Factors Affecting the Development of Graphing Competence 

Factors Affecting the Development of Graphing Competence 

The development of one’s graphing competence for communication and decision-making purposes is influenced by multiple, interacting factors. Like any learned practice, students’ prior knowledge of and experience with graphing affects their ability to successfully read, interpret, and transform graph data. Common challenges demonstrated in graphing across age groups and levels of expertise as well as potential barriers and opportunities to graph learning for all students should be considered for guiding the design and implementation of effective graph instruction.        

Common Challenges Students Encounter in Graphing

  • Learners of all ages – from elementary students to practicing scientists – face challenges in making sense of and using graph data. Despite earlier exposure to graphing, instructors should not assume that college students enter the classroom highly proficient in graphing practice and that attention should be directed to common misconceptions and difficulties that their students may hold.    
  • In graph interpretation, college students tend to have difficulties with:  
    • Mathematical functions (e.g., slope) 
    • Interpreting complex and/or multivariable graphs 
    • Identifying (global) trends from discrete (local) data points 
    • Data variability (e.g., error bars, dispersion)  
    • Understanding general graph layout and design features 
    • Making connections to the research question or relevant concepts  
  • Common graph construction difficulties include:  
    • The appropriate selection of graph type 
    • Translating data in numeric form to plotted points that represent variable relationships 
    • Proper graph layout 
    • Use of informative design features (e.g.,scale).   
  • Practicing scientists and students “see” graph data differently. Disparities between how experts and novices approach graph reading, interpretation, and construction activities reflect differences in one’s knowledge of graph content and data representation practices in the field.  
    • Being mindful that others may not approach graphing tasks as they do, instructors should consider the teaching and learning of graphing along a developmental trajectory as students transition from novice- to expert-like behavior over time.  
    • To assist the development of more expert like skills, students should be guided through practice how to systematically direct their attention toward salient information when reading a new graph and to think “between” and “beyond” data in graph construction and interpretation by considering the purpose and source of the graph data as well as relevant conceptual and statistical aspects.    
  • Reflective practices that incorporate graph choice and graph knowledge at every step in graph creation, reading, and interpretation need to be modeled by the instructor and encouraged in the classroom.  
    • Students need practice and guidance in reflecting on relevant variables, the best form of the data to plot, understanding the affordances and limitations of graph types, and thinking about the data in the context of the relevant biology and how the data were collected. (see the Design principles link)

Glazer, N. (2011). Challenges with graph interpretation: A review of the literature. Studies in science education, 47(2), 183-210. This review examines the importance of graph interpretation competence in the sciences and challenges that science learners encounter when interacting with and interpreting graph data. Drawing on empirical studies conducted with students at the elementary, secondary, and postsecondary levels, the author describes how graph interpretation is affected by several interacting factors: the display characteristics of a graph (e.g., format, type, and visual features like scale, aspect ratios, etc.) and one’s prior knowledge of graph content and graphing practices. The author also provides information about misconceptions and difficulties in graph interpretation that learners of varying ages hold, including: confusing slope and height; confusing an interval and a point; conceiving a graph as constructed of discrete points; developing an understanding of graph data presented in class; perceiving that graphs must pass a (0,0) point; interpreting complex line graphs that require series comparison; emphasis on x-y trends leading to incomplete interpretation; and difficulties with interpretation due to inappropriate graph format or visual features (e.g., color, scale, etc.). The effect of instructor knowledge is also reviewed as a potential obstacle to the development of students’ graphing competence due to the often-limited expertise and lack of awareness among teachers to the difficulties using graphs in graphing that their students may have.  Evidence of the importance of explicit instruction offered frequently and over time at increasingly higher proficiency levels as well as the need for pen-and-paper practice in graph construction for students to learn the conventions and technicalities of graphing is highlighted. Finally, the author concludes by identifying the need for further studies on the development of graph competence, raising teachers’ awareness of student difficulties with graphing, and the impact of explicit graph instruction and guidance in science curriculum to help students acquire graphing competences. 

