Concept Mapping the Earth's Radiative Balance

-- Elke Zeller --

At the start of the semester, I asked the students in my ENVS 270 course to take the Climate Change Concept Inventory (CCCI) questionnaire. This test was designed to assess students' understanding of climate concepts across seven conceptual areas (Jarrett et al., 2012), and it revealed that 64% of students struggled with questions in the “Interactions between greenhouse (GH) gases and electromagnetic radiation” category. Therefore, I wanted to use an active learning activity to help the student with this difficult subject.

While one way to do this is to calculate Earth's radiative budget, I noticed that some students in the class have limited math literacy—when calculating the surface area and volume of a cube, 25% of the students made errors—and decided against this. Concept mapping is an active learning activity with significant potential in Earth system science, as it can foster a holistic perspective (Vasconcelos et al. 2019) and improve students' performance (Freeman et al. 2014). Therefore, I wanted to test this out in my class and understand if this would help the students understanding of the Earth's radiative budget.

Made with gemini AI. Prompt: “Make an image depicting my TAR question: Does the use of a concept mapping improve the student’s understanding of Earth’s radiative balance?”

Does the Use of an Unstructured Concept Mapping Activate Improve the Students’ Understanding of the Earth's Radiative Balance?

Approach /Methods

Based on the initial CCCI, students struggled most with concepts related to greenhouse gases and radiation. Therefore, my main learning goal was for students to better understand how greenhouse gases interact with electromagnetic radiation and how this relates to Earth’s radiative budget. Instead of using a calculation-heavy approach, I used an active learning activity based on concept mapping. I was inspired by an online exercise from the National Oceanic and Atmospheric Administration (NOAA website), but I made the activity more open-ended and unstructured. Each student made a concept map to connect concepts such as solar radiation, infrared radiation, greenhouse gases, and the atmosphere. To assess learning, I used pre- and post-test data from the CCCI and compared students' scores. In addition, I qualitatively analyzed the concept maps for complexity, connections, and misconceptions related to radiation and greenhouse gases.

The students test scores for each of the questions, you can see that the scores for questions in the Radiation and Greenhouse gasses category are much lower than other questions. Both are key concepts for understanding the Earth's radiative balance.

Project timeline

Results

These concept maps were some of my favorites from the activity because they gave me insight not only into what students understood, but also into how they think and organize information. Some students created very linear maps, while others built highly connected systems with many feedback and cross-links. Looking through the maps almost felt like a small porthole into someone’s brain, showing how they mentally connect climate concepts and make sense of complex systems. Even when misconceptions were present, the maps helped reveal where students’ thinking was breaking down, which would have been much harder to see in a traditional quiz or short-answer response.

Overall, most students showed at least some improvement in connecting greenhouse gases, radiation, and Earth’s energy balance when taking the final CCCI. The strongest maps came from students who are also very engaged in the class and seem to have a moderate understanding of the climate system before the activity. Some students struggled a lot with the exercise and did not produce concept maps that really linked ideas. This helped me answer my TAR question, since the activity showed that concept mapping can support students’ understanding of Earth’s radiative budget and also gives insight into student thinking in a way that traditional assessments do not. At the same time, the results showed that completely open-ended concept mapping may not provide sufficient structure for all learners, especially for students unfamiliar with the technique and the concepts being mapped.

Reflections & Lessons Learned

If I do this again, I will make the exercise more closely aligned with the original NOAA activity I got the idea from. I pushed the students to "think for themselves," but some had difficulty producing the maps and just wrote down words to get points. Moving forward, I would make the following changes:

I would use a structured or semi-structured map (like the NOAA version). Giving the students an outline lets them focus on the science rather than get lost in the formatting.
25 minutes felt rushed for many. Deep mapping requires more breathing room, and I would give them more time.
When designing a full course I'll introduce concept mapping early on in the semester so the students can get familiar with the technique and use it regularly

About the Author

Elke is a paleoclimate scientist interested in interactions between past climates, flora, and fauna. Her current research focuses on understanding the interactions between global vegetation distributions and the global climate system, with a particular emphasis on warm periods that can serve as analogs for future climates. Research Interests include Paleoclimatology, Paleoecology, and Human Evolution.

AI statement

I wanted to test AI to help phrase some of my writing in a way that was more controlled. To do this I made an AI agent in gemini with the following instructions: “You are a writing coach that analyzes and explains writing structures and gives objective feedback. You do not write your own logic. You can only take logic that is given to you and rewrite it for clarity. You also help find synonyms (for words and phrases), when you do this give a minimum of 3 options with an explanation. Give context for words in English and translate up to 3 important words/ concepts to Dutch. You can read and write text in latex.”

The most common prompts were: "Translate and give synonym for: [word or phrase]”, “Analyse this text for clarity: [text]”, and “What would this be using Bloom’s taxonomy: [learning goal]”

I found the synonyms and Bloom’s prompts the most useful. AI was able to give me clarification about specific words and in what setting they would be used. When I was not sure about the specific definition it gave me I would still look this up online however I did not find any errors. When analysing text AI was more likely to rewrite in a way that lost the initial meaning of the sentence or paragraph. In general, I feel it can handle smaller sets of sentences much better than full paragraphs of writing. The most advantage for me is translation and that it gives me several options.

References

Freeman, Scott, et al. "Active learning increases student performance in science, engineering, and mathematics." Proceedings of the national academy of sciences 111.23 (2014): 8410-8415.

Jarrett, Lorna Elaine, Brian Ferry, and George Takacs. "Development and validation of a concept inventory for introductory-level climate change science." International Journal of Innovation in Science and Mathematics Education20.2 (2012).

NOAA: https://gml.noaa.gov/education/lesson_plans/Concept Mapping- Earth's Heat Balance.pdf accessed: February 4, 2025

Vasconcelos, Clara, et al. "Improved concept map-based teaching to promote a holistic earth system view." Geosciences 10.1 (2019): 8.

Page updated

Report abuse