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
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
Citations
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Dynamic Neural Program Embeddings for Program Repair
TL;DR: A novel semantic program embedding that is learned from program execution traces is proposed, showing that program states expressed as sequential tuples of live variable values not only captures program semantics more precisely, but also offer a more natural fit for Recurrent Neural Networks to model.
Proceedings ArticleDOI
Writing Reusable Code Feedback at Scale with Mixed-Initiative Program Synthesis
Andrew Head,Elena L. Glassman,Gustavo Soares,Ryo Suzuki,Lucas Figueredo,Loris D'Antoni,Björn Hartmann +6 more
TL;DR: A mixed-initiative approach which combines teacher expertise with data-driven program synthesis techniques is introduced which helps teachers better understand student bugs and write reusable feedback that scales to a massive introductory programming classroom.
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TL;DR: This work feeds embedded program submissions into a recurrent neural network and train it on the task of predicting the student's success on the subsequent programming exercise, and reliably predicts future student performance.
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code2seq: Generating Sequences from Structured Representations of Code
TL;DR: CODE2SEQ as mentioned in this paper represents a code snippet as the set of compositional paths in its abstract syntax tree (AST) and uses attention to select the relevant paths while decoding.
References
More filters
Proceedings Article
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.
TL;DR: Adaptive subgradient methods as discussed by the authors dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning, which allows us to find needles in haystacks in the form of very predictive but rarely seen features.
Journal Article
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
TL;DR: This work describes and analyze an apparatus for adaptively modifying the proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that are provably as good as the best proximal functions that can be chosen in hindsight.
Journal Article
Random search for hyper-parameter optimization
James Bergstra,Yoshua Bengio +1 more
TL;DR: This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid, and shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper- parameter optimization algorithms.
Proceedings Article
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.