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NAOMI: Welcome. This will be EduPsych Theory for Python Hackers.

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This is a half-hour talk by Mel Chua,

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we're going to try to have about five to eight minutes at the end for questions.

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Ok. Mel Chua!

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MEL: Hi. Hi, everyone.

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So, welcome to EduPsych Theory for Python Hackers.

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This is a very laptops-encouraged talk. The slides are on the web

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and there'll be a lot of points where I'll say "It's Wikipedia time!" if you want to look up something up or follow a resource down,

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because what I'm about to do is try to cram about 2 years of graduate school education classes into approximately 20 minutes.

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So there's going to be a lot that we cover very very quickly and there's going to be a lot that we don't cover but I'll go "there's a link!"

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So, my name is Mel, and in between bouts of academia I've been spending a lot of time wandering around sort of the hacking world,

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the coding world, the open source world, the Python world. And the reason I went back to academia was because there was a bug I wanted to fix,

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it was classrooms looked like that and not like this, and not like the sorts of really cool hacking, messing-around-with-things communities we all know and love.

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And there's this giant cultural gulf between the academic world and the world we know here.

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And my belief is that you folks here at PyCon are doing it right. So, in particular -- so one example, like test-driven development.

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Many of us would probably be familiar with this, that's a doctest for a function that returns a factorial of a number, and there's a test for it,

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and I was taught when I started programming that when you're writing code, you figure out what you want the code to do first,

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then write a test to make sure that the code you're going to write is performing the function you want it to perform, then you write the code.

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And when you design curriculum, you should do exactly the same thing.

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So a lot of people make the mistake of starting with the pedagogy phase. As in "that would be a really cool activity to do with students."

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So, pause, step back -- first figure out the content, the objectives you want.

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Then write the test. How will you assess that students are able to achieve the objectives you want.

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Then figure out the pedagogy of what activity you want them to do to get there.

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Understanding By Design is a lovely book if you want to learn a bit more about that approach.

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And if you're trying to figure out well, well, what's this content thing --

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Bloom's Taxonomy is a handy-dandy list of words that you can use. It goes up in a hierarchy.

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Do you want students to memorize and remember things? Which is very easily dismissed,

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and sometimes it's not a bad thing because there are only so many times you can say

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"the square brackets are lists, the curly ones are dictionaries" over and over again before you just want them to know it. D

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o you want them to be able to apply things in a step-by-step tutorial following instructions?

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Do you want them to be able to evaluate, like "here's two approaches, which one do you think is better? Why?"

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Do you want them to be able to create things? And the thing about the taxonomy is the stuff at the top, we usually think of as "better" --

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it's not necessarily "better." It takes more time.

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It's really hard to do the things at the top unless you've done some things at the bottom first.

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So, back to the whole cultural gulf between these two...

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One of the things that again, as Pythonistas, you already know is the world is socially constructed.

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And some people in school have a very hard time understanding this.

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But we already know that, because --

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well, why is the Python language the way it is? It's not because some higher power from above ordained it --

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well, uh, it kind of is [laughter]

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but they're called maintainers, and they're humans!

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And we can see them talking on the mailing lists!

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And you can see their commit messages!

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And you can see them going back and forth!

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And so we have this idea that the world is just as hackable as our technologies are. And that's a very, very powerful viewpoint to have.

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So. So here's the thing.

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We need to come up with some sort of translation between the two worlds, because what happened when I went back to graduate school

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and I started teaching again, and I was talking about "oh my gosh, we're all in these communities of makers and the open source world and the Python community

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and we're learning and it's fun and it's wonderful -- can I do this in my classroom, please?"

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They went "well, well... what is it you do there?" and I went "uhhh... we... make things and it's fun,"

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they're like "oh, you're just playing..."

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And so I needed to find ways to describe it in words they would understand. [laughter]

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And, uh... yeah.

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So, so let's go through some of this.

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Accidental learning. It's a fancy word for "we didn't plan it ahead of time." [laughter] All right?

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So say that you hang out in the IRC channel, people are talking, things are gonna come up, things you didn't expect to learn about that day.

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And this is the kind of hap- kind of thing that happens in our world all the time.

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You're in a conference, you're milling around, you meet people, so accidental learning or "authentic experiences" is another really useful phrase.

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And communities of practice. Python is a community of practice.

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Communities of Practice is another really big theoretical construct that a lot of education people use and it is within a domain, a community of people --

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so we're in the domain of programming. We are a community of people who share the practice of programming in Python.

