Again, we reinforced the need for students to set up some form of support group with their fellow students in the workshop. We even went as far asking students to change their seating arrangement in the classroom so that they were sitting by someone who was doing research similar to their own. The artificial creating of a learning community actually worked surprisingly well, and the room became much friendlier on the second day.
Tracy taught the morning section. This time, she covered data structures in Python – specifically, lists and dictionaries. We asked students to bring in their own data sets for the second day, and so they were able to practice importing their own data into lists. We also had a few standard examples and data files for students to do exercises with, which allowed for more structure lessons.
We started getting really interesting questions on the minute cards on the second day. It was exciting to see students realizing the potential power of programming languages for their own research.
Minute Card Responses:
Also, during the morning half of the lesson we started seeing the more experienced/advanced students monitoring questions in HipChat. They would often answer their peers’ questions before a TA or instructor had the chance to follow up. It was a really great way to utilize the existing student knowledge and experience in the classroom.
Successes: Students seemed to especially enjoy learning about data structures. They brought their own data files in, which made them more invested in what they were learning. We strongly encouraged the formation of a learning community in the class by pairing up students according to their similar research topics.
Recommendations: Use the student pairing scheme. Often, a student’s partner can help them troubleshoot when the student runs into small problems. Again, the IPython Notebook was really useful for teaching Python. Ask students to bring in their own data files to use in the class!