Week 2: Computers and Computer Culture, Mathophobia: the Fear of Learning
“What is happening now is an empirical question. What can happen is a technical question. But what will happen is a political question”
Things to think about:
- Papert describes the QWERTY phenomenon, where ideas get ‘locked in’ not because they’re good ideas but because they came first. What are some examples of QWERTY thinking from your experience? How might we push back against this phenomenon (and how are people already doing so)?
- If you have coding experience, has learning to code changed the way you think about any other aspects of your life, and if so, what and how?
- In the last page of Chapter 2 (page 54), Papert describes three principles for learning. To paraphrase, it should be 1. personally relatable, 2. empowering, and 3. socially relevant. What’s an example of an educational encounter in your life that did not fit these criteria? And can you think of any educational encounters that did fit these criteria?
- What ideas that you’d like to discuss most jumped out at you when reading this section?
- Papert sees computers as objects that can make abstract ideas more concrete. Pick an abstract idea that you’re familiar with. What object makes this idea more concrete? (It could be a toy, game, puzzle, comic, illustration, poem, essay, activity, computer program, etc.) If no existing object comes to mind, can you imagine such an object?
- Papert described ‘Piagetian learning’ as “learning without a curriculum”. Pick an abstract idea that you’re familiar with. What activity might teach this idea through exploration or play (e.g. in a manner similar to how Papert’s “concrete poetry” teaches grammar)?
Cool resources/links:
What talking about diagonals reminds Saber about
A different approach to coding
Sharon: "play with math <3"
A different approach to coding
Sharon: "play with math <3"
Responses/Links:
Sharon:
1. Hero keyboard app for mobile phones 2. YES! Not taking for granted PROCESS 3. American history tends to be that. What is “American”-----whose America? 4. ----frustration, how do you unlearn even me creating a project for this week was frustrating. 5. comics as an instructional tool. "Secret Coders", Jody Colkin
1. Hero keyboard app for mobile phones 2. YES! Not taking for granted PROCESS 3. American history tends to be that. What is “American”-----whose America? 4. ----frustration, how do you unlearn even me creating a project for this week was frustrating. 5. comics as an instructional tool. "Secret Coders", Jody Colkin
Aatish:
1. The US not being on the metric system is an example of QWERTY thinking. This has a ton of everyday consequences that make life more expensive & complicated for people. For one thing, science is done in metric (for good reasons.. metric was created to standardize units & connect them to things in nature you can measure) and students who aren't familiar with these units are at a bit of a disadvantage.
2. One coding concept that influenced me a lot was recursion. I remember being amazed at how it could solve problems that seemed so complicated like the Towers of Hanoi puzzle, by realizing that a big problem has a smaller problem contained within it, and the smaller problem a smaller one, and so on. Around the same time, I was learning about induction in math (a proof techinque that relies on recursively breaking a big problem into smaller steps) and so this was a new way of thinking about problems for me that felt empowering.
3. A lot of my school education didn't fit this criteria. E.g. it was very memorization heavy and the reason you had to memorize things were that somebody else had decided that you should know them (and we all knew the real reason was that it was easy to test). Playing with Lego Mindstorms robotics kit (which was strongly influenced by Papert) was an educational experience that did. I learned to program in C because I could use it to make my robot do stuff that it couldn't otherwise.
4. Ch. 1 taps into importance of diverse creators / educators, Ch. 2 taps into growth vs. fixed mindset
5. The towers of hanoi puzzle is a great way to be introducted to recursion! In solving it you learn how to take a big problem and break it down into slightly smaller problems in an interative way. Learning to solve a Rubik's Cube can be a great way to grapple with an idea in group theory called commutators, which is when A*B is not the same as B*A. (e.g. putting on your socks and then your shoes is not the same as putting on your shoes and then your socks, so these things don't 'commute'. The same is true for most Rubik's cube moves.)
