Summary
In this paper, some visual or graphical tools that help students to conceptualize the complex problems or systems, their roles in supporting systems thinking, and their affordances are described. These different visual tools are helpful in conceptualizing different aspects of systems thinking.
Concept mapping is the most common method to view connected concepts in educational settings, but they are not specifically designed to help systems thinking. This paper proposes an extension of concept maps, the Systems-Oriented Concept Map Extension (SOCME), that emphasizes expanding boundaries to include additional connections, as an opportunity to enhance the application of concept mapping tools in systems modeling. This extension in the concept maps helps users explore complex, multiply connected subsystems that are often present for situations requiring systems thinking. SOCME differs from a typical concept map by explicitly embracing the identification of subsystems and their putative boundaries. The key advantage of the SOCME formulation is the ability to conceptualize greater complexity within a subsystem framework that highlights characteristics within chemistry and in other science fields in defining the overall system of interest.
The Object–Process Methodology (OPM) visualization and conceptualization process represents one more advanced graphical tool. Although OPM has wide use in systems engineering contexts, it has also been utilized in science and science education. Drawing on both visual and textual modalities, OPM is tailored for processing information through both visual and verbal channels of the brain, thus reducing cognitive load. Features of conceptual modeling with OPM include the concept that objects may have parts and attributes, represented by structural links. These features demonstrate the power of OPM in providing a formal (objective) expression of system components, dynamics, and interrelationships. A key advantage of OPM over more simplified approaches to conceptual modeling (such as concept maps and SOCME) is OPM’s representations of zooming which provide the ability to present the system as a set of interconnected diagrams. Each OPD is simple enough, without an excessive number of objects, processes, and links.
Progress in time is perhaps the most important impact that can distinguish the systems from their individual components. System dynamicists begin to understand a system by first describing its behavior as a trend over time. The Behavior Over Time Graph (BOTG) is a representation on an xy plane, with time on the x-axis and the variable of interest on the y-axis. These graphs are used to understand how a variable is changing over time highlighting the practice’s focus on dynamic behavior. The key advantage of a BOTG is that it summarizes a net dynamic behavior.
Causal Loop Diagrams (CLD) also capture aspects of the dynamic behavior of complex systems. CLD are used widely to introduce systems thinking qualitatively or to summarize the conclusions of a quantitative systems model. Key advantages of CLDs are that they clearly indicate the directionality of connections and the presence of feedback loops. CLDs are a straightforward way to introduce system dynamics, using only the concepts of the variables of interest, causal relationships, and polarity. Thus, the development or the interpretation of a CLD will only depend upon the content knowledge of the system being described. Because of their simplicity, CLDs can be used to introduce the concept of feedback loops without taking up much course time. Their qualitative nature allows for consideration of causal linkages and feedback loops without any concern about how variables could be measured or where to obtain the data.
A Stock and Flow Diagram captures multiple connections and their influence on dynamic behavior. It is used to describe the types of variables in the system as well as the connectivity associated with the variables. Stocks represent where accumulation or storage takes place in the system and they summarize where a system stands. Flows are the conduits by which the stock material moves from one place in the system to another, and thus, they determine where the system will be in the future. Stock and Flow Diagrams provide additional useful information for formulating models of complex systems. The tool scaffolds classification of variables into their function within the system, which forces a meaningful understanding of environmental processes and moves away from surface understanding or memorization.
Moreover, authors present various data which suggest that students’ understanding of chemistry will be increased if they learn how to engage with and address tasks related to complex systems.
Relevance for Complex Systems Knowledge
The paper deals with systems thinking, complex systems and complexity.
Systems thinking is considered as a holistic approach that includes the examination of complexity, a focus on the connections between different chemical concepts, and a consideration of the dynamic nature of chemical processes. Authors argue that systems thinking allows students to develop the skills necessary to understand and deal with complex, real-world problems. Therefore, systems thinking is associated with deeper conceptual learning of content, the development of higher-order thinking skills, the ability to ask better questions, more retention of content knowledge and higher levels of problem-solving skills.
Complex systems are considered non- linear systems and thus three behaviors could be observed in complex chemical systems with multiple components, the last two being emergent system-level phenomena:
• Behaviors directly associated with individual components, which are simply observed in parallel within a large system.
• Behaviors that can be observed only in the complex system but can be predicted through detailed understanding of the properties of individual components.
• Behaviors that can be observed only in the complex system and cannot be predicted, no matter how thorough our understanding of individual components is.
Complexity is defined based on the interactions among elements of a system, on the extent to which systems may self-organize, and on the emergence of properties of the whole system that are not exhibited by the individual elements.
