This is a talk I gave this week in Advanced Biostatistics at McGill. The goal was to provide an gentle introduction to Bayesian methodology and to demonstrate how it is used for inference and prediction. There is a link to an accompanying R script in the slides.
As September draws nearer, my mind inevitably turns away from my lofty (and largely unmet) summer research goals, and toward teaching. This semester I will be trying out a teaching technique using live data collection and analysis as a tool to encourage student engagement. The idea is based on the electronic polling technology known as ‘clickers‘. The technology allows you to get instant feedback from students, check for understanding, and when used appropriately it can facilitate active engagement and peer learning.
Because I will be teaching in a computer lab, where all of the students will be sitting at a computer, I have the advantage of being able to bypass the little devices, and instead gather student responses using a web based interface. The advantages, as I see them, are:
- Students can enter more complex input than the 1-9 provided by clickers. Instead, students can enter any number or character vector response.
- Students can instantly download, plot, and analyze the class data. This step is facilitated by the
read.csv("http://data_url.csv") function in R, which allows data import directly from the web.
The first exercise I have planned using this technology is to have students enter their height, then have them plot a histogram of the data to introduce the normal distribution. Using the simple online interface I have created, this exercise can be done very quickly. I am calling the tool I am one of n.
If you have any suggestions for learning activities that could make effective use of this technology in an undergraduate Biostatistics (or other) course, drop me a note!