We took a lot of inspiration from Jupyter Book (and use Jupyter kernels under the hood), so nothing but respect for all things Jupyter.
Email hn@ycombinator.com if you want us to look over a draft (the same offer goes for anyone) - just please realize that we can't necessarily respond quickly; it depends on how brutal the inbox is that week/month.
"Derivative of sigmoid function"
function dsigmoid(z::Number)
return ??
end
I write a lot of Jupyter notebooks at my main job and I also lecture at a university thus Pathbird seems relevant.
You can demo the student experience with code JULIACON2020 as well.
It’s not the Jupyter notebook (or lab) frontend, it’s a custom webapp built for learning and specifically around exercise based, guided learning. So a student will go through exercises (mostly multiple choice and code-based “autograded” exercises) to check their understanding and guide them through a lesson.
This style of learning tends to work best with interactive languages (Julia and Python and R at present). Theoretically we could support other languages with Jupyter kernels (including Go and C++, etc) as well. I wonder how well those languages would work in this context considering it’s a bit hard to be “iterative” with those (but consider than a challenge rather than a limitation!).
Feel free to reach out with any questions/comments/concerns and I can answer in more long form!