For example, a well-known misconception among students learning about looping is the expectation that iteration can end in mid-loop, the instant a termination condition becomes true. And yet in this document I will present a language with looping constructs that work in just this way, are accepted as perfectly reasonable by inexperienced programmers, and are used by them to build effective robot behaviors. Does this mean that the old “misconception” can now be discarded as another artifact of the arbitrary requirements of an immature medium? Actually, in this case and many others, I think something much more interesting happens. What used to appear as a relatively isolated misconception is transformed into a part of a more general learning challenge with clearer connections to the rest of the world. In this case, for example, children still need (eventually) to master the differences between the two kinds of looping and the appropriate uses of each, along with parallel distionctions throughout the language. But in the meantime, they are free to use a construct that matches their intuitions and meets their needs.

Source: (Hancock, Unpublished Doctoral Dissertation, p. 17)

This book is an exercise in an applied genetic epistemology expanded beyond Piaget’s cognitive emphasis to include a concern with the affective. It develops a new perspective for education research focused on creating the conditions under which intellectual models will take root. For the last two decades this is what I have been trying to do. And in doing so I find myself frequently reminded of several aspects of my encounter with the differential gears. First, I remember that no one told me to learn about differential gears. Second, I remember that there was a feeling, love, as well as understanding in my relationship with gears. Third, I remember that my first encounter with them was in my second year. If any “scientific” educational psychologist had tried to “measure” the effects of this encounter, he would probably have failed. It had profound consequences but, I conjecture, only very many years later. A “pre– and post-” test at age two would have missed them. (Papert, 1980, pp. vii-viii); emphasis in original

As I begin my odyssey into the comps process, I’ve tried to start at a useful beginning: the writings of Seymour Papert. Papert’s 1980 book Mindstorms is regarded by many—at least in the fields of education and learning sciences—as a classic on how children learn with computers. In the beginning of the book he describes being curious about gears as a child and how that curiosity helped him build his own personal ways of knowing about mathematics. What I’d like to do in this post is take apart some of the assertions Papert makes in the above-quoted passage, which is excerpted from the book’s foreword: “The Gears of My Childhood.”

First, Papert calls his book an “exercise in an applied genetic epistemology.” But, what does he mean by genetic epistemology? For that matter, if there is an “applied” kind of genetic epistemology, could there be another kind? Let’s step back for just a moment. The phrase “genetic epistemology” itself traces as far back at least as Jean Piaget, with whom Papert worked for several years. In a previous paragraph, Papert writes:

I was developing a way of thinking that would be resonant with Piaget’s. The understanding of learning must be genetic. It must refer to the genesis of knowledge. What an individual can learn, and how he learns it, depends on what models he has available. This raises, recursively, the question of how he learned those models. Thus, the “laws of learning” must be about how intellectual structures grow out of one another and about how, in the process, they acquire both logical and emotional form. (Papert, 1980, p. vii; emphasis in original)

I think Papert’s summary of genetic epistemology is clear enough for now, though I’ll add one historical and interpretive note. In my understanding of Piaget—an understanding based on admittedly brief slice of the man’s work: his 1970 article on his own theory—what Piaget sought was an explanation for where knowledge comes from. Specifically, he wanted to know where structures of knowledge come from. Most scholars I’ve talked to who study Piaget argue that his training in both biology and philosophy deeply influenced his search for how structures of knowledge come to be. In short, if there is some base structure (viz. DNA) that co-directs how physical biological structures (arms, eyes, hurty parts on insects) emerge and develop, might one not also search for the epistemological developmental structures that co-direct how knowledge (and its forms) develop. That, in one wordy sentence, is my nutshell read of the relevant Piaget bits for this discussion. Back to Papert.

“both logical and emotional form”

Piaget’s original work—summarized in his 1970 article—sketches a possibility for the genesis of the logical forms of knowledge humans develop. It is, rather chiefly, concerned with structures responsible for our abilities to reason about quantities, forms, and conservation. But it’s not about emotion. Piaget didn’t attempt to explain how it feels to reason about coins and fluids. Piaget also doesn’t address the role of emotion in the building of cognitive structures.

Papert, in defense of Piaget, suggests Piaget was far from ignorant about the whole thing. Below, he argues that Piaget wasn’t blind to the role of emotion in assimilation—assimilation being Piaget’s word for how we graft new knowledge, in light of experience, to our current cognitive structures. Below, Papert explains:

[Piaget] talks almost entirely about cognitive aspects of assimilation. But there is also an affective component. Assimilating equations to gears certainly is a powerful way to bring old knowledge to bear on a new object. But it does more as well. I am sure that such assimilations helped to endow mathematics, for me, with a positive affective tone that can be traced back to my infantile experiences with cars. I believe Piaget really agrees. As I came to know him personally I understood that his neglect of the affective comes from a modest sense that little is known about it than from an arrogant sense of its irrelevance.

