Understanding the complexity of the natural world and making sense of phenomena is one of the main goals of science and science education. 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. 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. Computational modeling tools, for example, provide students with the opportunity to explore big data, run simulations and investigate complex systems. Therefore, both systems thinking and modeling approaches are important for science education when investigating complex phenomena.
In recent years, much attention has been placed on the teaching and learning of systems thinking and modeling competence in science education. Both systems thinking and modeling competence are important cognitive tools for investigating and reasoning about complex phenomena. Systems thinking is the ability to recognize, describe, and model a complex phenomenon in its structure, behavior, and function as a system, including the metacognitive awareness about systems and system characteristics. Modeling competence is the ability to engage in the process of developing and using models for reasoning in science, including the metacognitive awareness about models and modeling (‘metamodeling knowledge’).
This Research Topic aims to bring together studies focusing on bridging between systems thinking and modeling competence when investigating complex phenomena, and by this to promote the understanding of the interplay between these approaches in science education and to advance the discussion within the educational research community.
We welcome original empirical and theoretical contributions from all scientific disciplines. Contributions may include (but are not limited to) the following aspects:
• Relations and interplay between systems thinking and modeling competence in science teaching and learning
• Engagement of teachers and students in systems thinking and/ or modeling when investigating complex phenomena
• Assessment of systems thinking and/ or modeling competence when investigating complex phenomena using quantitative or qualitative approaches
• Development and enactment of curricula and programs for supporting teachers and students’ systems thinking and/ or modeling competence when investigating complex systems
Understanding the complexity of the natural world and making sense of phenomena is one of the main goals of science and science education. 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. 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. Computational modeling tools, for example, provide students with the opportunity to explore big data, run simulations and investigate complex systems. Therefore, both systems thinking and modeling approaches are important for science education when investigating complex phenomena.
In recent years, much attention has been placed on the teaching and learning of systems thinking and modeling competence in science education. Both systems thinking and modeling competence are important cognitive tools for investigating and reasoning about complex phenomena. Systems thinking is the ability to recognize, describe, and model a complex phenomenon in its structure, behavior, and function as a system, including the metacognitive awareness about systems and system characteristics. Modeling competence is the ability to engage in the process of developing and using models for reasoning in science, including the metacognitive awareness about models and modeling (‘metamodeling knowledge’).
This Research Topic aims to bring together studies focusing on bridging between systems thinking and modeling competence when investigating complex phenomena, and by this to promote the understanding of the interplay between these approaches in science education and to advance the discussion within the educational research community.
We welcome original empirical and theoretical contributions from all scientific disciplines. Contributions may include (but are not limited to) the following aspects:
• Relations and interplay between systems thinking and modeling competence in science teaching and learning
• Engagement of teachers and students in systems thinking and/ or modeling when investigating complex phenomena
• Assessment of systems thinking and/ or modeling competence when investigating complex phenomena using quantitative or qualitative approaches
• Development and enactment of curricula and programs for supporting teachers and students’ systems thinking and/ or modeling competence when investigating complex systems
