CommentaryDynamic visualizations and learning: getting to the difficult questions
Introduction
Recent advances in information technology and graphics have made it possible to produce powerful visualizations of scientific phenomena and more abstract information (Card, Mackinlay, & Schneiderman, 1999; Spence, 2001). With these developments, we can easily present diagrams as static or animated, and we can present images as still photographs or video clips. Furthermore, with developments in hypermedia systems and interactive interfaces, we can create documents that allow students to browse the information in any order, rather than being constrained by the linear ordering of information in printed books. Computer microworlds, in which students can make predictions and evaluate hypotheses by interacting with powerful simulations of scientific phenomena, are becoming more prevalent in educational programs.
There has been much excitement about the potential of these new dynamic visualizations for improving education and training. This is perhaps not surprising, because the same claims have been made about every new technology developed in the last century. For example, when the motion picture, radio, and television were invented, each was hailed as the answer to solving educational problems (Cuban, 1986, Mayer, 1999). The following quote from Thomas Edison about the development of the motion picture could just as likely be made by many proponents of dynamic visualization today.
I believe that the motion picture is destined to revolutionize our educational system and that in a few years it will supplant largely, if not entirely, the use of textbooks (cited in Cuban, 1986, p: 9).
It makes intuitive sense that there should be an advantage of dynamic over static media, especially for teaching students about dynamic phenomena. As Lowe (1999) has pointed out, dynamic media allow us to show processes explicitly such that there is an isomorphism between the process being represented in a dynamic medium and the medium being used to represent it. However, the first phase of research examining differences between dynamic and static displays failed to show a clear advantage for dynamic displays. Although some studies found positive effects of animated displays, for example, on student motivation and in implicit learning (Rieber, 1991), there have been few studies that have shown an advantage of static over animated displays in conceptual learning. Tversky, Morrison, and Betrancourt (2002) reviewed over 20 studies that compared learning from static and animated graphics. In the majority of these studies, there was no advantage of animations over static graphics. A small number of studies showed such an advantage, but in these studies, more information was presented in the animated graphics than in the static graphics, i.e., they were not informationally equivalent (cf. Larkin & Simon, 1987).
It is clear from this first phase of research on static versus dynamic displays that there is not a simple advantage of dynamic over static media. Just as in the case of the motion picture, radio, and TV (Cuban, 1986, Mayer, 1999) we have learned that improving education is not a simple matter of adopting a new technology. Yet most educators and researchers in this field continue to believe that dynamic media have enormous potential for instruction and training. This leads us to the much more interesting and challenging issues of understanding what conditions must be in place for dynamic visualizations to be effective in learning and how educational practice must be changed to capitalize on these new media. In this second phase of research on dynamic media, we have to reject the assumption that dynamic media are always better, in order to understand how to best use these new media in the educational process.
The papers in this special issue are clearly in the second phase of research on dynamic visualizations in education. Rather than assuming that dynamic visualizations are always better than static representations, these papers acknowledge that the effectiveness of dynamic visualization is not a simple issue and begin to address some of the complex factors that must be taken into account in evaluating their effectiveness. In my commentary I will first summarize some of the important factors that have been identified by the authors of this special issue, and then raise some other issues that also need to be addressed, but are perhaps receiving less emphasis in the literature at present.
Section snippets
Types of dynamic displays
One important insight represented in the papers in this special issue is that we need to go beyond making a simple distinction between static and dynamic displays, because there are in fact many different types of dynamic displays. Perhaps the prototypical example of a dynamic display is an animation of some visible phenomenon, such as a machine in motion. These displays are often characterized as very realistic, because they portray a visible sequence of events in real time, or at least
Cognitive demands of learning from dynamic displays
Another important insight that is shared by the authors in this volume is an appreciation of the fact that dynamic displays are not always easy to understand, and impose demands on human cognition that are not present with static displays. These demands are somewhat different for non-interactive and interactive-dynamic displays, so I will first discuss the difficulties that have been identified with non-interactive-dynamic displays and later discuss how these are affected by making displays
Other concerns
Comparing and contrasting the papers in this volume also raises some other issues that perhaps need more attention in the literature on learning from dynamic displays. The first issue is the type of material to be learned. Papers in this volume have examined learning of learning about meteorology (Lowe), knot tying (Schwann & Riempp), population dynamics (Ainsworth & Van Labeke), lakes as ecosystems (Zahn et al.), basic Newtonian physics (Rieber et al.), how mechanical systems work (Bodemer et
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