Sharepoint is your best bet here.
The only alternative I can think of is checking your docs into your source control.

Well, but that's part of the issue: not everyone has, or can have, the kind of high school experience we did. College needs to get you started with at least low-level theory early on to prepare you, but if we artificially push it to the extreme of being only math and low-level engineering, it will push people out of the major when they could have been very happy there given a more motivating introduction. I'm not advocating dumbing things down, the courses should be hard, but the students should understand why they are doing the hard work, and that requires at least some hands-on applications early on.

I also speak from the frustration of having had to teach CS stuff in certain contexts where I could be asked "can you give examples of where we would use this stuff?" and having no good answer, because I wasn't crazy about the curriculum myself (and I say this as a theoretician)... and from observations of how much better some students "get it" when they are able to apply even simple abstract concepts to code they write that actually does something. Not everyone learns the same way, but it should be possible for someone entering the discipline to find something they can get excited about right away, even if there are other parts they don't like as much...

I'm quite sure he was simplifying things and assumed a using statement was present.

Ah -- you mean, he was concealing details that will be explained later? Like he was criticizing the Java version for doing?

I wasn't being a smart-ass, I was pointing out a legitimate contradiction in his complaints. The C++ version concealed namespaces, functions, the whole mess of chaining applications via overloaded operators, and for God's sake, references (which are much worse in C++ than in Java). To me, that looks a lot worse than what he's objecting to...

Dijkstra's comments were right on the mark, and fairly obvious to people outside of CS. They were only contentious within the field, for some odd reason.

Err, did you see the part where he talks about how bad it is that calculus is taught by giving example problems from people's chosen field so they see how it's relevant? And how this just conceals how drastically new a tool calculus is, and it should be studied by itself in isolation, not as it is applied? I'm pretty sure there are people outside of CS who might disagree with that. I'm a fan of Dijkstra's research, but not so much his teaching philosophy...

There is nothing more fascinating than learning how computers really work.

I'm now working on a PhD in theoretical computer science, and am interested in how computers work (although that's not exactly my primary interest). But long before I knew any CS theory or systems programming or anything, what got me interested in computers was the neat things I could make them do. In high school I spent years making games and neat little graphics demos and things. As I understood more of what I was doing, I was able to work at a deeper level, modify kernels, mess with assembly, and generally learn "how computers really work".

But if someone had started me out that way, teaching me from the transistors up how computers worked while never talking about what I can do with them right now, I would have given it up and done something more interesting. I'm glad you weren't my teacher in high school, because these days I'm quite happy with my chosen field, and you're essentially saying (in your other response below) that because I learned about it in a way I found interesting, somehow this is illegitimate and I shouldn't really be in computer science at all.

I agree with you that it is vital that computer scientists learn how computers really work. But it isn't vital that this knowledge is forced on them before they have ever produced a running program, or before their interest has been piqued by other aspects of the field.

almost at the end of their Computer Science degree, and who didn't understand why their code was crashing with "null references" when "Java was supposed to get rid of all that memory stuff!".

If they can almost finish a degree and still be surprised when Java dies with a null reference, then the problem isn't that they were taught to program using Java, it's that they were taught Java very badly...

I'm quite sure you meant: std::cout << "output" << endl;

You seriously think that's easier to explain than System.out.println("output"); ? The C++ version is concealing even more hairy details than the Java version, while using even more symbols that can't be fully explained until later!

If you think Java shouldn't be the intro language, that's fine (I think there's a case to be made for it, but I have mixed feelings). But C++ is definitely worse.

I'd say in the modern era he's part of the problem, i.e. CS programs producing students who know loads and loads of theory and can't write a damn line of actual code.

I'm curious where you are finding these students. In my experience, it is much more common to find people who think they know loads of theory, but can't write a line of code. In reality, these people usually can't write a line of proof either.

The best theoreticians aren't always the best coders, but they're usually able to code pretty well when they want to. I've much more frequently seen people who were great coders but couldn't handle more abstract algorithmic questions than vice versa. And I certainly think it's hard to say that great CS theorists who can't code is "the problem" in CS education right now...

It's the equivalent of a biology class detailing the possibilities of life, by examining chemical interactions, without actually examining any actual living organisms.

