I teach mainly research methods, statistics, and computer science courses at the undergraduate level. I am currently developing courses in research methods and statistics for students in social science majors, with an emphasis on social and organizational change.
I have centered group work in these courses. Most recently, I provided students with data analysis packs on two social issues that have been top of mind in the U.S. over the last year: the spread of Covid-19 in the U.S. and police reform. Working in groups of 4 or 5, students completed analysis projects based on these data packs. Their projects had to demonstrate use of concepts typically covered in an introductory stats course: frequency distributions, central tendency, distribution spread and shape, etc.
For much of the data analysis pack for Covid I was able to borrow from a book I just authored (published by Packt) called the Python Data Cleaning Cookbook. For police reform, I had to start from scratch.
I was very pleased with how much this approach improved student engagement and helped to reduce the amount of math anxiety — remember that all students in this class were social science majors. But there were also challenges, and I am interested in ideas others might have for addressing them.
Most classes at my institution are now online and asynchronous due to the pandemic. That meant that much of the learning in my stats class happened when I was not around to see it, which is great. But that also meant that I could not facilitate at times when it got difficult — when students were less generous with each other that I had hoped. My stats classes always go better when students are excited about supporting other students. I heard from students that that did not always happen.
I would love to hear from anyone with tips for making group work go better, particularly in data science, stats, or research methods classes.