Expert geoscientists think in terms of systems that involve multiple processes with complex interactions. Earth system science has become increasingly important at the professional level, and an understanding of systems is a key learning goal at all levels of the earth science curriculum. In this paper, research in the cognitive and learning sciences is brought to bear on the question of how students learn systems thinking and on the challenges of developing effective instructional programs. The research suggests that learning systems concepts is difficult and that it involves extended learning progressions, requiring structured curricular integration across levels of K–16 instruction. Following a discussion of these challenges, current instructional innovations are outlined, and an agenda for needed research on learning and teaching systems thinking is proposed.

Most environmental issues involve near-surface earth systems that often exhibit complex spatial characteristics and dynamics. Conceptual understanding of complex earth systems influences the development of effective policy and management strategies. Students, like all people, organize knowledge and reason about environmental issues through manipulation of mental models. A mental model is a relatively enduring and accessible, but limited, cognitive representation of an external natural phenomenon. The nature of near-surface earth systems may present major cognitive difficulties to students in their development of authentic, accurate mental models of earth systems. These cognitive difficulties include conceptualization of natural earth environments as systems, understanding the complex characteristics of these systems, and the application of conceptual models of complex earth systems to support environmental problem solving. This paper reviews the nature of near-surface earth systems that exhibit complex behavior and the cognitive and epistemological issues that students may experience in understanding these systems. Finally, I suggest that the same learning issues that students face in the classroom also are encountered by experts, policy managers, and stakeholders while they develop solutions to environmental problems. Therefore, educational research of student learning in earth science may not only support the development of improved pedagogical practices and learning environments, but this research may also support improved environmental decision making.

Earth system science represents an important new way of looking at our planet, but it is difficult to help students learn, in an experiential mode, about the complex dynamics of earth systems. Here, I describe how the computer program STELLA can be used to construct and then experiment with a variety models to illustrate some important concepts of systems dynamics. A very simple model of a bath tub with a faucet and a drain serves to illustrate a wide range of systems concepts including residence time, response time, lag times, and feedback mechanisms. Variations on the bath tub model provide examples that illustrate the problem of model complexity versus simplicity. A more complicated model of the global carbon cycle is used to demonstrate one means of model validation, testing the model against the historical record of CO 2 buildup in the atmosphere, using as input the historical record of fossil fuel emissions and land use changes.

Systems analysis is the quantitative study of empirical and functional relations of a complex of interacting entities that comprise a specified system. Complex ecosystems can be properly understood only by utilizing the techniques and rationale of systems analysis. The multidisciplinary study of the Lake George, New York, ecosystem is given as an example of a comprehensive study in which geology is an integral part. Systems analysis involves the formulations of models, which are simplifications of the “real world” ; the Lake George study has used a progression of such models, representing different levels of abstraction. Conceptual models have been used to represent the more important components of the ecosystem and the principal transfer paths among these components; a comprehensive conceptual model has proved useful as a graphical representation of the interrelations among individual research projects. Data from the Lake George sampling program and from concomitant laboratory studies have led to the development of mathematical models of biologic, physical, and chemical processes. These have been expressed in computer logic and used in simulation studies; they have been evaluated by comparing predicted values with data collected in the field. Those models, such as the phytoplankton and hydrologic models, that yield realistic results and seem to be valid representations of actual processes are being analyzed in detail to determine how sensitive they are to various ecosystem changes, including man-induced effects. By combining functional models into a comprehensive ecosystem model, we can begin to forecast future consequences of management decisions and can apply the knowledge gained from all the associated studies in developing guidelines for environmental quality.

Systems analysis provides the procedural framework for the formulation, design, and analysis of symbolic models of systems. Usually the structural (organizational) aspects of a system are investigated by descriptive, analogue, or analytic modeling. Simulation modeling, which involves the merger of various structural models with a simulator and a simulation model, is usually employed to investigate the functional (operational) aspects of a system. The sequence of procedures used in systems analysis forms a detailed statement of the execution of the scientific method. On using these procedures the model builder is required to examine his subject from new analytical vantage points. Operational research techniques such as linear programming, game theory, congestion analysis, and list processing can provide the model builder with various types of analytical support. In systems analysis the model builder also requires a high level of tactical support in such areas as data quality control, planning, allocation of funds, equipment, and time, scheduling, computer services, numerical analysis, and experimental design. Suitable methodology for both analytical and tactical support is generally available to most systems investigators from institutional computing centers. As we continue to ask increasingly more complex questions concerning the structure and function of our modern and ancient environmental systems, we will find that the context of systems analysis is especially appropriate.