Making Sense of Graphs: Critical Factors Influencing Comprehension and Instructional Implications

Abstract

Our purpose is to bring together perspectives concerning the processing and use of statistical graphs to identify critical factors that appear to influence graph comprehension and to suggest instructional implications. After providing a synthesis of information about the nature and structure of graphs, we define graph comprehension. We consider 4 critical factors that appear to affect graph comprehension: the purposes for using graphs, task characteristics, discipline characteristics, and reader characteristics. A construct called graph sense is defined. A sequence for ordering the introduction of graphs is proposed. We conclude with a discussion of issues involved in making sense of quantitative information using graphs and ways instruction may be modified to promote such sense making.

Assessing and Understanding Line Graph Interpretations Using a Scoring Rubric of Organized Cited Factors

“…This perspective is predominantly found in the psychological and mathematics education research literature (e.g., Friel, Curcio, & Bright, 2001;Leinhardt, Zaslavsky, & Stein, 1990;Parmar & Signer, 2005) with several key pieces found in the science education literature (e.g., McKenzie & Padilla, 1986;Shah & Hoeffner, 2002). Unfortunately, most research on graphing in science in the psychological tradition has assessed only math skills and knowledge using tests and surveys, requiring no formal science skills and background knowledge to obtain high scores (e.g., McKenzie & Padilla, 1986;Tairab & Khalaf Al-Naqbi, 2004).…”

“…The focus of the psychological literature upheld how individuals' perceptions, cognitive and memory processing, and mathematical abilities affected performance on graphing tasks (e.g., Wainer, 1992). Researchers frequently noted that students lacked mathematical skills and knowledge to interpret simple graphs, let alone more complex graphs (e.g., Friel, Curcio, & Bright, 2001;Parmar & Signer, 2005). Specific problems included students' background knowledge, developmental readiness, learner characteristics, graph features, prediction tasks (e.g., interpolation and extrapolation), and iconic or literal interpretation problems.…”

“…Prior research has found that students with lower mathematical ability are less capable on graph interpretation tasks (Curcio, 1987;Gal, 1993), a finding corroborated by this study. However, these studies used multiple choice tests (e.g., Friel, Curcio, & Bright, 2001) and provided less detail about the specific mathematical methods participants used or failed to use.…”

“…The psychological literature focuses on general factors including skills, knowledge, and dispositions that inhibit students' ability to interpret graphs correctly (e.g., Berg & Smith, 1994;Friel, Curcio, & Bright, 2001;Shah & Hoeffner, 2002). The sociocultural literature, by contrast, presumes learning to graph is a socialization process that is highly contextually dependent (Marx, Blumenfeld, Krajcik, & Soloway, 1997;Roth & McGinn, 1998;Wu & Krajcik, 2006).…”

Mapping the Users’ Problem Solving Strategies in the Participatory Design of Visual Analytics Methods

“…In the context of data visualizations and visual analytics methods, three levels of graph comprehension can be differentiated [4]: (1) reading the data (i.e. extracting data, locating), (2) reading between the data (i.e.…”