Learning Program Embeddings to Propagate Feedback on Student Code

@InProceedings{pmlr-v37-piech15,
  title = 	 {Learning Program Embeddings to Propagate Feedback on Student Code},
  author = 	 {Piech, Chris and Huang, Jonathan and Nguyen, Andy and Phulsuksombati, Mike and Sahami, Mehran and Guibas, Leonidas},
  booktitle = 	 {Proceedings of the 32nd International Conference on Machine Learning},
  pages = 	 {1093--1102},
  year = 	 {2015},
  editor = 	 {Bach, Francis and Blei, David},
  volume = 	 {37},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Lille, France},
  month = 	 {07--09 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v37/piech15.pdf},
  url = 	 {https://proceedings.mlr.press/v37/piech15.html},
  abstract = 	 {Providing feedback, both assessing final work and giving hints to stuck students, is difficult for open-ended assignments in massive online classes which can range from thousands to millions of students. We introduce a neural network method to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and propose an algorithm for feedback at scale using these linear maps as features. We apply our algorithm to assessments from the Code.org Hour of Code and Stanford University’s CS1 course, where we propagate human comments on student assignments to orders of magnitude more submissions.}
}