His thesis boils down to this statement from the post: "I've seen all kinds of figures for drop-out rates in CS and they're usually
between 40% and 70%. The universities tend to see this as a waste; I think it's just a necessary culling of the people who aren't
going to be happy or successful in programming careers." The reason for those dropout rates?
"You used to start out in college with a course in data structures, with linked lists and hash tables and whatnot, with extensive use
of pointers. Those courses were often used as weedout courses: they were so hard that anyone that couldn't handle the mental
challenge of a CS degree would give up, which was a good thing, because if you thought pointers are hard, wait until you try to
prove things about fixed point theory."
A sensible course of study in any subject starts with basic concepts and moves by steps to the most difficult topics. The monastic
metaphor of perlmonks.org reflects the idea of helping individuals move forward. One makes progress initially by mastering the
basics and a process of trial and error. Naturally, more difficult topics are likely to take longer to master. I'd be willing to bet that
there are any number of monks who are happy and successful in programming careers who didn't bother with a jaunt through an
institution that espouses Spolsky's elitist views on higher education.
In my view, the inability of a student to master a subject can be as much a failing of the teacher as of the student.
Successful autodidacts would any of them handle the course material just fine. They may not have got along with a teacher, but a student can always find another teacher.
One of the advantages of a good education is that it teaches you to autodiddum without believing everything you read. If it doesn't teach that, it's not a good education.
After Compline,
Zaxo
and other sources. A teacher should provide guidance and encouragement.
The whole concept of introductory courses as "weed-out's" is repellent to me.
The whole concept of introductory courses as "weed-out's" is repellent to me.
How about the concept of intermediate courses as "weed-outs?" At some point, you have to seperate the ones who are serious about the subject and those who are just there because they don't know what they want to do for a career. Better to find out sooner than later. I was "weeded out" from the accounting major by the time i hit junior level and let me tell you, i am very thankful for that.
The only thing i find "repellent" about "weed-out" courses is when i am required to take one in my minor field. Other than that, a teacher can only provide so much guidance and encouragement -- they simply cannot be expected to do the work for the student.
You know what they call weed-out courses in the real world? "You're fired!"
jeffa
L-LL-L--L-LL-L--L-LL-L-- -R--R-RR-R--R-RR-R--R-RR B--B--B--B--B--B--B--B-- H---H---H---H---H---H--- (the triplet paradiddle with high-hat)
In the school I went to, the killer classes were biology and psychology. There, the failure rate was expected to be between 25-30% with no more than 10% being A's. Every school has the killer subjects - these are the subjects the school is known for, so they get a higher proportion of freshmen taking those courses.
My criteria for good software:
- Does it work?
- Can someone else come in, make a change, and be reasonably certain no bugs were introduced?
Spolsky is talking about universities teaching computer science instead of teaching a programming language. Computer science is a difficult discipline. It is founded on math & logic, and no one is claiming these are easy. Acquiring the appropriate foundational knowledge requires many difficult conceptual leaps. He argues that we should not abandon them just because they're hard. Or if we do abandon them, we shouldn't pretend it's computer science that we're teaching.
It's not as if the universities are just throwing irrelevant material at the students just because it's hard, or just to make themselves feel smart. The hard material is relevant and necessary for understanding the main results the field of computer science. A computer science degree should reflect knowledge of computer science.
As for "weeding out", I think that any student who goes into a CS degree program expecting nothing but Java lessons deserves to have a rude awakening, and as early as possible (for everyone's convenience). CS may require a lot more formality and mathematical rigor than a lot of college freshman are expecting.
I'd be willing to bet that there are any number of monks who are happy and successful in programming careers who didn't bother with a jaunt through an institution that espouses Spolsky's elitist views on higher education.Of course. There's nothing being taught in universities that you can't learn on your own. I also don't think that's the point at all.
blokhead
You've never been inside an actual university, have you? :-)
I'd hardly call Joel an elitist. He's not the one designing the curriculum or teaching the classes. He didn't invent the situation. He's just observing it.
You have to remember, however, that all sorts of people have romantic notions about the work they think they want to do in life, and once confronted with reality find out they don't really like it or don't want that lifestyle. That's really all he is saying.
Don't pull out a single essay to judge anyone. Take it in light of everything else they've written. I think you'll find Joel to be a lot less elitist than you accuse him of being. It's not like he's Paul Graham, who really is elitist :)
brian d foy
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I don't know Joel Spolsky or where he stands, but I do know that your little excerpt is maybe 5% of his point.
