MINERVA VOICES

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

by Assistant Professor Peter Zoogman

August 5, 2021

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.

Quick Facts

Name
Country
Class
Major

Social Sciences & Business

Business & Computational Sciences

Business and Social Sciences

Social Sciences and Business

Computational Sciences & Social Sciences

Computer Science & Arts and Humanities

Business and Computational Sciences

Business and Social Sciences

Natural Sciences

Arts and Humanities

Business, Social Sciences

Business & Arts and Humanities

Computational Sciences

Natural Sciences, Computer Science

Computational Sciences

Arts & Humanities

Computational Sciences, Social Sciences

Computational Sciences

Computational Sciences

Natural Sciences, Social Sciences

Social Sciences, Natural Sciences

Data Science, Statistics

Computational Sciences

Business

Computational Sciences, Data Science

Social Sciences

Natural Sciences

Business, Natural Sciences

Business, Social Sciences

Computational Sciences

Arts & Humanities, Social Sciences

Social Sciences

Computational Sciences, Natural Sciences

Natural Sciences

Computational Sciences, Social Sciences

Business, Social Sciences

Computational Sciences

Natural Sciences, Social Sciences

Social Sciences

Arts & Humanities, Social Sciences

Arts & Humanities, Social Science

Social Sciences, Business

Arts & Humanities

Computational Sciences, Social Science

Natural Sciences, Computer Science

Computational Science, Statistic Natural Sciences

Business & Social Sciences

Computational Science, Social Sciences

Social Sciences and Business

Business

Arts and Humanities

Computational Sciences

Social Sciences

Social Sciences and Computational Sciences

Social Sciences & Computational Sciences

Social Sciences & Arts and Humanities

Computational Science

Minor

Computational Science & Business

Economics

Social Sciences

Concentration

Computer Science and Artificial Intelligence & Cognition, Brain, and Behavior

Designing Societies & New Ventures

Strategic Finance & Data Science and Statistics

Brand Management and Designing Societies

Data Science & Economics

Machine Learning

Cells, Organisms, Data Science, Statistics

Arts & Literature and Historical Forces

Artificial Intelligence & Computer Science

Cells and Organisms, Mind and Emotion

Economics, Physics

Managing Operational Complexity and Strategic Finance

Global Development Studies and Brain, Cognition, and Behavior

Scalable Growth, Designing Societies

Business

Drug Discovery Research, Designing and Implementing Policies

Historical Forces, Cognition, Brain, and Behavior

Artificial Intelligence, Psychology

Designing Solutions, Data Science and Statistics

Data Science and Statistic, Theoretical Foundations of Natural Science

Strategic Finance, Politics, Government, and Society

Data Analysis, Cognition

Brand Management

Data Science and Statistics & Economics

Cognitive Science & Economics

Data Science and Statistics and Contemporary Knowledge Discovery

Internship
Higia Technologies
Project Development and Marketing Analyst Intern at VIVITA, a Mistletoe company
Business Development Intern, DoSomething.org
Business Analyst, Clean Energy Associates (CEA)

Conversation

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.