The Scientific Method: The World as Your Lab—Environmental Sciences

From Textbook to Terrain

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:

• An emphasis on real-world, hands-on, project-based learning. Students should have opportunities to make connections between the course content and the world around them.
• An emphasis on defining the learning outcomes. Assignments at Minerva are always designed around granular learning outcomes that relate directly to the learning outcomes of the course.
• An emphasis on the analysis. Getting the “right answers” isn’t the goal of these assignments. Instead, the focus is on the “right explanations.” Students are prompted to justify their approaches and interpret their results in full.
• Adaptability. Even though these are “location-based” assignments, they are not tied to a particular location — they can be done from anywhere! Further, depending on what is asked of students in the analysis, these assignments can be delivered at various levels of complexity and sophistication, from introductory courses to senior concentration classes.

Assignment 1: Sea Level Rise

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.

Assignment 2: Visibility and Pollution

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.