I've taught CS1 for going on 6 years now, to almost 10K students. I'll admit I found this post depressing to read. Because many of these pain points have obvious solutions that have been in use at many institutions for years. (Some of these solutions are in the paper, but are not novel.)

When / where are students struggling? Assess them frequently and you'll find out! We run weekly quizzes in my class. So we know exactly who's struggling with exactly what, and quickly. That allows us to do individual outreach, and for students to catch up before they get too far behind. We also use daily homework, for the same reasons. But a lot of CS1 courses are still using the outdated midterm and final model, maybe with some homework sprinkled in.

Frequently a glut of repetitive student questions points to bad course materials or poor course design. Make things more clear and make it easier for students to find information and at least some of the repetitive question asking will diminish.

Grading and TA support are related. Graduate TA quality does vary greatly, and you need to design around this. For example: Never put students in a position to suffer for an entire semester at the hands of a bad TA. (Many courses do.) Undergraduates are almost always better at assisting with early CS courses, and usually cheaper. We've been shifting gradually toward more undergraduate support for our CS1 course, and it has been working out well. They frequently outperform graduate staff.

But no amount course staff will be sufficient if you have them spend all of their time on tedious tasks that computers can do better: Like grading code! It's 2023. If you can't deploy your own autograder, buy one. Staff time grading code should be minimized or eliminated altogether. Freeing staff time for student support allows you to provide students with more practice, and accelerates the overall learning process. But many early CS courses are stuck in a situation where staff grading is bottlenecking how many problems they can assign. That's insane, when autograding is a well-established option. (Even if you want to devote some staff time to grading code quality, autograding should always be used to establish correctness. And you can automate many aspects of code quality as well.)

In my experience, what's at the root of a lot of these problems is simply that many people teaching introductory CS can't build things. Maybe they can implement Quicksort (again), but they can't create and deploy more complex user-facing systems. I mean, you can create an autograder using a shell script! Not a great one, but still far superior to manual human grading. Part of this is because these jobs pay poorly. Part is how we hire people for them, because the ability to build things isn't typical a criteria. Part of it is that there's little support for this in academia. It took me years of inane meetings to get a small cluster of machines to run courseware on for my 1000+ student class that generates millions of dollars in revenue.

But there's also a degree to which the CS educational community has started to stigmatize expert knowledge. If you do enjoy creating software and are good at it, you get a lot of side eye from certain people. "You know that students don't learn well from experts, right?" And so on. Yes, there is a degree to which knowing how to do something is not the same as being able to teach someone how to do it. But would you take music lessons from someone who was not only a mediocre player, but didn't seem to like music that much at all?