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

  1. Human-Centered Design (Empathizing with and understanding the learner's context)
  2. Learning Sciences Application (Designing evidence-based instructional strategies)
  3. Engineering & Instrumentation (Building, scaling, and embedding data-capture mechanisms into the learning environment)
  4. Data-Informed Decision Making (Analyzing learning analytics to iteratively refine and optimize the experience)

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.

Primary source

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