![]() ![]() In recent years, much attention has been placed on the teaching and learning of systems thinking and modeling competence in science education. Therefore, both systems thinking and modeling approaches are important for science education when investigating complex phenomena. Computational modeling tools, for example, provide students with the opportunity to explore big data, run simulations and investigate complex systems. Scientific models are tools that support students’ reasoning and understanding of complex systems, and students are expected to develop their modeling competence and to engage in the modeling process by constructing, testing, revising, and using models to explain and predict phenomena. When investigating complex phenomena, such as climate change or pandemic outbreaks, students are expected to engage in systems thinking by considering the boundaries of the investigated system, identifying the relevant components and their interactions, and exploring system attributes such as hierarchical organization, dynamicity, feedback loops, and emergence. Pushing students to inquire more will help them go beyond repeating things they’ve read, and go from learning facts to asking questions that reveal more about the world around them.Understanding the complexity of the natural world and making sense of phenomena is one of the main goals of science and science education. Even if students think they already know why it rains, they may discover that they actually can’t explain it. Note that an engaging phenomenon does not have to be flashy or unexpected. For example, exploring what we see in the sky will lead to different investigations depending on whether students live in an urban area or far from city lights.
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