From the most popular MOOC of all times, "Learning How to Learn", there is this book:
https://barbaraoakley.com/books/a-mind-for-numbers/
Although the "numbers" in the title, the book is about any kind of learning.
Might not be exactly what you're looking for but I recommend two books: "Peak" by Eric Anderson and "Mastery" by Robert Greene. Both of these books are really about getting to the top of your field but are both directly centered around how we learn. Mastery takes more of a macro approach to becoming the best by providing lots of real world anecdotal stories (like that of Leonardo Divinci, and Paul Graham) Peak goes a little bit deeper on the science of how we learn if I remember correctly, but both are definitely worth a read.
Brain Rules by John Medina and Make it Stick by Peter Brown are pretty good, and not too tough going. In terms of the mechanics of memory, they're pretty good particularly.
I've been reading the book Becoming Fluent, and it seems like a great introduction to the field. It's about how cognitive psychology can help adults learn a new language.
However, from another angle, it's basically about using the idea of language learning to teach basic cognitive psychology.
(My phd was in human memory, and I can't stop thinking about ways this book could be used as part of a cool learning course).
It should be noted, however, that learning a language is very different from many other kinds of learning (understanding complex systems, learning history, etc.) in that it engages very different patterns in declarative and procedural memory and possibly involves some specialised networks. I know some very frustrated scholars of second language acquisition that lament the over-applying of the general science of human learning to specifics. (Just making a general point here; the book you are reading sounds like it's specifically about language indeed.)
I am probably in what they would consider the over-applying camp ;). When it comes down to it, I think there's a tendency in many different fields to cast within-field learning problems as distinct from others, but in general, researchers in those fields often don't have a ton of experience on learning in other domains.
RE networks, I agree that there is likely evidence for distinct patterns of activation in various neuroimaging studies, but having worked in memory + neuroimaging, I think there's a serious risk that people will take something like "statistically significant difference in brain activity" and use it as a substitute for "substantial differences in learning behavior / retention". (this is a well known problem in imaging).
I'm not too familiar to L2 acquisition research, though, but those are my impressions from thumbing through some of the field. Would def love to hear some study recommendations :).
I build tools for teaching data science at DataCamp, so am really interested in this question! I think so, and suspect the ways in which a good language tutor assesses / recognizes where students can improve will have direct parallels for coding.
Greg Wilson (who founded Software Carpentry) has a great collection of thoughts on learning to program in general: http://third-bit.com/
Learning at scale via MOOCs seems to be enormously effective. EdX alone issued 250K certificates for 2.5M registered users. Mostly in CS.
I'd be interested to see YC Startup Schools own results as well. Do at least 10% of Startup School 2017 grads go on to full time work on their companies?
I also like "Refactoring your Wetware": https://pragprog.com/book/ahptl/pragmatic-thinking-and-learn...