Angra, A., & Gardner, S. M. (2017). Reflecting on graphs: Attributes of graph choice and construction practices in biology. CBE—Life Sciences Education, 16(3), ar53. In this study, the authors used the meta-representational competence and expert-novice frameworks to design, conduct, and analyze think-aloud interviews to reveal differences in graph construction reasoning and graph quality between  undergraduate biology students without research experience (n=10), with research experience (n=5), graduate students (n=15), and professors (n=5) in a pen-and-paper graph construction task. Participants were given either a bacteria or plant growth scenario, both containing a dependent variable, an independent variable, and two treatments with three replicates in each treatment. Simple numbers were provided in a table so participants could easily manipulate the data, if they chose to do so. Inductive analysis revealed that 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. Using a qualitative approach to compare the graphs attributes across participant groups, it was noted that 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. This study provides targets for undergraduate and graduate instruction in the lecture and laboratory setting. 

Harsh, J. A., Campillo, M., Murray, C., Myers, C., Nguyen, J., & Maltese, A. V. (2019). “Seeing” data like an expert: An eye-tracking study using graphical data representations. CBE—Life Sciences Education, 18(3), ar32. To examine the cognitive strategies of students and scientists as they read and interpret graphs, the study authors used eye-tracking data supplemented with interview questions . Data were collected from 36 participants of varying levels of scientific expertise (non science majors, early biology majors, advanced biology majors, biology graduate students, and science faculty) as they completed graph-based tasks focused on science-related topics drawn from everyday sources (e.g. medical pamphlets, ecological footprint, sports). Eye movement data were analyzed to examine where, when, how long, and in what order participants directed their attention (i.e. fixations and visual search patterns) at various graph scene elements (e.g., title, variable, and graph data). Study findings highlight variation along the expert-novice continuum as participants with higher levels of expertise allocated more attention toward contextual (i.e. graph title/caption, variables, legend/key, and data source) and graph data features relative to their more novice counterparts. Less experienced participants were also more likely to demonstrate sporadic search patterns and to lack alignment between their intended and actual cognitive strategies when faced with a new graph interpretation task. The authors suggest that explicit instruction using instructional scaffolding to target the differences observed in the study between expertise groups can help novices demonstrate more expert-like practices.  

 Maltese, A. V., Harsh, J. A., & Svetina, D. (2015). Data visualization literacy: Investigating data interpretation along the novice—expert continuum. Journal of College Science Teaching, 45(1), 84-90. Motivated by gaps in the literature in understanding how and when students develop graphing proficiencies, the authors developed and tested an instrument to measure differences in how individuals along a continuum of STEM expertise interpret graphical representations. A national sample of adult participants (n=202) with varying levels of academic training  – from first-year non-STEM undergraduates (novice) to practicing STEM professionals with an advanced degree (experts) – were recruited to complete the validated online assessment of 20 tasks focused on the reading and interpretation of science-related graphs and tables of varying complexity drawn from everyday sources. Analysis across seven groups (by academic level) found significant differences in the interpretative abilities of expert and novice end-members, with little differentiation between the middle groups (second-year undergraduates to those with a master’s degree). A moderate correlation was observed in the relationship between the amount of completed STEM coursework and performance, which in part reflected the difficulties that even participants with higher levels of expertise had with more complex representations (e.g., multi-scaled, those requiring simple calculations). The authors argue that instructors should not assume that undergraduate and graduate students enter their courses with a high proficiency in graph interpretation, and that explicit instruction may be necessary to help develop graphing skills, especially for complex representations that require readers to make connections between data and multiple graph elements.   

