As a PhD student at UC Berkeley, my duties involve some amount of teaching; so, this semester (Spring 2015), as well as last spring, I have been a teaching assistant for a class taught by my advisor, Tom Griffiths. The class, called Computational Models of Cognition (COGSCI 131), aims to introduce students to computational models of human behavior. The problem sets are a mixture of simple programming assignments—usually requiring students to implement pieces of different models—and written answers, in which students report and interpret the results of their code.
In the past, the problem sets were written in MATLAB. This year, however, we decided to make the switch to Python. In particular, we decided that the IPython/Jupyter notebook would be an ideal format for the assignments. The notebook is a cross-platform, browser-based application that seamlessly interleaves code, text, and images. With the notebook, it is possible for us to write instructions in the notebook, include a coding exercise after the instructions, and then ask for their interpretation of the results immediately after that. For an example of what the notebook looks like, you can check out try.jupyter.org for a demo.