Throughout the foreword, Papert argues that his own experience with gears wasn’t just knowledge-building. Rather, his interaction with gears became a form of emotional knowledge building. For me, it’s difficult to disagree with Papert. Affect, knowledge-building, and knowledge-use are interconnected to far too high a degree. If, for example, I think about a poem excerpt, I think both of how I felt when I read it, how reading it changed the way I read poetry, and how that resultant change gave me the confidence to keep reading poetry. At the Honors Convocation ceremony of my college graduation, my mentor read to us a poem called “At The Border” by Carl Dennis. The poem begins:

At the border between the past and the future
No sign on a post warns that your passport
Won’t let you return to your native land
As a citizen, just as a tourist

I was astounded. I was moved. I had never before thought of my graduation experience—or of any part of moving on in life—in the plainspoken yet profound way the poem suggested. But there I was, ironically, at the border, and being paradoxically warned for that brief moment in time that no sign would let me know I couldn’t come back.

The experience stuck with me. I’ve remembered the poem, but also the experience of trying to make sense of it. I’ve also remembered to keep trying; to look for new poetic experiences. I think that’s what Papert’s after when he talks about the importance of the emotional in building knowledge forms. It’s about much more than being happy when one learns; it’s about recognized that the feelings we have as we learn become part of the matrix of what we learn, and how that knowledge continues to integrate as we move forward with new experiences.

“creating the conditions under which intellectual models will take root”

This is what we’re all about. If learning derives from models, and we build models in part with and around emotional experiences, then understanding “the conditions under which intellectual models will take root” might well be the most important question we strive to answer with research on learning.

Except I think it’s easy to get distracted. It’s easy to think that what Papert means as “conditions” are things like how we build our schools, how we design our lessons, and what technology is (or isn’t) available. But those are material conditions. To me, they’re a subset of all the things we could think about when we mean conditions.

Again, let’s revisit Papert’s (1980, pp. vii-viii) three assertions in the original quote, paraphrasing where needed to make the point:

1. No one told me to learn about differential gears
2. I felt a feeling, love, as well as an understanding in my relationship with gears
3. I was two; a pre– post– test at the time was hardly an option to measure my learning

Point 1 isn’t about material conditions at all, really. If anything, it’s about the lack of externally-imposed constraints. Point 2, on which I elaborated quite a bit, seems to say that emotion is as much a building material in constructivism/constructionism as is any knowledge or sensory element. Point 3? We can have meaningful experiences with the world that many of the research methods we deploy in education would fail to capture.

References

Dennis, C. (2007). Unknown Friends. New York: Penguin Books. Retrieved from http://lccn.loc.gov/2006050716

Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. New York: Basic Books. Retrieved from http://lccn.loc.gov/79005200

Piaget, J. (1970). Piaget’s Theory. In P. H. Mussen (Ed.), Carmichael’s Manual of Child Psychology (3rd ed., Vol. 1, pp. 703–732). New York: Wiley. Retrieved from http://lccn.loc.gov/69016127

One highlight for me was talking with Mark Guzdial about pictures and sounds as transformations of binary information. As I continue to think about intersecting possibilities for our brand of scientific inquiry and what promise it might hold in CS, I’m definitely going to turn my attention toward core concepts in how we represent information. More importantly, perhaps, are issues of how we generate, modify, and understand information.

Another was raising issues of what counts as evidence, and how we define learning. I really look forward to having more interesting conversations about what we think we’re studying when we try to study student learning.

A third was getting to continue ongoing conversations about education with friends, both old and new. To me, the heart of a good conference is the way our interests and ideas about central problems in the field spill outside the bounds of sessions and talks. These conversations are what excite and energize me.

Finally, I had an amazing time at the pre-conference Doctoral Consortium (DC). I was able to meet colleagues from across the world who are starting (or, in some cases wrapping up) their dissertations. It was a thrill to see the variety of problems people are taking on and an amazing opportunity to both get and give detailed feedback on our work.

If I’m doing my job right, this post will grow into a much larger series of posts, and hopefully (when well-watered, cared for, and nourished) a dissertation. To get there, here are some of the core questions I think I’ll need to address.

1. What are epistemological dynamics? What do I mean when I’m talking about epistemologies and epistemological dynamics? What does that idea do as a technical construct?
2. Where do epistemological dynamics come from? What’s their genesis as a research construct? More to the point, what do they allow us to explain and predict?
3. Why do epistemological dynamics matter for learning to program? This has to go beyond simply arguing that “they matter.” Many, many things affect the ways individuals learn to program. The subquestions here surround how we can use the study of epistemological dynamics in programming?
   1. Can they push research efforts forward, illuminating erstwhile unseen problems?
   2. Can they push the theoretical discussion forward, explaining the otherwise un– or underexplained?
   3. Finally, can they inform pedagogy? Can epistemological dynamics help us craft educational experiences around programming that attract, retain, and empower students?

As part of my work preparing for the 2011 International Computing Education Research Conference (ICER), I’ll be working through a presentation that tries to flesh out these issues. The goal there is to craft a form of these dissertation ideas that conference attendees can critique. Keep your eyes on the blog as I continue to expand my ideas and hopefully tackle—or refine—the above roadmap of questions.