Macro-Design · Ongoing program lifecycle (applicable to 1-month to multi-year initiatives)
Learning Engineering Process
An agile, interdisciplinary framework that applies learning sciences, human-centered design, and engineering methodologies to create scalable learning experiences. It emphasizes continuous, data-informed iteration to optimize learner development and system performance.
When to use Learning Engineering Process
Ideal for designing scalable digital learning solutions, enterprise-level training programs, and educational technology platforms where continuous optimization and measurable outcomes are critical.
How Learning Engineering Process works
Integrate this framework by designing learning experiences with built-in data collection points (instrumentation). Use the resulting data to make iterative adjustments to content, delivery, and technology platforms, ensuring the design continuously adapts to learner needs.
Phases of Learning Engineering Process
- Human-Centered Design (Empathizing with and understanding the learner's context): The team studies learners, their context and the real performance challenge before designing anything. In a workshop, stakeholders build learner profiles, map pain points and define the challenge from the learner's point of view.
- Learning Sciences Application (Designing evidence-based instructional strategies): The team chooses strategies backed by learning research, such as retrieval practice, spacing or worked examples, to address the defined challenge. Participants match research principles to design options and explain why each should work.
- Engineering & Instrumentation (Building, scaling, and embedding data-capture mechanisms into the learning environment): The team builds the solution and decides in advance what data it will collect to show whether learning happens. In a session, this means prototyping the experience and defining measures and data points together.
- Data-Informed Decision Making (Analyzing learning analytics to iteratively refine and optimize the experience): The team analyses the collected data, compares it with the intended outcomes and decides what to change in the next iteration. Facilitated reviews walk through the evidence and agree concrete revisions.
Key principles
- Human-Centeredness: Prioritizing the learner's context, needs, and cognitive load.
- Scientific Grounding: Basing design decisions on established learning sciences and cognitive psychology.
- Engineering Rigor: Treating learning environments as scalable, instrumented systems.
- Data-Informed Iteration: Using continuous feedback loops and analytics to refine learning outcomes.
Best for
- Digital and hybrid learning systems
- Curriculum optimization
- Educational technology development
Considerations
- Requires cross-functional collaboration among instructional designers, engineers, and data analysts.
- Relies heavily on technical infrastructure capable of capturing and processing learning analytics.
Attribution & sources
Developed by the IEEE International Consortium for Innovation and Collaboration in Learning Engineering (ICICLE), 2017.
Design a session with Learning Engineering Process
METODIC builds a complete Learning Engineering Process-based session — methods, timing, worksheets, and facilitator guides — in minutes.