Saturday, 25 May 2013

Student choices between SAS and R in teaching presentations.

This is my second post in a series of posts (the first one is here) about a SAS/R course that I've recently finished teaching to MSc students at +Cardiff University.

I taught this course using a hybrid of flipped classrooms / IBL (although I'm cautious when using the term IBL as I'm not entirely sure my approach fits with any variation of Moore's methods). I gave students access to all the content of the class before hand (including notes, exercises and a series of screencasts - all the materials are here if they're of interest). The students were then given "challenges" and had to deliver their solutions to as presentations to the other students in the class. The aim of this was to get the students to teach themselves/each other and quite often I would not actually have to say much at all during a class (this allowed for a better use of 'me' by the students during the lab sessions).

(Here's a previous post about flipping the classroom and here's one about IBL)

In the previous post I described how students chose to use SAS and/or R in their class test. Most students chose SAS despite displaying a preference for R when asked.

The above is 1 of 3 assessments that the students have had to go through. This post is about the second assessment: a group presentation. In this presentation I asked students to teach an aspect of SAS or R that had not been covered in class (you can see the brief here).

I believe that this is a particularly important thing to assess as I in no way can pretend to teach them everything. It's important that they know how to learn new things that they might need in their career.

I was expecting groups to select a particular language and then a particular topic but interestingly most groups chose to look at both languages and compare strengths and merits.
I had 6 groups and here are the subjects that they looked at:
  • Time series forecasting: both in SAS and R;
  • Principal component analysis: both in SAS and R;
  • Random sampling: both in SAS and R;
  • Survey sampling (in SAS) and the creating a gif of the Mandelbrot set (in R);
  • Scorecard building in SAS;
  • Mapping and spatial analysis: in R.
The 3 groups who carried out a single thing in both SAS and R did a good job of describing strengths and weaknesses of both languages. It was a pleasure to see and again reassures me that an important message has gotten across to the students which is that there is not 1 best tool but an appropriate tool for a particular job.

I'm planning on putting the code/slide up for these talks for them to serve as resources for students doing the course next year but I want to wait till I've marked their final piece of work and asked the students if they mind. In the mean time I'll repost this gif of the Mandelbrot set made by 1 of the groups (I thought this was cool!):

I still want to write generally about the teaching/learning methods in this class and will do that later but if it's of interest my PCUTL portfolio is available here and in there I describe and justify a lot of what I'm doing.

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