Barriers to Developing Graphing Competence

  • Instructor knowledge and awareness as well as access to resources is critical in supporting the development of students’ quantitative literacy, including graphing competence. 
    • Freely available resources and professional development to improve college instructors’ ability to bring together their knowledge of the discipline and ways to teach quantitative literacy would benefit student skills. 
    • College instructors need to be aware of common struggles their students have with graphing for teaching and learning purposes.
    • It is not uncommon for pre-college teachers to have limited expertise in graphing, which may influence students’ ability to use and understand graphs upon entering the college classroom. Therefore, there is a distinct need for an emphasis to be placed on graphing in teacher preparation programs, such as in science content courses.
  • Students encounter graphs in a variety of contexts in their high school and college science courses, including textbooks.  This provides students with an inaccurate model of data visualization and nature of biological data and the systems from which they come. Textbooks often present graphs: 
    • That lack variability due to the representation of summarized, idealized data
    • Missing axis labels, units, or figure captions that present contextual information to reinforce the data collection methods and other details of measurement and data analysis. 
    • Mainly as bar, line, and scatter plots which takes away from exposing students to other types of graphs and data visualizations.
  • Graphs found in professional journals are often multivariate plots of messy data, representing the true nature of biological data.  However, reviews of figures in science journals reveal that authors often use graphs that fail to effectively report data by choosing inappropriate graph types for the presented data that may obscure variation or have design elements that detract from clear communication.


 Bowen, G. M., & Roth, W. M. (2005). Data and graph interpretation practices among preservice science teachers. Journal of Research in Science Teaching, 42(10), 1063-1088. As K-12 science reform documents commonly include graph-related actions students should be competent with, the authors of this study examined preservice teachers’ interpretation of data and graphs produced from their own inquiry-based investigations. Study participants (n=25) were candidates in a post-baccalaureate program for elementary and secondary science teachers, each having previously earned a science major or minor. In association with guided-inquiry activities in their methods course, participants completed multiple pen and paper data/graph interpretation and transformation tasks that drew on actions comparable to what they would be expected to teach. Through qualitative analysis it was found that preservice teachers had difficulties with structuring data and selecting an appropriate representation.  They also had difficulties with random variations in measurement as they held the view that data points must fall “in line” to make claims about variable relationships. While the study was conducted with preservice teachers, the findings highlight three points salient to college science instruction. First, despite having prepartion as science majors and minors, many participants demonstrated difficulties in graph-related practices identified as important for younger students. Second, as argued by the authors, “at present, preservice teachers do not seem ready to teach data collection and analysis in a way suggested by reform documents” (p. 1087). Thus, instructors of introductory science courses should be mindful that incoming students might not yet have received appropriate instruction to develop the graph-related competencies included in secondary reform documents. Third, in benefit to their teaching, study results suggest the need to engage preservice teachers in data representation and interpretation practices through authentic scientific activities. 

 Roth, W. M., Bowen, G. M., & McGinn, M. K. (1999). Differences in graph‐related practices between high school biology textbooks and scientific ecology journals. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 36(9), 977-1019. In light of the textbook-oriented approaches to teaching and learning regularly used in secondary science, the authors examined to what degree high school textbooks introduce students to graphing practices required to read scientific texts. The authors conducted two complementary analyses to compare the differences between six representative high school biology textbooks and five ecology-related journals in the graphs displayed and the practices required to read them. Findings revealed that while the overall frequency of inscriptions in the reviewed high school biology textbooks and scientific journals were comparable, the type and nature of the inscriptions used to relay information were notably different. High school biology textbooks predominantly relied on pictorial imagery (photographs, drawings, and diagrams) with fewer graphical models representing general trends, whereas Cartesian graphs of varying types constituted the major inscription type in scientific journal articles. Further comparisons showed that graphs in high school textbooks consistently lacked scales and units of analysis, presented smooth curves that did not reflect statistical variation in real data, and principally featured line and bar graphs.  Additionally, scientific journals were found to provide more resources in the caption and main text to assist readers in understanding the featured data and represented relationships. The authors argue that common graphing difficulties reported in the literature for secondary and postsecondary learners may (in part) reflect the observed discontinuities in graph-related practices between high school science textbooks and scientific journals. The authors conclude the article by summarizing this study as part of a larger project on the developmental trajectory of graphing practices, in which they highlight the instructional importance of teaching the reading, interpretation, and production of graphs as an integral part of doing science. 

