In the past academic year, I worked as a teaching intern with the Digital Social Science Center and Digital Science Center, hosting R open labs and workshops. Most of the people at R Open Lab are using R for their research projects; therefore in the past semester, I tried out several different teaching practices in search of the best way to enable the participants to harness the power of R as a research tool.

New Teaching Practices

1. Peer learning: It was the first method I tried out and it seemed to be the most helpful one. By talking with people with similar research interests and learning from each other’s experiences, many participants expanded their professional networks and found a better way to apply R to their research. Exchanging learning experiences with people from different academic backgrounds helped the participants to gain a broader understanding of the functionality of R. However, creating a stimulating environment for peer learning but not making the participants feel pressured can be challenging.
2. Group discussion: I tried to encourage group discussion by throwing open-ended questions at the participants during the instructions. The problem with this method is that more often than not, the discussions were very shallow, probably because the questions were not interesting enough. Looking forward, it might worth trying to prepare a thought-provoking question for each R Open Lab session and make group discussion as a standalone part.

Challenges and Solutions

Based upon my experience with both workshops and open labs, and the feedback from the attendees, the challenges facing the R Open Labs and possible ways to improve them are as follows:

1. Due to lack of a clear syllabus for the whole semester and its self-paced nature, most participants are not motivated to attend R Open Labs regularly. To help participants have a better idea of the progress of open labs, we can try to post topics for each session a month in advance on the workshop list. We can also maintain a GitHub repository and upload materials used for instruction after each session so that other interested individuals could easily access them.
2. Although the Swirl package is an immensely helpful starter kit, most people new to coding and statistics still struggle with very basic R operations. Furthermore, the huge gap between basic operations and being able to fully implement a research project can be frustrating and overwhelming. Therefore, instead of Swirl, we could provide participants with some sample code and links to GitHub repositories, so that they could use them as a starting point for their own projects and learn best practices of building a project with R
3. Some questions are frequently asked by the participants, so we can probably provide participants with a list of FAQs and the general answers to maximize efficiency.

To conclude, this internship motivated me to think deeper about teaching and better understand people’s needs. It was an incredibly challenging and rewarding experience. As data analysis becomes increasingly important in a multitude of research areas ranging from biology to history, R is becoming an essential research tool. I hope by continuing to make improvements to the contents and structure of R Open Lab, it could serve as a platform to introduce R as a useful digital tool and promoting collaboration between scholars interested in R.