Thanks all for the excellent thought-provoking comments. I want to clarify that these students are, for good or for ill, already doing all these analytical and graphical things for their projects. They are doing them with Excel and SPSS. One of my goals would be to teach them how they can be done (and I think done better) in R. Better for many reasons, not least of which is the reproducibility offered by lines of saved code.
It seems that many (not all) on the list agree with the science teachers that R is too difficult for high school students. Is R intrinsically more difficult to learn than commercial spreadsheet software? If so, why? Or is the issue that it is difficult to change to R after many years experience in the mind-set of spreadsheets? If a child was "brought up" on R for math/stats, in a developmentally progressive way, instead of Excel or a graphing calculator, would he/she perceive it as difficult? Are the intrinsic cognitive differences between high schoolers, college students, and graduate students substantial enough to explain why the last can learn R and the first can't? Or is it a matter of exposure, opportunity, etc? Indrajit, I'm curious: given your preference for hand-drawn graphs for learners (a very good point), why is Excel "fine" but R not? At any rate, I should probably migrate this thread over to the Teaching SIG listserve, which I didn't know about before. Thanks again. --Chris Christopher W. Ryan, MD SUNY Upstate Medical University Clinical Campus at Binghamton 425 Robinson Street, Binghamton, NY 13904 cryanatbinghamtondotedu "Observation is a more powerful force than you could possibly reckon. The invisible, the overlooked, and the unobserved are the most in danger of reaching the end of the spectrum. They lose the last of their light. >From there, anything can happen . . ." [God, in "Joan of Arcadia," episode entitled, "The Uncertainty Principle."] Bert Gunter wrote: > <...snipped> > >>> I anticipate keeping things very simple: >>> --objects and the fact that there is stuff inside them. str(), head(), >>> tail() >>> --how to get data into R >>> --dataframes, as I imagine they will mostly be using single, >>> "rectangular" datasets >>> --a lot of graphics (I can't imagine that plot(force, acceleration) >>> is beyond a high-schooler's capability.) >>> --simple descriptive statistics >>> --maybe t-tests, chi-square tests, and simple linear regression. >>> >> >> I have some experience in this and would have to agree with Indrajit >> that this is not a good idea. >> >> When I tried to teach R to a high school student it was not very >> successful. Certainly based on that experience the list above is way >> too complex. Don't teach anything on that list at all. The number of >> concepts involved in that is simply overwhelming. > > Oh amen amen! > > I'd go farther: It's overwhelming for college students. > > Farther yet: I've met very few scientists and engineers who understand > what a standard deviation is. Fewer still who understand the > difference between a sample statistic and a population parameter for > which it's an estimate. > > This approach to "basic" statistics is (imho) symptomatic of why our > discipline is so widely disliked and misunderstood. > > Cheers, > Bert > > Also avoid teaching >> anything that requires complex installation if you want them to be >> able to carry it forward by themselves. >> >> I would expect the reaction would be that most will have no interest >> and the ones that do will be frustrated by the large number of >> concepts needed to get going. >> >> The only part that seemed to trigger any interest was when I showed >> the large list of colors available in colors() and then playing with >> inserting different colors in: >> >> colors() >> plot(1:5, col = "violetred") >> >> Assuming you are committed to this and go ahead, I would divide it >> into two parts: >> >> 1. a graphics demo -- make it clear its a demonstration so they have >> an appreciation of what is possible and you are not actually teaching >> anything in this portion. >> >> 2. Teach them how to install R, run the above two commands >> (substituting in different colors), how to exit and point out that >> there are many tutorials in: >> http://cran.r-project.org/other-docs.html >> and they can pick one they like (since the official documents will be >> over their head). >> >> If you do that then perhaps a small number will have sufficient >> interest to try it some more at home but I wouldn't be surprised if >> none do and that most or all would prefer something with more >> immediate gratification. >> >> -- >> Statistics & Software Consulting >> GKX Group, GKX Associates Inc. >> tel: 1-877-GKX-GROUP >> email: ggrothendieck at gmail.com >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
