Mon, Aug 15, 2022: 5:00 PM-6:30 PM
ESA Exhibit Hall
Background/Question/MethodsBiologists often represent data in visual forms, such as graphs, to aid in analysis and communication of data. K-16 students, the public, and even scientists regularly struggle to construct and interpret graphs, especially in topics unfamiliar to them. This is unsurprising, as graph construction is a cognitively challenging task that requires integrating knowledge about data, research design, statistical concepts, and scientific communication. Although plotting points may be relatively easy, biology students have difficulty choosing amongst graph types, making decisions about which variables or data to include, incorporating conventions from specific science subdisciplines, and designing graph elements to effectively communicate experimental results. While some studies explore the challenges of graphing in particular contexts or in specific academic contexts, none have yet communicated a comprehensive framework of the knowledge and skills people need to use to construct graphs in biology.
Results/ConclusionsWe describe the development of a conceptual model of graph construction practices in biology; this is part of a broader study on graph construction assessment based on an Evidence Centered Design (ECD) framework. One component of ECD is the student model, which details the practices to be measured in assessment of competency in a performance task. Our first draft of the GCCM described a preliminary set of knowledge and skills needed for graph construction. This work reflected our collective knowledge from our research and teaching experiences, supplemented with a scoping literature review of journals in biology education, mathematics education, and statistics education. We identified four broad areas of knowledge for constructing a graph: data selection, data exploration, graph assembly, and graph reflection. In each area, we constructed activity statements to describe the specific skills and behaviors involved when constructing graphs of biological data. We further refined the GCCM through focus groups with experts in biology instruction, biology education, and statistics and quantitative education, and a second scoping literature review. Our hope is that the GCCM will help guide other course instructors, curriculum developers, and biology education researchers when designing instructional and assessment resources for graph construction in biology.
Results/ConclusionsWe describe the development of a conceptual model of graph construction practices in biology; this is part of a broader study on graph construction assessment based on an Evidence Centered Design (ECD) framework. One component of ECD is the student model, which details the practices to be measured in assessment of competency in a performance task. Our first draft of the GCCM described a preliminary set of knowledge and skills needed for graph construction. This work reflected our collective knowledge from our research and teaching experiences, supplemented with a scoping literature review of journals in biology education, mathematics education, and statistics education. We identified four broad areas of knowledge for constructing a graph: data selection, data exploration, graph assembly, and graph reflection. In each area, we constructed activity statements to describe the specific skills and behaviors involved when constructing graphs of biological data. We further refined the GCCM through focus groups with experts in biology instruction, biology education, and statistics and quantitative education, and a second scoping literature review. Our hope is that the GCCM will help guide other course instructors, curriculum developers, and biology education researchers when designing instructional and assessment resources for graph construction in biology.