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Within the domain of Python web frameworks, the Django community is a community that shares the practice of using the Django framework.

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And so if you think of a community of practice, it's actually a learning entity,

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and cognitive apprenticeship is how learning happens inside communities of practice.

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And if you think about a shop floor, how like a traditional handcraft like wood, woodworking, or delivering babies, or what have you,

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as an apprentice you see two different pathways.

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One is that you see the pathway from raw product to finish product. But you also see the pathway from new apprentice to master.

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And it's not just one pathway, it's I see the more experienced apprentices ahead of me,

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I see the journeymen ahead of them, and I don't just see one master, I see 50, I see 20, I see a thousand masters,

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so I get the idea that there are many different kinds of mastery and many different kinds of people, it's not a linear route I'm on,

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it's not "I'm comparing myself to him or her," but it's "what kind of master will I become?

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What does it mean for me to have mastery in this domain?"

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And the modeling, coaching, scaffolding, and fading -- because these communities of practice and these sort of apprentice shops are in our head,

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when we're programming, it's not like you can see, like, you know, bugfixing in your brain is not quite as visible as let me saw this drawer in this wood.

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And so modeling, coaching, scaffolding, and fading are four things that, as people we teach, we can think about.

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So modeling is doing the task yourself so the learner can see it,

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coaching is standing on the sidelines and giving them realtime feedback as they're attempting that task themselves.

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Scaffolding is designing a task so that they take on a little bit and then successively more.

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So for instance, the first time someone is running a program and it crashes, you might model "this is how you submit a bug report."

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The next time, you might scaffold that a little bit.

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You open up bugzilla and then say "you watched me type the subject line before, why don't you come up with a subject line now?

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All right, now that you've come up with a subject line, let's type the body together."

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And over time you give them more and more responsibility.

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And after that you start to fade slowly until they're doing the entire task on their own.

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And that link at the bottom if you want to learn more way more about cognitive apprenticeship is a paper I wrote on cognitive apprenticeship case studies in open source communities.

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So what happens when you go through a cognitive apprenticeship is you progress from novice to expert.

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And the Dreyfus Model of Skill Acquisition is a framework you can use to think about that progression.

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And the insight here that the Dreyfus brothers have was not that people progress from novice to expert, which is kind of obvious, but --

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uh, perhaps equally obvious -- is that when you are at any given state, it's very hard to tell and remember what the other stages are like.

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So if you are an advanced beginner, you've kinda forgotten what it's like to be a novice,

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and you have no idea what it's like to be proficient, or an expert.

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And so what that means is that the world looks very different to very different types of people.

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So for instance, if you are proficient or an expert or, you know, pretty comfortable with Python, working in communities remotely,

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an IRC channel might look like a place where you might get help faster, right?

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Find tasks faster -- these are all happy, happy tools for us. Alright?

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And we see them as things that can bring, like, the little green people on the outskirts into the little purple people on the inside of our community.

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Oh man. It'll be wonderful!

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And, um, but the green people on the outskirts don't necessarily see it that way, right?

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They might think this. "We will not talk to you until you use this strange new tool.

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Stop asking me what to do and go away.

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To a corner no one has touched for months." [laughter]

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And that's not really the impression we're trying to give them.

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But why do they think that?

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So: Piaget.

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One of the reason why -- this talk alone, if you can not read Piaget and get this,

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it's worth it because I had to read the original translations from the French, and ugh.

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So, Piaget has two ideas -- well, he has a lot more ideas,

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but the two I'm going to talk about here are assimilation and accomodation.

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Uh, assimilation is when you can add new information to the mental schemas you already have,

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and accommodation is when you have enough data that doesn't really kinda fit that you've got to refactor it,

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and we have a really great example over here.

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[laughter]

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Right?

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And so let's think about that, right?

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There are some people that, even as Python 3 was out, kept on using Python 2.5, 6, 7... and so you had two parallel streams.

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And so you have students -- some of them are going to keep on wanting to think the same way they've been thinking, until it really, really, really, really no longer works,

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and others are going to be early adopters, and telling them that is a switch that they're making in paradigms is something you can do

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to make it more conscious that that's the process that they're going through.

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And -- but, you know, back to that earlier image, right, to see things the wrong way can actually scare people, right?

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And the Dreyfus model talks about what, uh, context, what, what are novices missing? Novices are missing context.

