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Macro-Design

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.

4 phasesMacro-Design
When to Use This Framework

When you need to design a complete learning experience from scratch

You're planning a workshop, training, or learning session and need a proven structure to organize your content and activities.

Ideal for designing scalable digital learning solutions, enterprise-level training programs, and educational technology platforms where continuous optimization and measurable outcomes are critical.

The 4 Steps
Follow this sequence to apply 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)

What You'll Achieve

Ensures your session has clear goals, logical flow, and measurable outcomes.

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.

Practical Tips
How to get the most out of this framework
  • 1
    Start by defining what success looks like at the end
  • 2
    Work backwards from outcomes to activities
  • 3
    Build in checkpoints to verify learning
  • 4
    Allow time for practice and application
Best For
  • Digital and hybrid learning systems
  • Curriculum optimization
  • Educational technology development
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.
Watch Out For
  • Requires cross-functional collaboration among instructional designers, engineers, and data analysts.
  • Relies heavily on technical infrastructure capable of capturing and processing learning analytics.
Related Frameworks
Other Macro-Design frameworks you might find useful
70:20:10 ModelBalance experiential, social, and formal learning
Kepner-Tregoe (KT) Rational ProcessA systematic, data-driven methodology for objective decision-making and root cause analysis. It provides a structured language and set of tools to decouple emotion from complex organizational challenges, ensuring teams reach evidence-based conclusions.
Design ThinkingDesign Thinking is a human-centered, iterative problem-solving methodology. It focuses on understanding user needs, challenging assumptions, and creating innovative solutions through prototyping and testing.