In earlier posts, we considered cognitive load and the non-linear ways readers engage with text. Readers rarely move line by line; they scan for relevance and rely on cues to decide where to focus. In this post, we turn to visuals in scholarship, which carry intellectual weight in arguments as well as complement ideas. 

The Role of Visuals in Scholarly Communication 

Edward Tufte writes that “good visualizations present complex ideas with clarity and precision” (Tufte 2006, 23). When you design visuals with purpose, they reduce cognitive load and guide readers through data or information that may feel clear to you (the writer) but may not be clear to the reader, who’s absorbing this information for the first time. In the Scholarship of Teaching and Learning (SoTL), visuals generally fall into two categories: argumentative and decorative.  

Argumentative Visuals  

Argumentative visuals do rhetorical work. They select, organize, and emphasize data in ways that guide a reader toward a particular interpretation. As Jennie Goforth reminds us, “the form you choose frames how your reader interprets the data” (2022). For instance, a cluttered chart can obscure a trend, while a simplified one can make that same trend obvious. This means the form shapes what the reader sees first, what they compare, and what they conclude. 

A helpful way to approach this is to start with your claim, not your data. We’re not suggesting that you misrepresent your findings; your claim should reflect an ethical interpretation of your data. However, designing your visuals with your claim in mind prompts a focusing question: What do you want your reader to understand quickly—or differently—after encountering this visual? The answer determines which format your visual will take: 

• Comparisons: Side-by-side charts or grouped bar graphs highlight difference and magnitude  
• Processes: Flowcharts or timelines emphasize sequence, causality, or development over time  
• Relationships: Concept maps or network diagrams reveal connections, clusters, or hierarchies  
• Trends: Line or bar charts foreground change, growth, or decline across a variable  
• Patterns and synthesis: Infographics distill multiple data points into a coherent takeaway 

These formats can quickly express ideas, saving paragraphs of explanation. Let’s review an example.  

Data Visualization Example 

The example below, drawn from Jennie Goforth’s (2021) discussion of data visualization, show how these principles work in practice. The first figure (figure 1) is a default Excel chart that includes multiple visual elements competing for attention. What’s the message or focal point of this graph? The gridlines, borders, and styling choices don’t lead us to a clear takeaway. While none of these elements are inherently misrepresenting the information, the visual representation dilutes the chart’s focus. The reader is left to decide what matters, increasing effort required to interpret the data.  

By contrast, figure 2 demonstrates how design choices can better align a visual with its argumentative purpose. Here, unnecessary elements are removed, and emphasis is placed on the most important feature of the data through clear labeling and visual hierarchy. Rather than presenting the dataset in full, the chart guides the reader toward a specific takeaway. This shift from displaying information to directing interpretation is what makes the visual argumentative.  

These examples reinforce the idea that the effectiveness of a visual is determined by how clearly it communicates what matters.  

“Decorative” Visuals 

Decorative images occupy a tricky place in research on multimedia comprehension and learning. These visuals do not directly advance the text’s core ideas but appear alongside them. The cognitive theory of multimedia learning treats them cautiously because they can add extraneous cognitive load and distract from key concepts (Mayer 2014). In many studies, decorative pictures have shown neutral or even negative effects on comprehension when they compete with informational content for limited working memory (Rey 2012). 

Yet research also reveals another dimension to these images: they can influence learners’ affective and metacognitive states in ways that indirectly support engagement. Experimental work on decorative pictures that are related to a learning context finds that such images can facilitate knowledge acquisition or strengthen metacognitive monitoring even when they do not explain or further a text’s ideas directly (i.e., not an argumentative visual). In one study, participants who read with related decorative pictures produced higher scores on measures of knowledge recall, a pattern consistent with the idea that semantically linked images activate associated concepts in the reader’s memory (Scherer, Verkühlen, and Dutke 2023). 

Similarly, research on emotional design in multimedia finds that positively valenced pictures (those carrying positive emotional value) regardless of their informational relevance, can enhance learning performance through mediated effects on motivation and interest. Decorative pictures that evoke positive affect have been shown to foster retention and transfer in laboratory settings, suggesting that pleasure and engagement function as mediators between visual appeal and cognitive processes (Schneider, Nebel, and Rey 2016). 

This body of work suggests two reasons that decorative images continue in educational and scholarly contexts. First, they can shape the emotional and motivational landscape of a text, making readers more willing to engage with challenging material. Second, when decorative images are thematically related (even loosely) to the topic, they can prime associative networks that support memory and metacognition without directly communicating conceptual content.  

These effects are subtle and context‑dependent, but they explain why some authors choose decorative elements not for argumentation purposes, but for readership engagement and sustained interaction with the material. 

Guidance 

When deciding whether to include a visual, focus on rhetorical purpose rather than decoration. Start with a simple question: Is this visual doing argumentative work? 

