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6 references
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- Vol 3 RE: Chapter 1
cognitive architecturedeclarative knowledgeprocedural knowledgeproduction rulesskill acquisitioncognitive modellingproblem solvingintelligent tutoringBook Evidence summaryAt a glance
Anderson develops a unified computational account of cognition centred on the ACT architecture.
Key ideas
The book proposes that thinking emerges from interactions between declarative knowledge, represented as facts and structured information, and procedural knowledge, represented as productions that specify actions under particular conditions. Learning changes both the availability of knowledge and the efficiency of procedures, allowing performance to move from deliberate problem solving toward skilled execution. The architecture is intended to explain memory, reasoning, language, problem solving, and the acquisition of expertise within one framework. Its educational importance lies in treating learning as changes in organized knowledge and production rules rather than as undifferentiated practice.
Why it matters
Later ACT-R work revised many details, so the book is best read as a foundational model whose central questions continued to guide cognitive modelling and intelligent tutoring research.
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- Vol 4 TE: App C
intrinsic cognitive loadsubjective mental effortelement interactivitycognitive load measurementalgebra problem solvinglearner expertiseJournal article Evidence summaryAt a glance
Ayres investigates whether learners' own ratings of mental effort can detect changes in intrinsic cognitive load within a single task. In two experiments, students solved algebra problems and rated cognitive load after each computation.
Key ideas
Because the problems were designed to hold extraneous and germane load relatively constant, variation in ratings was interpreted as variation in element interactivity: the number and relation of information elements that must be processed together. The ratings were highly reliable, changed significantly across steps within problems, and were strongly related to errors. They also reflected learner expertise and procedural mistakes. The study therefore extends subjective rating scales beyond comparisons between whole tasks or instructional conditions.
Why it matters
For educators and researchers, it shows that a brief rating can reveal where a multistep problem becomes cognitively demanding, although ratings must be interpreted alongside performance and prior knowledge.
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- Vol 4 RE: Chapters 10, 14, 17, App A, App C
project-based learningauthentic assessmentstudent agencycollaborative problem solvingproject planningformative feedbackauthentic audiencesself-managementJournal article Evidence summaryAt a glance
Bell presents project-based learning as a student-driven approach in which learners investigate a meaningful question over time and create a product or performance.
Key ideas
Projects require inquiry, planning, research, collaboration, problem solving, technology use, and revision, allowing subject knowledge to develop alongside communication and self-management. The teacher establishes standards and milestones, teaches needed skills, monitors progress, and provides feedback rather than withdrawing guidance. Choice and authentic audiences can increase ownership, while teamwork makes participation and accountability important design concerns. The article connects these features with skills frequently described as necessary for contemporary study, work, and civic life.
Why it matters
For educators, its practical message is to align projects with explicit curriculum outcomes, assess both process and product, build in checkpoints and reflection, and scaffold independence so that engaging activity results in substantive learning.
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- Vol 4 TE: App C
progressive problem solvingadaptive expertiseadvancing expertiseself-regulationpromisingness judgmentstacit knowledgeexplicit knowledgereinvestment of mental resourcesBook Evidence summaryAt a glance
Bereiter and Scardamalia challenge views of expertise as simply accumulated knowledge, automatic skill, or exceptional talent.
Key ideas
Their central idea is progressive problem solving: accomplished people reinvest the mental resources released by experience into redefining tasks, noticing deeper problems, and extending what they can do. Routine experts use efficiency to make work easier; adaptive or advancing experts seek more demanding goals and continue learning at the edge of competence. The authors draw across domains to examine pattern recognition, tacit and explicit knowledge, self-regulation, promisingness judgments, and the social conditions that support expertise. For educators, the account shifts attention from accelerating novices toward polished performance to cultivating habits of inquiry, reflection, and reinvestment.
Why it matters
Learners need strong foundations, but also opportunities to identify worthwhile problems, improve methods, and treat competence as a platform for further growth.
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- Vol 1 RE: Chapters 2, 4, 5, 6, App B
- Vol 1 TE: App A
expertise in problem solvingdomain-specific knowledgeproblem representationdeep structural principlesqualitative analysisknowledge schemassurface-feature recognitionBook chapter Evidence summaryAt a glance
Chi, Glaser, and Rees synthesise research on expertise in problem solving, with particular attention to how knowledge is organised and used. Experts do not merely possess more facts or apply a general superior reasoning ability.
Key ideas
They recognise meaningful patterns, represent problems through deep domain principles, retrieve connected procedures efficiently, and use qualitative analysis to constrain a solution before calculating. Their advantages are largely domain-specific and arise from extensive structured knowledge and experience. Novices more often attend to surface features, search less selectively, and lack schemas that connect conditions with productive actions. Expertise can also bring limitations when familiar patterns are applied rigidly.
Why it matters
For educators, the chapter supports developing richly connected disciplinary knowledge, comparing surface-diverse problems with common structures, modelling problem representation and planning, and giving learners varied practice that helps them recognise when and why a principle applies.
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- Vol 1 RE: Chapters 9, 12, App B
- Vol 2 RE: Chapters 8, 9
self-explanationgenerative inferenceprior-knowledge integrationmental-model revisioncoherent mental modelstransfer problem solvingexplanation qualityJournal article Evidence summaryAt a glance
Chi and colleagues test whether prompting students to explain a science text to themselves improves understanding.
Key ideas
Eighth-grade learners read a passage about the human circulatory system; those prompted to self-explain generated inferences, connected statements with prior knowledge, repaired gaps, and constructed more coherent mental models. They subsequently learned more and solved transfer problems better than students who read without the same explanation activity. Analyses showed that the quality and content of explanations mattered: productive self-explanation was not mere repetition, and learners differed in how extensively they revised their models. The work demonstrates a generative mechanism through which learners can make sense of incomplete instructional material.
Why it matters
For educators, it supports inserting prompts such as “explain why,” “how does this connect,” or “what follows,” modelling useful explanations, and checking their substance rather than counting how often students speak or write.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
Citation checks
References are checked against publisher records, scholarly indexes, DOI or ISBN metadata, repositories and author records where available. An entry marked [Citation not verified] preserves the wording found in the relevant volume without attributing findings to an unconfirmed work.
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