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Computational Thinking to Learn Environmental Sustainability: A Learning Progression.

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Computational thinking (CT) enhances environmental science learning by integrating interdisciplinary approaches. This framework supports students and researchers in developing sustainable environmental solutions through scaffolded learning.

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Area of Science:

  • Environmental Science, Computational Science, Education

Background:

  • Environmental problems necessitate interdisciplinary solutions, often challenging for students and the public.
  • Computational thinking (CT) is increasingly vital in progressive science curricula.
  • Both environmental science and CT are inherently interdisciplinary.

Purpose of the Study:

  • To explore the synergy between computational thinking and environmental science education.
  • To propose a learning progression that integrates CT for understanding sustainable environmental solutions.
  • To highlight the interdisciplinary nature of environmental science and CT.

Main Methods:

  • Conceptual framework development integrating CT and environmental science.
  • Emphasis on computational components (input, integration, output, feedback) within a learning progression.
  • Consideration of sustainability, research, education, and economic perspectives.

Main Results:

  • A scaffolded learning progression is proposed to enhance both computational and environmental knowledge.
  • CT components support the learning process for environmental solutions.
  • The framework facilitates differentiated student progress.

Conclusions:

  • Integrating computational thinking into environmental science education is crucial for developing sustainable solutions.
  • The proposed learning progression offers a structured approach for educators and students.
  • This interdisciplinary approach has broad implications for researchers and environmental scientists.