 Weissgerber, T. L., Winham, S. J., Heinzen, E. P., Milin-Lazovic, J. S., Garcia-Valencia, O., Bukumiric, Z., … & Milic, N. M. (2019). Reveal, don’t conceal: transforming data visualization to improve transparency. Circulation, 140(18), 1506-1518. In response to reported problems with how biomedical data are commonly presented, and corresponding concerns as to the potential impact of poor data displays on health-related decision-making, the authors conducted a systematic review of studies published in prominent vascular disease journals to assess the prevalence of suboptimal visualization processes. Stemming from a review of over 200 papers in 13 journals, the authors identified a set of common visualization problems as well as basic steps investigators can take to correct these data display issues. In particular, the authors focused on the prevalent inappropriate use of bar graphs to present continuous data (observed in approximately half of the reviewed papers) that prohibits readers from critically and effectively evaluating the displayed data. The authors then provide a literature-based primer regarding how to (1) select the correct graphic for the given study design, sample size, and type of outcome variable; (2) effectively present summary statistics that reveals data distribution; and (3) limiting the potential for “mixed messages” in scientific literature by improving the alignment between figure structure with the study goals and design. In support of this, the authors provide a list of free tools and resources to help in the preparation of effective data displays. Of benefit to faculty  is that this article can be used both to inform instructional classroom practices as well as the training of future STEM practitioners.  

 Corwin, L. A., Kiser, S., LoRe, S. M., Miller, J. M., & Aikens, M. L. (2019). Community College Instructors’ Perceptions of Constraints and Affordances Related to Teaching Quantitative Biology Skills and Concepts. CBE—Life Sciences Education, 18(4), ar64. In this study, Corwin and colleagues investigated the challenges and affordances of the instruction of quantitative biology (QB) at community colleges. Semi-structured interview data were collected from 20 community college instructors, representing a national pool of institutions with varying levels of student diversity, about their perceptions and experiences teaching QB. Through qualitative analysis, the authors identified six themes of constraints of teaching QB and eight themes for instructional affordances. The authors summarize the instructor-identified challenges under three topics: perceived student-deficit models (in students’ math backgrounds), tensions between content coverage and time to teach skills, and teachers’ pedagogical content knowledge in QB instruction. These challenges align with those previously-identified for work around the integration of innovative curriculum changes in any college level STEM class.  Using the affordance themes and extant literature, several strategies were identified that community college instructors could use or leverage to facilitate QB instruction. Recommendations for professional development opportunities designed to support instructors in QB instruction are provided. As noted by the authors, while the exploratory study was conducted in the context of community college instruction, the presented findings are more broadly applicable as evinced by common challenges for any context and can be leveraged across institutional types to promote change in teaching QB in college biology.  

 Gardner SM, Suazo-Flores E, Maruca S, Abraham JK, Karippadath A, and Meir E (2021)  Biology Undergraduate Students’ Graphing Practice in Digital versus Pen and Paper Graphing Environments.  Journal of Science Education and Technology. Understanding the use of technology to create data visualizations, such as graphs, holds promise for effective teaching and assessing student graphing competence. The authors explored undergraduate biology students’ graphing practices with a complicated data set in two different graphing modalities.  Participants were provided with a conservation biology scenario and an overarching research hypothesis to evaluate using a data set of relevant and irrelevant quantitative and categorical variables.  Participants were randomly assigned to graph the data during a think-aloud interview using either pen-and-paper or using a digital tool called GraphSmarts.  GraphSmarts is an assessment tool which builds from previous research on student graphing and aims to constrain and focus students while providing them with a place to explore data and allows their actions to be monitored.  While both groups showed difficulties in selecting the relevant variables to plot, students plotting in the digital environment (GraphSmarts) were more likely to select and plot the most relevant variables, leading to higher quality graphs.  While not statistically significant, the GraphSmarts allowed for rapid iterations on data exploration and graphing compared to the pen-and-paper environment.  This was supported by participants’ verbal justifications for their graph type choices. GraphSmarts participants more often stated data exploration as their reason while the  ease of construction was the most common justification given by participants graphing with pen-and-paper.   Findings from this study demonstrate the affordances and potential limitations of the graphing environment in which students learn and practice graphing which should inform instructor choices for teaching and assessing student graphing competence. 

