Phil and I will be doing a webinar along with Echo 360’s Fred Singer for Inside Higher Ed tomorrow. We’ll be talking about how to think about adopting these platforms in ways that create opportunities to encourage conversation within the campus community about pedagogy and improving student outcomes.
The webinar is tomorrow—Wednesday, April 26th—at 2 PM EST. You can sign up here.
Phil wrote up some excellent observations yesterday about the announcement that Follett has invested in Lumen Learning and will be distributing some of their products. This deal has more significance for the curricular materials market than the (relatively) small dollar amount of the investment would indicate.
In my last post, I made an extended analogy between today’s ed tech and 19th Century medicine. My core argument was that effective ed tech cannot evolve without a trained profession of self-consciously empirical educators any more than effective medication could have evolved without a profession of self-consciously empirical physicians.
In this post, I’d like to go beyond analogies and look at the actual state of some cutting-edge cognitive science. I want to do this for several reasons. First, a lot of educators are skeptical or even cynical regarding the potential relevance of this work to the ways that they think about teaching. This is completely understandable, particularly given that most educators hear about this sort of research through product commercials or hyperbolic media puff pieces. By exploring the science in some detail, I want to show that having a basic understanding of even foundational research that has no direct classroom applications can stimulate the thinking of classroom educators in useful ways.
Second, I want to show that even educators with no background in science or math can achieve an empowering level of cognitive science literacy with a reasonable investment of time (like the time it takes to read a long blog post, for example).
And finally, I want to show that, after we strip away the hype and the ennui it engenders, we can recover a sense of wonder about the science while maintaining a sense of realism about its practical applicability. I have chosen to characterize the methodological paper I’ll be explaining as an attempt to create a “microscope of the mind.” That’s dangerously close to “robot tutor in the sky that can semi-read your mind” territory. I hope to demonstrate that there is a non-hyperbolic sense in which we can believe that my microscope of the mind analogy is a reasonable one.
Here’s how I’m going to do it:
I am going to explain a research study on cognitive neuroscience. It’s not a big, sexy paper that gets coverage in outlets like Wired. It’s a methodology study. I’m going to explain enough of the basic underlying concepts in math, physics, and cognitive psychology for you to be able to get the gist of the paper and judge its significance for yourself. I’m going to explain how fMRIs work and what machine learning is. And I’m going to explain the larger context of why the researchers tried this particular experiment and how it is relevant to our larger understanding of how people learn. Along the way, I will touch on topics as diverse as theology, Russian literature, and the miracle of selfies, but always with the goal of showing how the science can be accessible, interesting, and relevant to non-scientists.
This is not a short read, but I hope that it will reward your effort.
Mike Caulfield is one of a sadly dwindling number of truly interesting, deep-thinking bloggers about educational technology in higher ed. If you don’t follow his blog, you should. ((Mike, you really need to add an email subscription option to your site. You’ll be shocked to hear that not everybody uses RSS.))
He can be an acquired taste. He has his obsessions that take him into weird little intellectual corners sometimes. But as he lays out in his fantastic post, The Power of Explaining to Others, it all ties together into something important and compelling if you just give Mike enough time and patience to let him lay out his case.
His basic argument is that we learn best when we are explaining things. That certainly rings true to me personally. I often don’t fully know what I think on a topic until I write a blog post about it or answer a panel discussion question on it at a conference. It sharpens my thinking, which was my original motivation for starting a blog and which also has animated all aspects and phases of my career, whether I was a teacher, a consultant, an analyst or blogger, a product designer, or some combination of the above. I love learning and I learn best when I have to explain what I am learning to somebody else.
Mike’s intellectual canvas is enormous, so his posts related to this topic in one way or another range from micro-interventions in the design of a distributed wiki enormous societal problems and trends like how to confront the rise of fake news and political tribalism. I never blow off his pieces even when he seems to be off on a weird jag that I dont’ understand at first blush. There’s always something interesting going on underneath. If I’m willing to work for it, Mike’s pieces generally reward my investment. And his latest post is something of a Rosetta Stone to his blog.
A while back, I wrote a post arguing that algorithm-based ed tech like learning analytics and adaptive learning will not sell well as long as educators do not have the mathematical and scientific literacies necessary to understand the rationale and limitations of the ways in which the software makes evaluations. They won’t trust it because they can’t know when and how it works and when and how it fails. It is as if we are trying to invent the pharmaceutical industry in absence of a medical profession.
Today I will extend that analogy to argue that even revolutionary developments in educational technology will fail to have the dramatic impact (or “efficacy,” in the newly common parlance that was borrowed from medicine) without changes to the fundamental fabric of the institutions and culture of academe. By looking at the history of one particular medical innovation and imagining what might have happened if it had been discovered when the state of medical science and practice looked more like the state of today’s learning science and educational practice, we can learn a lot about how a technology needs to be embedded in a set of cultural and institutional supports in order to achieve widespread adoption, acceptance, and effective use. This has direct bearing on the situation with educational technology today.
There’s an interesting piece in EdSurge about the potential access to education challenge that CUNY will face if Governor Cuomo makes good on his proposal to make college tuition free:
According to the CUNY 2016 Student Experience Survey, 21 percent of the system’s community college students were not able to take required courses, most citing “lack of seat availability” as the reason. In an interview, George Otte, CUNY’s director of academic technology, described the “bottleneck” created by course shortages. Students were not able to take required courses because demand for space exceeded capacity—a problem that could get worse if more free tuition-seekers pour in. “We have always had lots of enrollment, which is one of the reasons I think we were late to the online course offerings. Our mission was never to gain students from the outside,” he explains, “but we are just beginning to realize that this has an enormous impact on capacity.”
“The real challenge is that eligibility is predicated on full-time enrollment,” Otte says, citing a caveat community colleges cannot ignore. Without taking 15 hours a semester, which equals full-time enrollment, students are not elible for Gov. Cuomo’s free tuition program—the Excelsior Scholarship. However, at least half of community college students work full-time. It is unlikely that such a population can opt-in to campus-based instruction that requires them to commute and show up for courses offered only at, say, 9 a.m. or 4 p.m.
It turns out that we’ve done a lot of work in this area, particularly in the state of California, and have some lessons learned that are worth sharing.
In a world where we are constantly barraged with product claims about “learning science,” most educators have very little sense of what that really means and how it is relevant to what they do. I was lucky enough to be able to hear from and interview some actual academic learning sciences last year at Carnegie Mellon University’s Simon Initiative. The trip itself was paid for by CMU—I was part of a group of “media fellows”—and the video production was paid for by a grant from the Bill & Melinda Gates Foundation. The result was a trio of interview videos that I’m particularly pleased to share. (more…)