He is trying to say that schools only teaching Java are doing a diservice to there students and the profession because Java makes things so easy that people without the critical thinking skills needed for programming can still pass the course.
Not having a degree, or widespread experience with other programmer i'm probably not qualified to aggree or disagree. However I took plenty of classes where there were students confused by concepts like variable, and "code", not to mention if statments or functions and subs. I would have been thrilled to have taken one of these tough classes to weed out the others so that my own experience would have been more than teaching the other kids in my class how exactly you login to a computer and run pascal. ;)
But then I'm your regular home brew hacker and think that anyone who needs taught computers probably doesn't realy want to work on them. In my experience programming and logic are things you just have to inherently have and they can't realy be taught (/me sat throw several 'logic' classes and watched many people consistently not get it). Maybe I'm wrong, dunno. Either way your critical analysis of this "worst blog ever" realy sucked and lacked any basis or argument. So, if you insist on posting something with such a title, please back it up with some content and critical thinking of your own.
___________
Eric Hodges
Bottom line though, I mostly agree with him - introductory programming courses are a good way to weed out the people who aren't commmited to getting a programming degree. What sort of person fails an introductory programming course? The sort of person who should be majoring in something else. Assuming your textbook is reasonably adequate, you have no excuse for failure.
You see, you might need calculus for non-linear optimization work, and you might need linear algebra and field theory if you decide to go into crytography research. So everyone has to take it.
And if you decide to just do a job doing boring, practical things like writing programs that work, instead of clever, abstract things like proving neat, theoretical boundries on toy problems for systems that can never actually exist in the real world, you're derided for not being clever and abstract and academic enough.
I don't know how many young kids I've seen wander out of a CS degree thinking a Turing Machine is Something Important(TM), as applied in the real world. They think that constants are irrelevant, because they learned order notation, but didn't learn quite enough.
In reality, all those boring little constants in front of your abstract little order notation symbols mean the difference between "highly profitable" and "completely worthless". In the real world, people need constant time speedup: and the difference between running in twenty minutes and running in a hour can make or break a program. In the real world, you worry about the scalablity of your algorithm only after it meets the inital performance requirements to begin with. If the the hardware to make the problem fast is too expensive to do what the business requires, it's a no go, no matter how much nicer your algorithm scales "towards infinity".
I don't know how many people have tried to use "Turing completeness" as a way to explain what a computer can or can't do, and gibber on and on about halting problems and so forth. It's far simpler than that: any computer you find in the real world will have finite memory, finite run time, and finite amount of cash available to construct and run it. No computer can do more than a finite state machine can, if only for reasons of economics. But finite state machines are boring, and we don't have a neat paradox for them, so I'm left listening to boring undergrads drone on and on about "undecidablity" as if it's a real world problem...
When they get to the real world, they'll learn that no one else cares about CS theory. No one else cares about whether P=NP. They just want the billing system to run, the accounting ledgers to add up, and the reports to look pretty, with colourful graphs that show the wiggly line going upward. If you do what the rest of the world needs, you get paid; if you don't, you don't.
Universities are largely in the business of training grad students to become professors; any other education they provide is mostly just incidental.
--
Ytrew
Exactly.
The most succinct encapsulation I've seen of your oh so eloquant dissertation above, is something I read in someone tag line somewhere. From memory it went something like:
In theory, theory is enough. In practice, it isn't!
In theory, theory is the same as practice. In practice, it's not.
--
Ytrew
attributed to Yogi Berra.
merlyn likes to give an alternative phrasing:
The difference between theory and practice in theory is much less
than the difference between theory and practice in practice.
— e.g. in this clpm post
Nearly everyone on this site will be able to point to an experience in their careers where they wrote something in Perl and it took them a couple days. Turned out to be really really useful and the PHB had it rewritten in Java. Took 15 people 6 months and it still doesn't work right.
Why is that? Perl isn't inherently a better language than Java. In fact, there are many things Java has better support for than Perl. However, Java projects generally take longer than the equivalent Perl projects and generally require more people.
My feeling is that Perl programmers tend to be more capable than Java programmers, precisely because we tend to have a stronger grasp of the fundamentals. Things like a Turing machine. In fact, I once implemented a Turing machine in production code because it was the correct and cost-effective solution to the requirements. It's easy to deride the theoreticals, but they're extremely useful, just not as presented. You actually have to think about how to apply them. :-p
My criteria for good software:
- Does it work?
