Two major advances are changing the nature of eLearning. One is emerging technology that has potential applications to learning. The other is the understanding of how we learn, and what we as L&D professionals should do to instruction to better align. At the intersection is a need to integrate the two to create optimal learning experiences. This has led to a call for learning engineering. What does learning engineering mean in principle and in practice?
The technology changes today are dramatic. Augmented reality (AR) and virtual reality (VR) have become affordable, thanks to reasonably accessible development tools. Similarly, artificial intelligence and machine learning are finding practical inroads in supporting performance and development. Data and analytics are burgeoning, as well.
While we have to be careful to separate the hype from real possibility, we are seeing practical applications. We can have automatic question answering on particular bodies of knowledge; we can make learning experiences adapt to models of the learner’s understanding; we can immerse learners in an artificial world; or we can annotate the world itself.
Taking advantage of these possibilities requires sophisticated digital engineering skills. It is a challenge to build adaptive systems, develop immersive experiences, and align information to contextual cues. To do so in ways that lead to specific performance and learning outcomes is another story.
Much of what has emerged in learning science not just recently, but over several decades, hasn’t fully penetrated practices. For example, the Serious eLearning Manifesto was an initiative to try to instill more learning science into eLearning practices. And the recurrent prevalence of myths in learning indicates that science still doesn’t rule our practices. Yet there’s a lot we can apply.
There’s a solid and growing body of evidence of what should be done. Books like Ericsson’s Peak and Brown, Roediger, & McDaniel’s Make it Stick highlight the findings. While cognitive awareness has moved to incorporate perspectives like situated, distributed, and social cognition, our practices in learning still largely come back to spaced, varied, and deliberate practice.
Introducing learning engineering
In general, engineering is the practical application of science. Biomedical engineering is the application of biomedical sciences, and electrical engineering is the application of knowledge of the science of electromagnetism. Following that thinking learning engineering should just mean “applied learning science,” however a new initiative is pushing this further.
The Institute of Electrical and Electronics Engineers (IEEE) is sponsoring ICICLE, the IC Industry Consortium on Learning Engineering. With representation from industry, academia, and government, the goal is “supporting the development of learning engineering as a profession and academic discipline.” Its definition is “merging engineering and systems thinking with learning science and theories of human development.” And this is a valuable perspective.
The focus is on applying learning science to new technologies. There is certainly room for improvement in existing technologies as well, such as learning management systems, authoring tools, mLearning, simulations, and more. Our industry needs a shift from what the market wants, to what it should be asking for.
There’s also a fair bit of ground to cover. When you take a full ecosystem perspective, you are looking at formal learning, performance support, and social and informal learning. Thus, you’re about more than just learning science, but the full spectrum of cognitive science. Similarly, the wide variety of technologies includes associated processes like software engineering, usability, and project management. Also, awareness of the contexts of application—K-12 and higher education, organizations, as well as “none-of-the-above”—would be required.
The inclusion of systems thinking is also important. Full solutions include an environmental scan outward from the core experience to the legal, social, and other implications. History tells us that just trying to insert a new learning technology is fraught with complications. Thinking systemically has the potential to anticipate problems not foreseen by just designing a solution. You could look at design thinking here, as well.
What does this mean for our industry? Where are learning engineers needed, and how can they be found and developed? As we move forward, there will be an increasing demand for this skill set. Meeting it moves into prominence.
At the very least L&D sorely needs a deeper background in learning science. The previously discussed gaps need addressing. Further, the increasing capabilities in augmenting intellect delivered by technology suggest a bigger picture.
More organizations are, or should be, looking at the afore-mentioned ecosystem approach. To create a true user-centric learning and performance environment, a successful integration of the skills suggested would be necessary, including knowledge of the domains and of process. While conversations between IT and L&D could achieve the balance, a successful approach ideally would be guided by someone with sufficient background in both sides.
Further, the rise of educational technology ventures is noticeable. Research documents a growing investment. Most of these new initiatives are integrations of new technology to learning. Thus, these efforts require a skillset composed of technology awareness, learning science comprehension, the ability to envision the integration, and the ability to execute against that vision.
The full suite of competencies is still under development. However, an awareness of the need and the initiative promise a revision in the suite of skills that will be required as L&D moves forward. While not everyone needs every skill, someone has to oversee the integration, and it helps when there’s a shared understanding of the overall picture. Recognizing the components is an important step, so this ICICLE initiative has the potential to benefit us all.