Summary
This paper describes students' mental models of complex chemical systems in terms of noncontent‐specific complexity thinking components. It describes a study that explores students' reasoning in terms of emergent complex systems in chemistry, as it develops through learning with curricula that are either normative or complexity‐based. Complex systems are made up of many entities (or “agents”), which interact among themselves and with their environment. The interactions of numerous elements result in a higher‐order or collective behavior; the system self‐organizes in coherent global patterns. Emergence is a central concept associated with complexity and is the process by which the actions and interactions of the system's entities transform into global patterns. A wide variety of chemical phenomena can be described through an emergent perspective, especially nonlinear systems, such as oscillatory reactions, polymer systems, stirring, and mixing; flux through a catalytic pathway in metabolism, distribution of greenhouse gases in the atmosphere, and fracture toughness of a polymer.
Authors present an extensive body of literature related to people's reasoning about complex systems, points out various biases which divert people from noticing nonlinear, invisible causes and bottom‐up processes of emergence, they claim that it is common for students at all levels to inappropriately apply systems thinking to complex systems, according to which the system operates thanks to a simple mechanism of cause and effect, controlled by a single top‐down cause.
Complexity approaches to making sense of systems have received great attention in various domains of science and education. Such approaches (also known as agent‐based modeling) are based on the following idea: a system can be represented as many entities that operate according to a small set of simple rules; and have demonstrated important advantages to learning through a complexity approach to support conceptual change in several scientific domains such as chemistry, physics, biology, materials science, robotics, or in systems thinking as a more general form of reasoning. Learning through this approach focuses on entities and their actions, such as movement, interactions, and global flows, and allows students to comprehend parallel processes by which emergent phenomena form. In this manner, students can understand the mechanisms driving the systemic patterns. Finally, this approach provides a general framework that addresses the need to connect between different systemic phenomena.
In contrast to a complexity approach to learning concepts, the current widely used and established normative approach to scientific systems is usually not based on bottom‐up representations, but rather tends to focus on states rather than processes. In learning about systems, the normative approach lacks a coherent common framework, ignores the interactions between the parts of the system, and is learned in a sequential way (e.g., input‐process‐output structures), rather than providing a basis for emergent causality which operates in a parallel fashion.
Authors argue that in the chemistry discipline, reasoning involves exploring how the mechanism that causes macro‐level phenomena can be explained by the interactions between many atoms or molecules, at the micro‐level. Chemistry has relied heavily on the ability of ensemble properties that are obtained through thermodynamics and statistical mechanics to make unnecessary to consider the behavior of individual molecules. Moreover, recent research in chemistry has started including emergent perspectives for phenomena such as chemically fueled molecular motion and oscillators. This form of reasoning is aligned with the complexity science approach of relating such micro‐ and macro‐levels. Furthermore, the systemic nature of chemistry as it deals with the structure of matter, allows tracing mental models of students about complex systems by means of their explanations of phenomena associated with the structure of matter.
In this study, students' reasoning in chemistry in terms of emergent complex systems is explored for two curricula: a normative and a complexity‐based one, so that the interaction could be examined under both conditions. A quasi‐experimental pretest‐intervention‐posttest comparison group design was used to explore student's learning, complemented with interview data. The experimental group (n = 47) studied the topic of gases with a complexity‐based curriculum. A comparison group (n = 45) studied with a normative curriculum for the same duration. Students' answers to questionnaires were coded with a complexity‐based approach that included levels (distinguishing micro‐ and macro‐levels), stochastic particle behaviors, the emergence of macro‐level patterns from micro‐level behaviors, and the source of control in the system. It was found that students' reasoning about chemistry concepts in terms of complex systems falls into three distinct and coherent mental models. A sophisticated mental model included most of the above‐described complexity features, while the non-sophisticated model included none. The intermediate model is typified by distinguishing between levels, but not by stochastic and emergent behaviors. The non-sophisticated mental model was used mostly in the pretest. In the posttest, the experimental group used the intermediate and sophisticated models, while the comparison group used the non-sophisticated and intermediate models.
The current study shows that learning chemistry as a discipline leads to the development of principles for understanding complex systems. Chemistry deals with materials. To understand chemical processes, it is necessary to understand the structure of matter at the micro‐level. Going back and forth between these two levels is natural to both chemistry and reasoning about complex systems, so that learning chemistry contributes to the development of the learner's systems thinking within the domain.
The construction of systems knowledge is a “free” byproduct of learning chemistry. On the one hand, learning content through generic systems thinking can improve the understanding of chemistry. On the other hand, systems thinking can also be found among students who did not study with a complex systems approach.
Relevance for Complex Systems Knowledge
The paper deals with systems thinking, complex systems, and complexity.
It conceptualizes systems thinking as a broad term which is used to describe the concepts and thinking strategies that focus on understanding, deciphering, predicting behavior, and problem‐solving regarding systems. It emphasizes the interactions and interdependence within a system that together form a functioning whole. Systems thinking is also defined as high‐order thinking, since it requires investing considerable mental effort and self‐regulation and is characterized by uncertainty in the search for structure in apparent disorder. Complex systems are considered as a general‐purpose reasoning scheme, used in a wide range of disciplines. Many researchers address complexity as a universal structure. This means that the same heuristics and tools can be used to analyze complex systems from different domains. Among the range of cognitive capacities uniquely required for reasoning about complex systems, the following components are used in the present research: (a) identification of the system components, (b) noticing the interactions between system components, (c) distinguishing of several levels of organization, (d) transitioning between levels, (e) understanding nonlinearity, (f) considering the stochastic nature of complex phenomena, (g) discerning dynamic equilibrium in the system, and (h) understanding of control mechanisms.
