I had the deeply unsettling experience recently of feeling like the stupidest person in the room. This type of experience is (both fortunately and unfortunately) fairly rare for the typical educational researcher, though it’s far more common for members of the learning communities researchers study. For this reason, I believe it’s incredibly important for researchers to examine the contexts that make them feel stupid, if only so they can better understand the groups they’re studying.
The context was a graduate-level class. I’m one of just under a dozen students; the class, “Computational Technologies in Educational Ecosystems,” draws students from my university’s school of education and from the Informatics Department. A key assignment in the course is design, reflection on, and revision of a model that represents our take on the role of technologies in learning environments.
I have previously noted my despair over my apparent inability to complete this assignment in a meaningful way. The most progress I’ve been able to make was in presenting an unfinished model that draws the vaguest possible connection between humans and technology:
Then in class this week we spent a large chunk of time working with a representation developed by the instructor, the fanTASTIC Joshua Danish. His representation, which is also available on his website, is intended to point to key features of the week’s readings on cognitive tutors, Teachable Agents, and computer-aided instruction. Here’s the representation:
This representation literally carries no meaning for me. I mean, I get the basic idea behind it, but only because I did the assigned reading and get the basic themes and goals of computer-aided instruction. I get that research in this area focuses on domain-oriented issues, learning theories, and the role of these tools in classroom environments; but I do not understand how the above representation articulates this focus.
Yet I sat there in class and listened to my classmates interpreting the representation. They understood it; they could ‘read’ it; they could point to areas of weakness and suggest corrections to improve it.
The experience reminded me of the time I tried to learn rugby by joining an intramural team. After 20 minutes of basic instruction, we all got thrown into a game and the first time I got the ball, I apparently did something wrong and the team captain tackled me hard, hollering at me as she pulled me down. I never did find out what I’d done wrong. And actually, I didn’t much care. That was the last time I tried rugby.
Of course, Joshua’s never tackled anybody. He’s a fantastic teacher–one of the best I’ve ever had–who’s deeply invested in fostering an authentic learning community and supporting his students in their growth. But I sat there, watching my classmates speak a language I didn’t understand, getting more and more frustrated, and I absolutely felt like walking right off the field and never coming back.
At least two important lessons are nested in this experience, and one is linked to the other.
1. There are kids who feel this way all the time, every day. It’s easy for educational researchers to forget this point, mainly because most (though certainly not all) of us have experienced raging success in our own educational experiences. We got A’s in everything. Or we found a niche within a certain content area and pursued it with a fair amount of success. Or we figured out how to game the system, so that even if we didn’t get A’s in everything, we still felt somehow smarter than everyone else. Or if we had bad experiences with school early on, we still came to think of ourselves as smart, or at least smart enough to deserve advanced study in education.
So maybe we know in theory that schools are stacked against some kids, that the entire education system is designed on the premise that some kids will always be labeled the failures, the losers, the learning disabled, the stupid. (If it weren’t for the stupid kids, after all, how would we know what an A student is worth?) We know in theory that some kids feel frustrated and lost in school, and that some kids end up feeling like it’s hopeless to even bother trying.
But the fact is that we don’t know how it feels in practice. We can’t know how it feels. And we should never be allowed to forget this.
Even as I was feeling like the stupidest person in the room, I also felt an absolute certainty that this wasn’t my fault. Here, too, my experience diverges from that of many learners in the classrooms we study. I knew that my experience was neither right, nor fair, nor my fault; because of this, I knew to curb my strong initial impulse, which was to throw things, to disrupt the class, to walk out and never return. Instead of following my gut, I saved up all that frustration and spent it on a short burst of research. Which is how I got to my second point:
2. Modeling ability is a disposition, one that is (or is not) cultivated through sustained educational focus. Andrea diSessa calls this disposition “metarepresentational competence”; by this, he means a learner’s ability to:
- Invent or design new representations.
- Critique and compare the adequacy of representations and judge their suitability for various tasks.
- Understand the purposes of representations generally and in particular contexts and understand how representations do the work they do for us.
- Explain representations (i.e., the ability to articulate their competence with the preceding items).
- Learn new representations quickly and with minimal instruction.
As Richard Lehrer and Leona Schauble point out, model-based reasoning is not only essential to the established practices within many varied domains, but it’s also a set of proficiencies that can and must be cultivated through focused instruction. In offering their own discussion of metarepresentational competence, they write:
Modeling is much more likely to take root and flourish in students who are building on a history of pressing toward meta-representational competence (diSessa, 2004). Developing, revising, and manipulating representations and inscriptions to figure things out, explain, or persuade others are key to modeling but are not typically nurtured in schooling. Instead, students are often taught conventional representational devices as stand-alone topics at a prescribed point in the curriculum, and may be given little or no sense of the kind of problems that these conventions were invented to address. For example, students might be taught in a formulaic manner how to construct pie graphs, but with no problem or question at hand to motivate the utility of that design over any other, students are unlikely to consider the communicational or persuasive trade-offs of that or any alternative representational form.
Though modeling has its application in most, if not all, content areas, it’s typically emphasized in science and math classes and de-emphasized or ignored in the social sciences and read
ing and writing instruction. At best, students are told to make a timeline to represent the events of the Civil War (without being shown the affordances and constraints of this sort of representation); or they’re required to make a diorama (or, now, a digital version of a diorama) to prove they understand a key scene in a literary text.
Representations don’t always take the shape of graphs or pictures; in fact, we might say that a musical score or a piece of descriptive writing is a representation in its own right. But as Lehrer and Shauble point out, a thing is only a model insofar as it is treated as such. “One might suggest,” they write, “that a pendulum is a model system for periodic motion. Yet, for most, the pendulum simply swings back and forth and does not stand in for anything other than itself.”
Some disciplines, in fact, actively resist the notion of representation, of language as representational. In a previous iteration, I was a poet and even spent several years’ worth of sustained study in an undergraduate, then a graduate, creative writing program. In the MFA program especially, I was immersed in a sustained discipline-wide effort to divorce language from its representative nature. There was an effort to fight against narrative, against what many writer-types believed was “easy” poetry. This is, as poets are wont to remind us, the basis of Postmodernism.
Though I’m in a Learning Sciences graduate program, I am by no means a scientist, at least in the more general sense of the term. This is even more true if we think of modeling as a key element of scientific practice. For multiple reasons, I do not have what diSessa calls “native competence,” which he explains is a proficiency that develops over time both in and out of school. I could point, for example, to the shame I felt in 6th grade when I was required to build a model of the solar system using styrofoam and coat hangers; my final product, the absolute best work I could have done, was pitiful and humiliating. I remember thinking: everyone else can do this; what’s wrong with me?
Now I know it’s not a problem with me but with a system of schooling, which helps me direct my rage outward but still doesn’t really solve the problem of how I’ll ever build a goddam model that makes any sort of sense to anybody at all.
In case you’re interested in reading the work I reference above, here are the citations:
diSessa, A. A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22, 293-331.
Lehrer, R., & Schauble, L. (2006). Cultivating Model-Based Reasoning in Science Education. In R. Keith Sawyer (ed.), The Cambridge Handbook of the Learning Sciences. Cambridge: Cambridge University Press.