Towards A Science Of Complex Experiences

Towards a Science of Complex Experiences

Science | Environment | Health

This book provides a fascinating insight into the on-going process of self- reflection in the Science|Environment|Health (S|E|H) community. The basic vision of a new S|E|H pedagogy is to establish a transdisciplinary dialogue between the three educational fields of science education, environmental education, and health education. This approach finds growing interest among science educators. Since 2014, the ESERA special interest group S|E|H has united both experienced and junior researchers all over Europe in a burgeoning research community. This book presents a selection of results of these vibrant activities. Systems theory has turned out to be a stimulating theoretical framework for S|E|H. The limits of predictability in complex living systems result in structural uncertainty for decision-making, and they ask for emphasising and rethinking the role of pedagogical concepts like informed citizenship and scientific literacy. They challenge crude scientific determinism in environmental and health education, which all too often ends up with students’ eco- and health depression. Instead, S|E|H conceives coping with uncertainty in terms of an interplay between cognitive and affective factors. The horizon of the future remains always open. Hope must never die in a new S|E|H pedagogy. Chapter 3 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Investigating Complex Phenomena: Bridging between Systems Thinking and Modeling in Science Education

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.