Journal of Educational Psychology · 1999 · 225 citations · 25 references
ComputingVisualizationGraph VisualizationInformation VisualizationKnowledge RepresentationGraph DrawingGraph TheoryGraph-based LearningKnowledge GraphsGraph LiteracyGraph-based Knowledge RepresentationReasoning With Graphs
Graphical displays are widely used to convey quantitative data, yet viewers often struggle to interpret them because graph comprehension relies on both simple pattern perception and more error‑prone inferential transformations. Experiments 2 and 3 identified perceptual organization—the grouping of data points into visual chunks—as the critical factor that influences how viewers interpret graphs. When quantitative information is perceptually grouped into visual chunks, viewers accurately describe trends, whereas ungrouped data leads to poor trend comprehension.
Graphical displays are frequently used to express quantitative information in texts, but viewers are sometimes unable to comprehend and learn the relevant information. According to a cognitive analysis, graph interpretation involves (a) relatively simple pattern perception and association processes in which viewers can associate graphic patterns to quantitative referents and (b) more complex and error-prone inferential processes in which viewers must mentally transform data. Experiment 1 establishes that graphs can be redesigned to improve viewers' interpretations by minimizing the inferential processes and maximizing the pattern association processes required to interpret relevant information. In Experiments 2 and 3, the researchers isolated one important factor that affects viewers' interpretation (i.e., the perceptual organization of the information in graphs). If relevant quantitative information is perceptually grouped to form visual chunks (because relevant data points are either connected in line graphs or close together in bar graphs), then viewers describe relevant trends. If relevant information is not perceptually grouped, viewers are less likely to comprehend relevant trends.
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