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Authored from the Learning Engineering Toolkit — pending expert review

This page was authored on 2026-07-16 directly from the source text of the Learning Engineering Toolkit (Jim Goodell & Janet Kolodner (Eds.), 2023). Every factual claim carries an inline <cite> citation to the specific chapter it draws on; the full references are listed at the foot of the page. The prose is grounded in the primary source but has not yet been validated by a subject-matter expert. Use the Edit button to validate, correct, or expand.

Chapters: LET-02 (Chapter 2)

3.4.3 Task Analysis and Cognitive Task Analysis

Source:lebok.wiki/en/knowledge-area-3-learning-sciences-foundations/3-4-application-of-learning-sciences-in-learning-engineering/3-4-3-task-analysis-and-cognitive-task-analysis
Retrieved:2026-04-17
Confidence:HIGH

This subtopic covers task analysis and Cognitive Task Analysis and their use to understand the nature of the skill to be learned. Breaking down complex tasks into smaller, manageable steps helps in designing instructional sequences and performance tasks to measure learning and to provide learners with clear guidance in tackling complex problems.

From the Learning Engineering Toolkit

The Learning Engineering Toolkit presents cognitive task analysis as a way to get from broad competency definitions to the specific competencies learners must actually develop. [LET-02] The book explains that competency definitions used for job descriptions or academic standards are "necessarily defined broadly and leave room for interpretation," so they must be unpacked before they can be reliably taught or assessed. [LET-02] A common mistake, it warns, is assuming a competency addresses a single measurable skill when it actually represents two or more skills that need further unpacking. [LET-02]

To reach what the book calls "data-justified specificity," it describes two complementary approaches: a systematic process for understanding the cognitive elements of a broadly defined competency—cognitive task analysis—and a data-analytics method that uses student learning data to validate or discover errors in an initial set of competency definitions. [LET-02] Learning engineers assume the first draft of competency definitions won't be perfect, and data expose those imperfections so they can be iteratively fixed. [LET-02]

This unpacking matters because of the expert blind spot. The Toolkit notes that experts often cannot say why they know something or how they do it, having conditionalized their processing to be automatic, and that advanced content knowledge without well-developed knowledge of how novices learn can produce expert-based views of curricula that are at odds with the learning process. [LET-02] Learning scientists' methodologies for unpacking expertise let teams define competencies down to an atomic level—in the KLI framework, a knowledge component—and then design finely grained assessment items and learning supports around the components that make up complex tasks. [LET-02]

Sources from the Learning Engineering Toolkit

  1. [LET-02]Jim Goodell, Janet Kolodner & Aaron Kessler (2023). Chapter 2: Learning Engineering Applies the Learning Sciences. In Jim Goodell & Janet Kolodner, Learning Engineering Toolkit (pp. 47–81). Routledge / Taylor & Francis. Open Access

Further Reading

Source: wrgr/lecommons — curated by the learning engineering community. Confidence: medium — lecommons-curated; not yet independently expert-validated in this context. To validate or challenge any item: use the Edit button on this page. Upgrading confidence from medium → high requires expert sign-off.

Landmark Academic Papers

  • SOAR: An Architecture for General Intelligence — John E. Laird, Allen Newell, Paul S. Rosenbloom (1987). Artificial Intelligence · doi:10.1016/0004-3702(87)90050-6 · ~5,000 citations · tier: foundational

    Established SOAR as a unified cognitive architecture. The chunking mechanism in SOAR provides a computational model of procedural learning from practice — directly informing how ITS should structure problem sequences and when to apply mastery criteria. Foundational for cognitive modeling in learning engineering. Source: lecommons/landscape/data/papers.json · ID: LE-LS-AP-015 · confidence: medium · expert-validated: false

  • Cognitive load during problem solving: Effects on learning — John Sweller (1988). Cognitive Science · doi:10.1207/s15516709cog1202_4 · ~4,000 citations · tier: foundational

    The founding paper of Cognitive Load Theory. Established that working memory limitations impose hard constraints on instruction design. CLT-derived principles (worked examples, split-attention, redundancy) are among the most widely replicated findings in educational psychology and are standard design rules in learning engineering. Source: lecommons/landscape/data/papers.json · ID: LE-LS-AP-008 · confidence: medium · expert-validated: false

  • Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge — Albert T. Corbett, John R. Anderson (1994). User Modeling and User-Adapted Interaction · doi:10.1007/BF01099821 · ~2,500 citations · tier: foundational

    The formal presentation of Bayesian Knowledge Tracing (BKT) — the probabilistic model for tracking per-student, per-skill mastery. BKT remains the most widely deployed student model in production ITS and learning platforms worldwide, forming the core of personalization algorithms. Source: lecommons/landscape/data/papers.json · ID: LE-LS-AP-011 · confidence: medium · expert-validated: false

  • Immersive interfaces for engagement and learning — Chris Dede (2009). Science · doi:10.1126/science.1167311 · ~2,000 citations · tier: highly_cited