Rybarczyk, B. (2011). Visual literacy in biology: A comparison of visual representations in textbooks and journal articles. Journal of College Science Teaching, 41(1), 106. This article compared visual representations from three common sources utilized by  a typical biology undergraduate student: general biology textbooks, discipline-specific textbooks, and primary journal articles. Analysis of visual representations from only the main text of the textbook chapters across five different types of textbooks (general and discipline-specific) and 30 articles from seven journal titles revealed five major categories of visual representations: 1) diagrams, line drawings, and computer-generated graphics, 2) graphs/charts, 3) tables, 4) photomicrographs, digital images, and photographs, and 5) gel electrophoresis images. Substantial differences were observed across textbook and journal sources in the visualizations used. In particular, graphs and figures appeared more frequently in journals than in textbooks (general and discipline-specific). Beyond frequency, journal articles were also found to represent data in graphs and figures using a greater variety of display types and with more complexity compared to textbook imagery. More broadly, analysis of end-of-chapter questions showed that discipline-specific textbooks integrated experimental data at a significantly higher frequency than the general biology textbooks, meaning that students at the introductory level of biology had fewer opportunities to practice data analysis skills. The discrepancy in visual representations makes it even more urgent for instructors to not only integrate visualizations from multiple sources but also provide ample opportunities for students at all education levels to engage with experimental data and improve visual and data literacy.   

Inclusive Teaching Practices

  • Instructors can make use of inclusive teaching and learning strategies that support the engagement and achievement of all students to foster the development of graphing competence within their classroom. These general practices include: 
    • Incorporating teaching and assessment strategies using contexts that resonate with their student populations.
    • Encouraging a combination of individual participation and strategic collaborative learning.
    • Having students participate in reflective activities.
  • Students enter the undergraduate science classroom having extensive prior experience with graphing. The diversity and depth of these experiences can lead to different instructional needs for students in support of their continued development of graph competence. 
    • Instructors need to be aware of student graphing abilities and can use early, frequent formative assessment practices to identify areas of improvement and guide teaching practices in response.    
  • As visual objects, instructor decisions regarding graph design and presentation can affect the accessibility to blind, visually impaired, and colorblind students. 
    • The use of freely available resources to guide design choices (e.g., color selection) and to provide accessible data displays (e.g., tactile graphics) can remove barriers for these students. 
    • Simple strategies can improve how all students engage with visual data, but the effects are disproportionately greater for visually impaired students. These include explicit verbal descriptions, peer instruction, using alternative models (e.g., physical modeling), and varying assessments of competencies.


Stone, B. W., Kay, D., & Reynolds, A. (2019). Teaching Visually Impaired College Students in Introductory Statistics. Journal of Statistics Education, 27(3), 225-237. As classroom instruction and supporting resources in postsecondary STEM courses relies heavily on visual information (e.g., graph data, instructor gesturing, textbook diagrams), blind and visually impaired (BVI) students often face access barriers to building foundational statistical skills. This review explores the challenges of teaching statistics to BVI students and provides a variety of accommodations informed by research in cognition and learning. Here, considerations relevant to graphing are highlighted, but the article also examines aspects of teaching statistics to BVI students (e.g., use of equations, analysis of data sets), that are relevant for biology instructors given the quantitative nature of the field.  Within the classroom context, accommodations related to instructor behavior (explicitly verbalizing what is shown in presented graphs and how to interpret) and the use of collaborative problem solving with sighted peers to help make graph data more accessible to BVI students are outlined. Outside the classroom, a variety of accommodation ideas for teaching BVI students to create and interpret graphs are discussed (e.g., being attentive to the accessibility limitations of online/digital homework systems, making resources available during an exam that students have practice with). The strengths and weaknesses of varying types of auditory and tactile learning aids (i.e. tactile graphics, hands-on education models, 3D printing) are also reviewed as an evidence-supported means to engage BVI students with data. The authors conclude the article noting that a combination of strategies and resources will likely be necessary to accommodate BVI students in learning quantitative skills at the college level. Additionally, the employed strategies should be guided by extensive student-instructor communication and considered on a case-by-case basis as, like all learners, what may work for one student may not work for another.   The considerations and approaches outlined in this article in the context of supporting BVI students represent responsive and inclusive practices that would likely support all students in the creation and reading of graphs. 

