Worked Examples Types¶

Strategy · All strategies
Evidence · 5 claims (3 for, 2 mixed) · 13 studies (6 review, 5 causal, 1 quant-synthesis, 1 theoretical), q1–q3 · 0 of 13 report an effect size · 1 claim rests on one study

Description¶

Worked example types form a guidance continuum rather than a single technique. At the maximum-support end sit fully worked examples — a complete solution presented step by step, often with expert reasoning narrated. Completion problems (also called faded or fadeout examples) present a fully solved model followed by a partially solved problem the learner must finish. Erroneous examples present a flawed solution for learners to diagnose and correct, and case-based examples embed the worked solution in a rich, situated scenario. Selecting among these types is a decision about how much structure to provide at a given point in skill acquisition.

Design Implications¶

The choice of worked example type should track learner expertise: high guidance early, fading as competence grows. Alternating examples with problems (example–problem pairs) consistently outperforms either examples alone or problem solving alone for novices Example-problem sequences reduce cognitive load and improve learning for novices. [+M]. As expertise develops, the same guidance becomes redundant and can even impose extraneous load Guidance that helps novices becomes less effective or counterproductive as expertise grows. [~S].

Context¶

Requirements¶

• A well-structured task domain with clear solution steps; worked examples are poorly suited to ill-structured problems with no canonical solution path
• Subgoal labeling or annotation that explains why each step is taken, not just what is done — unlabeled steps force learners to infer structure on their own
• A sequencing plan: which type to use at which point, and how support will be faded (Fading)
• Paired practice problems matched in structure to each example

Constraints¶

• Presenting fully worked examples to learners with substantial prior knowledge wastes time and can depress performance relative to unguided practice Guidance that helps novices becomes less effective or counterproductive as expertise grows. [-M]
• Examples without accompanying problem solving produce shallow encoding and overconfident self-assessment; learners who only study examples often believe they can solve problems they cannot [-S]
• Splitting attention between a problem statement, the solution, and separate explanatory text (split-attention effect) negates much of the example's benefit; integrate steps and commentary physically [-S]
• Erroneous examples used with novices who lack the knowledge to spot the flaw can reinforce the error rather than correct it Erroneous examples build conceptual knowledge when learners can engage the error productively. [~M]

Implementation Variability¶

• Fully worked examples: maximum support; best for the first encounter with a new task category
• Completion problems: intermediate support; the learner completes the final steps, then progressively earlier steps are removed — the standard fading sequence
• Erroneous examples: learners find and fix a planted error; effective for building conceptual understanding and error detection once basic procedures are known Erroneous examples build conceptual knowledge when learners can engage the error productively. [+M]
• Case-based examples: the worked solution is embedded in a narrative or professional scenario; adds context but also added load, so reserve for learners who can handle the extra processing
• Example–problem pairs: each example is immediately followed by an isomorphic problem; the most robustly supported arrangement Example-problem sequences reduce cognitive load and improve learning for novices. [+M]

Target Learners¶

• Novices in a structured domain (algebra, programming, physics, grammar), for whom worked examples substitute for effortful, error-prone search Example-problem sequences reduce cognitive load and improve learning for novices. [+M]
• Learners with low prior knowledge benefit most; for them, unguided problem solving imposes search-related extraneous load
• More advanced learners should shift toward completion problems and then independent problems, since full examples become redundant Guidance that helps novices becomes less effective or counterproductive as expertise grows. [~S]

Target Learning Goals¶

• Procedural fluency: acquiring and automating solution schemas in well-structured domains
• Schema construction: building mental models of problem categories and their canonical solution paths
• Error detection and conceptual understanding: diagnosing flawed solutions sharpens discrimination between correct and incorrect reasoning Erroneous examples build conceptual knowledge when learners can engage the error productively. [+M]

Instructions¶

1. Identify the problem category and write a complete, correct solution with each step annotated for its rationale (subgoal labeling).
2. Present the fully worked example first, integrated with the problem statement to avoid split attention; chunk steps so each is processed as a unit Chunking reduces working memory load. [+M].
3. Follow immediately with an isomorphic problem the learner solves alone (example–problem pairing) Example-problem sequences reduce cognitive load and improve learning for novices. [+M].
4. Fade support across the sequence: move from full examples to completion problems, removing progressively more of the solution (Fading) Fading support promotes transfer of responsibility to the learner. [+M].
5. Once procedures are secure, introduce erroneous examples for learners to diagnose, deepening conceptual understanding Erroneous examples build conceptual knowledge when learners can engage the error productively. [+M].
6. Monitor expertise and drop example support as soon as learners solve problems efficiently — continuing examples past this point wastes time Guidance that helps novices becomes less effective or counterproductive as expertise grows. [~S].

Related Strategies¶

• Fading — the mechanism by which example types progress from full support to independence
• Erroneous Examples [+M] — the diagnosis-and-correction variant that builds conceptual knowledge
• Completion Problems — the intermediate type on the guidance continuum

Examples¶

• Sweller & Cooper's algebra studies — the foundational example–problem pair design: learners studied a fully worked algebra example, then solved an isomorphic problem, outperforming learners who solved all problems themselves.
• Khan Academy — narrated step-by-step video examples followed by practice exercises with on-demand hints, which function as progressive completion problems.
• Codecademy — annotated code examples presented inline before learners write their own version, a completion-problem structure in a programming context.
• Fractions erroneous-example tutoring (Adams et al.) — tutoring systems that present incorrect fraction solutions for diagnosis improved conceptual knowledge beyond correct-only examples.

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
• Renkl, A. (2014). Toward an instructionally oriented theory of example-based learning. Cognitive Science, 38(1), 1–37. doi:10.1111/cogs.12086
• 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
• Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31. doi:10.1207/S15326985EP3801_4
• Adams, D. M., McLaren, B. M., Durkin, K., Mayer, R. E., Rittle-Johnson, B., Isotani, S., & van Velsen, M. (2014). Using erroneous examples to improve mathematics learning with a tutoring system. Computers & Education, 72, 323–337. doi:10.1016/j.chb.2014.03.053