    Established the engineering parameters for using immersive virtual environments in STEM education. Demonstrated that multi-user virtual environments and augmented reality can support complex cognition and inquiry skills not achievable through conventional instruction. Positioned XR as a serious learning engineering domain. Source: lecommons/landscape/data/papers.json · ID: LE-LS-AP-006 · confidence: medium · expert-validated: false

  • Computer Support for Knowledge-Building Communities — Marlene Scardamalia, Carl Bereiter (1994). Journal of the Learning Sciences · doi:10.1207/s15327809jls0303_3 · ~1,800 citations · tier: foundational

    Engineered the socio-cognitive framework for collaborative digital learning platforms. Established that effective online learning environments must support collective knowledge construction. Foundational for CSCL platform design, modern learning management systems, and collaborative LE tools. Source: lecommons/landscape/data/papers.json · ID: LE-LS-AP-004 · confidence: medium · expert-validated: false

  • Cognitive Tutors: Lessons Learned — John R. Anderson, Albert T. Corbett, Kenneth R. Koedinger et al. (1995). Journal of the Learning Sciences · doi:10.1207/s15327809jls0402_2 · ~1,800 citations · tier: foundational

    The comprehensive review of a decade of Cognitive Tutor development and deployment. Documented both the theory (ACT-R production rules, BKT) and the empirical learning gains in real schools. One of the most influential synthesis papers in ITS, directly shaping subsequent adaptive learning system design. Source: lecommons/landscape/data/papers.json · ID: LE-LS-AP-014 · confidence: medium · expert-validated: false

Key People

  • Herbert A. Simon (profile), Carnegie Mellon University (1916–2001) — Originator of the 'learning engineer' concept; Nobel laureate

    Coined the term 'learning engineer' in the 1967 Educational Record essay 'The Job of a College President' Source: lecommons/landscape/data/people.json · ID: LE-LS-PP-001 · confidence: medium · expert-validated: false

  • John R. Anderson (profile), Carnegie Mellon University, HCII (1947–present) — Cognitive architect; creator of ACT-R; pioneer of Cognitive Tutors

    Developed ACT-R (Adaptive Control of Thought–Rational), the dominant cognitive architecture for modeling skill learning Source: lecommons/landscape/data/people.json · ID: LE-LS-PP-002 · confidence: medium · expert-validated: false

  • Allen Newell (profile), Carnegie Mellon University (1927–1992) — Co-creator of SOAR cognitive architecture; pioneer of AI and cognitive science

    Co-developed SOAR, a unified theory of cognition modeling problem solving and learning via chunking Source: lecommons/landscape/data/people.json · ID: LE-LS-PP-003 · confidence: medium · expert-validated: false

  • John Sweller (profile), University of New South Wales (1946–present) — Developer of Cognitive Load Theory

    Formalized Cognitive Load Theory (CLT) distinguishing intrinsic, extraneous, and germane load Source: lecommons/landscape/data/people.json · ID: LE-LS-PP-004 · confidence: medium · expert-validated: false

  • Kenneth R. Koedinger (profile), Carnegie Mellon University, HCII (active 1988–present) — Co-originator of learning engineering as a named field; Cognitive Tutor pioneer; DataShop founder

    Led development of Cognitive Tutors deployed in thousands of schools; co-founded Carnegie Learning Inc. Source: lecommons/landscape/data/people.json · ID: LE-LS-PP-005 · confidence: medium · expert-validated: false

Organizations, Conferences & Journals

  • HCII — Human-Computer Interaction Institute, Carnegie Mellon (research_center) · link

    Source: lecommons/landscape/data/organizations.json · ID: LE-LS-CO-005 · confidence: medium · expert-validated: false

  • Learning Sciences Research Institute, UIC (research_center) · link

    Source: lecommons/landscape/data/organizations.json · ID: LE-LS-CO-007 · confidence: medium · expert-validated: false

  • International Society of the Learning Sciences Annual Meeting (conference) · link

    Source: lecommons/landscape/data/organizations.json · ID: LE-LS-CE-005 · confidence: medium · expert-validated: false

  • Journal of the Learning Sciences (journal) · link

    Source: lecommons/landscape/data/organizations.json · ID: LE-LS-JO-001 · confidence: medium · expert-validated: false

  • International Journal of STEM Education (journal) · link

    Source: lecommons/landscape/data/organizations.json · ID: LE-LS-JO-007 · confidence: medium · expert-validated: false

Programs & Initiatives

  • International Society for the Learning Sciences (ISLS) (CO) · link

    Home of CSCL and ICLS conferences. Bridges learning science and design. Important for T01 foundation layer. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-042 · confidence: medium · expert-validated: false

  • University of Washington — Learning sciences & design (CoE, HCDE, iSchool) (PC) · link

    Graduate hub in Learning Sciences & Human Development plus related units (e.g., Learning, Epistemology, and Design Lab in HCDE; learning sciences at the Information School) for research on learning, design, and technology in formal and informal settings—UW’s closest cluster to learning engineering even without a single LE degree name. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-088 · confidence: medium · expert-validated: false

Lecommons enrichment applied 2026-04-17. All items pending expert validation. See wrgr/lecommons for source data and curation methodology.

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3.4 Each Learner’s Role in Learning→