Andrew,
I am just finishing my first year teaching a graduate level stats course
to Agriculture and other life sciences students. The course is
practically oriented, and most of the students have taken 1
undergraduate course previous to mine.
1) What are the instructional decisions that a person needs to make if
they are going to be teaching statistics using R?
I think that the biggest decision is whether we are going to teach a lot
of calculations by hand, or whether we are going to "black box" a lot of
the mathematics and teach the process of data analysis.
2) What decisions have you yourself made and what were your reasons?
I have chosen to focus on data analysis as a process. I think for the
average MSc. student it is most important that they be able to choose an
appropriate method and apply it correctly than to understand the theory
of statistics. I have chosen to use R rather than a program like SAS
because it is so much more useful to the students.
Most of my students will be going to work for small companies after
graduation that do not have the resources to purchase something like
SAS. There was a bit of pushback from faculty who didn't like the idea
that the students were leaning a program other than their favorite one,
but they fully accepted the value of wider availability.
3) How do you teach with R? Do you have sessions on R and other sessions
where content is taught? Is the computing fully integrated with the
content? Or some combinationn?
More than anything I like running examples "live" in class using R. I
notice a real difference in my students between a segment of powerpoint
(passive, unresponsive...) to when I am running examples (engaged,
suggesting ideas on how an anlysis should proceed...). I provide them
with all of the scripts developed in and for class so they can re-run
analyses later on their own.
The students have weekly assignments where they are expected to apply
the tests they have seen in class to new datasets. I find the process of
having them see an example and then figure out how to apply it in a new
situation to be very effective.
I use Crawley's "The R Book" extensively. I like the books approach,
integrating both theory and practice. The examples are also very
suitable to my agriculture / life sciences students.
4) If you have the heterogeneous group of students (some going on to
program in R, others just trying to get through, etc.) how do we deal
with this? Do we need to have different types of assignments and
materials for the different students?
Everyone has to do the same assignments. Different groups have different
challenges, however. I have some students with an engineering background
that are very comfortable with the idea of coding but have no background
in things such as experimental design and messy data. Oh the other hand
I have ag students who found the idea of telling a computer what to do
by typing very intimidating. Within 4 weeks of hands on practice I found
most students to be very comfortable with using the command line
interfaces, and since then almost all discusson and questions have
focussed on the use and interpretation of statistics.
I hope this helps. In general I have found R to be rewarding to work
with, and the students have in general responded very well to it.
Cheers, Eric
--
Eric Lamb
Assistant Professor, Plant Ecology and Biostatistics
Dept. of Plant Sciences, University of Saskatchewan
http://homepage.usask.ca/egl388/index.html
4D68 Agriculture Building
306-966-1799
[email protected]
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