Learning Engineering (LE) Spotlight: Your Weekly Dive into LE Research & Practice (Sept. 15-21)

The Power of Learning Engineering: From Process to Practice

Learning engineering is a fascinating concept that's gaining momentum in education. While the term might sound like a mix of two very different worlds—learning and engineering—it's actually a powerful, emerging field that combines both to create scalable, impactful learning solutions.

What is Learning Engineering?

At its core, learning engineering applies evidence-based practices from the learning sciences, data-informed decision-making, and human-centered design to optimize how we learn. It's not about "engineering" the act of learning itself but creating environments and conditions that help learners thrive.

Herbert Simon, a Nobel laureate and a key figure in the field, coined the term more than 50 years ago. His work laid the foundation for what learning engineering is today: a multidisciplinary approach that involves experts in instructional design, learning sciences, data science, and even user experience.

Why Learning Engineering Matters

Think of learning engineering as a team sport. Just like civil engineers collaborate to build bridges, learning engineers work with diverse professionals to design learning systems. Whether it's the physical space of a classroom, the technology learners use, or the data collected to measure progress, learning engineers bring together these elements to create optimal learning experiences.

One powerful example of learning engineering in action is Duolingo, the language learning app. The team at Duolingo combines cognitive psychology, user data, and AI to personalize the learning experience for over 500 million users. They constantly run controlled experiments to improve the app, tweaking everything from user engagement features to when you’re prompted to practice a word you’ve almost forgotten. This data-driven, iterative approach is what makes learning engineering unique.

What Makes Learning Engineering Different?

Unlike traditional teaching methods, which often rely on one-size-fits-all solutions, learning engineering recognizes that there’s no such thing as an “average” learner. Instead, it focuses on personalization and adaptability, using data and technology to meet learners where they are. For example, learning engineers analyze what’s working, iterate designs based on data, and then implement changes to optimize the learning experience for individual students.

Learning engineering isn’t just about technology, though. It’s about problem-solving. Sometimes, the solution to a learning challenge might be as simple as redesigning a classroom layout or creating a low-tech resource that works in a specific context, like the donkey cart solution used in Gambia to help children get to school.

Key (Must-Have) Resources

Check out the following openly-licensed materials from the "Learning Engineering Toolkit" to help get started with applying the processes and practices important to individuals and teams looking to add learning engineering to their repertoires:

Access open materials (e.g., infographics, images, slides) from the Learning Engineering Toolkit.

Read more: Goodell, J., & Kolodner, J. (Eds.). (2022).?Learning engineering toolkit: Evidence-based practices from the learning sciences, instructional design, and beyond. Taylor & Francis. https://doi.org/10.4324/9781003276579

The Takeaway

As education continues to evolve, learning engineering offers a way to blend the best of science and engineering to improve learning outcomes. By focusing on both the learner and the learning environment, this field helps us rethink how we approach education in a world that’s increasingly complex and data-driven.

Stay tuned for more insights as we explore how learning engineering can transform education, one learner at a time.

Acknowledgements: Many thanks to the team of editors ( Jim Goodell and Janet Kolodner ), authors ( Avron Barr , Michelle Barrett, Erin S. Barry, Laura Casey, Jesse Chuang, Erin Czerwinski , Scotty D. Craig, Chris Dede , Diana Delgado, Tanvi Domadia , Jim Goodell, Scott W. Greenwald, Andrew J. Hampton, Daniel Jacobs, Michael Jay , Aaron Kessler , Janet Kolodner, Dina Kurzweil , Jodi Lis , Prasad Ram, Brandt Redd , Steve Ritter , Bror Saxberg , Jordan Richard Schoenherr, PhD , Sae Schatz , Robert Sottilare, Ph.D. , Khanh-Phuong Thai, 佟佳睿Richard , Dr. J.J. Walcutt ), and contributors (listed in the book's front matter) who made this work come to life by sharing their many years of experiences and wealth of knowledge in the field of learning engineering!

*This newsletter was generated with the help of ChatGPT 4o (Sept 16 version).