Today I am sharing the first video out of the Empirical Educator Project (EEP) 2019 summit, and with it, a central concerns of the project. Much of the basic machinery our learning processes work so naturally and automatically so much of the time that they are invisible to us. So pervasively invisible, in fact, that most of us are barely aware that it even exists. And that’s a problem. If you believe that the job of education is to work within what psychologist Lev Vygotski called the “zone of proximal development”—the kind of learning challenge that would be too hard for a student to learn on her own but not so hard that she can’t learn it at all—then we have to have a very finely tuned understanding of that learning machinery, to the point where we can accurately find each student’s zone of proximal development with a high level of consistency.
We fail to do this all the time. Some students are bored while others struggle. The more heterogeneous the student population is, the bigger a problem this is. As higher education as a sector becomes more committed to serving post-traditional students, first-generation students, and students with 40-year educational relationships to the school rather than 4-year relationships, then this need to be able to see and understand these invisible learning processes becomes more acute. For this reason among others, fostering academic literacy around the mental machinery of learning—making the invisible visible—is one of the central goals of EEP. I therefore wanted to start the 2019 EEP summit by highlighting this challenge. So I invited three Carnegie Mellon University (CMU) professors with complementary areas of expertise to participate in a panel that could highlight several dimensions of the problem.
This wasn’t the first time I had interviewed these three particular academics. I had been fortunate enough to be invited to a CMU press fellowship three years earlier. I brought my video camera along and happened to be able to get some air time with these very three people, two of whom I had never met before. The interviews turned out to be formative for me, particularly with regard to my thinking about EEP. I’m going to write a little about the complimentary insights that these three academics gave to me and then share both the interview video from the summit and the original interview videos from two years ago.
Expert blind spots
As we get old and forgetful, we like to joke that our minds have to make room for the new information by clearing out old information. It turns out that there’s truth behind this joke in multiple ways. First, we have different kinds of memory. If I asked you to list the steps required to tie your shoe, those steps would probably not come tripping off your tongue. Does that mean that you don’t know how to tie your shoe? No, it doesn’t. It means that you’ve moved that knowledge to a more efficient memory space in your brain. One that’s quick and efficient enough that you can easily bend down and tie your shoes while performing other, more demanding cognitive tasks. But that knowledge is not accessible to your conscious mind. It is “tacit” knowledge. Your brain is very efficient at shunting information that it needs to access but does not need to consciously examine into a different compartment than the one it was in when you were learning a skill.
There was a time when you could list the steps in tying your shoe, because that was how you first learned those steps. Your brain put that information into a box once it no longer needed conscious access to it. Chances are good that you don’t remember that time well and that you don’t remember the experience of those steps fading from your conscious memory. I tried to recreate this experience recently for myself. I am learning to swim. In the first weeks, I was thinking about about very basic aspects of moving my arms and, separately, moving my legs. That period was about nine months ago. I decided to try a little experiment with memory encoding in the process. Every two weeks, I would try to remember the steps that I learned in my first lesson. I didn’t try to memorize those steps. That would be triggering a different memory process and would invalidate the experiment. I just tried to reconstruct the steps in my mind. Meanwhile, I spent most of my time at the pool learning to be a better swimmer.
As the weeks went on, I found myself thinking less about what my arms and legs were doing separately and more about what my whole body was doing. I also found it harder and harder to remember what the original steps were that I learned in my first lesson. Nine months in, I barely remember anything about how I first thought about what I was doing. If I had to teach somebody to swim from scratch, I couldn’t just reproduce the lesson that was taught to me. I’d have to make something up. Nor could I reproduce the learning steps I took—many of which I made on my own, without my instructor—to get from my beginner’s understanding to the level of expertise I have achieved as of today. I might be able to draw on some of my knowledge and experience, but I would have to invent more of my teaching moves than most teachers like to admit, through trial and error, by working with students.
So our brains do, in fact, make room for new information by boxing up old information and putting into storage. In addition to the memory changes, we also process information differently as our domain knowledge gets more sophisticated. When you’re learning math, or cooking, or yoga, or any other discipline with integrated skills that build on each other, at first, you’re learning each skill separately. Over time, your mind integrates steps and makes general rules. As novice cooks become expert cooks, their way of thinking about cooking looks less like meticulously following one out of hundreds of completely separate recipes and more like following some generalized principles that they’ve drawn from their experience of making so many recipes. They stop thinking algorithmically and start thinking heuristically.
