Hi Robert For our 1st yr geoscience undergraduates we teach an introductory programming course with python (~90 students). The content is different from SC but we pretty much cloned all the teaching methodologies (thanks Greg!). Learning outcomes are much better than before and the student experience is good. We also did a workshop covering shell and git last year. This also went very well.
If you are taking on the fight to change the curriculum I have two pieces of advice. First pile up your evidence: lots of literature on what are good and bad first programming languages, what is the community using (do a survey if you have to), what is the competitor risk (i.e. "everyone else is going to be kicking our ass if we don’t do something"), what are the teaching/research staff using (they can be good allies - project students etc). This will work with rational opposition. Irrational opposition is usually confined to one vocal person (lunatic) - but rather than waiting for them to retire you just work with everyone else to form a consensus. Scientists are usually pretty well behaved when you present evidence. If that fails then look for a job elsewhere ;-) Secondly, be stupidly well prepared if you do get your way. The first couple of years when I taught python it was pretty much a disaster mostly due to poor (traditional) teaching methodologies. I thoughts just switching from c++ to Python and being an enthusiastic teaching would be enough…it wasn’t. And the fact is that you look pretty lame if you strongly advocate something and then blow it… Over time we made quite a few changes to improve the course but we only really nailed it after we did the SC instructor training ;-) One difference from SC is that for undergraduate teaching the classes can get much larger. The solution for this is an army of teaching assistants (in our case these are mostly grad students) which are familiar with SC. Regards Gerard On 21 Apr 2015, at 22:59, David Martin (Staff) <[email protected]<mailto:[email protected]>> wrote: Those look very interesting. We teach stats in RStudio from almost the minute they walk through the door in 1st year. We expect all graphs/representations to be literate and reports to demonstrate reproducibility through inclusion of the scripts. With a cohort of 200 (and rising) it is a challenge to embed those skills but by the time they get through to 3rd year they start to do some good stuff and are confident in large scale data wrangling. Overheard in the library 'Why are you messing around with Excel? it is so much easier in R' from a cohort who are the antithesis of the nerdy geek programmer. So we have successfully introduced R into the undergrad curriculum and are slowly training faculty who get a shock when the student in the genetics class ask for the raw data and do a proper ANOVA rather than the Fisher Price version the instructor had been doing for years because the student's didn't have the skills. This amounts to about 5-6 contact hours in level 1 and substantially more (16+) in level 2. Level 1 covers basic plotting and descriptive stats. Level 2 covers statistical models, fitting and testing to multiway ANOVA. We only teach the basic plots as I am not brave enough to attempt to teach 200 18 year old biologists ggplot2 - their brains are too delicate. I'm working on getting a reasonable amount of Python in but that is difficult to find time and the appropriate context to engage the students. Happy to chat and share materials if folk are interested. ..d Dr David Martin Lecturer in Bioinformatics College of Life Sciences University of Dundee ________________________________ From: Discuss <[email protected]<mailto:[email protected]>> on behalf of Karen Cranston <[email protected]<mailto:[email protected]>> Sent: 21 April 2015 19:08 To: Robert M. Flight Cc: Jennifer Bryan; Software Carpentry Discussion Subject: Re: [Discuss] undergrad curriculum examples I've been really impressed with Mine Çetinkaya-Rundel at Duke who teaches intro stats using RStudio and literate programming [1]. There is a nice summary of the approach in this article [2]. Mine and I (and Jenny!) were at a reproducible science workshop last fall, which is where I learned about this course. Materials on GitHub [3]. Cheers, Karen [1] https://stat.duke.edu/~mc301/teaching/ [2] http://chance.amstat.org/2014/09/reproducible-paradigm/ [3] https://github.com/mine-cetinkaya-rundel/sta101_sp15 On Tue, Apr 21, 2015 at 1:43 PM, Robert M. Flight <[email protected]<mailto:[email protected]>> wrote: Does anyone know of any examples where software carpentry type skills have been integrated into an undergraduate science curriculum? It seems to me that the various skills taught in software carpentry could be integrated into an undergraduate science curriculum if done correctly, given the prevalence of data manipulations that are frequently performed in undergraduate science labs (chemistry titrations / conversions, physics equation fitting, biology number manipulations), at least in my experience over 10 years ago. I don't imagine that things have changed, and have likely gotten worse. I know that Jenny Bryan is integrating a lot of this stuff into her advanced stats class (which is awesome), but the more I think about it, it seems that it would be useful to introduce things earlier rather than later. I would be very appreciative if anyone has any specific examples from their own or others teaching. Regards, -Robert Robert M Flight, PhD Bioinformatics Research Associate Resource Center for Stable Isotope Resolved Metabolomics Markey Cancer Center University of Kentucky Lexington, KY Twitter: @rmflight Web: rmflight.github.io<http://rmflight.github.io/> EM [email protected]<mailto:[email protected]> PH 502-509-1827<tel:502-509-1827> The most exciting phrase to hear in science, the one that heralds new discoveries, is not "Eureka!" (I found it!) but "That's funny ..." - Isaac Asimov _______________________________________________ Discuss mailing list [email protected]<mailto:[email protected]> http://lists.software-carpentry.org/mailman/listinfo/discuss_lists.software-carpentry.org -- ~~~~~~~~~~~~~~~~~~~~~~~ [email protected]<mailto:[email protected]> @kcranstn ~~~~~~~~~~~~~~~~~~~~~~~ The University of Dundee is a registered Scottish Charity, No: SC015096_______________________________________________ Discuss mailing list [email protected]<mailto:[email protected]> http://lists.software-carpentry.org/mailman/listinfo/discuss_lists.software-carpentry.org
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