Exemplar Analysis¶

Strategy · All strategies
Evidence · 4 claims (4 for) · 8 studies (5 causal, 2 quant-synthesis, 1 review), q3–q4 · 1 of 8 report an effect size · 1 claim rests on one study

Description¶

Exemplar analysis presents learners with one or more complete instances of high-quality performance — a solved problem, a model essay, a finished design — and structures their examination of it. Rather than passively viewing the exemplar, learners are prompted to identify its features, explain why it works, and compare it against other cases or non-examples. The strategy converts an expert product into an object of study, making implicit quality criteria explicit.

Design Implications¶

Exemplars give novices a concrete reference model, reducing unguided search during early skill acquisition Example-problem pairs lower cognitive load relative to unsupported problem solving. [+M]. Their effectiveness depends on active analysis: learners who explain why an exemplar succeeds learn more than those who merely read it Elaborative interrogation improves learning. [+M]. Studying multiple varied exemplars supports abstraction of general principles, whereas a single exemplar risks binding features to that specific case Multiple varied cases support flexible, transferable knowledge structures. [+M].

Context¶

Requirements¶

• One or more authentic, high-quality exemplars of the target performance
• Analysis prompts that direct attention to structure and decision points, not surface features (Advance Organizers can frame what to look for)
• Contrast material — weaker versions, Non-Examples, or a second exemplar varying surface details — to support discrimination
• A production task in which learners apply the extracted criteria (Practice)

Constraints¶

• Copying without analysis: learners imitate surface features rather than extracting principles, producing near-identical but poorly understood work [-M]
• A single exemplar can anchor learners to one approach and impair transfer to varied problems [~M]
• Learners with high prior knowledge gain little and may find exemplar study redundant or tedious [~M]
• Poorly chosen exemplars (too advanced, or with unexplained idiosyncrasies) can transmit misconceptions [-W]

Implementation Variability¶

• Single-to-multiple progression: begin with one annotated exemplar, add contrasting cases as analysis skill grows
• Erroneous exemplars: analyzing a flawed example and diagnosing its faults builds conceptual understanding Analyzing erroneous examples builds conceptual knowledge. [+M]
• Completion variants: learners finish a partially completed exemplar, bridging analysis and production
• Comparative ranking: learners rank several exemplars and justify the ordering, forcing criterion articulation

Target Learners¶

• Novices who lack a mental model of what "good" looks like in the domain Example-problem pairs lower cognitive load relative to unsupported problem solving. [+M]
• Intermediate learners ready to discriminate quality levels, especially with contrasting or erroneous exemplars Analyzing erroneous examples builds conceptual knowledge. [+M]
• Less valuable for experts, who already possess the criteria the exemplar would teach [~M]

Target Learning Goals¶

• Quality-criterion acquisition: understanding what distinguishes strong from weak performance
• Schema formation: abstracting general principles from concrete instances
• Evaluative judgment: developing the capacity to critique one's own and others' work
• Conceptual understanding, particularly through error diagnosis in flawed exemplars

Instructions¶

1. Select an authentic exemplar matched to the target performance level — complete but not so expert that its moves are invisible to novices.
2. Frame the analysis with an Advance Organizer naming the dimensions to examine (structure, reasoning, evidence, craft).
3. Have learners annotate or interrogate the exemplar: why this choice, what makes this section effective (Elaborative Interrogation).
4. Introduce a contrasting case or flawed version and ask learners to articulate the differences and their consequences.
5. Support learners in stating explicit criteria extracted from the analysis — a rubric or checklist they generate or co-construct.
6. Move immediately to a production task (Practice) where learners apply the criteria, followed by self- or peer-assessment against them (Assessment).

Related Strategies¶

• Worked Examples — exemplar analysis applied to problem solving; the analysis prompts add the elaboration that plain worked examples often lack
• Comparing Cases — the multi-exemplar variant that drives abstraction of general principles
• Error Analysis — using flawed exemplars as the object of diagnosis

Examples¶

• Writing instruction (model text analysis): Students read a mentor text, annotate how the author builds an argument, compare it with a weaker sample, then draft their own piece against co-constructed criteria — a core routine in units modeled on the Reading & Writing Project approach.
• Codecademy — learners study a complete, working code solution with annotations before modifying or writing their own, then receive feedback against the reference implementation.
• Design studio "precedent studies": Architecture and HCI courses require students to analyze exemplary designs, extract the principles at work, and present the analysis before beginning their own design work.

Key Sources¶

• Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59–89. doi:10.1207/s1532690xci0201_3
• van Gog, T., & Rummel, N. (2010). Example-based learning: Integrating cognitive and social-cognitive research perspectives. Educational Psychology Review, 22(2), 155–174. doi:10.1007/s10648-010-9134-7
• Durkin, K., & Rittle-Johnson, B. (2012). The effectiveness of using incorrect examples to support learning about decimal magnitude. Learning and Instruction, 22(3), 206-214. doi:10.1016/j.learninstruc.2011.11.001
• Renkl, A. (2014). Toward an instructionally oriented theory of example-based learning. Cognitive Science, 38(1), 1-37. doi:10.1111/cogs.12086