Our ongoing development and deployment of an online robotics education platform highlighted a gap in providing an interactive, feedback-rich learning environment essential for mastering programming concepts in robotics, which they were not getting with the traditional code-simulate-turn in workflow. Since teaching resources are limited, students would benefit from feedback in real-time to find and fix their mistakes in the programming assignments. To address these concerns, this paper will focus on creating a system for unit testing while integrating it into the course workflow. We facilitate this real-time feedback by including unit testing in the design of programming assignments so students can understand and fix their errors on their own and without the prior help of instructors/TAs serving as a bottleneck. In line with the framework's personalized student-centered approach, this method makes it easier for students to revise, and debug their programming work, encouraging hands-on learning. The course workflow updated to include unit tests will strengthen the learning environment and make it more interactive so that students can learn how to program robots in a self-guided fashion.

@inproceedings{shill2024wip:,
  title={WIP: A Unit Testing Framework for Self-Guided Personalized Online Robotics Learning},
  author={Ponkoj C. Shill and David Feil-Seifer and Rui Wu},
  year={2024},
  month={May},
  url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Wfm3Z_YAAAAJ&citation_for_view=Wfm3Z_YAAAAJ:Y0pCki6q_DkC},
  publisher={IEEE},
  booktitle={Frontiers in Education Conference (FIE), Washington DC, Oct 2024},
}

Collaborative Research: A Student-Centered Personalized Learning Framework to Advance Undergraduate Robotics Education, National Science Foundation PI: David Feil-Seifer, co-PI: Fredrick Harris, Sergiu Dascalu, Amount: $320,214, June 1, 2022 - May 31, 2025