This topic covers Modeling, an essential component of engineering design that involves creating representations of systems or solutions to predict outcomes, identify potential issues, and refine designs before full-scale implementation.
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From the Learning Engineering Toolkit
One way engineers cope with the complexity of the unknown is by building and using models [LET-04]. A model can act as a stripped-down stand-in for a complicated system, or it can simulate a system in ways that would be hard or expensive to try in reality [LET-04]. Civil engineers, for example, draw on models of population growth, material strength, weather, and material decay to keep bridges and roads suitably scaled and safe throughout their working life [LET-04].
Not every model is exact. Some are estimates that carry a safety margin the engineer needs to grasp, and some are as basic as the trigonometry or multiplication tables an engineer extrapolates from to reach finer detail [LET-04]. Others, such as computational fluid dynamics models, are high-fidelity; the aerospace industry uses them to test and validate new designs like wing shapes, keeping or even adding complexities that are difficult or impossible to test physically [LET-04].
The same reasoning carries over to learning engineering, where models of learners can serve to test learning systems or ideas [LET-04]. Piotr Mitros's team, for instance, drew on models of learning such as the progression from passive to active and on to constructive learning [LET-04].
Models capture aspects of a system along with the mathematical relationships needed to understand and analyze it, and several models of one system can be built to study it from different angles [LET-04]. The Open edX platform could be rendered as one model of learner interactions, another of software architecture, another of course content, and another of the system's data [LET-04]. Because systems are designed with models of varying fidelity, selecting a fitting model—and appreciating what it cannot capture—is central to sound engineering design [LET-04].
Sources from the Learning Engineering Toolkit
- [LET-04]Avron Barr, Brandt Dargue, Jim Goodell & Brandt Redd (2023). Chapter 4: Learning Engineering is Engineering. In Jim Goodell & Janet Kolodner, Learning Engineering Toolkit (pp. 125–151). Routledge / Taylor & Francis. doi:10.4324/9781003276579
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 frommedium→highrequires expert sign-off.
Landmark Academic Papers
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Design principles for virtual humans in educational technology environments — Scotty D. Craig, Nicolaus L. Schroeder (2018). International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-017-0148-y · ~150 citations · tier: contemporary
Synthesized human-factors engineering principles for designing pedagogical agents. Showed that voice, appearance, and persona of virtual instructors systematically affect learner cognitive load and perceived credibility — establishing that LE must integrate psychological design alongside algorithmic design. Source: lecommons/landscape/data/papers.json · ID: LE-LS-AP-009 · confidence: medium · expert-validated: false
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The voice quality of pedagogical agent impacts learning and agent perceptions — Ryan F. Siegle, Scotty D. Craig (2024). Journal of Computer Assisted Learning · doi:10.1111/jcal.12997 · ~20 citations · tier: contemporary
Empirically demonstrated that voice quality (prosody, naturalness, warmth) of a pedagogical agent significantly affects both learning outcomes and learner perception. Illustrates the meticulous human factors engineering required in modern AI-driven learning systems. Source: lecommons/landscape/data/papers.json · ID: LE-LS-AP-010 · confidence: medium · expert-validated: false
Key People
- Scotty D. Craig (profile), Arizona State University (active 2000s–present) — Human factors researcher; virtual humans and pedagogical agents specialist
Systematically investigated design principles for virtual humans and pedagogical agents in learning environments Source: lecommons/landscape/data/people.json · ID: LE-LS-PP-014 · confidence: medium · expert-validated: false
Programs & Initiatives
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Human Factors and Ergonomics Society (CO) · link
Professional society for human factors and ergonomics. Publishes Human Factors journal and organizes the annual HFES conference. The Training Systems technical group directly addresses LE-relevant human factors in instructional systems. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-133 · confidence: medium · expert-validated: false
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INCOSE Systems Engineering Body of Knowledge (SEBoK) (CO) · link
Open wiki-based body of knowledge for systems engineering. Includes the Human Systems Integration knowledge area. The SE process framework (requirements, architecture, integration, verification) is the engineering process backbone that LE adapts for learning system development. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-134 · 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.