What do I mean by Computing Education Research? The Computer Science Perspective

 

Last week, I talked about how I explain what I do to social scientists. This time, let me explain what I do to computer scientists. I haven’t given this talk yet, and have only tried the ideas out on a few people. So consider this an experiment, and I’d appreciate your feedback.

Let’s simplify the problem of computing education research (maybe a case of a spherical cow). Let’s imagine that instead of classes of Real Humans, we are teaching programming to Human-like Turing Machines (HTMs). I’m not arguing that Turing machines are sufficient to represent human beings. I’m asking you to believe that (a) we might be able to create Turing Machines that could simulate humans, like those we have in our classes, (b) RH’s would only have additional capabilities beyond what HTM’s have, and (c) HTM’s and RH’s would similar mechanisms for cognition and learning. (Carl Hewitt has a great CACM blog post arguing that message passing is more powerful than TM’s or first order logic, so maybe these should be HMP, Human Message Passers. I don’t think I need more than TM’s for this post.)

This isn’t a radical simplification. Cognitive science started out using computation as a model for understanding cognition (see history here). Information processing theory in psychology starts from a belief that humans process information like a computer (see Wikipedia article and Ed Psychology reference). Newell and Simon won the ACM Turing award and in their Turing Award lecture introduced the physical symbol system hypothesis, “A physical symbol system has the necessary and sufficient means for general intelligent action.” If we have a program on a Turing machine that gives it the ability to process the world in symbols, our theory suggests that it would be capable of intelligence, even human-like intelligence. I’m applying this lens to how we think about humans learning to program.

This simplification buys me two claims:

• The Geek Gene is off the table. The Geek Gene is the belief that some people can’t learn to program (see blog post for more). Any Turing machine can simulate any other Turing machine. Our HTM’s are capable of tracing a program. If any HTM can also write code, then all HTM’s can write code. Everyone has the same computational capability. (If HTM’s can all code, then RH’s can all code, because HTM’s have a subset of RH cognitive capabilities.)
• Learning of our students can be analyzed and understood as information processing. The behavior of Turing machines is understandable with analysis. HTM’s are sophisticated Turing machines. The core mechanism of HTM’s can be analyzed and understood. If we think about our students as HTM’s, we might reason about their learning about computing.

Here are some of the research questions that I find interesting, within this framing.

How do HTM’s learn to program?

All HTM’s must learn, and learn at a level where their initial programming (the bootstrap code written on their tape when they come into our world) becomes indistinguishable from learned capabilities. HTM’s must have built-in programming to eat and to sleep. They learn to walk and run and decipher symbols like “A,” such that it’s hard to tell what was pre-programmed and what was learned. HTM’s can extend their programming.

There are lots of models that describe how HTM’s could learn, such as SOAR and ACT-R. But none so far has learned to program. The closest are the models used to build the cognitive tutors for programming, but those couldn’t debug and couldn’t design programs. They could work from a definition of a program to assemble a program, but that’s not what most of us would call coding. How would they do it?

How would HTM’s think about code? How would it be represented in memory (whether that memory is a tape, RAM, or human brains)? There is growing research interest in how people construct mental models of notional machines. Even experts don’t really know the formal semantics of a language. So instead, they have a common, “notional” way of thinking about the language. How does that notional machine get represented, and how does it get developed?

How do we teach HTM’s to learn to program?

You shouldn’t be able to just reprogram HTM’s or extend their programs by some manipulation of the HTM’s. That would be dangerous. The HTM might be damaged, or learn something that led them into danger. Instead, extending HTM’s programming can only be done by conscious effort by the HTM. That’s a core principle of Piaget’s Theory of Cognitive Development — children (RH’s and HTM’s) learn by consciously constructing a model of the world.

So, we can’t just tell an HTM how to program. Instead, we have to give them experiences and situations where they learn to program when trying to make sense of their world. We could just make them program a lot, on increasingly harder programs. Not only is that de-motivating (maybe not an issue for HTM’s, but certainly is for RH’s), but it’s inefficient. Turns out that we can use worked examples with subgoal labeling and techniques like Parson’s problems and peer instruction to dramatically improve learning in less time.

