The (coming) new normal: Instructional designers may find themselves gaining new roles as partners in learning engineering efforts.
Since formalized training came into being, instructional designers designed instruction. They created classroom courses, and people signed in on paper. With eLearning and computer systems, including LMSs, we found new ways of creating and offering courses and keeping up with who took them. Some stayed attached to the classroom, some moved to online-based approaches, and some did both, but otherwise the basic work itself didn’t change much.
Things have changed significantly, especially over the last decade. Workers are accessing learning opportunities through many in-house and external avenues, including electronic performance support tools, Google searches, YouTube videos, infographics, and virtual reality lab experiences. We can gather and parse data in ways and in volumes unimaginable to those working in the field even 10 years ago. No longer just responsible for building courses, the ID is now called on to help create apps, build dashboards, track informal learning, link sensor data to training data, and embed analytics in the workflow. For a while we—or, at least I—managed this by juggling relationships with IT, HR, and a gifted data-wizard coworker. But the juggling is starting to require too many balls in the air.
Enter learning engineering. It’s basically meshing an understanding of learning theory, learning science, and learning ecosystems with expertise in engineering design. (Think both/and.) While the specifics are evolving, a typical learning engineer would have expertise across a number of areas including AI and machine learning, systems, user experience design, product testing, data and analytics, and development of policies, regulations, and standards. The juggling of teams and relationships is also required.
Who and how?
The field of learning engineering is evolving quickly, and specifics are not yet carved in stone. IEEE has special interest groups looking at standards for learning engineers in both academic and workplace settings. We’ve started to see organizations recruiting for learning engineer positions and Carnegie Mellon is offering a master’s degree in educational technology and applied learning science designed to prepare graduates for a number of roles—including learning engineer. For now, the idea of learning engineering offers the ID practitioner a chance to stretch into some new areas across learning science, data science, and computer science. There’s room, for instance, for those who want to build apps, learn to tell stories with data, or become more involved with product testing. It may involve working on micro-level projects, as well as working to build systems and architectures. It may involve more time devoted exclusively to researching and working with emerging technologies.
Educause offers this primer on learning engineering, including some comments on downsides and implications for teaching and learning. Also, see recent Learning Solutions articles from Ellen Wagner and others: “Learning Engineering and the Future of eLearning” and “Learning Engineering: The Next Wave of eLearning”. The idea of learning engineering is opening up exciting new conversations and opportunities for instructional designers.