Learning Engineering: New Profession or Transformational Process?
Grush: What changes might you wish to see in the general perception of learning engineering, that would be helpful in aligning these different interests better?
Wagner: We need to find more reasons to believe that learning engineering offers something of great enough value that it behooves us to figure out how to use it better. And as I've noted, for me the most important benefit that learning engineering presents is that it offers a process for applying research about how human learning works in real-life learning settings.
I like to think of this, as I mentioned earlier, as a process for connecting science, solutions, and scale in the service of better learning:
We start with evidence from learning science; we design solutions that apply research for use on specific outcomes, with specific audiences, in specific settings; and once we figure out what works we engineer those solutions to scale our results.
Once we figure out what works we engineer those solutions to scale our results.
More than anything, learning engineering can give us a transformational process that turns research results into designs that guide people into applying what the research tells us will work best. It makes research actionable. People forget just how hard it is to apply research evidence in practice settings, and just how much testing it takes to ensure that a so-called learning solution actually works. We forget that not every single thing we think of is worthy of scale.
Learning engineering can give us a transformational process that turns research results into designs that guide people into applying what the research tells us will work best. It makes research actionable.
If we are talking about producing learning products at scale, implied in that statement is that someone will be giving someone else money to purchase the solutions being produced. Products will need to be really good for that to happen. Academic colleagues testing assertions of efficacy are likely to find that taking that experimental product to scale is an entirely different undertaking than developing a research tool. It's not for the inexperienced or the faint of heart.
Grush: And so, the learning scientists will remain involved, way down the road?
Wagner: Of course. We need the science driving our collective thinking about better learning to be sharp and relevant.
Actively applying learning science in the design and development of our learning assets, objects, and experiences will improve the quality of the products we produce. It's really as simple as that.
Grush: What role does ontology play in clarifying the perceptions of learning engineering, or even identifying communities of practice?
Wagner: Ontology is a technique for organizing and structuring a body of knowledge. It helps define a common vocabulary for contributors who share information within and across domains. It articulates the essential elements that establish a new discipline's place in the world. An ontology creates shared understanding within a domain, enabling better communication and interoperability. It uses descriptors and relationships among constructs to determine if a prospective new discipline is truly unique and special in the place it holds, or if it is simply a new and different way of talking about something that people already do.
Grush: Can you give one or two examples of groups or individuals whose work could serve as models for learning engineering?
Wagner: To be clear, some of the work going on is quite impressive. In particular I really like Ryan Baker, Ulrich Bosser, and Erica Snow's work on the Learning Engineering Research Framework. Norman Bier and colleagues at Carnegie Mellon University are deeply involved in the Open Learning Initiative, developing free, open tools for educators to use when creating open curricula and content. Danielle McNamara and her colleagues at Arizona State University will be co-hosting the upcoming IEEE ICICLE Learning Engineering Conference July 22-24.