2023 ASEE Annual Conference & Exposition

Auto-graded Scaffolding Exercises For Theoretical Computer Science

Presented at Computing and Information Technology Division (CIT) Technical Session 3

This paper describes an ongoing effort to develop auto-graded scaffolding exercises to support an upper-division theoretical computer science class at a large Midwestern public university. The course covers a mixture of formal languages, automata theory, and design and analysis of algorithms. The course has a steady-state enrollment of 400 students per semester, almost all undergraduates majoring in computer science or computer engineering, for whom the course is required.

Most of our auto-graded exercises are organized as guided problem sets. Each guided problem set consists of a small number of multi-stage exercises, implemented as a sequence of questions that guide students through the process of solving a design or proof question. Our guided problem sets support multiple correct solutions, detect common mistakes, automatically provide counterexamples for incorrect answers, provide helpful narrative feedback, and award partial credit consistent with grading rubrics for written homeworks and exams. Some exercises incorporate new interactive elements that enable students to submit solutions similar to written homework. These elements allow drawing finite state machines, writing structured sentences that are auto-graded and provide feedback, and drag-and-drop blocks for writing proofs and pseudocode.

We report the results of a student survey to gauge the effectiveness of our scaffolding exercises to help students master the material, and just as importantly, to improve their confidence in that mastery.

Authors

Prof. Jeff Erickson University of Illinois Urbana-Champaign [biography]

Jeff Erickson is a Sohaib and Sara Abbasi Professor in the Department of Computer Science. He received an BS in computer science and mathematical sciences from Rice University, an MS in information and computer science from UC Irvine, and an PhD in computer science from UC Berkeley. His past research has focused on computational geometry, computational topology, graph algorithms, and their applications; he is also the author a popular free algorithms textbook. His awards include a Sloan Research Fellowship, an NSF CAREER award, and numerous teaching and research awards from the University of Illinois.
Jason Xia University of Illinois at Urbana - Champaign
Eliot Wong Robson
Tue Do
Aidan Tzur Glickman
Zhuofan Jia
Eric Jin
Jiwon Lee
Patrick Lin
Steven Pan
Samuel Ruggerio
Tomoko Sakurayama University of Illinois, Urbana-Champaign
Andrew Yin
Yael Gertner University of Illinois, Urbana-Champaign [biography]

Dr Gertner joined the Computer Science Department at the University of Illinois in 2020 as a Teaching Assistant Professor. She received her B.S. and MEng in Electrical Engineering and Computer Science from MIT, and Ph.D. in Computer and Information Science at the University of Pennsylvania. She was a Beckman Fellow at the University of Illinois Urbana-Champaign. Her current focus is on broadening participation in Computer Science and Computer Science Education She has been developing materials and teaching for iCAN, a new program for broadening participation in CS for students who have a bachelor’s degree in a field other than computer science.
Brad Solomon University of Illinois, Urbana-Champaign

Download paper (849 KB)

Are you a researcher? Would you like to cite this paper? Visit the ASEE document repository at peer.asee.org for more tools and easy citations.

» Download paper

« View session

For those interested in:

computer science