- Can someone else come in, make a change, and be reasonably certain no bugs were introduced?
I once implemented a Turing machine in production code because it was the correct and cost-effective solution
I think it is all too easy to forget how much practical experience it requires to be able to reach that determination.
Just as theory without practice--real-world practical application--is just so much hot air; so you can practice as much as you like, but without the theory to back you up and allow you to choose the right starting point, the likely outcome of your practice is that you will become very good at doing the wrong thing.
From previous discussion, I think that you are likely in tune with the theory and practice of 'balance in all things'.
In programming as in life, balance is everything, and inbalance--the over concentration on one aspect to the exclusion of others--the source of most woes.
Really? Please explain where you got the infinitely long tape, and how your software made markings on it. If you didn't do that, then you didn't make Turing's machine; and any computing device with an infinite datastore that we can concieve of is computationally equivalent to a Turing machine.
Turing machines are just a theoretical device for discussions of computational equivalence; you can't "implement" one in any sense of the word. You can create a state machine with an associated finite datastore, but we tend to call those devices "computers"; the hardware already does that for us.
There's no sense of the word in which I can find it meaningful to claim one has "implemented" a Turing machine; it's a thought experiment, not a device you can actually build.
--
Ytrew
There's no sense of the word in which I can find it meaningful to claim one has "implemented" a Turing machine; it's a thought experiment, not a device you can actually build.
A thought experiment? You mean like Schrödinger's half-dead cat in a box?
Please explain where you got the infinitely long tape...
You are mistaken. There is no requirement in the definition of Turing machine that the tape be actually infinite in size. It merely needs to be unbounded. As long as an implementation, in its execution, doesn't exceed the limits of its "tape", there's no reason it can't be a Turing machine.
When they get to the real world, they'll learn that no one else cares about CS theory. No one else cares about whether P=NP. They just want the billing system to run, the accounting ledgers to add up, and the reports to look pretty, with colourful graphs that show the wiggly line going upward. If you do what the rest of the world needs, you get paid; if you don't, you don't.
True.
Of course sometimes knowing about N=NP, big O notation, etc. is exactly what you need to get the job done.
I've encountered my fair share of fresh CS graduates who think they know everything and are terrible at their job. The thing is I've also encountered my fair share of non-graduates who've been working in the industry for years and think they know everything and are terrible at their job. I think a lot of this has to do with the person - rather than whether they come from an academic or industry background.
Universities are largely in the business of training grad students to become professors; any other education they provide is mostly just incidental.
Back when I was at uni I remember being taught tons of purely "academic" content. People I knew who were working in industry told me I'd never use it in the real world. Silly things like object orientation, virtual machines and garbage collection.
I certainly don't think a university education provides you will all of the skills needed to do the job. I'm actually glad that they don't since I don't think universities should be in the job of just vocational education. They do provide a bunch of useful skills though. IMHO as ever ;-)
When? Academic learning isn't at all bad, but those two examples are a very poor choice. Measuring computational complexity at all well requires far more than order notation (and perhaps more than graduate level computational complexity theory). Performance modeling remains a poorly understood and active field of research, and the gains are being made quite slowly.
So in practice, the concept of "P vs NP", and order notation are largely useless. "Polynomial space/time" explodes quadradically for a polynomial as low as two! The choice of P vs NP boils down to "too slow to be workable" versus "really too slow to be workable". The exception, of course, if the constants are nice, and N is small enough to be workable: but that's exactly what order notation and most computational complexity theory ignores in the first place!
Personally, I found that while academic learning is interesting, it's rarely useful. It's nice that you can write your own compiler, but your job will involve producing graphs and reports, not writing compilers. And when and if some of that deep, complex academic learning is required, your company will just hire a PhD: so unless you're willing to give your life to CS theory, there's no great benefit to a mere undergrad degree. Perhaps that's why there's so many open source languages: people desperate to find an excuse to write their own compiler, now that they've wasted thousands of dollars learning how!
One guy I worked with was so desperate to do something "academic" with his job that he wrote his own recursive descent parser ... for a configuration language that he invented himself ... for an EBICDIC to ASCII translator ... which only needed a very limited set of options ... and which never actually changed. But hey, he got to be all "academic"; and now I've got a tonne of painfully useless code to untangle if I ever have to maintain his over-engineered monstrosity.
