Project Update: Co-Taught Classes

Brooke McKelvey, Nicole Connell, and Rebecca Hicke

The progress we’ve made on our project has been in the data collection area, which we’ve identified as the most time consuming area of our project. We have gotten about 25% of the way through our initial data collection, which involves identifying classes listed in Carleton’s academic catalogs as having multiple professors. We will check whether these classes are actually co-taught or whether different professors are teaching different sections by cross-checking our spreadsheet of data against backlogs of ENROLL.

Most of the problems we’ve encountered so far have involved the time necessary to complete various sections of the project. The hand retrieval of data for the first half of the catalogs is taking more time than we had predicted and the coding section is more complicated than we had initially expected and will therefore also take more time than planned. Both of these problems have been exacerbated by a lack of consistency in how the data is formatted in the academic catalogs, which makes it harder to quickly scan the documents by eye and to craft regular expressions to pull the data we would like. There are also typos in the course catalogs themselves that lead to confusing data. There has also been some confusion on whether to categorize some things as co-taught classes. Specifically, we were unsure of whether to include colloquiums and integrative exercises. We decided to include colloquiums but not integrative exercises based on how closely they resemble the structure of what we consider a co-taught class, but in the process have realized that we should set a definition of what we consider to be a co-taught class so as to avoid this confusion in the future. Luckily, however, we have not yet encountered any problems large enough that we need to change our initial plan.

We still plan on using Gephi to visualize our network once we reach that point, but for now are using Google Sheet to collect our data in a place that is easily accessible by all of us. We have used the Automator application on Macs to create a PDF to TXT convertor to turn the second half of the course catalogs into text files readable by Python. To clean that data, we have begun using TextEdit and Python in conjunction with regular expressions.

In terms of our projected timeline, we will probably have the hand collected data ready on track by Friday, but the data to be collected by code will not be ready. Because of this we will probably start on cross-checking ENROLL for the first half of the data before we have data for the second half.