How to Engineer eLearning: An evidence-based human-centered design process for modern times
A Review of Learning Engineering Toolkit: Evidence-based Practices from the Learning Sciences, Instructional Design, and Beyond

I first came across Learning Engineering Toolkit: Evidence-based Practices from the Learning Sciences, Instructional Design, and Beyond (Routledge, 2023) when a colleague handed me a review copy at our first, post-pandemic faculty retreat. Once I started reading, I couldn’t put it down. Learning Engineering Toolkit is a timely, thoughtful, and well-written guidebook for eLearning professionals who are passionate about applying systematic, evidence-based principles of learning engineering to eLearning projects. The book’s editors, Jim Goodell and Janet Kolodner, are renowned experts in engineering and computer science education.  They solicited nearly 30 prominent experts in the field to contribute individual chapters. This comprehensive and unique volume melds together the diverse fields of learning sciences, instructional design, engineering, and edtech.

Foreword and Preface

The foreword written by Harvard professor Chris Dede opines that learning engineers are well-equipped to tackle the wicked learning problems of our time, such as the inadequacies of emergency remote teaching laid bare by the COVID-19 pandemic; however, the profession can’t scale until there’s much wider adoption of its tools and processes. A remarkable computer scientist and Harvard-trained medical doctor turned learning engineer, Bror Saxburg’s preface positions learning engineers as among the best-trained and most flexible technology workers when it comes to turning good design, data points, and human-centered design into viable learning solutions. Now for some history.

Introduction

While the term “learning engineering” was coined by Carnegie Mellon professor Herbert Simon more than 50 years ago, Goodell and Kolodner offer a new perspective, indicating that it is both a process and practice that incorporates human-centered engineering design methodologies to support learners and their development. This message permeates throughout Learning Engineering Toolkit, which not only offers a wide range of eLearning strategies but also ample practical examples of successful learning engineering applications. In a video interview about the book, Goodell defines learning engineering to emphasize its distinctions in a field crowded with related disciplines, but he also ties in similarities to other more established fields like instructional design.

In the introduction to Learning Engineering Toolkit, Goodell describes learning engineering as a "process and practice” that “applies learning sciences, using human-centered engineering design methodologies, and data-informed decision-making…to support learners and their development" [1].

Foundations and Tools

The editors recommend reading the content sequentially before revisiting specific segments related to the tools and processes of particular relevance for the learning context of interest. I followed this advice and immediately applied elements of the chapters on user experience research and human-centered design to iterate portions of a first-run AR/VR-focused e-learning online course I developed. Learning Engineering Toolkit encourages a kaizen approach, one that encourages even small changes to learning interventions. By definition, learning engineering is a scalable, iterative process, not the common one-and-done approach, which distinguishes it from the way eLearning design is practiced in some organizations.

I found the comprehensive references at the end of each chapter useful, as they allowed me to find cited sources more easily than if I had to search a long list of references at the end of the volume. The in-text references to specific pages in other chapters were also highly beneficial when I wanted to learn more about a particular topic. There are also many infographics and blue-boxed narratives that provide even more insights.

Learning engineering is a process.

Toolkit contents reflect the editors’ assertion that learning engineering is "a series of actions or steps taken to achieve a particular end, and it has inputs, process steps, and outputs" [1]. However, this is not a linear but rather an iterative process that starts with a design challenge and may follow a series of different paths toward a solution, or a collection of solutions with different characteristics.

Human-centered design strategies.

Learning Engineering Toolkit places a particular emphasis on human-centered design, which requires getting to know the learners and empathizing with them to identify their needs and expectations. As this approach is active and takes account of learner variability, it can include task analysis familiar to instructional designers and e-learning specialists, the 5 Whys technique, fishbone analysis, failure mode and effects analysis, or any other suitable method. In that respect, learning engineering, instructional design, and eLearning development share complementary processes, and the specific strategies adopted will be based on the identified needs, root causes, and other relevant criteria. Chapter 3 includes a great example of how a collaborative interdisciplinary team applied a learning engineering process to improve healthcare delivery. This informal case study describes the use of human-centered design principles to develop The Medic Mobile Community Health App aimed at diverse learners who spoke different languages, came from different cultures, and were in some cases even illiterate. 

Learning engineering is based on the learning sciences.

