I'm taking a bunch less coursework this semester, and then I find out I need 30 points coursework to finish my MSc instead of 18. I would have been done with 18 this semester. I have to publish a thesis either way; it's a matter of whether they accept a non-Technion four-year-degree as a four-year degree or a three-year degree. I hate these anal, bureaucratic requirements; I just want to concentrate on research and in-depth issues rather than continually taking courses!
I just want to add one more issue:
4) In both teaching/coursework and research, academia is extremely detail-obsessed, constantly burrowing away from larger, important questions towards small, easily-answerable ones. For a good example, look at how many different kinds of differential equations the average engineering major at a really good university is required to learn to solve, and then check how often they actually solve those equations in either original research or in their jobs. Sometimes, yes, they do, but enough to justify having two or three distinct courses in just differential equations versus, say, a single full course in fundamental statistics? Oh, but there are a thousand different approaches to statistics!
The result is a system that, seen from the outside, appears to be trying to actively avoid tackling truly major scientific problems. Sure, it can give you a seminar on the latest approach to convex optimization problems or pure subtyping theories, but ask us what problems these solve and we academics will look at you sort of blankly.
> In both teaching/coursework and research, academia is extremely detail-obsessed, constantly burrowing away from larger, important questions towards small, easily-answerable ones.
In the research circles, this is simply because smaller, easily-answerable questions are the ones that fit the short-term grant applications. It's very unfortunate, indeed.
In the teaching circles, there is a slightly related case, which manifests through this:
> For a good example, look at how many different kinds of differential equations the average engineering major at a really good university is required to learn to solve, and then check how often they actually solve those equations in either original research or in their jobs.
This is because the mathematics courses need to reach a compromise between teaching enough fundamentals to be meaningful as math courses, and enough "practical" applications to warrant their presence in an engineering curricula.
I was also annoyed by taking math courses for three. Fucking. Semesters. But looking back to it, while I have forgotten much of the actual details they taught me, the type of reasoning they taught me stuck with me, and it's ok. I'm not sure if there's a better way to teach that.
I'm taking a bunch less coursework this semester, and then I find out I need 30 points coursework to finish my MSc instead of 18. I would have been done with 18 this semester. I have to publish a thesis either way; it's a matter of whether they accept a non-Technion four-year-degree as a four-year degree or a three-year degree. I hate these anal, bureaucratic requirements; I just want to concentrate on research and in-depth issues rather than continually taking courses!
I just want to add one more issue:
4) In both teaching/coursework and research, academia is extremely detail-obsessed, constantly burrowing away from larger, important questions towards small, easily-answerable ones. For a good example, look at how many different kinds of differential equations the average engineering major at a really good university is required to learn to solve, and then check how often they actually solve those equations in either original research or in their jobs. Sometimes, yes, they do, but enough to justify having two or three distinct courses in just differential equations versus, say, a single full course in fundamental statistics? Oh, but there are a thousand different approaches to statistics!
The result is a system that, seen from the outside, appears to be trying to actively avoid tackling truly major scientific problems. Sure, it can give you a seminar on the latest approach to convex optimization problems or pure subtyping theories, but ask us what problems these solve and we academics will look at you sort of blankly.