chrisparnin
Joined 13 years ago
Joined 13 years ago
You can build some cool stuff with RMarkdown and LearnR:
* https://rmarkdown.rstudio.com/
* https://rmarkdown.rstudio.com/docs/articles/rmarkdown.html
One interesting distinction is "tutorials" vs. "lessons"... LearnR and datacamp are much better for _lessons_, where you ask a learner to type in some code and check to see if you got it right, with hidden cells with the appropriate test cases backend.
We have a limited set of notebooks published online to demo how they work. For example:
* https://docable.cloud/chrisparnin/examples/basics/script.md
* https://docable.cloud/chrisparnin/examples/tutorials/Git.md
Right now, we're using the notebooks to build better lecture materials + workshops, especially for my DevOps course: https://github.com/CSC-DevOps/Course
We've seen some interest in having support for live documentation + OneOps (simple runbooks for one-off devops tasks), so we'll probably continue to explore this more.
https://medium.com/@gameweld/the-case-for-the-private-techni....
Even notebooks still are problematic, for example, this study found that only 25% of Jupyter notebooks could be executed, and of those, only 4% actually reproduced the same results.
http://www.ic.uff.br/~leomurta/papers/pimentel2019a.pdf
One compromise is to evaluate the paper separate from it's artifacts, which are reviewed for availability, reproducibility, and reusability. In software engineering conferences, this is becoming a standard, and while there is a huge burden for reviewers to evaluate these things, I think it does take us in the right direction. So in this case, we also submitted our paper for evaluation for its artifacts.