Summary
This paper discusses the understanding and reasoning about complex systems. Authors argue that learning about complex systems is hard because (a) complex systems integrate a vast range of subject matter; (b) there are many systems concepts that persons have not directly experienced or they violate human intuition; (c) learning about complex systems requires cognitive, metacognitive, and social resources. Authors also highlight several issues that the learning sciences should better understand if they are going to support students’ learning about complex systems.
Authors point out that although learning about complex systems is foundational and offersr the potential to integrate across many disciplines. (e.g. understanding aquatic systems can integrate biology, chemistry, physical science, and social science), most people consider complex systems as collections of parts with little understanding of how the system works.
They believe that studying the use of metacognitive processes in understanding complex systems is critical to understanding how learning (students’ emerging understanding, the aspects of their learning context, and also their conceptual growth) about these systems can be facilitated, as students engage in multiple learning activities.
Moreover, authors argue that besides learning mechanisms, reasoning skills may concurrently be used to support learning about complex systems. They also consider that students need generic knowledge about the nature of models, domain knowledge, general skills (cognitive and metacognitive skills and motivational strategies), and scientific reasoning skills (e.g. hypothesis generation, experimentation, data collection, data analysis, and communication of results) to understand, simulate, and model complex systems.
As the study of complex systems comes from several fields with different underlying assumptions, goals, methodologies, and analytical methods, the authors argue that there is an intense need to amalgamate diverging theoretical frameworks to analyze the complexities in learning about complex systems. Some current theoretical frameworks include complexity science, structure-behavior-function theory, conceptual change, knowledge integration, and self-regulated learning. They also point out the need to collect multiple data sources and use mixed methods to triangulate between qualitative data and quantitative data. Authors claim that different types of data are necessary to have a full understanding of the complexities related to learning about complex systems. Another important issue is related to the strengths and weaknesses of various types of studies being conducted.
Relevance for Complex Systems Knowledge
The paper deals with complex systems which are considered as having a hierarchical nature and multiple interacting levels. The aggregate nature of a complex system is not predictable from isolated components but occurs through the interaction of multiple components.
