We are a team developing resources on the PrairieLearn platform to support the teaching of algorithms, data structures, and other theoretical aspects of computer science, at several different levels of the computer science curriculum at the University of Illinois. This development effort started with CS 374 (Algorithms and Models of Computation), but has since expanded to include several other classes at Illinois, including CS 173 (Discrete Structures), CS 225 (Data Structures}, CS 277 (Algorithms and Data Structures for Data Science), and CS 401/403 (Accelerated Fundamentals of Algorithms).
Jason Xia and Craig Zilles. Using context-free grammars to scaffold and automate feedback in precise mathematical writing. Proc. 54th SIGSCE, 479–485, 2023.
Jeff Erickson, Jason Xia, Eliot Wong Robson, Tue Do, Aidan Glickman, Zhuofan Jia, Eric Jin, Jiwon Lee, Patrick Lin, Steven Pan, Samuel Ruggerio, Tomoko Sakurayama, Andrew Yin, Yael Gertner, and Brad Solomon. Auto-graded scaffolding exercises for theoretical computer science . To appear in Proc. 2023 ASEE Annual Conference, 2023.
Instructors that have used resources in this repository in their courses:
Funding: Since August 2022, this project has been funded by the Grainger College of Engineering, through the Strategic Instructional Innovations Program, under the auspices of the Academy for Excellence in Engineering Education, with matching funds from the Department of Computer Science since July 2023. The current faculty members of the SIIP team are Jeff Erickson (PI), Carl Evans, Yael Gertner, and Brad Solomon; the 2022–23 startup SIIP team also included Tiffani Williams.
Other Acknowledgments: Many thanks to the core PrairieLearn development team, espeially Dave Mussulman, Seth Poulson, Nathan Walters, and Matt West. FInally, we thank the roughly 3500 students in CS 374, CS 225, and CS 173 who have used these resources, for their patience, frustration, and helpful feedback.