Life Sciences Seminar: Let’s move beyond the bars: How biology students interpret variability in graphs
Life Sciences Seminar
Let’s move beyond the bars: How biology students interpret variability in graphs
Dr. Lauren Stoczynski
Assistant Professor, Biological and Environmental Sciences
LeMoyne College
Abstract:
Quantitative reasoning skills are important for any student in Science, Technology, Engineering, and Math to master. Students’ ability to interpret variability within graphs falls within the data literacy skills of quantitative reasoning. While student knowledge of variability within graphing has been analyzed using histograms and dot plots, students are instead asked to construct bar graphs, line graphs, and scatterplots. Student understanding of how to incorporate variability within these charts or how to read graphs that show forms of variability shows a wide variation in understanding. This talk dives into two projects on students’ understanding of variability in graphing. The first project focuses on the analysis of over 3500 student responses to a dropdown question on how students interpret variability within a treatment and an open-ended question about what information error bars are portraying on a bar graph. Interestingly, student responses to these questions show patterns with their own constructed graphs, which provides insight into how well students may understand some aspects of variability based on the graph they make. The second project will show data from 33 interviews with students across a wide range of institutions, year, socioeconomic backgrounds, and research experience. There were different ways in which students talked about variability depending on the graph type (bar graph, quantitative scatterplot, or categorical scatterplot). One graph type, the categorical scatterplot, was novel to most students and we saw an interesting dichotomy in students' understanding of the utility these graphs provide instead of bar graphs. These studies provide detailed information on what students know and misunderstandings around variability within graphing to provide a foundation to build teaching tools in the future to address student knowledge gaps. I will also make the argument for incorporating more categorical scatterplots in your teaching and leaving the bar graphs behind.
Intended Audience:
Beginners, undergraduates, graduates. Those with interest in the topic.
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