ArticleCBE life sciences education2017
Reflecting on Graphs: Attributes of Graph Choice and Construction Practices in Biology.
Article in CBE life sciences education, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Revealing undergraduate biology students' conception of variability and error bars within graphing.PloS one · 2026Article
- DataVersify: A Framework for Data Literacy Instruction Featuring Scientist Stories.Integrative organismal biology (Oxford, England) · 2026Article
- Unveiling Undergraduate Research: Employing Ecological Momentary Assessment to Characterize and Compare Undergraduate Research Experiences.CBE life sciences education · 2025Article
- The graph construction competency model for biology (GCCM-Bio): A framework for instruction and assessment of graph construction.Bioscience · 2025Article
- Supporting Student Competencies in Graph Reading, Interpretation, Construction, and Evaluation.CBE life sciences education · 2024Article
- The DNA Landscape: Development and Application of a New Framework for Visual Communication about DNA.CBE life sciences education · 2022Article
- Two graphs walk into a bar: Readout-based measurement reveals the Bar-Tip Limit error, a common, categorical misinterpretation of mean bar graphs.Journal of vision · 2021Article
- Paired Multiple-Choice Questions Reveal Students' Incomplete Statistical Thinking about Variation during Data Analysis.Journal of microbiology & biology education · 2021Article
- "Seeing" Data Like an Expert: An Eye-Tracking Study Using Graphical Data Representations.CBE life sciences education · 2019Article
- Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy.CBE life sciences education · 2019Article
- An Introductory Module and Experiments To Improve the Graphing Skills of Non-Science Majors.Journal of microbiology & biology education · 2019Article
- The Graph Rubric: Development of a Teaching, Learning, and Research Tool.CBE life sciences education · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Undergraduate biology education reform aims to engage students in scientific practices such as experimental design, experimentation, and data analysis and communication. Graphs are ubiquitous in the biological sciences, and creating effective graphical representations involves quantitative and disciplinary concepts and skills. Past studies document student difficulties with graphing within the contexts of classroom or national assessments without evaluating student reasoning. Operating under the metarepresentational competence framework, we conducted think-aloud interviews to reveal differences in reasoning and graph quality between undergraduate biology students, graduate students, and professors in a pen-and-paper graphing task. All professors planned and thought about data before graph construction. When reflecting on their graphs, professors and graduate students focused on the function of graphs and experimental design, while most undergraduate students relied on intuition and data provided in the task. Most undergraduate students meticulously plotted all data with scaled axes, while professors and some graduate students transformed the data, aligned the graph with the research question, and reflected on statistics and sample size. Differences in reasoning and approaches taken in graph choice and construction corroborate and extend previous findings and provide rich targets for undergraduate and graduate instruction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.