The examples in the textbooks are not representative of real world problems

When you're teaching something new, you want to be able to isolate that concept/technique and focus in on it. A simplified example, or a question with strict parameters, both allow the student(s) to concentrate on the key idea.

If you want to teach someone about SELECT in SQL, for example, it's useful to abstract away the database setup for that particular lesson. It's also useful to use a simplified database concerned with familiar entities, so that SELECT remains the focus rather than one new element of many.

Of course, these simplified examples shouldn't be used in isolation. Instead, you should have a number of them, eaching illustrating/assessing some small point or idea. Once the students have absorbed those ideas, then you start combining the initial building blocks into more complex ideas, just as you'd take a tiered approach when testing software; start with unit tests, then build up.

Effective education moves in steps.

My applied probability and statistics course for engineers was all about manufacturing quality control. We didn't have magic urns with colored balls that no one cared about. But we did pull samples off the line for destructive testing. Imagining myself troubleshooting a manufacturing problem made me more creative, tenacious, and engaged than if it was a sterile math problem with no context.

Point is, you don't necessarily have to lose anything when making a topic more relatable.

University education, in general, suffers from the implicit assumption that everyone should think like a savant. It's even worse when a topic was invented or discovered to solve a real problem, as opposed to base research, and the teaching strips all context and motivation from the presentation.

I see some other comments criticizing classes that teach certain software tools at the expense of concepts. And yeah, that's training, not education.

I'm not suggesting that compsci teaching should change, but that there shouldn't be too much expectation that the theory taught to students probably should reflect the real world. The real world is too noisy. It'd be impossible to learn anything. Leave those lessons to experience.

It's a huge amount of work, and needs to last a long time, because changing it is a bear.

Also, It. Cannot. Be. Wrong. Very often, the students I'm training are smarter and more experienced than I am. This tiny little corner of things is the only place that I have anything to tell them.

This means that keeping examples as small as possible, is to my advantage.

I usually consider any class I take, or give, as a "starting point." All I do, is plant the seed.

I think teaching good techniques and heuristics is more important than teaching rote.

    As you can see, I have no overarching theory here; it just seems like a topic worth thinking about. 

On the topic called out, though. I'm sympathetic. I recall in grade school a classmate missing the question "without air resistance, what happens to a piece of paper you throw out the window of a moving car?" To me, it was somewhat clear that the "without air resistence" part was important, but my classmate missed it. Others ridiculed them for missing the point, but the more interesting question, to me, was what caused something thrown out to fall behind the car, and how did that relate to the question? That is, on hearing the wrong answer, I was more interested on following up with "and why is that?" Not jumping to "stupid! That is air resistance!"

How do you pull this to interesting topics, though? Programming, in particular, is interesting in that moving to "first principals" is stupid hard for anything graphical. Unless you are wanting to build an engine, you are far more productive using an existing environment. And, basic environments are much harder to force people into today; compared to when the computer booted to a prompt and didn't really store anything for when it was turned off.

Then there is modelling and general ML. Turns out, simple models still dominate all but the most advanced engineering today. And there is no real reason to think that will change. Sure, with enough data, I think deep learning is borderline magic. Turns out, even places that have terabytes of data often have far less relevant data. I'm convinced there is a Nyquist/sampling relation out there for most data. But... I have no idea what that would be. So, we are stuck with simplistic housing price models for the time being. All the while, real estate is basically "whatever sold in the neighborhood recently is what you can expect."

If we look back to our school days in maths classes with Sir getting all excited about teaching the class how to do y = 2x - 2, solve for x.

All your average kid does is quite rightly ask themselves "What's the **ing point, I'll never use this junk in real life".

Then decades later, people like me occasionally find myself (trying to) understand interesting sounding papers on machine learning, but the maths just flies right over my head.

I'm all for a practical approach to teaching. Its more interesting to learn and as a result it leaves you with a higher chance of the theory sticking in the student's head.

Its better than the alternative approach of just whacking students over the head with dry theory books in the hope that some of it sticks.

We have to present simplified problems, and in order to get a sampling across many possible complications that our students could be going out into the world and seeing (because while all of them are likely to end up solving stereotyped problems in some domain, we don't have any idea which ones they are yet!), we need to sample things in an unrealistic manner. It is an unsolvable problem.

I still have my opinions about how education is done; I think one of the most fundamental errors it makes that is not talked about is that it is virtually impossible to provide a solution to someone who doesn't have the problem in the first place, and our entire system is top-to-bottom structured around providing solutions. This, I think, is an even more fundamental view of the disconnect underlying the issue of textbook problems not being representative... "textbook problems" are a misnomer, really, because they aren't problems. I don't care if Sally does or does not get her 1/7th of an apple, and the technique of connecting a grade to whether or not I answer correctly is really quite clumsy and of dubious effectiveness. But it would take a top-to-bottom rethink of the curriculum of the sort I've never really seen anyone try. I've seen people peck around the edges and I've seen people throw the baby out with the bathwater but a problem-first-centric education would be such a radical departure that I don't think people can even think it.

Learners can get too caught up on adventitious details in the example like the wording or certain irrelevant details.

If the example is a real life example they are familiar with it could lead them away from the essential features the example is trying to reason from.

It's a common trope in movies to have a sassy kid show up the teacher by pointing out how artificial their example is and then proceeding to solve the problem realistically most of the time involving street smarts.

While this can be amusing this is also missing the point. The example is there as a learning aid to learn a particular concept.

    Using a dataset of your choice [apply some principle from the chapter]. 

I'm a firm believer that you learn more when you act, and you act better when you use a dataset that interests you.

The funny part is that I keep getting people all me for the solutions to the exercises...

I haven't ever really learned about the theorem except in passing through some articles and a brief mention in highschool once.

Even so, I still ended up basically using Bayes Theorem to successfully do something IRL. What was it?

Hunting ghosts in a game called phasmophobia. Some friends and myself were playing it on discord and were having a hard time figuring out which ghost it was we were supposed to be hunting. That is, until I started trying to figure out the negatives before figuring out the positives. The others were curious as to what I was doing, as it seemed to be working finally, and so I told them; "I'm fairly certain this game is using a rudimentary form of Bayes theorem, which is how we are finally getting these correct."

And correct we were, time and time again; despite never having gotten all the respective 'positive' clues.

So, in regards to the comic...

I understood it well enough?