6. Nim is an example of a simple game you can play as a kid. It turns out that there's an optimal strategy where the 1st player will always lose, and learning this winning strategy involves learning about binary numbers. So the need for binary numbers comes up in a natural way.
http://www.archimedes-lab.org/game_nim/play_nim_game.html
Vandana
2. Learning to code definitely changed the way I started thinking about other things. I think I became more 'procedural' in the way I thought about other things in day-to-day life, even if only slightly. I also believe that it helped in driving my interest in linguistics, the field I turned to after college was over. It definitely gave me an immedate affinity to formal semantics when I learned that it was based on many similar principles. 3. Sadly, I think my own experience of learning programming may have not fit these criteria. I recall having to code things that at the time felt so far from anything that could be relevant or important. I remember having to make tiny programs to calculate body mass index, convert Fahrenheit to Celsius, etc., and not feeling particularly empowered through the experiences. 4. Papert argues for the ease of children learning artificial languages (programming) by connecting it to what we know about child language acquisition for natural language. I wonder if Papert then thinks that there is a similar “critical period” for learning programming well as there is believed to be for becoming a native speaker of a spoken language. Furthermore, does he believe that it becomes increasingly more difficult for someone to become well-versed in programming in adulthood? Pushing this idea further, does Papert suppose that there is an innate component to the child’s capacity to learn programming as some linguists believe there is for the child’s capacity to learn a natural language? A second item from the chapters that jumped out to me was the portion or two where Papert describes his thoughts about how the current classroom model of teaching does not work well for trying to learn certain skills, one being programming. This is a provocative argument, but one that was interesting food for thought. 5. I’m not sure how different of an example this is from what Papert already spells out, but what immediately comes to my mind is a toy I very recently purchased for a three-year-old. It’s called a “Code-A-Pillar”, marketed as appropriate for kids aged 3 to 6, and intends to get them to understand relative directions (left, right, straight forward, etc.) by providing a caterpillar head plus multiple detachable segments. Each segment is marked with an arrow indicating the direction, and the expectation is that the child will spend time detaching and attaching the segments in different orders, turning the caterpillar on, and then seeing what the resulting path it takes is, and how it differs from when the segments are reordered. 6. Having a child play around with tossing a coin might help them understand probabilities.
2. Learning to code definitely changed the way I started thinking about other things. I think I became more 'procedural' in the way I thought about other things in day-to-day life, even if only slightly. I also believe that it helped in driving my interest in linguistics, the field I turned to after college was over. It definitely gave me an immedate affinity to formal semantics when I learned that it was based on many similar principles. 3. Sadly, I think my own experience of learning programming may have not fit these criteria. I recall having to code things that at the time felt so far from anything that could be relevant or important. I remember having to make tiny programs to calculate body mass index, convert Fahrenheit to Celsius, etc., and not feeling particularly empowered through the experiences. 4. Papert argues for the ease of children learning artificial languages (programming) by connecting it to what we know about child language acquisition for natural language. I wonder if Papert then thinks that there is a similar “critical period” for learning programming well as there is believed to be for becoming a native speaker of a spoken language. Furthermore, does he believe that it becomes increasingly more difficult for someone to become well-versed in programming in adulthood? Pushing this idea further, does Papert suppose that there is an innate component to the child’s capacity to learn programming as some linguists believe there is for the child’s capacity to learn a natural language? A second item from the chapters that jumped out to me was the portion or two where Papert describes his thoughts about how the current classroom model of teaching does not work well for trying to learn certain skills, one being programming. This is a provocative argument, but one that was interesting food for thought. 5. I’m not sure how different of an example this is from what Papert already spells out, but what immediately comes to my mind is a toy I very recently purchased for a three-year-old. It’s called a “Code-A-Pillar”, marketed as appropriate for kids aged 3 to 6, and intends to get them to understand relative directions (left, right, straight forward, etc.) by providing a caterpillar head plus multiple detachable segments. Each segment is marked with an arrow indicating the direction, and the expectation is that the child will spend time detaching and attaching the segments in different orders, turning the caterpillar on, and then seeing what the resulting path it takes is, and how it differs from when the segments are reordered. 6. Having a child play around with tossing a coin might help them understand probabilities.
