Search for: All records

Abstract Biologists represent data in visual forms, such as graphs, to aid data analysis and communication. However, students struggle to construct effective graphs. Although some studies explore these difficulties, we lack a comprehensive framework of the knowledge and skills needed to construct graphs in biology. In the present article, we describe the development of the Graph Construction Competency Model for Biology (GCCM-Bio), a framework of the components and activities associated with graph construction. We identified four broad knowledge areas for graph construction in biology: data selection, data exploration, graph assembly, and graph reflection. Under each area, we identified activities undertaken when constructing graphs of biological data and refined the GCCM-Bio through focus groups with experts in biology and statistics education. We also ran a scoping literature review to verify that these activities were represented in the graphing literature. The GCCM-Bio could support instructors, curriculum developers, and researchers when designing instruction and assessment of biology graph construction. 

Abstract Graphing is an important practice for scientists and in K-16 science curricula. Graphs can be constructed using an array of software packages as well as by hand, with pen-and-paper. However, we have an incomplete understanding of how students’ graphing practice vary by graphing environment; differences could affect how best to teach and assess graphing. Here we explore the role of two graphing environments in students’ graphing practice. We studied 43 undergraduate biology students’ graphing practice using either pen-and-paper (PP) ( n  = 21 students) or a digital graphing tool GraphSmarts (GS) ( n  = 22 students). Participants’ graphs and verbal justifications were analyzed to identify features such as the variables plotted, number of graphs created, raw data versus summarized data plotted, and graph types (e.g., scatter plot, line graph, or bar graph) as well as participants’ reasoning for their graphing choices. Several aspects of participant graphs were similar regardless of graphing environment, including plotting raw vs. summarized data, graph type, and overall graph quality, while GS participants were more likely to plot the most relevant variables. In GS, participants could easily make more graphs than in PP and this may have helped some participants show latent features of their graphing practice. Those students using PP tended to focus more on ease of constructing the graph than GS. This study illuminates how the different characteristics of the graphing environment have implications for instruction and interpretation of assessments of student graphing practices.