For some time, work in cognitive science has been attempting to understand learning in complex domains that involve multiple variables and processes. All of the papers in this volume deal with learning in complex domains, e.g., chemical reactions, weather phenomena, functional relationships in economics, optics, and metabolism. Frequently, the variables and processes in these domains seem to operate in ways that appear random, with nondeterministic outcomes––unless of course you are an expert in the domain. A mark of that expertise is being able to see the patterns that are meaningful in the domain and that portend effects in relatively deterministic ways. How is it that one comes to understand these complex domains? What sorts of representations do experts use to help them understand the patterns and relationships among variables? How do nonexperts in the domain gain access to these patterns and relationships? The papers in this special issue are united around the common theme of attempting to understand the impact of verbal and nonverbal representations in acquiring greater expertise in a variety of complex domains. In other words, the researchers are all concerned with how learners make sense of important concepts and relationships in complex domains based on verbal and visual input information. Efforts to understand how learners integrate and capitalize on verbal and visual information are not new (e.g., Levie; Mandl and Willows). However, the advent of ubiquitous multimedia resources stimulated renewed interest in the role of nonverbal representations, especially those that convey dynamic relationships. At first blush it seemed that multimedia resources would `solve the problems' of learning in complex domains because they could show learners how variables interacted and related to one another. But as has happened any number of times with new technologies, research undertaken to demonstrate an advantage for multimedia systems soon found that learners could fail to learn what the designers intended just as easily from multimedia as they could from unimedia (see for discussion Cognition and Kozma). That is, merely showing the learner a dynamic process did not miraculously produce understanding of that process. The papers by Mayer and Kozma discuss some of these `first generation' efforts and contrast them with their own later efforts to more precisely understand the impact of multiple forms of information representation on learning. Such `second generation' interests characterize the five other papers in this issue.
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