If you study any other related field like math or physics you become accustomed to learning a formal system for the context of a particular problem.
CS students tend to have this weird careerist view where every page just directly help them get a job.
Schools are so desperate to keep up enrollment numbers today that many have capitulated and are giving students what they want instead of what the faculty thinks they need.
If all someone wants is the practical benefits of programming and has no interest in the underlying theory, they shouldn't waste their their time and money on a CS degree. All the practical information is available for free or at very low cost.
Call me a hopeless optimist, but I think there's a better way out there.
Universities should start their own AI-tutor development programs, in co-operation with others because, only way AI-tutors can become better is by practice practive practice.
So I'n not sure if this is a new viewpoint or not, but it is not only students that need training, it is also teachers who need to be trained more in teaching. AI is all about "training", understanding is about training. Training is the new paradigm for me.
Is that why they are so bad at adapting to foreign languages and frameworks? Maybe they should go back to the basics.
Somewhat understandable considering that student loans put you into indentured servitude unless you have rich parents. Although I still think they're shortsighted. A good CS graduate should understand that programming languages are just syntactic sugar over the underlying concepts and have little trouble translating/picking up the basics of new languages.
A more fair comparison is engineering or applied math major, not pure math at MIT.
Engineers rarely do laplace transforms by hand either.
The book is written for 1st year stem undergrads at MIT. So maybe 2nd or 3rd year at state school.