> Even today, if you're designing a truly novel algorithm, you're very likely to spend the bulk of your time not coding, and then coding only at the end as a sort of check to make sure you ideas work out.
If you cannot code, you cannot check that your ideas work out on a computer.
> Which is to say, from my observation, coding-as-problem-solving is not very effective until after you've thought things through in more general, mathematical terms.
I don't see how that relates to my comment, I specifically referred to 'writing' an algorithm, as in implementing the algorithm in code, not devising the algorithm in the first place.
> If you cannot code, you cannot check that your ideas work out on a computer.
Believe it or not, it's possible to write provable correct algorithms without writing a single test case :-)
Of course implementing is a useful sanity check. But it's not the important part -- it's not the skill set we should most emphasize in education.
> as in implementing the algorithm in code, not devising the algorithm in the first place.
Is there a particularly pressing reason to teach students who to translate pseudo-code from Wikipedia into <insert favorite language>? Sounds like a perhaps useful but pretty menial skill to me. Not the sort of core skill you'd organize a curriculum revision around for sure.
My point was that "copy an algorithm into programming language X" isn't something which needs to be taught in the first place, and isn't an economically valuable skill because literally anybody can be taught this skill in a few weeks.
If I had to choose between teaching why an avl balanced binary search tree works and forcing students to implement in a chosen programming language from pseudocode, I would choose the former every time. The intellectual maturity is far more valuable -- and harder to acquire -- than the rote skill.
> My point was that "copy an algorithm into programming language X" isn't something which needs to be taught in the first place, and isn't an economically valuable skill because literally anybody can be taught this skill in a few weeks.
That depends on the language, I doubt you could learn much about programming in a few weeks, it requires lots of practise.
> If I had to choose between teaching why an avl balanced binary search tree works and forcing students to implement in a chosen programming language from pseudocode, I would choose the former every time. The intellectual maturity is far more valuable -- and harder to acquire -- than the rote skill.
You are giving yourself a hard choice, just demonstrate both sides.
From personal experience, I've been looking at bst's recently. While messing around in my code, out of the blue I decided to write the struct for the node like this:
typedef struct avl_node_typed {
avl_node_typed *left, *right;
void *data;
unsigned height, population; /* avl algorithm requires height, not number of nodes */
}avl_node;
I could access the nth node in a tree. I had rediscovered the algorithm of order statistic trees, which can be treated syntactically like an array (this can be applied to all ordered trees, not just avl). During this time I was looking up knowledge on avl trees and coding away on my machine. If I was purely focused on the algorithmic side, I don't think I would have found it.
> I specifically referred to 'writing' an algorithm, as in implementing the algorithm in code, not devising the algorithm in the first place.
That's confusing usage of "write an algorithm". There used to be a distinction between "programming" (creatig program structure; sequence selection iteration and all tha stuff) qnd "coding" (taking those paper notes and translating them into the particular programming language being used).
It's easy to find people who could create an algorithm but who cannot also code. For example, you can create an algorithm, but you know nothing about {insert the name of a language which you know nothing about here}.
> That's confusing usage of "write an algorithm". There used to be a distinction between "programming" (creatig program structure; sequence selection iteration and all tha stuff) qnd "coding" (taking those paper notes and translating them into the particular programming language being used).
Apologies for being vague, I presumed the phrase 'writing an algorithm' was sufficiently precise.
> It's easy to find people who could create an algorithm but who cannot also code.
I rephrase that: It's easy to find people who could create an algorithm but can only target a specific architecture, human grey matter.
> It's easy to find people who could create an algorithm but can only target a specific architecture, human grey matter.
"Programs should be written for people to read, and only incidentally for machines to execute."
The key difference is that if the skill of implementing an algorithm in <insert programming language> code can be learned in a few weeks max. It's a matter of whether you've bothered to learn the skill, not whether you're capable of learning the skill.
Learning the problem solving skills to create new algorithms is not so trivial, and often can't be self-taught.
> Harold Abelson, yeah? I was wondering what an assembly programmer would say to this.
Taking my comment within the wider context of the conversation, and the general conflation of "writing an algorithm" with "copying down into a programming language from existing pseudocode", I like to think most notable computer scientists would agree with the sentiment that we should be prioritizing the teaching of algorithms over the teaching of programming.
edit: Most importantly, no one is claiming that these two are somehow mutually exclusive. Only that the latter should be in service to the former, and not the other way around.
