The Graph Choice Chart: A Tool to Help Students Turn Data into Evidence

Data literacy is complex. When students investigate the natural world, they must be able to gather data, organize it in tables and spreadsheets, analyze it in context, and describe and interpret it--usually as to support a scientific argument (Jimenez-Aleixandre, Bugallo Rodriguez, and Duschl 2000; Kilpatrick 1985; Schoenfeld 1992). These skills are echoed in the science and engineering practices of the Next Generation Science Standards: Because raw data as such have little meaning, a major practice of scientists is to organize and interpret data through tabulating, graphing, or statistical analysis. Such analysis can bring out the meaning of data--and their relevance--so that they may be used as evidence (NGSS Lead States 2013, Appendix F, p. 9). But before students can identify patterns in data or use it as evidence, they must be able to graph it. In 2007, we began working with scientists and teachers in Maine to explore students' data literacy skills. We found that when students began to organize, graph, and interpret their data, many were unsure about what kind of graph to make. Most made bar graphs, regardless of their research question. They also treated the graph like an end product in itself, instead of using it to see patterns and make arguments. Although students had the mechanical skills to generate graphs, they did not logically decide what kind of graph would best suit their particular research question. Consequently, we developed the Graph Choice Chart (GCC), a tool to help students choose the appropriate graph. This article describes the GCC and gives examples of how our partner teachers used it in their classrooms. Background Early in our project, we surveyed more than 200 high school students and asked them to draw graphs to illustrate simple comparisons between two groups and the relationships between two variables (Figure 1). In the first part, we asked students to draw a graph to help them determine whether the type of stream bottom--rocky or muddy--affected dragonfly abundance. The second part asked them to graphically show the correlation between fish size and the concentration of mercury. In the case of the dragonflies, only 23% of students made a graph--a frequency plot or a bar graph of group averages--that visually compared dragonfly abundance in the two habitats. In the fish example, only 58% of students correctly made a scatterplot to display the correlation between mercury concentration and fish weight. Based on our follow-up interviews with students, we concluded that, for many, the question What kind of graph should I use? did not occur to them. Thus, the GCC we created takes the form of a decision tree, where a choice at each node, or decision point, leads to other choices and finally, to an outcome, or type of graph, for each branch (Figure 2, p. 40). This helps students make an informed decision about what kind of graph to use. Focusing on the research question The starting point for the GCC--and a requirement for it to work--is a precisely worded research question. Writing the question forces students to be clear and consistent--and to stick with one question--as they move through their analysis. Changing the wording of a question midstream can produce a different kind of graph and cause confusion. In the classroom, our partner teachers find that much of this confusion can be resolved by having students reconsider their research questions. The process of fitting a graph to a question encourages them to think more deeply about their data as they develop a claim or argument. Classroom example For example, one partner teacher works with her students to locate bird nests in a forest and measure the distance from each nest site to the nearby lakeshore. After looking at their data table and the bar graphs some draw, students conclude that birds build nests closer to the water because there may be more predators in the deep woods, and thus it is safer by the water--a conclusion that takes leave of the data and ventures into speculation. …

Topic: Genetics, Bioinformatics, and Biomedical Research
Subfield: Molecular Biology
Field: Biochemistry, Genetics and Molecular Biology
Domain: Life Sciences