Jones, J. L., Jones, K. A., & Vermette, P. J. (2011). Planning Learning Experiences in the Inclusive Classroom: Implementing the Three Core UDL Principles to Motivate, Challenge and Engage All Learners. Electronic Journal for Inclusive Education, 2(7), 6. This essay explores the use and interplay of two curriculum and instructional decision-making frameworks for the intentional development of learning experiences that allow all students opportunities for success. The authors first outline the planned learning experience (PLE) framework that guides teachers through a four-part structure to help them apply differentiated instruction by considering student needs and the management of learning interventions and assessments to facilitate individual growth. The Universal Design for Learning (UDL) framework, an approach to optimize equal learning opportunities for all students, is then introduced focusing on the three principal interrelated design features that includes providing students multiple means of representation, action and expression, and engagement. The authors synthesize the core principles of these two frameworks to provide general guidance in applying an integrated UDL supported lesson planning model. The article concludes with an example case-study based statistics lesson (that includes graphing) to demonstrate how the two frameworks can benefit inclusive instructional practices. While the presented lesson is intended for 7th grade mathematics students, the discussion and application of the core PLE and UDL features can be broadly applied to topics in the K-16 classroom.  

Levine, A. (2019) True Colors: Optimizing Charts for Readers with Color Vision Deficiencies. As 8 percent of males and 0.5 percent of females are color blind (color vision deficiency), instructors and researchers should consider the use of barrier-free colors in graphs and other data visualizations when preparing learning materials and scholarly articles. This webpage from Wichita State University highlights general principles to design accessible visualizations as well as additional supporting resources, links to color blindness simulators to test figures, and step-wise examples on how to address sample problematic graphs.  

Describing Science Images for Learners with Disabilities. This site from the National Center of Accessible Media (NCAM) provides guidelines drawn from NSF funded research for the presentation of visual STEM information to blind and visually impaired students and scientists. Examples as to how the general guidelines can be implemented across a range of visualization types (e.g., scatter plot, line graph) are provided.     

 Color Oracle. This site provides a free software tool that allows users to quickly see how figures may look to a person with common colorblindness impairments: deuteranopia, protanopia and tritanopia. The colorblindness simulator is available in Windows, Mac and Linux formats.

Braille Authority of North America (2010). Guidelines and Standards for Tactile Graphics. This link offers guidelines provided by the Braille Authority of North America and the Canadian Braille Authority to standardize best practices for the use and creation of braille and/or tactile graphics for visually impaired tactile readers. The 12-part online training manual provides information and guidelines across a range of topics for educators and others, including recommendations for the design of tactile mathematical and scientific diagrams. 

Tanner K (2013)  Structure Matters:  Twenty-One Teaching Strategies to Promote Student Engagement and Cultivate Classroom Equity.  CBE-Life Sciences Education 12(3). This practical guide provides a summary of some of the best practices for equitable teaching practices and advocates for a purposeful management of the learning environment to foster student engagement and sense of belonging.  Many of these techniques will be useful in supporting and inviting all students to learn graphing by supporting their individual thinking and reflection, strategies for eliciting diverse ideas and perspectives, managing to classroom to model the social practice of science, including graphing. 

Inclusive Teaching Guide. This evidence-based teaching guide provides a broad framework of research literature and resources that can help instructors develop self-awareness and empathy toward others, create a supportive classroom environment, and utilize inclusive teaching practices in the college science classroom to provide all students opportunities for success.

 CAST Universal Design for Learning (UDL) Guidelines . This website provides guidelines to the implementation of Universal Design for Learning (UDL), described as a framework to improve and optimize teaching and learning for all learners based on scientific insights. The guidelines offer a cross-domain set of evidence-based recommendations that can be broadly implemented by educators, curriculum developers, researchers, and parents. The concrete UDL guidelines focus on three aspects of learning, including engagement (interest and motivation), representation (information presentation), and action and expression (activity and assessment). 

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Cite this guide:
Gardner SM, Angra A, Harsh JA. (2023) Evidence Based Teaching Guide: Graphing in Biology. CBE Life Science Education. Retrieved from https://lse.ascb.org/graphing-in-biology/
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