Learning Engineering Primer
(version 1.0, created 04/01/26, last updated 06/13/26)
Learning engineering integrates scientific, engineering, and design principles to systematically improve learner conditions and learning outcomes. The proposed framework models learning engineering as a structured domain composed of interdependent foundational dimensions that operate through iterative, evidence-based processes.
At the highest level, learning engineering is grounded in
- scientific principles, which explain and predict learning phenomena,
- design principles, which shape learner experience and interaction, and
- engineering principles, which guide optimization under constraints
all of which converge to form the bedrock of the domain.
This convergence gives rise to five primary dimensions:
- Learning sciences
investigates how learning occurs—including cognitive, motivational, and social processes. - Learning design
shapes instructional experiences through intentional structuring of content, activities, and interactions. - Learning technology
supports the implementation and scalability of learning interventions. - Systems thinking
coordinates the relationships among components while accounting for contextual, institutional, and resource constraints. - Data analytics
enables the measurement, analysis, and inference of learning processes and outcomes.
which function as interdependent components, each contributing a necessary applied function in the creation, implementation, investigation, and iterative improvement of learning under highly-contextualized environmental constraints (cf. Learning Engineering Process Model).
This applied layer consists of
- Learner
- Learner activity
- Content
- Context
- Constraint
- Technology
- Evidence
- Outcome
- Iteration
which contribute to the optimization of (human and non-human) system performance related to learning effectiveness, efficiency, and scale.
