This subtopic covers understanding and application of feedback in closed loop control systems.
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Open-Loop Control: Involves setting up a learning environment that operates without feedback, often used in predictable, stable scenarios.
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Closed-Loop (Feedback) Control: Monitors learners’ progress and adapts instructional strategies based on feedback, commonly used in adaptive learning systems where content is adjusted according to performance.
From the Learning Engineering Toolkit
Within control theory, closed-loop systems tune the input by computing the difference between the feedback and the reference, and since a difference is a subtraction, the Toolkit terms this negative feedback. [LET-04] Positive feedback, on the other hand, throws systems badly out of control because it magnifies whatever error is present, the classic case being the squeal that results when a microphone is aimed at a speaker. [LET-04] The chapter emphasizes that this exact engineering vocabulary runs counter to everyday speech: a closed loop beats an open loop, and negative feedback is the good kind while positive feedback is the bad kind. [LET-04]
The mechanism hinges on a sensor. A sensor reads the output value—in cruise control, the car's speed—and returns it to the controller, which works out the difference from the reference and changes the input to match. [LET-04] In the learning parallel, assessment serves as the sensor that gauges skills so that instruction can be adjusted. [LET-04]
To stop feedback from over-correcting, engineered systems bring in filters and dampers. [LET-04] The chapter cautions that feedback tuned too tightly in a cruise control would keep alternating between braking and accelerating without settling. [LET-04] It defines a filter, in signal processing, as a device or process that strips unwanted parts out of a signal, and a damper as something that lessens the amplitude of mechanical vibration, such as a piano's soft pedal or a car's shock absorbers. [LET-04] A skilled one-on-one tutor fills this role, serving at once as filter, damper, and controller by letting learners spot and fix their own mistakes rather than correcting them right away. [LET-04] Effective learning, the chapter adds, draws on several nested feedback loops—an inner loop that responds while a learner works on an activity and an outer loop that responds between activities. [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.