Learning Engineering Spotlight (Nov. 10-16): Your Weekly Dive into LE Research & Practice

"Learning engineering is a process and practice that applies the learning sciences using human-centered engineering design methodologies and data-informed decision-making to support learners and their development" (Goodell et al., 2022, p. 47).

Pioneering the Future of Learning with Evidence-Based Innovation

Just as the chemical engineers at Pfizer turned Alexander Fleming’s discovery of penicillin into a life-saving medicine accessible to all, today’s breakthroughs in the learning sciences benefit from an innovative process to scale their transformative potential. Enter Learning Engineering: a discipline that combines the latest in cognitive science, human-centered design, and data-driven methodologies to create tailored, impactful learning experiences.

The Fundamentals of Learning Science

Herb Simon (1967), the father of learning engineering, once said, "Learning is a complex psychological process, and it would be na?ve to design effective learning environments without a mastery of what is known scientifically about that process" (p. 73). Today, we know more than ever about how people learn, but much of this knowledge remains untapped.

Key principles include:

• Mental Models as the Building Blocks of Expertise - Learning is not about starting from scratch but building on existing knowledge and experiences. Mental models (or schemas) are internal representations of how the world works, refined through new experiences. Expertise develops by continually improving these models and connecting them across domains.
• Cognitive Load and the Power of Scaffolding - Learning occurs best when the difficulty level is “just right.” Too much cognitive load overwhelms working memory, while tasks that are too easy can lead to boredom. Scaffolding—hints, prompts, or structured supports—helps learners tackle more complex challenges, turning frustration into productive effort.
• No One-Size-Fits-All Approach - Learners bring unique cultural contexts, developmental levels, and prior knowledge to the table. Effective learning solutions consider these variabilities, adapting methods to meet learners where they are.

From Novice to Expert: The Journey of Learning

Research shows that becoming an expert involves more than just accumulating knowledge. It’s about rewiring the brain for efficiency, adaptability, and deeper understanding.

• Deliberate Practice and Feedback - Expertise requires focused, intentional practice with regular feedback. Short, spaced-out practice sessions (spaced repetition) lead to better retention and application than cramming.
• Metacognition: Thinking About Thinking - Reflecting on one’s learning process is crucial. Metacognitive strategies, like self-regulated learning and goal-setting, empower learners to adjust their approaches and overcome obstacles.
• The Role of Executive Function - Skills like working memory, attention, and self-control play a significant role in learning. These capacities help learners stay focused, connect new information to prior knowledge, and persevere through challenges.

The Role of Learning Engineering

Learning engineering bridges the gap between theory and practice by applying insights from cognitive science to design better learning environments.

Key tools and methodologies include:

• Competency Modeling - Breaking down complex skills into measurable components helps identify gaps and tailor learning experiences.
• Data-Driven Learner Models - Using student interaction data to infer cognitive and motivational states enables adaptive learning systems to provide personalized feedback and support.
• Iterative Design and Testing - Borrowing from engineering disciplines, learning solutions are refined through continuous cycles of implementation, assessment, and redesign.

For example, children struggling to grasp negative numbers, benefit from hands-on activities like number lines and relatable analogies—helping their mental models evolve from simplistic understandings to a nuanced comprehension of mathematical concepts.

Lifelong Learning in a Changing World

The demands of the 21st-century workplace make learning a lifelong endeavor. Jobs in cybersecurity, for instance, require constant upskilling and adaptability. Mastering the art of learning—metacognition, perseverance, and strategic thinking—is no longer optional.

Even later in life, as the brain undergoes changes associated with aging, it retains the ability to learn. Neuroplasticity allows for the development of new skills and the retention of cognitive sharpness, provided the learning environment is engaging and supportive.

Looking Ahead: A Call to Action

The golden age of brain research is here, but it’s time to translate these discoveries into the golden age of human learning. Learning engineering is a key to this transformation, turning insights into scalable innovations that benefit all learners—children, adults, and professionals alike.

As we reimagine education, let’s embrace the complexity of learning and design systems that not only deliver knowledge but empower individuals to thrive in an ever-changing world.

Read the full chapter (open access):

Goodell, J., Kolodner, J., & Kessler, A. (2022). Learning engineering applies the learning sciences. In J. Goodell, & J. Kolodner (Eds.). Learning engineering toolkit: Evidence-based practices from the learning sciences, instructional design, and beyond (pp. 47-82). Taylor & Francis. https://doi.org/10.4324/9781003276579-6

Acknowledgements: A special thanks to Jim Goodell , Janet Kolodner , and Aaron Kessler for authoring an extremely practical, yet thoughtful exposition on this topic.

*This newsletter was generated with the help of ChatGPT 4o (Nov 12 version).