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Learning and Modern Programming Languages


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A brief look at learnability of a few modern programming languages, drawing a lot from Bret Victor's Learnable Programming.

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Learning and Modern Programming Languages

  1. 1. Are Modern Programming Languages Easier to Learn? Ray Toal, Loyola Marymount University @rtoal
  2. 2. What will this talk be about? ● "Anyone Can Program" (really?) ● What does it mean to learn or understand programming? ● How are modern languages helping (or not)?
  3. 3. I was asked to first talk about myself
  4. 4. … and about LMU CS
  5. 5. But enough about me
  6. 6. Really, Michael? A noble gesture to garner the NYC tech community vote, for sure, but if the mayor of New York City actually needs to sling JavaScript code to do his job, something is deeply, horribly, terribly wrong with politics in the state of New York. --- Jeff Atwood
  7. 7. Who's going to teach everyone?
  8. 8. At least it is not as hard as this
  9. 9. Because of this visionary "But Grace, then anyone will be able to write programs!"
  10. 10. But alas, not everyone can All images from
  11. 11. Seriously, it can't be this hard?
  12. 12. And it's just not about "mistakes" What is WRONG with you people?
  13. 13. We need to ask How do we get people to understand programming?
  14. 14. This guy knows
  15. 15. He wrote a really great essay
  16. 16. With this answer "We change programming. We turn it into something that's understandable by people."
  17. 17. Why did he write this essay? In Inventing on Principle (2011), Bret Victor demonstrated a remarkable live coding environment. Inspired, Khan Academy implemented it in their programming section. But KA missed the point: he was talking about creating, not learning.
  18. 18. What does he say? A system should ● support and encourage powerful ways of thinking ● allow people to see and understand the execution of their code.
  19. 19. What's wrong with KA's approach? "A live-coding Processing environment addresses neither of these goals. JavaScript and Processing are poorly-designed languages that support weak ways of thinking, and ignore decades of learning about learning. And live coding, as a standalone feature, is worthless."
  20. 20. Bret Victor's Nine Principles: The learner should be able to... E1. See labeled code (not consult manuals) E2. Follow the flow E3. See the state E4. Start somewhere, then sculpt E5. Start somewhere, then generalize L1. Work with sensible metaphors L2. Decompose thoughts L3. Glue thoughts together L4. Know what the code means just by reading
  21. 21. Languages and Learning What can the language do for the learner?
  22. 22. It's been done right before!
  23. 23. What did they do right? ● Concepts are directly related to the programmer's world (metaphor) ● Elements decompose into things people can think about independently ● Program elements can be composed from other programs and molded to new uses ● State is minimized, or at least explicit ● Syntax (It matters!) ● Names (they matter!)
  24. 24. Metaphor GOOD ● Turtle ● Objects and messages ● Stack of cards ● Movable players POOR ● Shuffling bits ● Memory cell LACK OF ● rect(0,0,10,10)
  25. 25. Decomposition NICE THINGS modules objects functions BARRIERS Top level event handlers Without objects how do you make animations, multiple copies, vary behavior?
  26. 26. Recomposition BAD STUFF: ● mutable state ● invisible state ● global variables ● lack of encapsulation ● "leakiness"
  27. 27. Syntax draw_circle(center=(2,5), radius=10) drawCircleCenteredAt: (2,5) withRadius: 10 [3, 5, 9] {name: "Rex", breed: "G-SHEP", age: 5} drawCircle(2,5,10) … however you make arrays in C or Java … however you make maps in C or Java
  28. 28. Names vectorFromStartAndEndPoint(start, end) // you can tell this is constructing and returning // a new vector vectorFrom:To: fill(...) // to set a fill color // why not at least set_fill_color? rectangle(....) // should be draw_rectangle concat(a, b) // ambiguous if b is an array, no?
  29. 29. So, the BIG things to look for are Primary metaphors Identifiable objects Independent modules Streamlined syntax (no clutter) No ambiguity (almost) Language support for modelessness Parts of speech used properly in keywords and libraries Parameter names in calls Nice environments (IDEs, Playgrounds)
  30. 30. How about some new languages?
  31. 31. They're all adopting nice features ● Loop through structures without "for i" ● "for k,v in map" and other destructurings ● Ways to avoid loops (higher order functions) ● String interpolation ● Multiline strings ● x,y = y,x ● a[3..5] ● Patterns ● Optional or inferential typing, for the statically typed languages
  32. 32. Explain some of those, please for dog in kennel: dog.bark() sum(n*n for n in numbers if n % 2 == 0) sum(map(square, filter(odd, numbers))) numbers | filter odd | map square | sum
  33. 33. CoffeeScript number = -42 if opposite square = (x) -> x * x list = [1, 2, 3, 4, 5] math = root: Math.sqrt square: square cube: (x) -> x * square x race = (winner, runners...) -> print winner, runners alert "I knew it!" if elvis? cubes = (math.cube num for num in list) These examples are from
  34. 34. Clojure "A general-purpose language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming." Lots of statelessness, powerful combining forms (defn scramble [s] (if (empty? s) "" (-> s seq shuffle join))) See Rich Hickey's Simple Made Easy
  35. 35. Go "Did the C++ committee really believe that was wrong with C++ was that it didn't have enough features? Surely … it would be a greater achievement to simplify the language rather than to add to it." -- Rob Pike In one of his talks, Pike identifies 35 "significant simplifications in Go over C and C++".
  36. 36. Rust Rust is a systems programming language that runs blazingly fast, prevents almost all crashes*, and eliminates data races. So…. what are the chances it is easy to learn? "Rust's pointers are one of its more unique and compelling features. Pointers are also one of the more confusing topics for newcomers to Rust. They can also be confusing for people coming from other languages that support pointers" But it's pretty clean, with  pattern matching  closures  type inference
  37. 37. Julia The Julia programming language fills this role: it is a flexible dynamic language, appropriate for scientific and numerical computing, with performance comparable to traditional statically-typed languages. x = [1,2,3] y = [1 2 3] A = [1 2 3 4; 5 6 7 8; 9 10 11 12] A[2,1] = 0 u, v = (15.03, 1.2e-27) f(x) = 3x x -> 3x x[2:12] x[2:end] A[5,1:3] A[5,:] for animal in ["dog", "cat", "mouse"] println("$animal is a mammal") end map(x -> x^2 + 2x - 1, [1,3,-1]) [add_10(i) for i in [1, 2, 3]] ... and keyword args too
  38. 38. Swift I think Apple did a good job here: ● Named Parameters o counter.incrementBy(5, numberOfTimes: 3) ● Closures ● Tuples and multiple return values ● Fast and concise iteration over a range or collection ● map and filter ● Eliminates much unsafe code: Variables are always initialized before use, arrays and integers are checked for overflow, and memory is managed automatically. ● Option chaining! (e.g., ● Great interactive playground (within XCode) ● Uses the readable Cocoa API
  39. 39. So…? ● All have REPLs or Playgrounds and great online examples. ● All adopt nice features to make programming a little more pleasant ● But all are industrial strength - do we expect the learner to quickly master all 7 types of Rust pointers? ● And none really have the turtle metaphor!
  40. 40. Takeaways ● We have decades of research on teaching and learning that is often ignored ● Learning and understanding programming requires more than just live coding ● Language should expose metaphor, allow decomposition and recomposition, and make meaning transparent ● We saw some modern languages and saw they did a few things right
  41. 41. Okay that's it Questions? Discussion. Thanks!