This subtopic addresses the process of identifying components of the learning system of systems such as components in the learning environments, pedagogical models and methods employed, social environment, learning content, learning context, technological tools, and motivational factors.
From the Learning Engineering Toolkit
The Learning Engineering Toolkit explains that engineers tame complexity by picturing a system as a set of interoperable components, or modules [LET-04]. Splitting a system into modules lets engineers break one large problem into smaller, more manageable subsystems, each carrying design limits and tolerances suited to the job it does within the whole [LET-04]. Picking out the correct components is therefore a foundational act of engineering: a system works only if it contains modules covering every function it must perform [LET-04].
Every module comes with its own specifications. The book notes that a module has design constraints and tolerances, where a tolerance sets how much variation is allowed, and that a module should permit only those variations that will not meaningfully disturb the wider system under the conditions in which it is meant to run [LET-04]. In specifying a component, an engineer can also state the normal conditions it is expected to face and the acceptable band within which it has to keep working [LET-04].
The chapter grounds component identification in a concrete learning example. When in-person teaching broke down during the pandemic, the analysis worked by naming the specific pieces that had previously kept students participating, the physical space, the cultural norms, and, in public K-12 systems, truancy laws and bussing, and then recognizing which of those pieces no longer functioned under the changed conditions [LET-04]. The most inventive institutions responded by building new modules to shore up or replace the failed parts of their systems and by developing fresh sensors and instrumentation to track student engagement remotely [LET-04]. Identifying components thus amounts to listing the parts of the learning system of systems, from environments and content to the tools and monitoring instruments, so that each can be designed, bounded, and later reworked as needed [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.