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- Vol 1 RE: Chapters 3, 4, 10, 12, App A, App B
- Vol 2 RE: Chapters 1, 3, 4, App A, App C, App D
- Vol 2 TE: App B
- Vol 3 RE: Chapters 2, 4, 11, App A
- Vol 3 TE: App C
- Vol 4 RE: Chapters 1, 2, 3, App A, App C
- Vol 4 TE: App C
- Vol 5 RE: App B
- Vol 5 TE: App C
- Vol 7 PE: App D
- Vol 7 RE: Chapters 1, 2, 4, App A, App B, App D
cognitive loadmeans–ends analysisgoal-free problemsworked examplesschema acquisitionproblem-solving searchJournal article Evidence summaryAt a glance
Sweller argues that conventional means–ends problem solving can consume the limited processing capacity needed for learning. Experts differ from novices largely through domain-specific schemas that organise problem states and solution procedures.
Key ideas
Yet novices solving unfamiliar problems may focus on repeatedly comparing the current state with the goal, selecting operators to reduce differences, and tracking subgoals. This search can produce a correct answer without directing attention to the structural relations needed for schema acquisition. Computational analysis and experiments compare conventional problems with alternatives such as goal-free problems that reduce means–ends search. Learners under reduced search demands can devote more capacity to studying problem structure and later transfer more effectively. The article helped establish cognitive load theory but does not imply that problem solving is inherently unproductive; its instructional value changes with prior knowledge and guidance.
Why it matters
For educators, novices benefit from worked examples, completion tasks, explicit subgoals, and reduced search before independent practice. Assessment should distinguish immediate solution success from later learning, and support should fade as schemas develop so that learners eventually practise full problem solving.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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