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If you are just beginning to learn how to cook, you really want a cookbook that says

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"this is a chicken. A chicken is a bird. [laughter]

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This is what you do with it. Turn on the oven. [laughter]"

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And on the other hand if you're an experiened chef, you can go and you compete in Iron chef, and they say "SARDINES!"

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and you go "oh, wow!" [laughter] Because you have the context of how to improvise in that world.

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And so the resources that work for an expert or someone higher up in the Dreyfus scale -- acquisition scale -- aren't going to work for a novice.

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And so that's another thing. Looking at where your students are, what they need, where they feel they are,

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and how much context they can handle so you can scaffold them appropriately.

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So -- quick bit. Assessment -- because I was thinking about food, and formative versus summative assessment is a good aside to put in here.

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Formative assessment is like tasting food as you're cooking it to make sure it's going to turn out okay.

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Summative assessment is tasting the food at the end once you've served it and it's done.

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Summative assessment is the kind of assessment that usually happens in schools; you get a grade at the end of the semester.

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Formative assessment is the kind of assessment that happens in here.

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You have conversations on things on your blog, you get people's feedback, you review their code,

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and so when you talk to educators and they say "well, how are you going to assess your students?"

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and the answer is "well, I don't, I don't wanna grade them," you can say

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well, well, they have many opportunities for informal formative assessment with experienced members of the community, and it'll sound good.

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So, back to Dreyfus. One of the things that that dispels is the myth that you can't contribute until you're "good enough." Right.

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There are still things that novices can do if they are scaffolded appropriately.

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And two things that can help you think about that is -- in a cognitive apprenticeship within a community of practice

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there's the idea of the zone of proximal development and the idea of legitimate peripheral participation. And they're related.

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So, proximal development is -- so here's the things I can already do.

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And then are the things on the other end that I can never do no matter how much help I get -- at this point in time.

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And in the middle of that, there's the stuff that I can do if and only if someone else helps me.

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That is the zone of proximal development.

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And so -- bike riding, it's when you're at that stage where you can kinda wobble on the bike if your,

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if your brother is hanging on to keep you hanging.

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So what's the equivalent in the Python community?

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It might look like pair programming, it might look like code review,

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it might look like any sort of interaction between experienced people and less experienced people.

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And one of the cool things about the zone of proximal development is that you don't need experienced versus less experienced --

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peers can create zones of proximal development for each other.

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But it's the enablement that an extra person beside you or with you online creates.

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Legitimate peripheral partipation is what -- the means for allowing people to contribute without being the core already.

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So there are mission critical tasks, and, you know, these things that nobody really cares about.

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And legitimate peripheral participation opportunities come with the jobs that --

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it would be really, really cool if we could do this, but none of the core people have time for.

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And so that's a lot of where Google Summer of Code projects sorta fall into that category,

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a lot of good projects for students in classes fall into that category,

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if you have things that your core community would love to see fixed, so they'll actually help people who are coming in to fix it,

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but they're not so vital that if they don't fix it the build gets delayed

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because this one student couldn't figure out how to write the code --

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that's a great opportunity.

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And -- now, switching gears for a bit,

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going back to that first example of test-driven development,

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this is also a good example of behaviorist thinking.

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The fact that, you know, we've got that little function that says

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[robotic voice] we will test these students, they will produce an output,

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it will tell us the true and accurate mentality of how much they know --

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[normal voice] That is a fair assumption to make,

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but it's an assumption you should know you're making.

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And so what I'm going to do, uh, for, for the grand finale of this --

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well, semi-grand finale of this --

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is to go through 50 years of cognitive paradigms in teaching and learning very, very quickly,

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and if you want the less-abridged version, um, there are resources there. So.

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This is 50 years of educational psychology history on one page.

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In the beginning there was behaviorism, which is the carrot-and-stick stimulus-response thinking, all right?

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You have a student, they're a black box, you poke at them, there's an output, it will be the correct answer!

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We hope. If not, we'll poke at them more and kinda beat them with a stick

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and wave a little carrot in front of them, and they'll give the correct answer!

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And sometimes we go "ah, behaviorism is bad, it's outmoded and outdated"

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-- but it's really useful.

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For instance. The Boston Python Workshop uses CodingBat to teach stuff.

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Anytime we talk about automating learning, automating experience, we're taking a behaviorist mentality, alright?

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So we say, write this code, type it in, press the button,

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did you get the right answer, yes/no.

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So it's not an evil thing, it's a very, very useful tool,

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but it's something to be conscious of.