What native capabilities of HTM’s are used when they learn to code?

We know that learning to read involves re-using more primitive mechanisms to see patterns (see article here). When HTM’s learn to program, what parts of the native programming are being re-used for programming?

Programming in RH’s may involve re-use of our built-in ability to reason about space and language. My colleague Wes Weimer (website) is doing FMRI studies showing that programmers tend to use the parts of their brain associated with language and spatial reasoning. In our work, we have been studying the role of spatial reasoning and gesture in learning to program (see summaries of our ICER 2018 papers). We don’t know why spatial reasoning might be playing a role in learning to program. Maybe it’s not spatial reasoning, but some aspect of spatial reasoning or maybe it’s even some other native ability that is related to spatial reasoning.

How does code work as an external representation of HTM’s, and where does it help?

We can safely assume that HTM’s, like RH’s, would enhance their cognition through the use of external representations. Cognition and memory are limited. Even an infinite tape has limitations in terms of time to access. Human cognitive systems are limited in terms of how much can be attended to at once. RH’s use external representations (writing notes, making diagrams, sketches) to enhance their cognition. We’re assuming that HTM’s have a subset of RH abilities, so external representations would help HTM’s, too.

My students and I talk about a wonderful paper by David Kirsh, Thinking with External Representations (see link here). It’s a compelling view of how external representations give us abilities to think that we don’t have with just our brain alone.

How can program code be a useful external representation for HTM’s? When does it help, e.g., with what cognitive tasks is code a useful external representation? For example, a natural one is modeling and simulation — we can model more complex situations with program code than we can keep in our head, and we can simulate that model for a much larger range of time and possible values. Are there cognitive tasks where code by itself, as a notation like written language or mathematics, can enhance cognition? Here I’m thinking about the ability of code to represent causal relationships (e.g., as in Bruce Sherin’s work) or algebraic forms (e.g., as in Bootstrap) — see here for discussion of both.  I’m intrigued by the idea of the affordances of reading code even before writing it.

What makes programming worth learning for HTM’s?

Why should an HTM learn programming? Let’s assume that an HTM’s basic programming is going to be about staying alive, e.g., Maslow’s hierarchy of needs. When would an HTM want to learn programming?

The most obvious reason to learn programming is because you can get paid to do it. It’s about meeting physiological needs and safety. But, if you can meet those needs doing something that’s easier or more pleasant or has fewer barriers, you’ll likely do that.

Sometimes, you’ll want to learn programming because it makes easier something you want to do anyway. Brian Dorn’s graphic designers wanted to learn programming (see here) because they used Photoshop or GIMP and wanted a way to do that easier and faster. Maybe that’s about safety and physiological needs, but maybe it was about esteem or even self-actualization (if HTM’s care about those things).

Where my simplification breaks down: Real humans learn in situated and social contexts

Our learning theory about RH’s say that they are unlikely to start a new subject unless there’s social pressure to do so (see Pat Alexander’s Model of Domain Learning). Would HTM’s feel social pressure? Maybe.

As I described in the previous blog post, much of my work is framed around sociocultural models of learning, like Lave and Wenger’s situated learning. I use Communities of Practice to understand a lot of the situations that I explore. We can only go so far in thinking about programming as just being inside of individual minds (HTM or RH). Much of the interesting stuff comes when we realize that (a) our cognition interacts with the environments and situations around us, and (b) our motivation, affect, and cognition are influenced by our social world.

Setting aside whether it’s social science or computer science, I am still driven by a paper I read in 1982, which was five years after it was written: “Personal Dynamic Media” by Alan Kay and Adele Goldberg (see copy here).  I want people to be to use coding like they use other literacies, to create a literature, and in a casual, informal and still insightful way.  Mitchel Resnick often talks about people using Scratch to write a card to their mother or grandmother — that’s the kind of thing I want to see.  I want people to be able to make small computational models that answer questions, in the same way that people do “back of the envelope” calculations today. I also want great literature — we need Shakespeares and daVinci’s who convey great thoughts with computing (an argument that Andrea diSessa made recently at the PPIG conference which Felienne Hermans blogged about here.) That’s the vision that drives me, whether I’m using cognitive science or situated learning.