Portland, Oregon
June 23, 2024
June 23, 2024
June 26, 2024
NSF Grantees Poster Session
11
10.18260/1-2--46804
https://peer.asee.org/46804
70
Leo C. Ureel II is an Assistant Professor in Computer Science and in Cognitive and Learning Sciences at Michigan Technological University. He has worked extensively in the field of educational software development. His research interests include intelligent learning environments, computer science education, and Artificial Intelligence
Dr. Jarvie-Eggart is a registered professional engineer with over a decade of experience as an environmental engineer. She is an Assistant Professor of Engineering Fundamentals at Michigan Technological University. Her research interests include technology adoption, problem based and service learning, and sustainability.
Jon Sticklen is an Associate Professor with the Engineering Fundamentals Department (EF) and Affiliated Faculty with the Department of Cognitive and Learning Sciences (CLS). He served as Chair of EF from 2014-2020, leading a successful effort to design a
After completing a bachelor's degree in computer science, Laura Albrant decided to challenge how she viewed software development, by switching departments. Currently working towards a PhD in human factors at Michigan Technological University, Laura pursues interests on both sides of the fence through computer science education research.
PhD Student in the Dept. of Civil, Environmental, & Geospatial Engineering at Michigan Technological university.
The "Rich, Immediate Critique of Antipatterns in Student Code" (RICA) project aims to provide rich, relevant, and immediate feedback to students learning to program in their first year of engineering education. This feedback is indispensable in effective student learning, particularly in introductory computing courses. Students often need help understanding compilation or run-time messages, and code structures that initially seem intuitive can have unintended and poorly understood consequences. Conventional classroom feedback mechanisms fall short here, partly because large-scale courses like those in First-Year Engineering (FYE) often strain the instructional team's capacity to deliver timely feedback. Our work-in-progress project aims to address this challenge by developing real-time Code Critiquers specifically tailored for First-Year Engineering (FYE).
Our ongoing project is developing a real-time Code Critiquer system, WebTA, that identifies, categorizes, and provides feedback on code antipatterns in student-submitted MATLAB code. In programming, where the learning process is iterative and often fraught with errors, immediate feedback can serve as a critical form of scaffolding.
The RICA project aligns with broader educational theory that supports the vital role of immediate feedback. However, it takes it a step further by focusing on the "richness" and "relevance" of this feedback. The project exists in the intersection of computer science, engineering, and cognitive & learning sciences. By focusing on antipatterns, it addresses the mental models that students form while learning to code.
While autograders and other automated assessment tools have been instrumental in scaling up coding education, their primary limitation lies in evaluating syntactical and functional correctness, often overlooking the "antipatterns" in student code. Antipatterns represent code structure, which, while usually syntactically correct, could lead to unintended consequences: errors, inefficiencies, or complexities.
The context of the project is a First-Year Engineering Program. At our institution, FYE has a typical total enrollment of approximately 1,000 students matriculating each fall into the College of Engineering. FYE is a common first-year engineering experience taken by all first-year students in the College of Engineering. During an Engineering Fundamentals course, students are taught programming in MATLAB.
The poster focuses on research conducted by our graduate students over the past year. This research includes preliminary analysis of classroom data, work developing a Machine Learning algorithm to detect antipatterns, exploration of the impact of feedback on student self-efficacy, and efforts to develop a common Abstract Syntax Tree representation for multiple languages (in particular Java, MATLAB, and Python).
Ureel, L. C., & Brown, L. E., & Jarvie-Eggart, M. E., & Sticklen, J., & Albrant, L., & Benjamin, M., & Masker, D., & Pendse, P., & Teahen, J. R. (2024, June), Board 234: Current Progress of Providing Rich Immediate Critique of Anti-patterns in Student Code Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--46804
ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2024 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015