Off-topic, I know, but I always disliked the time pressure of exams. All the bad scores I got were down to not feeling quite right at the time of the exam: maybe I didn't sleep well, the food I ate did not quite sit with me, had a fever, etc. None of these things actually impair too badly but if the time is short, there is an impact.

I suppose it's similar to sports where you have to, at least somewhat, adapt on the go. Many teams botch a game here and there. But... your final exam? Botch it and you're screwed. It just does not feel right.

maybe this is good for weeding out students who would not be a good fit for high stakes speed math.

Undergraduate exams tend to be short. Which means that a perfect grade should be interpreted as "meets the expectations".

What skill are you grading? Can you expect some students to be able to churn out the problems? Are they doing design work? Is it more inventive or more creative? Is there more than one solution? How rigorous is the solution?

My point is that you can design an exam to test for speed and ability to do the day to day well. You prepare for this exam with lots of practice. There are a few problem types but you might not have seen every one. There's a basic level of extrapolation you're expecting.

You can design an exam for abstracting theory, and you prep for this exam by doing the hardest problems you can find, reading more theory, etc. You might not have seen anything like this but the test is to use the tools you're given in a class well. The test is the extrapolation.

I had theory courses where I expected the latter and practical courses where I expected the former.

There's more than one type of mastery; I bet your math professors would get destroyed by gifted 14 year olds in algebra competitions that involved arithmetic and timing. Do those 14 year olds not have more mastery?

Being able to do deep thought quickly isn't a valuable skill, and you don't pick it up on the way ti mastery with lots of practice like you do the speed for rote problems

Griggs says that you can't employ IQ tests to discriminate for jobs where the IQ threshold you established is immaterial to the job. For modern knowledge work, that's a trivial bar to clear. It is essentially the case that Griggs is a nonfactor in white collar employment.

My motivation for calling this out isn't that I'm OK with discriminating against Black people; quite the opposite, in fact. IQ tests aren't used more widely because they aren't effective at qualifying employees. IQ is an idee fixee among A Certain Part Of The Internet, and part of their pitch is that IQ tests are a secret cheat code that would enable hiring purely on merit, if only the woke wouldn't prevent it. No, false.

Ambiguity: there is a lot of ambiguity at uni as well. So you should have had the practice, especially working in groups of students.

> It requires accurate and constant self reflection

Skills I sharpened at university by studying psychology. Do you know how big the mind fuck was to grok the reproducibility crisis while being a 3rd year student in that program (5th year student overall)? I had to re-examine my whole degree.

I also had courses where self-reflection was a weekly part of the course. And I took that seriously.

> whereas learning is relational

I consider it a mode.

Try mathacademy.com it will teach you the “shut up and calculate” version of math really well, and there is nothing relational about (since it’s an app).

I also had courses that were about computer and network security in C and assembly. In it, I self-taught vim, assembly, C and git. The self-teaching was partially the point.

One thing I have learned from university is that what many people see as good practices is more akin to a culture rather than an actual good practice, because the actual empirical work in figuring out what works hasn’t been done. And I find what you say to strongly fit a particular culture of software engineering. But there are many forms. For example, in the Smalltalk/Pharo community it is recommended to extend the base classes to your liking. Whereas in other languages they look at you with horror. Back in the day (2012) Airdrop was what people used and didn’t want to use git, and these people have programmed for decades (I disagree with that one personally, just showing the culture).

You will learn some things through trial and error entirely on your own of course, but you will also take a lot of time independently re-learning things that are well known. Which isn't necessarily a bad thing. I'm a huge DIYer. Sometimes you have to try and see for yourself. But I do acknowledge that I could have learned a lot faster about, e.g., carpentry, as an apprentice, than I did mucking about in my backyard with youtube videos. I think the concept of "balance" is more important than taking sides on this matter. They're both incredibly useful tools to be used in conjunction. Read the manual. Then go muck around. Then read the manual again and go, "oh now I get what they were talking about."

I think what I'm learning from this is that a lot of the "entirely self-taught" folks don't realize how much they've benefited from "thinkism" and standing on the shoulders of giants.

Some I ended up using more during my career than others, but knowing more definitely reduced my tendency to think "ah, that should be easy".

That's a fair question and I'll do my best to answer this. It'll come in an edit. I think it's fair to say: not all courses were created equal in this regard but I'll do it course by course. I studied a bachelor information science but I tweaked my program so close to computer science that I almost daresay it's computer science (if I had 3 courses different, it was). I studied a bachelor in psychology. A two year master in computer science and a one year master called game studies (officially a specialization of information studies, but in practice it wasn't and it really was game studies as a whole field that we studied).

