Integration processes during frequency graph comprehension: Performance and eye movements while processing tree maps versus pie charts

Abstract

SummaryFrequency graph types differ in the way how data are translated into visual representations. We compared 2 visualization methods, a traditional circular representation (pie chart) and a rectangular representation (constant column width tree map), which were hypothesized to differ regarding the cognitive ease of visual comparison processes. Performance was evaluated in tasks involving proportion and comparison judgments under both highly controlled and more realistic circumstances. The results showed performance benefits (in terms of reduced response times or error rates) for rectangular representations. Additional eye movement analyses revealed that this benefit was mainly due to a facilitation of scanning the graph for relevant information. The results suggest that facilitating comparison processes by representing the critical variable in less complex visual dimensions (i.e., straight length with constant orientation instead of surface area or curved length) eventually enhances the efficiency of integration processes during graph comprehension.

Ranking of Risks for Existing and New Building Works

“…Court case records of falls from a height are colour-coded and counted, and stop words are used to remove irrelevant words. The results are then visualised in the form of a tree map: a two-dimensional space-filling approach where each entity is a rectangle and the area is proportional to an attribute such as node size [122]. Different colours or grey shading are used within each region for visual clarity and represent different types of entities [123].…”

Longitudinal impact of interactive science activities: Developing, implementing, and validating a graphing integration inventory

“…Given the challenges that previous researchers have shown in studying the development of graph analysis skills on their own or combined with different disciplinary content (cf., Åberg‐Bengtsson & Ottosson, 2006; Huang et al, 2016; Huestegge & Pötzsch, 2018; Mautone & Mayer, 2007; Ratwani et al, 2008; Saß et al, 2017), our study suggests that careful attention should be paid to how technology can support such learning and assessment. Indeed, as we have shown in our past experiments (Lai et al, 2016; Matuk et al, 2019) and meta‐analysis (Donnelly‐Hermosillo et al, 2020), technology enhanced learning environments play a pivotal role in the development of such capabilities to use graphs to learn disciplinary knowledge in complex ways.…”

“…To guide graph comprehension, scaffolds help learners attend to specific aspects of graphs and to organize the ideas therein (cf., Huang, Huang, & Lin, 2016; Huestegge & Pötzsch, 2018; Mautone & Mayer, 2007; Ratwani, Trafton, & Boehm‐Davis, 2008). Indeed, Mautone & Mayer's 2007 study found that combining both a priming signal to focus attention on particular graph features, and graphic organizers to help learners integrate the provided depiction, led to the greatest gains in relational and causal explanations—a primary goal for the current science standards wherein K‐12 students are asked to develop cause and effect and pattern‐specific justifications of data (NGSS Lead States, 2013).…”

Inferring task performance and confidence from displays of eye movements

“…For instance, could they infer how difficult a performer experienced the task to be? Or given that eye movements have the potential to reveal (multimodal) graph‐comprehension processes, such as information integration difficulties (Acartürk & Habel, 2012; Huestegge & Pötzsch, 2018), would observers be able to pick up on those? In this context, we do not know if task familiarity plays a role in (the accuracy of) inference making.…”