2026 ASEE Annual Conference & Exposition

Understanding the Impact of Ungraded Assignments on Student Learning in Introductory Computing

Presented at CIT Technical Session 12: Foundations and Emerging Topics.

This experience report explores the effects of ungraded assignments on the learning experience of students in an introductory computing course. Our study examines the impact of ungraded assignments on student engagement, understanding, and overall academic performance. We developed and administered new ungraded assignments for a required introductory computing course in the first year of the computer engineering curriculum. To assess the effectiveness of our ungraded assignments, we employed a mixed-methods approach, including surveys, interviews, and performance analysis. Our analysis reveals a positive correlation between participation in ungraded assignments and overall course performance. However, this relationship appears to reflect pre-existing student motivation rather than the direct learning effects of the assignments themselves.

Authors

Yehya Sleiman Tellawi http://orcid.org/0009-0002-5347-5678 University of Illinois at Urbana - Champaign [biography]

Yehya Sleiman Tellawi is a Ph.D. student in Electrical and Computer Engineering (ECE) at the University of Illinois Urbana–Champaign. He previously served as a Teaching Assistant for ECE 120: Introduction to Computing. He is currently a Research Assistant advised by Rakesh Kumar, conducting research on extreme silicon specialization for large language model inference. His work focuses on computer architecture and hardware acceleration techniques. He earned his B.S. in Computer Engineering from the University of Illinois Urbana–Champaign.
Abhishek Umrawal http://orcid.org/0000-0003-4460-7499 University of Illinois Urbana-Champaign [biography]

Dr. Abhishek Umrawal is a Teaching Assistant Professor of Electrical and Computer Engineering (ECE) at the University of Illinois Urbana–Champaign, with affiliations in the Coordinated Science Laboratory (CSL) and the National Center for Supercomputing Applications (NCSA). He earned his Ph.D. in Industrial Engineering (Operations Research) from Purdue University, where his doctoral work developed efficient machine learning algorithms for influence maximization on social networks. His research integrates machine learning, operations research, and causal inference to design intelligent and trustworthy decision making systems, with applications in algorithmic marketing, digital platforms, and safe and controllable generative AI. His scholarship also includes teaching‑integrated work in computing and algorithms education, including the responsible use of AI in instruction and assessment. He also holds an M.S. in Economics from Purdue University’s Daniels School of Business and an M.S. in Statistics from the Indian Institute of Technology (IIT) Kanpur.

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