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
More filters
Posted Content
Deep Learning for Bug-Localization in Student Programs.
TL;DR: This work presents the first deep learning based technique that can localize bugs in a faulty program w.r.t. a failing test, without even running the program.
Proceedings ArticleDOI
Towards Open Natural Language Feedback Generation for Novice Programmers using Large Language Models
TL;DR: In this article , the authors present an approach for automatically constructed formative feedback, written in natural language, that builds on two streams of research: (1) automatic program repair, and (2) automatically generating descriptions of programs.
Journal ArticleDOI
An XGBoost-Based Knowledge Tracing Model
TL;DR: In this article , the authors apply XGBoost algorithm to knowledge tracing model to improve the prediction performance, which can accurately evaluate the learning state of students and conduct personalized instruction according to the characteristics of different students.
Posted Content
Improving the Robustness to Data Inconsistency between Training and Testing for Code Completion by Hierarchical Language Model.
TL;DR: A novel Hierarchical Language Model (HLM) is proposed to improve the robustness of LSTM model to gain the capacity about dealing with the inconsistency of data distribution between training and testing and achieves averagely 11.2\% improvement in prediction accuracy.
Posted Content
Amanuensis: The Programmer's Apprentice.
Thomas Dean,Maurice Chiang,Marcus Gomez,Nate Gruver,Yousef Hindy,Michelle Lam,Peter Lu,Sophia Sanchez,Rohun Saxena,Michael R. Smith,Lucy Wang,Catherine Wong +11 more
TL;DR: This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018 that draws upon insight from cognitive and systems neuroscience to implement hybrid connectionist and symbolic reasoning systems that leverage and extend the state of the art in machine learning by integrating human and machine intelligence.
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.