Realism in Dynamic, Static-Sequential, and Static-Simultaneous Visualizations during Knowledge Acquisition on Locomotion Patterns

Realism in Dynamic, Static-Sequential, and Static-Simultaneous Visualizations during Knowledge Acquisition on Locomotion Patterns Birgit Imhof (b.imhof@iwm-kmrc.de) Knowledge Media Research Center, Konrad-Adenauer-Strasse 40, 72072 Tuebingen, Germany Katharina Scheiter (k.scheiter@iwm-kmrc.de) Knowledge Media Research Center, Konrad-Adenauer-Strasse 40, 72072 Tuebingen, Germany Peter Gerjets (p.gerjets@iwm-kmrc.de) Knowledge Media Research Center, Konrad-Adenauer-Strasse 40, 72072 Tuebingen, Germany Abstract In the current study the level of realism in visualizations and the role of diverse presentation formats of dynamic and different static visualizations in a complex, dynamic domain (locomotion pattern classification) were investigated. In a two-by-three design with the two independent factors realism (realistic, schematic) and presentation format (dynamic, static-sequential, static-simultaneous) one hundred-and- twenty university students were randomly assigned to six conditions. Learners had to learn how to classify fish according to their locomotion pattern. Learning outcomes were measured by two pictorial tests, assessing recognition and transfer performance. Data analyses showed an advantage of the dynamic conditions over the sequential conditions in both recognition and transfer performance. Simultaneous visualizations did not lead to different learning outcomes than either dynamic or sequential visualizations. Moreover, there was no main effect for realism or an interaction of realism with presentation format. Implications for the design of instructional materials are discussed. Keywords: Animation; static pictures; realism; visualization. Amount of realistic detail In general, visualizations have the potential to support learners’ understanding in complex and dynamic domains. However, visualizations are a broad field with various formats and functions (cf. Scheiter, Wiebe, & Holsanova, 2008). An important question is under what conditions the specific benefits of different visualization formats occur (e.g., Tversky, Bauer-Morrison, & Betrancourt, 2002). As identified by Hoffler and Leutner (2007) in their meta- analysis, an important dimension of visualization design concerns the amount of realistic details depicted. According to Rieber (1994) “realism is somehow measured against the likeness of the object the picture is supposed to represent” (p. 148). This similarity is achieved by copying the real- world referent with respect to shape, details, texture, or color. The few empirical comparisons of realistic and schematic visualizations have yielded inconsistent results so far (cf. an overview Scheiter et al., in press). Due to their high resemblance with depicted real objects realistic visualizations may facilitate their recognition (e.g., Goldstone & Son, 2005; Hoffler & Leutner, 2007). However, they also entail more irrelevant details and therefore might direct learners’ attention away from the important aspects (Dwyer, 1976). By schematizing visualizations, relevant aspects, which might be difficult to detect in reality, can be presented in an easier-to-perceive way. On the other hand, learners studying schematic visualizations might have difficulties when being confronted with real-world phenomena. Dwyer (e.g., 1976) did a lot of research on the question what amount of realistic detail depicted is useful in static visualizations. It could be shown that the relative efficiency of realistic and schematic visualizations depends on several factors. First of all, the learning goal has an influence, because what has to be learned may lead to different needs for information regarding details or more schematized aspects. Another important aspect is the presentation method, that is, the interactivity in the form of self-controlled versus system- paced learning environments. Our assumption is that in a learning task about movement pattern classification only details necessary for movement recognition should play an important role. Additionally, the presentation format of the visualizations also might have an influence. In line with the latter assumption, the meta- analysis of Hoffler and Leutner (2007) found dynamic visualizations particularly effective when they are realistic. Presentation formats of visualizations A second and probably the most common differentiation of visualizations is the one between dynamic and static ones. There are several meta-analyses concerning comparisons between animated and static displays, which led to equivocal results so far. Park and Hopkins (1993) found that dynamic visualizations were better than static ones in 15 out of 27 comparisons, whereas there were no differences between dynamic and static visualizations in the remaining 12 comparisons. Interestingly, in none of the studies static visualizations were superior to dynamic ones. In the meta-analysis of Hoffler and Leutner (2007) an overall advantage of instructional animations (i.e., dynamic visualizations) over static visualizations could be found. Tversky and colleagues (2002) questioned in their review the findings that dynamic visualizations are in general superior to static ones and identified two aspects, which may explain the advantages of dynamic visualizations. First, in a couple of studies (see Tversky et al., 2002, for an