2025 ASEE Annual Conference & Exposition

A Complete Redesign of CS1 for Engineering Students

Introductory computer science (CS) courses, also referred to as CS1, are essential for equipping computer science and other engineering students alike with foundational programming skills. However, traditional CS1 courses are tailored for CS majors, leaving engineering students without the specialized programming knowledge needed for their disciplines. This paper presents a complete redesign of the CS 101 course at the University of Illinois Urbana-Champaign to meet the specific needs of fundamental programming for engineering students. The new curriculum prioritizes teaching foundational programming concepts in lectures while providing engineering-focused applications in lab activities and mini-projects. New topics, such as pseudocode and debugging, were introduced in lectures to deepen engineering students' understanding of programming fundamentals and enhance adaptability to new programming languages. Homework and exams were restructured into computer-based assessments featuring auto-grading and randomized problem variations to encourage mastery learning. Bi-weekly mini-projects were designed to connect programming concepts and skills with practical engineering applications. To evaluate the impact of the redesigned CS 101 course, a CS1 assessment was developed to measure students' understanding of programming fundamentals, pseudocode interpretation, and Python-specific skills. Future work will focus on incorporating group activities into lab sessions, expanding mini-project offerings, and refining the assessment tools to further align with the needs of engineering students.

Authors

Mr. Yuxuan Chen University of Illinois Urbana-Champaign [biography]

Yuxuan Chen is a Master of Science student in Computer Science at the University of Illinois Urbana-Champaign. His primary research interests focus on computer science education and artificial intelligence. He is dedicated to enhancing student learning experiences and accessibility in computing education through both innovative technology and research-driven teaching practices.
Mr. Chenyan Zhao http://orcid.org/0000-0001-9520-2438 University of Illinois Urbana-Champaign [biography]

Master of Science student in Computer Science at University of Illinois Urbana-Champaign. Research interest in Automatic Short Answer Grading using AI
Mr. Kangyu Feng University of Illinois Urbana-Champaign [biography]

Kangyu Feng is a student in the Master of Computer Science program at the University of Illinois Urbana-Champaign. His primary interests include artificial intelligence, machine learning, game development, and computer science education. He is passionate about enhancing the structure and content of foundational math and computer science courses.
Dr. Mattox Alan Beckman http://orcid.org/0000-0002-0935-365X University of Illinois Urbana-Champaign [biography]

Mattox Beckman is a teaching associate professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign. He earned his doctorate from UIUC in 2003 under Sam Kamin,
specializing in programming languages. He was a senior lecturer at the Illinois
Institute of Technology for thirteen years before returning to UIUC as a teaching faculty. He is currently interested in CS Education, particularly in regards to upper level courses such as programming languages and competitive programming.
Prof. Mariana Silva University of Illinois Urbana-Champaign [biography]

Mariana Silva is a Teaching Associate Professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign and co-founder and CEO of PrairieLearn Inc., a company dedicated to empowering instructors with tools to enhance teaching workflows without compromising educational quality. Before joining CS@Illinois in 2017, she was a lecturer in the Department of Mechanical Science and Engineering at the same university for five years. Silva has extensive experience in course development across engineering, computer science, and mathematics and is passionate about advancing teaching innovations that benefit students and instructors alike. She is an expert in the development and application of computer-based tools for teaching and learning in large STEM university courses. Her current research investigates the use of educational technologies to enhance computer-based assessments and centralized computer-based testing centers. This includes leveraging Large Language Models (LLMs) for automated short-answer grading and the creation of robust, randomized question generators to improve equity, accessibility, and scalability in teaching, learning and testing practices.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025

For those interested in:

computer science
engineering
undergraduate