SBIR Phase I: Empowering Distance Learning for CS/CE Students with Real-time, Personalized Feedback through Anti-Pattern Detection and Correction (COVID-19)

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to address the increasing demand placed on educators in computer science (CS) and computer engineering (CE) through an automated feedback mechanism. Courses in CS and CE are growing rapidly. This project will develop a platform providing automated feedback by leveraging machine learning. This will allow courses to scale without compromising quality. In particular, this project would lead to the development of a system that provides real-time, personalized feedback to students. This system may also help attract and retain under-represented populations within CS and CE. This product will greatly enhance the scientific and technical understanding of computing for all students. This Small Business Innovation Research (SBIR) Phase I project will create a novel intelligent tutoring system to provide real-time, personalized feedback to CS and CE students. In particular, this project will develop a deep learning architecture that detects errors in student code and clusters them into anti-patterns according to semantic and behavioral similarity. The proposed research will design and tune the deep learning architecture to reach sufficient accuracy and precision. To realize this goal, several technical hurdles must be overcome, including abstract syntax tree analysis and downstream localization. The ultimate task is to find patches through edit-paths from incorrect code to correct code and report these repairs in a human-readable way. This project will advance a comprehensive learning management system that emphasizes student learning and performance. In particular, the platform will allow for students to submit course material and rapidly receive personalized feedback. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.