Abstract

The need for developing information literacy, higher order thinking and problem-solving skills for an information society is well-documented (e.g., Reich, 1991). This paper argues that systems thinking and system dynamics can play a promising role in promoting conceptual connections and developing integrative reasoning across academic disciplines, which can be seen as key skills for promoting effective thinking and problem-solving skills required of lifelong learners of the twenty-first century. This can be fostered by adopting the cognitive tool perspective as a comprehensive framework for research and learning with system dynamics modeling. An extended example from relevant field research on learning system dynamics modeling is discussed.

INTRODUCTION

System dynamics researchers have long advocated the use of learner-centered models of active learning (e.g., Forrester, 1992; Richmond, 1993, 1994). Longitudinal field studies (Zaraza & Fisher, 1996; Mandinach & Cline, 1994) have applied instructional approaches to help learners actively construct models of their understanding of dynamic phenomena. However, although system dynamics practitioners advocate principles of active learning and knowledge construction in many contexts, there is not currently a comprehensive framework for conducting research and practice on learning with system dynamics modeling.

For the purpose of this paper, a cognitive tool is defined as a type of software that is used in ways that enable, extend, or reorganize the processing capabilities of normal human cognition. Using the cognitive tool perspective, it is possible to situate system dynamics as part of the larger issue of promoting learning with technology, and as part of the debate on learning and media research in educational technology (Jonassen, 1996).

Computers as Cognitive Tools

Tools have effected human development from the earliest use of artifacts to the use of information technologies in work and learning. However, many tools have been used simply to "amplify" human aptitudes, which, as term suggests, describes an increase in intensity with no real qualitative change, such as a signal traveling over a longer distance in a computer network. By contrast, cognitive tools can be designed to go "beyond amplification," to function as "intelligent technologies" by acting as "reorganizer[s] of mental functioning." (Pea, 1985).

Research related to the effects of tools on cognitive functioning originated long before computer technologies in the study of written language (Olson, 1985) and mathematical notation (For a summary of the historical perspectives of cognitive technologies, see Pea, 1985). However, unlike previous approaches to studying literacy or other non-computer cognitive tools in natural contexts, Salomon, Perkins, & Globerson suggest that we study how cognitive tools can be made to improve or reorganize human cognitive functioning, asking: "Can a cognitive effect of technology be 'engineered' by designing the technology, the activity, and the setting to foster mindful abstraction of thinking skills and strategies?" (1991, p.7).

Intellectual Partnerships

Specifically, Salomon, Perkins, & Globerson (1991) distinguish between two types of effects in relation to technology. The first type is called effects with technology, which emphasizes the combined performance of the human-computer system view of intellectual partnership. The second type refers to the effects of technology, which emphasizes the "cognitive residue" or "analytic" approach in which an intellectual skill or effect of interacting with the tool can be measured in isolation, especially when learners attempt to apply cognitive skills in novel contexts. While mindful engagement of the human partner is required for both types of effect, engagement alone it is not sufficient for the effects of cognitive tools, which also require a learner to mindfully abstract a generalizable cognitive skill from interaction with the tool.

System Dynamics As A Cognitive Tool

Salomon (1988) identifies two general types of functions of cognitive tools: 1) enabling, and 2) guidance and modeling functions. When a cognitive tool has been internalized, this refers specifically to the guidance and modeling functions; the learner can still be interacting with the enabling function of the tool, which performs the lower level or repetitive processing aspect of a task, such as using system dynamics simulation software to process complex model equations.

Research on Learning with Cognitive Tools

Jonassen & Reeves (1996) summarize the research on learning with cognitive tools in several categories. Jonassen (1996) categorizes STELLA as an exemplar of a cognitive tool in the category of microworlds, which he defines as "constrained problem spaces that resemble existing problems in the real world" (p. 237). As an approach to learning that promotes internalization of the guidance and modeling functions of a cognitive tool, system dynamics seems to further the ability of a microworld to "integrate knowledge, skills, and attitudes through problem-solving activity" (Jonassen, 1996, p. 251).

Conditions for tool internalization: A System Dynamics Example

The concept of the cognitive tool builds on the Vygotskian perspective of a zone of proximal development in which a mental skill or symbolic process is internalized in two stages: first, through interaction with more capable social or cognitive technological peers in the environment, and then as internalized psychological tools or formalized conceptual structures. Salomon (1988) builds on these foundations and further outlines five conditions for the internalization of cognitive tools. These conditions are identified in the following example, in which Zaraza & Fisher (1996) describe their experiences training teachers to model as part of the longitudinal CC-STADUS (Cross Curricular Systems Thinking and Dynamics Using STELLA) project. Based on their experiences over at least three years, they advocate an approach to teaching modeling that begins with simple, single-discipline models that the teachers can develop in their own disciplines before attempting to learn from or how to construct the complex, multi-disciplinary models that are the long-term goal of the project.

The first condition for tool internalization is that "for the computer-based strategies and modes of representation to become cognitive they must be such that they could potentially be carried out in one's mind." (p. 6). While this would hold true of the simple models Zaraza and Fisher call "single-discipline;" it would not be seem to hold for a complex, or multi-disciplinary model whose output could hardly be expected to be carried out in a mental visualization.

The second condition for internalization of a cognitive tool requires that the process be generalizable, or have the potential to be applied to new contexts. System dynamics models and systems thinking principles meet this requirement of applicability to multiple contexts and domains.

Next, "the 'intelligence' encountered through the partnership with the tool should serve a novel and useful function" from the viewpoint of the learner (p. 7). With learners new to modeling, this condition would be also be met by the simple, single-domain models.

Fourth, the tool's processing must be "explicit" so that its operations and processes are visible to the learner. In this condition, the need for explicit documentation of both the content and construction of a model emphasized by Zaraza & Fisher (1996) supports this requirement for internalization. Much of the success of internalizing systems principles will rely on the use of instructional strategies and methods for making both the cognitive processes of an instructor, as well as underlying model processes, visible to the learner throughout the learning process.

The final condition for internalization relates to how the learner interacts with the tool. Essentially, for "far" transfer to take place to dissimilar contexts "entails processes of mindful abstraction, that is, deliberate, effortful and metacognitively guided decontextualization of a principle, main idea, strategy, concept or rule" (Salomon, 1988, p. 8). This condition can be met by a combination of practice and skillful facilitation or scaffolding in the teaching process, as learners work with system dynamics models from single domains. It seems to follow that using an initial single-domain with which a learner is comfortable for their first modeling efforts, it is probably easier for the learner to focus on the relevant aspects of systems and modeling principles against a background of known information, possibly making the principles easier to abstract and decontextualize for use in subsequent, more complex, cross-curricular modeling efforts.

Conclusions

The practice and writings of key system dynamics researchers and educators exemplifies learning strategies consistent with a cognitive tool approach. However, there is a need for a comprehensive and explicit framework for formalizing an approach to the practice and research related to learning with system dynamics modeling. I believe that the cognitive tool approach provides an "activist research paradigm" as advocated by Pea (1985) by providing the ability to simultaneously study and change human cognitive functioning. It is my hope that by articulating and extending the cognitive tool approach in relation to system dynamics, researchers and practitioners will find value in this perspective.

References

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