Providing better feedback in auto-grading: we are using the Autolab autograding system in the CS Department’s Introduction to Computer Science class, where we are handling upward of 1500 students annually. Auto-grading has been a critical technology for us in reaching this scale. However, students are often frustrated because of insufficient feedback from the autograder, leaving them at a loss on how to improve their programs. Thus, we are currently adding a feedback component to Autolab that allows the instructors to provide concepts and skills that students are expected to learn from an assignment, testing results that indicate commonly found misunderstandings of the concepts/skills, and hints that can be provided to students for them to review and improve their programs. We plan to deploy this component in the Fall, and will make it publicly available after gaining more experience with the system. If you are potentially interested in our work, please contact Andrew Tjang or Thu Nguyen. Our larger goal is to gain a better understanding of how students interact with auto-grading, and make the auto-grading system a tool for learning in addition to grading.
Understanding and improving diversity of the CS student body: we believe that it is important to increase diversity in the CS study population. Our work in this area starts with analyzing and understanding a volume of data about our student body in order to identify areas where intervention may have maximum impact. We have completed a study of gender diversity in our CS classes; see the publication below.
Learning assistants: a number of years back, our department adopted a Peer Leader model in the Introduction to Computer Science course, where undergraduate Peer Leaders run peer groups of 8-12 students (1 hour each week in addition to lectures) that work on problem solving as opposed to large recitation sections with a graduate student TAs that are much closer to lectures in format (although somewhat smaller than the class lecture). We are currently working with the Rutgers Learning Center to formally convert our Peer Leader model to the Learning Assistant model, which includes a 3-credit pedagogy class. We are currently developing a pedagogy class that is specific to the ~40 undergraduate Learning Assistants that we hire each semester. We thank the MaGE Training program for sharing material with us, and we in turn will make our material publicly available soon.
We would be happy to share the surveys that we used for this study. Please contact Andrew Tjang (email@example.com) or Thu D. Nguyen (firstname.lastname@example.org). We thank NCWIT and the Rutgers Center for Women and Work for their help with designing and improving the surveys. Our work has been partially supported by a Google 3×3 grant and NSF grant DUE-1504775.