Feedback from Lawrence Berkeley National Lab
This post originally appeared on the Software Carpentry website.
Katy Huff, Justin Kitzes, and I wrapped up our LBL workshop yesterday. We had ~25 participants with a broad range of backgrounds and levels of experience. Here's our traditional table of good/bad feedback:
| Good | Bad |
|---|---|
| can use python better | don't know how to apply git to work |
| covered lots of material | covered too much material |
| can use git better now | group felt too large for number of helpers |
| covered from basics to advanced programming | felt unprepared for git instruction |
| helpers were knowledgeable and nice | couldn't run VM, add system reqs |
| motivated version control well | python level was too easy |
| started with basics, had good help | need well defined goals for exercises |
| got exposed to a lot | group people by experience |
| all the material is available on GitHub | would have liked a more detailed description of course |
| learned enough to get started | didn't get any emails on the waitlist |
| have hope | getting info ahead of time |
| good overview for later | whole thing too fast |
| format - work through code live | topics jumped around |
| testing cases | maybe too language (Python) dependent, no objects |
| self-consistent complete package | burnt out at end of first day (fast) |
| inspirational | 9-4:30 draining, more breaks between topics |
| great intro to vocabulary, how programmers think | didn't expect to program at end, was right |
| gained confidence from seeing experts | switch morning git with afternoon python on 2nd day |
| seeing github, advice on resources | not clear that mac users needed to install software ahead, why certain steps |
| had to do exercises, version control in beginner class | some hard to follow along, relies on previous success with commands |
| instructors :-D, with real-world experience | more relevant examples to audience, day to day data/problems |
| demo writing, troubleshooting, testing very valuable | what was xcode for? |
| falling behind in python - 2 tiered exercises | |
| documentation needs comments | |
| is there help for release management? | |
| advanced math? | |
| third day - integrative exercise, case study - start to finish | |
| follow up project, final suggested homework assignment |
The good news is that they liked our material and they liked us (and seemed to think we knew what we were talking about). The bad news is a lot of them had a hard time keeping up and a couple complained it was too slow. These are typical complaints of our shotgun approach to teaching to a random sampling of scientists. We'd like to do more discipline specific bootcamps where we can tailor things better. Another typical complaint was they they aren't sure how to apply what they've learned to their research. We're still working on that one.
Dialogue & Discussion
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