Everything old is new again.
Educational research has been happening for many years, with useful information about what works and what doesn’t. Richard Clark reminded us at LWMOOC that a lot of very smart people have spent countless hours studying the cognitive science of learning. That research tells us that the best learning happens using methods that may seem counterintuitive. Guided, evidenced-based pedagogy is the strategy that results in increased learning and greater motivation.
Before embarking on new educational research in digital environments, we would be doing a disservice to the field if we did not do a deep literature review first. Not doing so, says Clark, could result in a worsening of learning outcomes. It’s much more difficult to unlearn something in error than it is to learn it correctly the first time. Why then, with so much valuable information in educational research, have those best practices not been embraced more deeply in the disciplines of higher education? And why the sudden renewed interest in the science of learning?
Data is gold for the learning sciences, at least it is for those who can read it, structure it, and analyze it. We now have massive amounts of data from digital environments to test against well-founded principles of learning. And I firmly believe we should do this. Higher education has the human capital to do this effectively – to build on prior knowledge, iterate in the classroom, and refine theories of learning that should be reflected in the residential classroom and online.
It will take a combination of data, from digital environments and qualitative studies of the unobservable learner experience, to fully understand learning. This is where Ed Tech and Higher Ed should be in conversation, with better strategies for collaboration. If practitioners are expected to do something actionable with data that sometimes takes additional training to understand deeply, they will need better tools. We can have the age-old tools versus method debate here, but without the right tools in the hands of our faculty, we cannot tweak methods for optimal learning. At the same time, those tools should be informed by the rich data behind good qualitative research.
I see LWMOOC as a call for action. Academic technologists, who sit in these liminal spaces between teaching, learning, and Ed Tech, have a unique view on these intersections. We can and should liaise with computer scientists and faculty in support of educational research – advocating for the complementary strengths each side has to offer, and actively supporting campus research endeavors that both build upon and add to the existing rich body of knowledge.