It took me four years of Ph.D. work to explore learning sciences through primary resources such as Merriam [2], The Cambridge Handbook of the Learning Sciences [3], How People Learn [4], and innumerable peer-reviewed journal articles. Thus, I was impressed by the 30-page overview provided on this vast domain in the book. 

Learning Engineering Toolkit is also a valuable resource for those wishing to learn the basics of neuroanatomy. There is even an artist’s rendering of a neuron and associated structures worthy of any anatomy and physiology textbook like the ones I used as an undergraduate microbiology student. According to the National Institutes of Health, there are more than 100 billion of these physical structures shaped by human experience and learning in the human brain [5], and they work together like light switches. If we multiply the number of switches and account for the wiring among them, according to the Toolkit, we will have a basic model that explains the neuroscience of learning. Through learning, these networks of switches and connections change, evolve, and adapt to new stimuli or inputs from the senses, the concept known as neuroplasticity.

Learning engineering uses data analytics.

The book also emphasizes the importance of analyzing in the context of eLearning, both at the course and at the organizational level. These analyses should be based on solid evaluation plans and reliable inputs to generate actionable outputs. In practice, learning engineers and data analysts working in collaborative learning engineering teams typically sift through and explore vast amounts of data generated by learning interventions, as this approach better reflects the real-world contexts than do carefully controlled experimental models. To accomplish this successfully, data instrumentation must be incorporated into the learning intervention design from the outset, as demonstrated in the chapter on xAPI or experience application programming interface. Still, data-informed decision-making is not unique to learning engineering; I first learned about it as a Quality Enhancement Plan faculty co-leader on the community college team that won the first Malcolm Baldridge Award for Performance Excellence in 2005. Nonetheless, Learning Engineering Toolkit is an excellent source on this subject, especially for those aiming to improve existing practices and revise widely held assumptions about e-learning to achieve measurable and scalable improvements.

Tools.

xAPI is s just one of many data instrumentation approaches explored in the book. There are 11 chapters on analytical and design tools with detailed descriptions of the techniques and resource links to understand design challenges, teaming, lean-agile development, data instrumentation, software and technology standards, motivational tools, implementation tools, ethical decision-making tools, and data analysis. That’s a lot of tools in one book that maps everything out for would-be learning engineers or those who simply want to improve their work as eLearning designers, developers, and managers.

Critical Voices

As in any field, learning engineering is not without critics, and in this context, it is worth noting the views of Audrey Watters [6] and Victor R. Lee [7] suggesting learning engineering is just another term to describe practices that are already performed as a part of different fields. Others are also concerned about its contribution to surveillance capitalism, and, to this end, Learning Engineering Toolkit provides a comprehensive chapter on ethics and ethical decision-making, thus offering a framework for eLearning designers at each stage of learning intervention development. This is vital in an age where rampant challenges to privacy in online, hybrid, and face-to-face classrooms present threats for everyone working in the e-learning profession.

Conclusion

Learning Engineering Toolkit ends with several insightful stories about the tools, processes, and strategies associated with learning engineering applied to a future world that includes expanded use of AI-drive intelligent tutors and more. It is a fitting conclusion to a book that everyone involved in eLearning should consider adding to their professional library. Having this valuable volume at our disposal will be indispensable when the right approach demands human-centered design, lean and agile strategies, rapid prototyping, and data-informed decision-making to iteratively develop increasingly complex learning environments that are sure to challenge even the most adept e-learning designers.

References

About the Author

Dr. Doug Wilson serves as an assistant professor of learning technologies at George Mason University, where he provides instruction to graduate students in the Learning, Design, and Technology (LDT) Program. Before joining George Mason, Dr. Wilson taught graduate-level instructional design and educational technology courses online in the higher education and learning technologies program at Texas A&M University, Commerce. His teaching experience also includes more than 10 years of service as a professor of journalism and basic writing at Richland College. Dr. Wilson’s credentials include a Ph.D. in learning, design, & technology from The Pennsylvania State University, a Master of Science in journalism from the Columbia University Graduate School of Journalism, and a Bachelor of Science in microbiology from Xavier University of Louisiana. Dr. Wilson’s current research interests include augmented and virtual reality, learning experience design, instructional design, and online teaching and learning. His work has been published in several peer-reviewed academic journals and conference proceedings. Prior to becoming an academic, Dr. Wilson worked for more than a decade as a major market television news report