This subtopic includes the skill and process of analysing learning as a system of systems with the learner at the center, including learning environments, learning professionals, learning content, technological tools, and other components that influence social, cognitive, and motivational factors.
From the Learning Engineering Toolkit
The Learning Engineering Toolkit characterizes the engineering mindset by its habit of viewing any problem as embedded in a larger system [LET-04]. Systems thinking takes a broad vantage point, attending to the structures, recurring patterns, and cycles that run through a system instead of fixating on individual isolated events [LET-04]. For learning engineering, that vantage point deliberately takes in the setting where learning happens, including how a given environment shapes someone's capacity to learn [LET-04].
A central move in the book is to situate the learner within the system rather than treating them as an external user [LET-04]. It raises questions like how the system tailors experiences for people whose backgrounds and needs vary widely, which features let learners steer that tailoring, and how the adaptation in turn affects a learner's ability to learn [LET-04]. On this reading, systems thinking demands reasoning about the full arrangement and the learner's role inside it, not merely about detached teaching moments [LET-04].
The chapter frames a system as a collection of subsystems that each operate on their own while still adding something to the larger whole, and it emphasizes that a system is best seen as dynamic rather than fixed [LET-04]. Because the same system can be captured by several models built for different purposes, the book points out that the Open edX platform might be modeled through the lens of learner interactions, software architecture, course content, or system data, each offering a distinct view of the same system of systems [LET-04]. The anchoring engineering example is Piotr Mitros's team, which had to account for how the design served not just learners but instructors, teaching assistants, authors, school administrators, IT staff, and managers, regarding every one of them as part of a single interconnected learning system [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.