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Then there are people who went "well, the behaviorists think that inside the brain's a black box,

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but what goes inside there is actually important."

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And so how do we structure material?

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How do we store it in our memory?

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How do we organize things so they're easier for people to learn?

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And a really good example of that is actually you folks.

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Why do so many people like programming in Python, as opposed to other languages?

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It fits in your brain pretty nicely.

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You can read the code, it makes sense, as opposed to --

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I had to program in assembly back in the day and that --

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it didn't fit in my brain quite so well.

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And so using Python is already using cognitive schemas

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and working with people who value the use of good cognitive schemas.

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Then we move into the situative domain, people were like

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"alright, stuff is happening in your cranium, you're responding to input...

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but really, this stuff happens in a community.

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Learning happens in a context.

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Knowledge only really makes sense if other people know it too, and validate it."

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And that's a lot of what we were talking about with the cognitive apprenticeship earlier.

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But another good example of that is Sugar Labs.

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So this is a learning environment for kids, written in Python, where they can, um, play,

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play with different games for learning, but one of the cool things is --

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we were talking about modeling, coaching, scaffolding, and fading --

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that's also built into the design of the activity. So you can play the game,

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and at another level you can click on the little gear thing and make your own abacus,

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and on another level you can click on another button and see the Python code and work on it,

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and so there's different levels of scaffolding and you can see the work of multiple people

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and share your code with others and that's getting out of the

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"I'm a person on my computer working alone" and into the

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"I'm a person working in a broader community connecting with other people"

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socially constructing a world with them.

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So, in a parallel thread to that timeline of behaviorist, um,

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cognitive thinking, and, um, situated learning,

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was the ideas of different theories of motivation. A

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nd I'm going to go through a few of them really quickly.

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One of them is self-efficacy, which is the idea of how much you believe you can do this.

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And that is, in rank order, the things they found affected self-efficacy the most.

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If you care about making people believe they can do things, this is the list you want to pay attention to.

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So the most impactful thing is doing it, because if you did it before you can probably do it again.

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The second one is seeing people do it -- people that look like you.

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The converse of that is if you see people that look like you fail at it, then you start thinking "maybe I can't do it either."

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And the third one, social persuasion, is other people coming up and saying

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"you can do this, you can try this, you should come, you should come to this talk,

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you should go to this tutorial, you should give another talk."

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And the interesting thing is that those three things override your physical body,

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so if you have butterflies in your stomach, and you're really scared about hitting the Enter key,

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but the people beside you are going "yeah, you can do this!"

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and you're watching people around you succeeding,

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then you're really likely to believe you can you can take that leap.

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Attribution theory -- another thing.

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Do you walk into the room thinking that "my talent in coding is innate,"

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or do you walk into the room thinking "it's a muscle, if I exercise it, I'll get better,

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and if people are really good, they're good because they work really hard."?

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And so what attitude do your students come into the room with,

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and what attitude as a teacher do you project?

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And this is a really good example here,

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because I think people who come to things like PyCon are coming because they want to learn things,

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because they know that it's not a magical talent we're born with,

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we gain those through exposure and working with others that are interested.

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Then there's motivation,

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and moving from amotivaton, which is "I don't care," to

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"other people make me do it,"

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"I do it because it's good for me and it'll help me get a good job,"

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into "this is really really cool" --

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which is, for the most part where we want people to end up --

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can be influenced by making, increasing autonomy, relatedness, and competence.

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So, autonomy is the freedom you have to decide what you're gonna do.

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Relatedness is how close your project is to someting I already care about,

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and competence is self-efficacy we talked about earlier.

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How good do I think I am at doing it,

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not how good I am at it really,

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but how good do I think I am.

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So if you want to increase the stuff on the left, turn the sliders up for the stuff on the right.

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And so now with that, this, this paragraph should now start making a little bit more sense.

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So... why do I do this? Why am I talking about these kinds of things?

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And the thing is, because we can only see a little bit of the world.

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We can only see a little bit of what students do and what they think.

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And as teachers we have a lot of privilege.

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And we don't necessarily see what our students are assuming about themselves,

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or about why things may not be working for them yet.

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We tend to teach the way we learn. A

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nd so sometimes we think "oh, well, the way I teach is the way it should be taught,"

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or maybe "the way I teach is better than the way they teach in school, but clearly it's the way it should be taught."

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And that's just not necessarily true.

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For your students, the first couple steps do not feel like progress.

