Open AccessProceedings Article
Learning Program Embeddings to Propagate Feedback on Student Code
Chris Piech,Jonathan Huang,Andy Nguyen,Mike Phulsuksombati,Mehran Sahami,Leonidas J. Guibas +5 more
- pp 1093-1102
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TLDR
A neural network method is introduced to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and an algorithm for feedback at scale is proposed using these linear maps as features.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.read more
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Deep Reinforcement Learning for Syntactic Error Repair in Student Programs
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References
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James Bergstra,Yoshua Bengio +1 more
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Richard Socher,Alex Perelygin,Jean Y. Wu,Jason Chuang,Christopher D. Manning,Andrew Y. Ng,Christopher Potts +6 more
TL;DR: A Sentiment Treebank that includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality, and introduces the Recursive Neural Tensor Network.
Book
A complexity measure
TL;DR: In this paper, a graph-theoretic complexity measure for managing and controlling program complexity is presented. But the complexity is independent of physical size, and complexity depends only on the decision structure of a program.