Back in school, I took a lot of courses in things like multidimensional calculus, vector algebra, and group theory. None of it is terribly useful for producing billing reports and the other assorted drudge work that actually pays the bills. In some sense, I understand why my co-worker decided to waste company funds on his wierd design; but I certainly can't condone it.
In any case, I've been left with a distaste for breathless undergrads, and people who think that "more complicated is better", or people who think "new is better": most of the time, the boring, obvious encoding is the most maintainable encoding, and when it's not, you can at least understand what was done, and slot in your clever little algorithm where it's needed.
--
Ytrew
So in practice, the concept of "P vs NP", and order notation are largely useless.
I've not found this to be so. For example I can remember one instance of a problem that, in a blinding flash of the obvious, could be mapped onto an NP problem - which immediately told us that we wanted to be doing something like simulated annealing to give us a reasonable answer in a reasonable time.
Use big O notation all of the time when I did work in the financial sector. Very handy in giving us clues on how things will scale up on very large data sets.
Personally, I found that while academic learning is interesting, it's rarely useful. It's nice that you can write your own compiler, but your job will involve producing graphs and reports, not writing compilers.
Depends on your job. I've been paid to write compilers several times during my career. The ones I did after my undergrad compiler course were a lot better than the ones I wrote before it ;-)
In any case, I've been left with a distaste for breathless undergrads, and people who think that "more complicated is better", or people who think "new is better": most of the time, the boring, obvious encoding is the most maintainable encoding, and when it's not, you can at least understand what was done, and slot in your clever little algorithm where it's needed.
s/undergrads/youngsters/ and I'll agree with you 100%. For me its a symptom of age and experience rather than an academic or industry background.
You're 100% correct there!
When I took my first Perl course a few years back, one of the people in the class was balking at figuring out a simple program to calculate the volume of a planet, given the variables needed to do the calculation. And this was one of the first assignments!
Fortunately, that was an isolated incident and that person dropped from the course within a couple of meetings.
I take it you would not be keen on my proposed course, "Freshman Diligence Seminar", a mandatory seventeen-hour course (3 hrs Mon-Fri plus 2 hrs Saturday) for first-semester Freshmen, designed to impart the skills needed to succeed in college (e.g., verbatim memorization, good note-taking, library research, the ability to wade through voluminous quantites of collateral reading, and the ability to put down what one has learned in writing in a little blue book on short notice).
Sanity? Oh, yeah, I've got all kinds of sanity. In fact, I've developed whole new kinds of sanity. Why, I've got so much sanity it's driving me crazy.
When I was an undergrad at RPI, the Symbolic Logic course was grouped with the Philosophy courses, and so it met the humanities credit distribution requirement. Everyone took symbolic logic. :-)
You'd think they'd teach that in grade 1. I weep for the future of mankind.
I think the subject used to be called "Rhetoric", and it was once taught in college (sometimes even earlier). I agree that some effort should be made to teach people how to reason in a disciplined manner. Without that, much education is merely stuffing people full of facts that they may retain for a lifetime, but which they can't make much use of.
This can be true, but often it is not the current teacher's fault, but a previous teacher the student had. In high school I breezed through math and even took beginning Calculus concurrently at college. I found out that my Trig knowledge was woefully deficient and soon hit a brick wall in the second semester when the two were combined. That wasn't the Calculus teacher's fault, but it was too late by then. I don't think the Calculus teacher should have slowed the class down to my level just because of an inept high school education -- that would have been unfair to the rest of the class.
I made a personal decision not to be weeded out. I believed that I could indeed handle the mental challenge, I just didn't have a good foundation at the time. Sadly, there *are* people that can't handle the mental challenge, no matter the previous foundation. I believe he's just saying these folks should find something else -- I really doubt he'd say you'd have to get it 100% right the first time.
I'd be willing to bet that there are any number of monks who are happy and successful in programming careers who didn't bother with a jaunt through an institution that espouses Spolsky's elitist views on higher education.
I think what you really want to find is monks that are happy and successful that didn't go to school and that don't have the ability to understand C pointers and other fundamental CS concepts even after repeated attempts. I'm not saying these folks don't exist, but I'd like to see an example. Of course I don't think anyone really understands automata theory, for more than 5 minutes at a time ...