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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Book ChapterDOI
Flexible Program Alignment to Deliver Data-Driven Feedback to Novice Programmers
TL;DR: In this article, a flexible program alignment based on program dependence graphs is proposed, which enrich with semantic information extracted from the programs, i.e., operations and calls, to find correspondences at the statement level between them.
Book ChapterDOI
API Misuse Detection Based on Stacked LSTM
TL;DR: Wang et al. as mentioned in this paper employed stacked LSTM to learn the API usage specification to detect the API misuse defects, and then they generated API sequences and transformed the sequences into for training.
Journal ArticleDOI
Cross-Lingual Adversarial Domain Adaptation for Novice Programming
TL;DR: This work focuses on two essential SMP tasks: program classification and early prediction of student success and proposes a Cross-Lingual Adversarial Domain Adaptation (CrossLing) framework that can leverage a large programming dataset to learn features that can improve SMP's build using a much smaller dataset in a different programming language.
Dissertation
Artificial intelligence in computer science and mathematics education
TL;DR: This thesis examines how Artificial Intelligence (AI) techniques can help Computer Science students learn programming and mathematics skills more efficiently using algorithms and concepts such as Predictive Modelling, Machine Learning, Deep Learning, Representational Learning, Recommender Systems and Graph Theory.
Proceedings ArticleDOI
Giving Feedback on Interactive Student Programs with Meta-Exploration
TL;DR: This work shows that exploring to discover errors can be cast as a meta-exploration problem, which enables it to construct a principled objective for discovering errors and an algorithm for optimizing this objective, which provides fine-grained feedback.
References
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James Bergstra,Yoshua Bengio +1 more
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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.
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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.