Alice
2. Coding definitely causes me to think more about efficiency and about the delegation of tasks that may be better performed by a computer program. Namely, computers are amazing at rapid iteration. In one instance, analysis of biological data has increasingly come to rely on computational methods. However, input data were not always recorded in ways amenable to computational parsing and thus computational research. Thus, we must consider how we generate and record data, for there exists some divide between human and computer-readability. I also remember an early instance of this occuring while I was completing my calculus homework (we were learning about Euler's method). We had to do about 10 problems in which we were to manually crunch out the intermediate answers that build up to the final solution. After about three problems, I grew exceedingly bored (oops) while feeling that I had sufficient understanding of the topic. Thus, I wrote a computer program to generate answers for me. (And I'd like to think this helped me internalize the concepts in new ways as well.)
3. Relating what Papert says about combinatorial thinking
I think we should question our classification systems – why do we classify the way we do and how do our methods speak to our societies are constructed
What are the underlying assumptions
Walter Ong – Orality
Also just generally a lot of works from media theory go hand-in-hand with ideas Papert discusses (he even cites McCluhan directly.
4. Optical illusions: It is difficult to understand and grasp that which we do not yet even know, to understand beyond that offered by our perceptions. For one, it is hard for us to understand the limits of our perceptive capacities. For instance, we are told of "impossible colors," but how do we even begin to imagine what they look like. Optical illusions essentially take the limits of our perception and throws them at our faces.
6. http://www.npr.org/sections/alltechconsidered/2017/07/19/537961841/musks-warning-sparks-call-for-regulating-artificial-intelligence Elon needs to put down his Kurzweil and pick up some Kafka. It's nice to hear him acknowledge that tech isn't an unalloyed good, but the immediate threat of AI isn't so much machine uprising as it is machine-assisted oppression. We can't build the utopias of tomorrow if the societies of today are crushed by algorithm-assisted dystopian impulses.
The immediate examples I cited were state oppression, but there are so many other wonderful ways AI can and has amplified the trends he noted, from black-box sentencing algorithms and crime predictions to subtle algorithmic influences on the culture and media we're exposed to, shaping our worldview, taste, and even mood in ways we're often unaware of
Other commends: some underlying currents of thought:
Efficiency
Building intuition
2. Coding definitely causes me to think more about efficiency and about the delegation of tasks that may be better performed by a computer program. Namely, computers are amazing at rapid iteration. In one instance, analysis of biological data has increasingly come to rely on computational methods. However, input data were not always recorded in ways amenable to computational parsing and thus computational research. Thus, we must consider how we generate and record data, for there exists some divide between human and computer-readability. I also remember an early instance of this occuring while I was completing my calculus homework (we were learning about Euler's method). We had to do about 10 problems in which we were to manually crunch out the intermediate answers that build up to the final solution. After about three problems, I grew exceedingly bored (oops) while feeling that I had sufficient understanding of the topic. Thus, I wrote a computer program to generate answers for me. (And I'd like to think this helped me internalize the concepts in new ways as well.)
3. Relating what Papert says about combinatorial thinking
I think we should question our classification systems – why do we classify the way we do and how do our methods speak to our societies are constructed
What are the underlying assumptions
Walter Ong – Orality
Also just generally a lot of works from media theory go hand-in-hand with ideas Papert discusses (he even cites McCluhan directly.
4. Optical illusions: It is difficult to understand and grasp that which we do not yet even know, to understand beyond that offered by our perceptions. For one, it is hard for us to understand the limits of our perceptive capacities. For instance, we are told of "impossible colors," but how do we even begin to imagine what they look like. Optical illusions essentially take the limits of our perception and throws them at our faces.
6. http://www.npr.org/sections/alltechconsidered/2017/07/19/537961841/musks-warning-sparks-call-for-regulating-artificial-intelligence Elon needs to put down his Kurzweil and pick up some Kafka. It's nice to hear him acknowledge that tech isn't an unalloyed good, but the immediate threat of AI isn't so much machine uprising as it is machine-assisted oppression. We can't build the utopias of tomorrow if the societies of today are crushed by algorithm-assisted dystopian impulses.
The immediate examples I cited were state oppression, but there are so many other wonderful ways AI can and has amplified the trends he noted, from black-box sentencing algorithms and crime predictions to subtle algorithmic influences on the culture and media we're exposed to, shaping our worldview, taste, and even mood in ways we're often unaware of
Other commends: some underlying currents of thought:
Efficiency
Building intuition
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