Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference


  • Mike Wu Stanford University
  • Milan Mosse Stanford University
  • Noah Goodman Stanford University
  • Chris Piech Stanford University



In modern computer science education, massive open online courses (MOOCs) log thousands of hours of data about how students solve coding challenges. Being so rich in data, these platforms have garnered the interest of the machine learning community, with many new algorithms attempting to autonomously provide feedback to help future students learn. But what about those first hundred thousand students? In most educational contexts (i.e. classrooms), assignments do not have enough historical data for supervised learning. In this paper, we introduce a human-in-the-loop “rubric sampling” approach to tackle the “zero shot” feedback challenge. We are able to provide autonomous feedback for the first students working on an introductory programming assignment with accuracy that substantially outperforms data-hungry algorithms and approaches human level fidelity. Rubric sampling requires minimal teacher effort, can associate feedback with specific parts of a student’s solution and can articulate a student’s misconceptions in the language of the instructor. Deep learning inference enables rubric sampling to further improve as more assignment specific student data is acquired. We demonstrate our results on a novel dataset from, the world’s largest programming education platform.




How to Cite

Wu, M., Mosse, M., Goodman, N., & Piech, C. (2019). Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 782-790.



AAAI Special Technical Track: AI for Social Impact