Leo Ureel (CS/ICC-CompEd) is principal investigator (PI) on a project that has received a $599,732 research and development grant from the National Science Foundation. The project, “Rich, Immediate Critique of Antipatterns (RICA) in Novice Programmer Code: Broadening Adoption, Supporting Student Learning, and Enhancing Programming Competencies,” includes Co-PIs Laura Brown (CS/ICC-CompEd, DataS), Jon Sticklen (EF/ICC) and Michelle Jarvie-Eggart (EF/ICC).
This project aims to serve the national interest by developing a system that will automatically detect coding mistakes made by students in programming classes. Providing rich, immediate feedback to students significantly improves learning outcomes resulting in better coding skills. Providing feedback in a timely manner using human graders is difficult due to rapidly increasing class sizes and instructor shortages. The goal of this project is to develop a system that can automatically provide high-quality feedback to students in introductory programming classes. Once developed, the system can be used by departments with limited resources to provide timely feedback to students. The result will be students with strong coding skills who will be ready to meet the current demand for programmers in the technology workforce.
The project team will extend an existing system, WebTA, to study the common errors that are made by students in introductory programming courses. A mixed-methods approach will be used to evaluate the impacts of using WebTA as a learning tool in a first-year engineering course. The hypothesis is that this intervention will: (a) improve students’ computing skills, (b) improve students’ computing and engineering self-efficacy, and (c) enable students and instructors to converse in a common language around antipatterns. Students will use WebTA to engage in a tight learning cycle of coding, reflecting on feedback, and making changes. First-year instructors will assist in identifying and encoding novice antipatterns, resulting in a MATLAB antipattern library. The project team will partner with course instructors to embed the terminology and examples of antipatterns in their instructional materials.
WebTA will be developed as a freely available software system. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.