals are responsible for visibility impacts in their environment, and which emissions may have led to the formation of these specific pollutants. This requires an understanding of atmospheric chemistry as well as an investigation of local human and biogenic activity.
Fluid Motion: As dynamics can also be a main driver of visibility, students also analyze local and regional weather conditions in an attempt to understand if their observed changes in visibility are caused by local emissions, regional emissions, or natural variability. This is critical for their recommendation of what action, if any, this location should take to ameliorate poor visibility.
Connect course concepts to real life: Have students make connections between what they experience in everyday life and more nuanced course concepts. In the visibility example, it was very valuable for the students to see (and even sometimes smell!) the influence on their hometown of chemical species that are merely some letters on a whiteboard in class.
Use state-of-the-science data that is freely available and has broad coverage: In the sea level assignment, this took the form of global models of sea-level rise and the associated topographical datasets. In the visibility, this was data from NASA satellites that have global coverage and are open-access. Most US government agencies are required to make data publicly available! (Data access from other countries can be more difficult.)
Do what you can with what you have: Even without a perfect set-up or the exact context you want, students can get a great deal of understanding from seeing what is possible, working somewhat independently. The learning outcomes in the examples above could be completed almost entirely online, or focus more heavily on specific site visits. To get the most out of site visits, utilize the technology that most students carry (e.g. phones). The final analysis can then be as quantitatively rigorous as is appropriate for your course.