Eh? No, it's the equivalent of studying the simplest possible living organisms to prepare for the later work of understanding more complex ones. It's still the same subject matter, but it lets you focus on the most important concepts before getting caught up in the specific details that vary from case to case.

Teaching on a fake OS lets you write your own kernel while bypassing the tedium of bootstrapping and such. Teaching fake assembly lets you write your own compiler while avoiding some of the irrelevant gotchas or (initially) unneeded sophistication of real machine code. I've done both of these things, and my understanding of kernels and compilers is much better for it. The same result would not be possible if you gave students 6 weeks to write a real kernel from scratch that ran on real x86 system...

Yes, it's not like they taught such new-fangled subjects as MATH or HISTORY back then... The way in which we teach is drastically different today, but the core subjects are not.

Just "HISTORY"? It must have been a survey course....

Seriously, what do you imagine is the "core subject" of "history" such that two people who studied "history" 60 years apart should perform similarly on identical exams? My wife is a PhD student in history, but would probably have trouble answering simple questions about people or periods she's never studied, even if she could give some broader context.

Or the "core subject" of "math"? I'm a PhD student in theoretical computer science, and do a lot of "math", but you could easily trip me up by asking for computations in areas I don't study (like, I suspect, some of the integrals that might appear on a 1950s chemistry exam)...

GP is right. Subjects change (you can claim "the same facts are still true", but the same facts are not still studied on the same timeline today, nor should they be). As another comment said, what we should really be concerned about isn't performance on some arbitrary historical standardized exam, but on how well education is preparing students. In many cases the answer is probably "not that well," and that's what we should be worrying about. If students are performing well after highschool and college, then who cares about a standardized exam from generations ago? Since when did we start thinking that doing well on specific tests is really an objective measure of a quality education?

I have not looked into it but can you be certain that the top teams are not using additional metadata on the movies?

Pretty sure. IAITTT = I Am In The Top Ten ;)

The winning progress prize entry from 2007 had to publish the full details of their algorithm, and they don't use anything. I don't use anything. PragmaticTheory even wrote a blog post about how they don't use anything. Others have said the same thing. It's impossible to say that no one will ever come up with a useful way to use metadata, but so far the "metadata" produced by the algorithms themselves is far more accurate than that generated by human observers on the same data.

It may wind up being something not intuitive (like release month/year, production company, gap score of economic state during release year vs current, or something like that)

Well, that's beyond just counterintuitive to actually demonstrably unhelpful -- it seems a priori unlikely that someone's rating would depend on the production company, for example, but even if it was, that would be much more easily detected by the actual movie average (i.e. if a particular production company gets good ratings, then we will know that just because the movie has a lot of good ratings, and the company becomes superfluous). On the other hand, if you're suggesting that specific people have varying opinions of particular companies, well that again seems odd, but again it's irrelevant -- if such a correlation exists, SVD will find it, and so some of the dimensions of user-movie vectors will correlate to production company.

Similar with the other properties you mention: since SVD is already finding *all* of the (linear) correlations in the data, it's not very helpful to try to come up with a huge list of farfetched ones yourself hoping one of them will work out...

I'm now thankful I didn't waste more time, given that there are corporate entries such as ATT to compete against (with all their resources and database staff to assist in the contest).

The ATT people are an extreme case, but even so there are multiple amateurs within close range of them -- as of a few days ago, at least one amateur team (PragmaticTheory) was beating them.

But isn't farfetched what you're looking for?

Well, no, "accurate" is what you're looking for ;)

But part of what I was getting at is that it doesn't matter how farfetched it is, SVD has no preconceptions (or at least, only the minimum needed for accuracy), it just finds the correlations themselves. If a correlation isn't detectable in the data, then it doesn't matter how plausible or farfetched something is, it won't be useful. If it is detectable in the data, then again, farfetched or not doesn't matter. Time is better spent modeling different ways for functions to correlate and then applying SVD to that than it is in coming up with as many hard-coded correlations as possible...

You recall incorrectly. None of the top teams currently use external data sources (some have tried, but it doesn't help that much once you get up towards the top 10). The last team that (probably, not official yet) won a milestone used the combined predictions of the first and second place teams, interpolated to improve the final score, but nothing external. Same thing in the previous year, the winning team used only computations on the data given.

IAITTT ;)