It's been a while since I took a Coursera course but I LOVED it at the beginning. Between Machine Learning, the (numerical) optimisation courses and NAND-To-Tetris (even for the platform alone) it had so many great courses to pick from.
I also tried the sequel to the Scala course at one point, and the Cryptography course, but I dropped out from those after finding out they were a bit too hard - I spent way more time on the coursework than I'd intended.
But I can't say I like the direction it's taken in recent years.
"Model Thinking" was great!
And I really liked the gamification course by Kevin Werbach (The topic was still hot back then) - something I used extensively at my start up.
And yeah: Gamification became shallow real fast. Even the Gamification techniques in games! I think the reason is that everyone focused on adopting the easy part (checkboxes, achievements, levels) while skipping the real core (player types, intrinsic motivation)... But the course even warned about this level of shallow implementation.
(Btw: A few months later I enrolled in a course on educational psychology on coursera that was supposed to showcase some SOTA techniques... They canceled it because they could not work out the details. I think academia is often just not good at pulling things off.)
There was also a very interesting introduction to Programming Languages (by Dan... something? He was from the University of Washington I think) which covered multiple paradigms and had interesting things to say about the ML family.
Reading online it seems like most people got the impression that it was establishing that all models are essentially useless. Instead it was showing that each of these models were an extremely efficient way to understand some dynamic situations, but that it’s still absurd to focus on only one model when trying to understand the world.