Developing a flexible learning activity on biodiversity and spatial scale concepts using open-access vegetation...

Area of Science:

• Ecology
• Spatial Sciences
• Environmental Science Education

Background:

• Biodiversity is a fundamental concept for ecology students, requiring understanding of patterns across spatial scales and their relation to ecological processes.
• Developing data science skills and understanding macroscale influences on local ecosystems are crucial for modern ecologists.
• Existing curricula often lack opportunities for students to work with large ecological datasets.

Purpose of the Study:

• To design and evaluate a flexible learning activity for teaching macroscale biodiversity concepts.
• To improve undergraduate students' understanding of macroscale ecology and biodiversity at multiple spatial scales.
• To enhance students' familiarity with analyzing large spatio-ecological datasets and relevant software.

Main Methods:

• An interdisciplinary team developed a learning activity using National Ecological Observatory Network (NEON) data.
• The activity was piloted in six courses with 109 undergraduate students across various ecology and spatial science disciplines.
• A pre/post-assessment framework evaluated changes in student understanding and data analysis skills.

Main Results:

• The learning activity significantly improved student comprehension of biological diversity, biodiversity metrics, and spatial patterns.
• Students demonstrated increased familiarity with quantitative analysis techniques and large ecological datasets.
• Faculty feedback provided insights for refining the activity and implementation strategies.

Conclusions:

• The learning activity effectively enhanced student understanding of macroscale ecology and biodiversity.
• It successfully built essential data science skills for working with big data in ecological research.
• The activity serves as a valuable model for integrating macroscale concepts and big data into undergraduate science education.