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You're guarding their thoughts, you're scared, and you might be quiet,

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but underneath the silence, stuff is happening inside.

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Reason is stirring in the background.

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And it's very important that confirmation that you belong to a community starts when you begin to try

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rather than being an end goal you try to get.

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Once you take the first step, you already belong.

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And that's all I had. Time for questions?

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[applause]

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NAOMI: Ok. If you have questions, we've got a microphone there and we've got a microphone here.

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AUDIENCE MEMBER: Yeah, um, can you go back a few slides? I missed the quote.

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MEL: Um, this one? Ok.

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AUDIENCE MEMBER: Um, yeah. First, I want to thank you, that was -- oh, go ahead.

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MEL: No, go ahead.

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AUDIENCE MEMBER: So I want to thank you, that was really interesting

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and not at all what I thought I would be learning when I came to PyCon

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so it was an accidental learning opportunity.

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[laughter]

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And then, um, could you recommend a book that we could...

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MEL: Come again?

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AUDIENCE MEMBER: Could you recommend a book, that, on this topic?

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Like, some kind of [indistinguishable] book?

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MEL: On which topic?

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AUDIENCE MEMBER: On educational psychology, it's an interesting field which I know nothing about except, um, this [laughter]

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and it sounds quite big but I don't want to go to graduate school either.

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[laughter]

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MEL: Ah. So, uh, if I could only recommend one book, I would...

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um, I believe it's called "Theories of Development" by Crain,

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C-R-A-I-N,

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and I can write that down for you afterwards if you'd like.

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And what that is, is a snapshot of individual researchers that have developed --

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so Bandura's in there, Dweck is in there, a couple of the things we went through are in there,

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and it gives 5-page summaries of all the very complicated papers.

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And it's lovely.

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NAOMI: Ok, another question.

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AUDIENCE MEMBER: Thanks.

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Thanks again for doing this talk.

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I was just curious what are some of the next steps for you, what do you think,

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do you think you can make engineering, this kind of teaching methodology based on our open source movement is really interesting.

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MEL: Well, I'm interested in 2.5 things.

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[laughter]

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Well, one, one is --

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so the reason I'm giving this talk is I'm assuming y'all are all people who do Python things and care about teaching,

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and I wanted to try and give you some language to explain what you're doing,

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to validate it in the world of professional schooling.

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And to go and be able to say "this is a great thing to have your class do,

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this is a great thing to have, you know, as part of the curriculum."

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It's not something should be relegated to just after-school clubs that are just playing around,

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because putting it into more mainstream schooling will give more people access to it

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instead of just the people that have the resources in both time and equipment to play around with it on the side.

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And so that's one of the things --

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and so if you are interested in, um, trying to help explain the project that you're doing to educational institutions

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I'd be really happy to help with that.

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Another thing I'm interested in is going in the opposite direction,

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working with teachers who are interested in working with folks like you.

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And doing things in their classroom.

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So Jeff and Steve up in the front row are brilliant examples.

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I wish I could clone them.

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Jeff is K-12 and Steve does college, and how do they move in the opposite direction

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and get to know what it's like to work in these sorts of projects and communities.

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And the point-five is that a number of people have been trying to bug me to write this up in longer form as a bidirectional translation for both sides

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and I'm not sure if more than three people are actually interested in that

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but that might be something to do once I finish up my classes next semester.

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AUDIENCE MEMBER: Thank you.

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AUDIENCE MEMBER: So if there was one thing that you could get the average computer science teacher

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to change or stop doing or start doing, what would it be?

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MEL: It would be... mm, looking up...

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It would be working on the content.

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A lot of teachers, their learning objectives are filled with "we have to hit standard XYZ,

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and there's 20 learning objectives

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and we have to get through all the chapters of the textbook" and...

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where is it, there we go,

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and so it's a very linear thing because you need to have a predefined outcome,

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but loosening that up and changing things so that your goal is

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"let me have students become confident wandering in an unknown world" --

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being comfortable being, um,

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"productively lost" is a phrase I like to use a lot.

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Having them be comfortable being productively lost.

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So even if they don't get specific bits of content or specific bits of material,

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that they're comfortable moving around and improvising,

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and that's the learning objective -- that's the one thing I'd like to see changed

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but it's a huge, huge, huge change

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and it's a very risky change for a lot of people, so it's a hard one.

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NAOMI: Ok. Any more questions?

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MEL: I think that's time.

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NAOMI: Thank you very much.