Creating Visuals¶

Strategy · All strategies
Evidence · 4 claims (3 for, 1 against) · 11 studies (5 causal, 3 quant-synthesis, 2 review, 1 associational), q2–q4 · 2 of 11 report an effect size · 1 claim rests on one study

Description¶

Creating Visuals asks learners to generate their own visual representations — diagrams, sketches, concept maps, infographics, storyboards — of the content they are studying, rather than only viewing instructor-provided graphics. The act of constructing a visual forces learners to select, organize, and integrate information, making it a generative learning activity rather than a passive one.

Design Implications¶

Student-generated visuals are effective because drawing requires translating verbal information into spatial form, which promotes integrative processing between verbal and pictorial mental models Drawing supports learning by forcing learners to construct and integrate verbal and pictorial representations. [+M]. The benefit comes from the generative processing the construction demands, not from the aesthetic quality of the product — a rough sketch that forces selection and organization outperforms a polished but copied illustration [~M]. Visuals also externalize understanding, making misconceptions visible to both learner and instructor and creating a surface for Self-Explanation and feedback.

Context¶

Requirements¶

• Source material that has a mappable structure (processes, systems, hierarchies, relationships) — visuals of arbitrary lists add little
• Low-stakes framing: the goal is reasoning made visible, not artistic quality
• A prompt that specifies what the visual must show (e.g., "show what happens to X when Y changes"), not just "draw it"
• Tools appropriate to the medium: paper, whiteboards, or digital tools (see Examples)

Constraints¶

• Drawing can overload working memory when learners must simultaneously search for the pictorial format and reason about content; providing a partially completed structure or reference images reduces this load [~M] — see Cognitive Load Theory
• Learners with weak prior knowledge may copy surface features without understanding; pairing the visual with Self-Explanation [-M] prompts mitigates this
• Benefits shrink when the material is purely verbal or when learners already have fluent mental models — the expertise-reversal pattern applies [~M]
• Time-intensive relative to reading or discussion; reserve for high-value, structurally complex content

Implementation Variability¶

• Structured formats: concept maps, flowcharts, timelines, and comparison matrices scaffold learners who cannot yet invent a format
• Free-form sketching: best when learners have enough knowledge to choose their own representation
• Progressive completion: provide a partial diagram learners finish, fading support over time
• Group visuals: collaborative diagramming surfaces disagreements about relationships that individual work hides

Target Learners¶

• Novices and intermediate learners encountering relational or systemic content for the first time [+M]
• Learners who benefit from dual-channel encoding of verbal material Media combinations affect recall and retention. [+M]
• Less effective for learners with strong existing mental models, for whom construction is redundant [~M]

Target Learning Goals¶

• Conceptual understanding of systems, processes, and relationships Self-explanation improves conceptual understanding. [+M]
• Abstraction: comparing multiple student visuals or contrasting cases supports generalization Multiple contrasting cases support abstraction. [+M]
• Communication: translating understanding into a form others can read

Instructions¶

1. Select content with a mappable structure and define what the visual must communicate.
2. Activate relevant prior knowledge before construction (Activation).
3. Have learners construct the visual individually or in pairs, using paper or a digital tool.
4. Prompt learners to explain their visual aloud or in writing — the explanation, not the drawing, carries much of the learning benefit Self-explanation improves conceptual understanding. [+M]
5. Compare visuals across students or against an expert version to surface alternative structures and misconceptions.
6. Revise: have learners redraw after feedback or new information, making conceptual change visible.

Related Strategies¶

• Annotating — text-based sibling strategy; both make processing visible on the artifact
• Concept Mapping — a specific structured format for visual construction
• Comparing Cases — contrasting multiple visuals drives abstraction

Related Elements¶

• Analogies — visual analogies help learners map unfamiliar structures onto familiar ones
• Chunking — a well-designed visual is itself a chunking device, grouping related information spatially Chunking reduces working memory load. [+M]

Examples¶

• BioRender — students construct biological pathway diagrams from a library of scientific icons, combining structured support with generative construction.
• Canva for Education — infographic design for summarizing research findings; the constraint of fitting content into a layout forces prioritization.
• Sketchnoting in history classes — students produce visual notes combining symbols, spatial layout, and text; the spatial arrangement encodes relationships that linear notes miss.
• Model-based drawing in science (e.g., the Ambitious Science Teaching practice of "puzzling phenomena") — students draw initial models of a phenomenon, then revise them as evidence accumulates.

Key Sources¶

• Ainsworth, S., Prain, V., & Tytler, R. (2011). Drawing to learn in science. Science, 333(6046), 1096–1097. doi:10.1126/science.1204153
• Van Meter, P., & Garner, J. (2005). The promise and practice of learner-generated drawing: Literature review and directions for future research. Educational Psychology Review, 17(4), 285–325. doi:10.1007/s10648-005-7024-5
• Fiorella, L., & Mayer, R. E. (2015). Learning as a generative activity: Eight learning strategies that promote understanding. Cambridge University Press. doi:10.1017/cbo9781107707085
• Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press. doi:10.1017/9781316941355