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
Citations
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Proceedings ArticleDOI
Mapping Python Programs to Vectors using Recursive Neural Encodings
TL;DR: Ast2vec, a neural network that maps Python syntax trees to vectors and back, thereby enabling about a hundred data mining techniques that were previously not applicable, is presented and hoped to augment the educational data mining toolkit by making analyses of computer programs easier, richer, and more efficient.
Journal ArticleDOI
Fold2Vec: Towards a Statement-Based Representation of Code for Code Comprehension
TL;DR: A novel approach to source code representation to be used in combination with neural networks designed to permit the production of a continuous vector for each code statement, which shows how models trained on code summarization and models training on statement separation can be combined to address methods with tangled responsibilities.
Journal ArticleDOI
Beyond binary correctness: Classification of students’ answers in learning systems
Radek Pelánek,Tomáš Effenberger +1 more
TL;DR: This work proposes to use answer classification as an interface between raw data about student performance and algorithms for adaptive behavior and suggests that the proposed classification is broadly applicable and makes the use of additional interaction data much more feasible.
Proceedings ArticleDOI
VizProg: Identifying Misunderstandings By Visualizing Students’ Coding Progress
Ge Zhang,Yan Chen,Steve Oney +2 more
TL;DR: VizProg as mentioned in this paper represents students' statuses as a 2D Euclidean spatial map that encodes the students' problem-solving approaches and progress in real-time.
Dissertation
Aspectos de adquisición de lenguaje en la enseñanza de programación
TL;DR: Tesis (Doctor en Ciencias de la Computación) as discussed by the authors, Universidad Nacional de Cordoba, Facultad de Matematica, Astronomia, Fisica y Computacion, 2020.
References
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Proceedings Article
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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
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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.