We don’t generally notice these changes in our cognition as we move from novices to experts in a topic. They’re not directly observable and not usually consciously experienced. They just happen. This is a problem for teaching because professors, as experts, have undergone all of these changes in their learning processes. They no longer think they way their students do. They don’t think about cooking as following individual recipes. Further, because their evolution as thinkers was largely silent, and because most professors have no professional development in these processes, it’s not always obvious to them the extent to which their brains process information in fundamentally different ways than those of their students. Ironically, it is their very expertise that causes them to struggle sometimes to understand how their students think about their subjects or how to work with them in that zone of proximal development. CMU Professor Ken Koedinger, Director of LearnLab at the Pittsburg Science of Learning Center, is an expert in this conundrum.
Expert teaching blind spots
There’s a related phenomenon that I’ll call an expert teaching blind spot, even though I don’t think that’s an official term of art. Just as it is possible to not consciously know what you know in any domain of knowledge, it’s possible to have tacit knowledge specifically in teaching. In addition to the reasons above, I’ll add another one: Interpersonal skills, including teaching skills, are somewhere in the middle of learning spectrum between things that we are hardwired to learn without anyone specifically teaching us (like spoken language as young children), and something that is an intellectual creation which must be consciously learned (like political science). Many educators have what we colloquially refer to as teaching “instincts,” and that word is not far from the truth. We have tacit interpersonal knowledge, sometimes including tacit knowledge about learning processes of our students. We know some things about how to teach in a very real sense, but that knowledge is not fully consciously accessible to us.
As a result, it can be very difficult to talk to even highly skilled teachers about what they do, because in many cases they’ve never even tried to put what they do into language. They just do what seems right and obvious to them. And if they do verbalize what they’re doing, they usually aren’t using terms of art because they usually haven’t been taught any. Their insights seem personal because nobody has talked to them that beyond the personal and phenomenological there could be a sharable, learnable, teachable body of knowledge that their instincts are tapping into. CMU’s Marsha Lovett, Director of Eberly Center for Teaching Excellence & Educational Innovation is an expert in this problem domain.
If we don’t have a coherent answer, then we make one up
If you put all of this together, it adds up to a very significant challenge to serious educators. They don’t have easy ways of knowing how they think differently than their students or easy access to their own cognitive journeys that got them from novice learners to expert learners. And yet, most of us have vivid memories of our formative experiences as students. On top of that, teachers teach, and students learn. It happens all the time. Humans are such incredible learning machines, and the machinery is so well hidden from us, that many people tend to assume that there really isn’t much to it (when nothing could be further from the truth). Most professors are good at academic learning. That’s how they ended up as professors.
And they usually had at least one experience that really inspired them to learn about their chosen field. That association is often all it takes for educators to attribute causality. “Well, I had an amazing experience in Professor Smith’s class, and Professor Smith did X, so X must be a great way to teach.” Given that most professors diligently worked through five to seven years of graduate school without being exposed to the tiniest hint of any of the above and then were expected to somehow magically know how to teach well, what tends to happen is that professors make up their own stories about what effective teaching is based on their own personal experiences—which is the only data they have, really—and they go on that. And they don’t change their minds about it very much or very easily. CMU anthropologist and Simon Research Faculty Lauren Herckis has conducted some fascinating research in this area.
We have a literacy problem
If you put all of this together, it’s clear that we’re not going to make substantial progress on improving education until educators are taught to see that which is currently invisible. We have to develop a common cultural understanding that learning involves a complex set of cognitive processes, that being an expert in a knowledge domain is not sufficient to be a good teacher of novices, that good teaching instincts are often based on tacit knowledge which we can make explicit and therefore more sharable and useful. Only by doing this together, as a sector, can we make substantial progress on improving student success. One of the main goals of Empirical Educator Project is to begin fostering the cultural infrastructure that we need in order to do that.
Here are the three original video interviews I conducted of Marsha, Ken, and Lauren two years ago:
I got lucky with those interviews. The coherence in the interviews is a product of the coherent body of work at CMU’s Simon Initiative as represented by the three people who happened to be available to interview rather than through some master plan of mine.
At the summit, I chose to frame up both the discussion and the event more consciously. In addition to their work, I asked the three to reflect on their personal journeys as educators to embrace views about teaching and learning that may have seemed surprising or even counter-intuitive to them:
The journeys that these experts describe are emblematic of the bigger picture that EEP is all about. And not just in classroom work specifically, but in every aspect of serving students.
I have said before that academia needs to move from a philosophical commitment to student success toward operational excellence at supporting student success. The implied gap is knowhow. It will show up differently in the classroom than it will in, say, advising, but the pattern is going to be the same, and I think academics will be most comfortable thinking about it as starting with a literacy problem. There is some discipline, either new or existing, that they must learn to some degree of competence in order to serve their students well. They might not have to be expert in it—they don’t have to have PhDs in cognitive psychology, for example—but they do need to be literate in it.