I'll try to do it in order per study program too.

AI-kaleidoscope: general overview of AI algo's. It's a shame we didn't know how to program or that we knew the usefulness of BFS or DFS but we learned it here. Not a useful course due to scaffolding issues (teach programming first).

Business mathematics: partial derivatives, etc.

Problem solving: useless course (teach programming first).

Privacy and security: we didn't learn much about security. We learned a thing or two about privacy. Should've been a TED talk, not a course.

Graph theory: graph visualization (and when not to do it, helped me out as a data analyst later), mathematical proofs, social networks (helped me to actually network a bit), graph algo's (made leetcode easier), not being scared of math notation. This was a really math heavy course as it was taught by someone that studied math in undergrad and grad and then switched to CS as his PhD (and by the time he taught it he was a full professor). He did not skip on the math which was wild since I was under the impression that I did a "business informatics" bachelor so I didn't need to have advanced math as a high school prerequisite. But I definitely needed that here so this course was hard.

Web technology: this was a bit too early but it was a good overview of web programming at the time. I remember being explained what the DOM was and I remember thinking "wtf is the point?" That was because they explained it way too theoretically and simply should've opened firebug or something to really show it.

Language of Logic and Methods of Reasoning: propositional logic and predicate logic. Practically speaking: after this course if statements are not a problem. This was true for me at least.

Pervasive computing: fun course but could've been a TED talk about how tech is used in interesting ways.

Introduction to programming: basic programming stuff in Java. I learned that Java is a terrible language to start programming in. I recommend JavaScript for app/web-oriented people and Python for "just pick a language" people. Nevertheless, while it was a terrible start, it did teach what it needed to teach which was a basic understanding, and skill, in programming.

Empirical methods: the better name is statistics 1. We learned about statistics and we had to program in R. Since our actual programming ability was quite weak it was a double course. It was the second language ever that I had to take seriously and it taught me a lot of programming stuff and statistics stuff at the same time. It helped that I had friends studying psychology at the time as that degree has a lot of stats in it, so I knew the lingo. Almost everyone else was hopelessly lost.

Programming project: no lectures, just one big programming assignment. After this it was expected that the student could write readable code. I failed this course the first time by a hair. And when I came back to it the second year I realized that I failed because my code was unreadable. So I refactored the whole thing and learned how to write much more readable code. We created the game engine for an Othello game. The graphics library was provided by the TA's. So we also learned to program with a library that was way beyond our heads at the time. And that also implicitly teaches you to trust certain abstractions.

Interactive multimedia project: we learned XIMPEL a hypermedia framework, kind of useless. But through XIMPEL we also learned about storyboarding, creating scenes and general video editing. This teacher was super hands off and allowed students to be creative. So I also learned to create simple PHP websites and learned my way around bash a bit. Then I decided to create an upload script where I used PHP to call the ftp command on bash. I thought it was impossible but it wasn't and had to rethink about what web applications could really do. None of these things were formal course requirements but this teacher encouraged this type of explorations so I do credit it to him that "I learned it in school". It's part of these efforts that he also gave me an amazingly high grade as my XIMPEL story graph was a bit meh but my creativity and extra explorations where a 10 out of 10 effort. So he gave me a 9 out of 10 in total. Also deepened my HTML/CSS knowledge.

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I'll write the rest later in another comment this is about the first year and I studied 9 of them. As you can see, I was just learning the basics here. But there are a few patterns:

* Some teachers allowed us to work on real stuff if the student chose to (e.g. Interactive Multimedia) and that got way more serious later on as I created an iPhone app for a client when the teacher taught Multimedia Authoring in the master.

* Some courses were quite useless or could've been condensed to a TED talk.

* Some courses taught something useful or semi-useful but it's not up to industry standard. But as you'll see, this will lead up to a level where - while not quite industry standard - makes the gap between industry standard and whatever I did small enough to just make the jump easily by simply applying what I thought was common sense.

We have a literature full of spatial indexing algorithms that can't work in any real system because they assume cache replacement algorithms. This problem isn't even mentioned in modern academic papers. That is extremely low-value research. That's like doing physics research under the assumption that the fundamental laws of physics don't apply. It might be an intellectually interesting exercise but it isn't useful.

It isn't all like this. The spatial indexing literature is actively bad to an unusual extent. If you look at e.g. academic graph analytic algorithm research, where I also worked, it is mostly just decades behind the non-academic state-of-the-art. The literature won't mislead you but it also won't tell you where the frontier is.