That is, when teaching CS, we should teach concepts, and include instruction on how to implement programs because it's a useful tool for learning CS. "Turtle Geometry" is a fantastic example of this, BTW. The focus is on the concepts, with programming as a tool for understanding them. But -- to the refutation of your core argument -- it's definitely never insinuated in TG that the ideas don't exist independently of the programming. Such an insinuation, that geometry does not exist independently of a programming environment, is as absurd as the notion that algorithms can't be written without a machine.
But anyways, your initial claim was that you need to be able to program in order to write algorithms. Which, aside from your highly non-standard use of the phrase, is just clearly not the case.
Also, of course programming has its own set of hurdles. But there's a difference between writing highly optimized assembly and copying an algorithm into Python.
> Getting someone to put themselves 'in the shoes of a computer', is just as difficult.
In my experience teaching, this isn't the case.
If you can teach the student to think precisely and unambiguously about a single algorithm, the unrelenting logic of the machine is no stumbling block; quite the opposite, actually.
It's far more effective and less degrading than having students fight with the machine and get confused about why it won't "do what they want it to do".
> But -- to the refutation of your core argument -- it's definitely never insinuated in TG that the ideas don't exist independently of the programming. Such an insinuation, that geometry does not exist independently of a programming environment, is as absurd as the notion that algorithms can't be written without a machine.
Was that my core argument? My original comment (as pointed out by the omnipresent DanBC) was poorly phrased, and was rephrased as:
> If you can teach the student to think precisely and unambiguously about a single algorithm, the unrelenting logic of the machine is no stumbling block; quite the opposite, actually.
Of course it isn't a stumbling block, logical thinking is required for both, which would also explain why they are both difficult.
Both of the statements in that link are just tautologies...
You can teach computer science without teaching coding. And you can write algorithms without testing them on a computer.
So the tautology that "you can't code if you can't code" isn't a particularly important pedagogical insight when it comes to teaching CS, since CS != coding.
> Of course it isn't a stumbling block, logical thinking is required for both, which would also explain why they are both difficult.
Right. And I'm saying that, in my experience teaching, it's better to teach CS and then let programming be something that kind of just falls out of that naturally, as opposed to focusing on programming itself.
> Both of the statements in that link are just tautologies...
I do not understand, and there are four statements, not two.
> Right. And I'm saying that, in my experience teaching, it's better to teach CS and then let programming be something that kind of just falls out of that naturally, as opposed to focusing on programming itself.
Where did I say that the focus should purely be on programming?
> Where did I say that the focus should purely be on programming?
If you'd be okay with teaching a CS course without programming, we might be talking past one another.
> I do not understand, and there are four statements, not two.
I'm confused. Maybe you posted the wrong link?
Here are the two comments from the linked post:
> If you cannot code, you cannot check that your ideas work out on a computer.
This is only true when it just reduces to "if you cannot code, you cannot code".
Taken literally, the statement is just blatantly false. You could use a computer to figure out properties of your algorithm or for designing an algorithm without actually implementing and running the algorithm; e.g. for getting closed forms of sums, or for finding/verifying the convergent behavior of a series which the algorithm relies on.
So the statement really is only true if it's a tautology.
> I don't see how that relates to my comment, I specifically referred to 'writing' an algorithm, as in implementing the algorithm in code, not devising the algorithm in the first place.
'writing' an algorithm = implement the algorithm in code
So, substituting into your original statement:
"I fail to see how you could implement the algorithm in code if you don't know how to code."
No, you're just confused. You mentioned the two statements in that link, but you have only described one. The other statement you referred to is from my original comment.
Do you have a point, or are you just being pedantic? That is, is there some essence of your position which I haven't adequately addressed? If so, what is that essence?
FWIW Your original statement is referentially transparent wrt the meaning of 'writing' an algorithm ("I specifically referred..."). You were clarifying what you meant by this phrase. So the substitution of your intended meaning into your original statement is justified; in fact, is the entire purpose of your clarification. Otherwise you're just giving a definition with absolutely no context or purpose.
> That is, is there some essence of your position which I haven't adequately addressed? If so, what is that essence?
That essence is something you and I have in common. I believe this thread sprouted because of how my original comment was interpreted. I think you are the first HN reader to actually give a decent word for word analysis of my comments. A lot of the time I just have to endure obvious misinterpretations, elaborations, misquotes or sometimes just plain fabrications. It's good to come across someone who takes time to read boring old English.
If you cannot code, you cannot check that your ideas work out on a computer.
> Which is to say, from my observation, coding-as-problem-solving is not very effective until after you've thought things through in more general, mathematical terms.
I don't see how that relates to my comment, I specifically referred to 'writing' an algorithm, as in implementing the algorithm in code, not devising the algorithm in the first place.