Autograded resources for theoretical computer science

View My GitHub Profile


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).






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.