This is good stuff but I'll say that it isn't as comprehensive as all that. These studies and findings are almost entirely focused on simple recall knowledge (isolated facts, vocabulary). That's an important part of learning certainly and it informs research on learning higher order concepts but it's not the full story.
Just to name one example, folks might look into research on conceptual change theory (eg Chi or Posner). This theory helps explain why a concept like electricity is so challenging to learn. The reason, in brief, being that naive conceptions make a category error and think of electricity as a thing rather than a process. And this theory then informs instructional practice. Specifically, teachers should be aware of difficult concepts and should design activities that force students to confront the contradictions between their naive models and more accurate/complete ones.
Mickie Chi also has fascinations research on active learning (ICAP) and related work on the effectiveness of peer learning.
Thanks for your example of the category error here. Are there other cases though? For example, sometimes I doubt my understanding of gradient descent because it is very hard to implement in code on data and show error reducing over time (writing from scratch). But in some examples of a few nodes I can calculate it perfectly. I can do the Maths manually.
What error might I be making? For the future, is there a list of types of errors?
From a more applied angle, a book like "10 steps to complex learning" might be helpful.
I come from a similar cog psych background as the Bjork Lab, so am a big fan of their research, but books like 10 steps come from instructional design, which is a bit more focused on the big picture (designing a whole course vs individual mechanisms).
It is super unlikely that an algorithm that was made based on feels would turn out to be the most effective. Especially since it requires some learning and errors to even learn how to use it.