• If yes, identify the specific claim it supports.  
• If no, consider whether its value lies in engagement, orientation, or affect, and whether that value justifies its inclusion.  

From there, focus on placement and integration. Visuals are most effective when they are introduced rather than dropped in without context. Signal their purpose in the text, place them near the relevant discussion, and reference them explicitly so readers understand how to use them. 

Design choices should reinforce, not compete with, your argument. To maximize efficacy, visuals should be readable, scannable, and easy to interpret and understand. This means prioritizing clarity over aesthetics: 

• Emphasize key information through hierarchy (size, position, contrast)  
• Remove unnecessary elements that add visual noise  
• Use color intentionally to distinguish or group information  
• Label clearly so readers don’t have to infer meaning 
• Keep accessibility in mind, paying attention to captions and alt-text 

Conclusion 

When used strategically, both argumentative and decorative visuals can improve comprehension of your text. Choosing the right visual can feel overwhelming, but it helps to think of visuals as a “visual roadmap” for your argument (Schriver 2013). For that reason, it’s useful to articulate the takeaway explicitly before designing a visual: What should someone be able to say after seeing this? If the answer isn’t clear, the visual likely won’t be either.  

In our next post, we continue this conversation by examining accessibility as an intellectual and ethical practice, not just a box to check. 

Suggested Reading 

Ferdio. n.d. Data Viz Project. https://datavizproject.com/. 

Redish, Janice. Letting Go of the Words, 2012. 

Dougherty, Jack, and Ilya Ilyankou. 2026. Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code. Last updated April 16, 2026. https://handsondataviz.org/table-design.html. 

Toronto Metropolitan University. n.d. Critical Data Literacy. https://pressbooks.library.torontomu.ca/criticaldataliteracy/. 

Tufte, Edward. Beautiful Evidence, 2006. 

Yau, Nathan. n.d. Learn Visualization. FlowingData. https://flowingdata.com/learning/. 

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References 

Dougherty, Jack, and Ilya Ilyankou. 2026. Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code. Last updated April 16, 2026. https://handsondataviz.org. 

Goforth, Jennie. 2021. “Four Steps to Better Data Visualizations.” Center for Engaged Learning (blog). March 4, 2021. https://www.centerforengagedlearning.org/four-steps-to-better-data-visualizations. 

Goforth, Jennie. 2022. “Academic Publishing: Diagrams, Photos and Illustrations.” Center for Engaged Learning (blog), Elon University. January 18, 2022. https://www.centerforengagedlearning.org/academic-publishing-diagrams-photos-and-illustrations. 

Mayer, Richard E. 2014. The Cambridge Handbook of Multimedia Learning. 2nd ed. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139547369.  

Rey, Geoffrey D. 2012. “A Review of Research and a Meta‑Analysis of the Seductive Detail Effect.” Educational Research Review 7 (December): 216–37. https://doi.org/10.1016/j.edurev.2012.05.003.  

Scherer, Demian, Annika Verkühlen, and Stephan Dutke. 2023. “Effects of Related Decorative Pictures on Learning and Metacognition.” Instructional Science 51: 571–594. https://doi.org/10.1007/s11251-023-09618-8.  

Schneider, Sascha, Steve Nebel, and Günter D. Rey. 2016. “Decorative Pictures and Emotional Design in Multimedia Learning.” Learning and Instruction 44: 65–73. https://doi.org/10.1016/j.learninstruc.2016.03.002.  

Schriver, Karen. 2013. “What Do Technical Communicators Need to Know About Information Design?” In Solving Problems in Technical Communication, edited by Johndan Johnson-Eilola and Stuart A. Selber. University of Chicago Press. https://www.karenschriverassociates.com/wp-content/uploads/2020/03/6-Schriver-What-Do-Technical-Communicators-Need-to-Know-Information-Design.pdf.  

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About the Authors 

Sophie Grabiec is the Center for Engaged Learning’s Managing Editor, where she oversees the production of CEL’s books, open access resources, and blog. Before joining Elon University she lived and worked in Washington, DC at Georgetown University where she earned her M.A. in English and taught first-year writing. 

Sophia Sta. Rosa works as a publishing intern at the Center for Engaged Learning. An undergraduate student majoring in both Strategic Communications and Professional Writing & Rhetoric, she has a passion for reading and engaging critically with media and hopes to enter the publishing industry as a book editor after graduating. Along with her internship at CEL, Sophia is also a Communications Fellow. 

How to Cite This Post 

Grabiec, Sophie, and Sophia Sta. Rosa. 2026. “Visuals as Arguments.” Center for Engaged Learning (blog), Elon University. May 5, 2026. https://www.centerforengagedlearning.org/visuals-as-arguments.