Learning Program Embeddings to Propagate Feedback on Student Code
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Cites background from "Learning Program Embeddings to Prop..."
...ecent techniques that approach the problem of embedding programs (or, more generally, symbolic-expressions/trees) in unique ways. Using input/output pairs as the input data for learning, Piech et al. [42] and Parisotto et al. [39] learn to embed whole programs. Using sequences of live variable values, Wang et al. [49] produce embeddings to aid in program repair tasks. Allamanis et al. [4] learn to emb...
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59 citations
Cites background from "Learning Program Embeddings to Prop..."
...[49] also studied data collected from hundreds of thousands of users working on Code....
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48 citations
Cites background or methods from "Learning Program Embeddings to Prop..."
...Other approaches rely on one or more reference solutions to a given assignment that are compared over student submissions [14], [15], [27], [28], [33], [36], [40]....
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...[28] exploit machine learning to provide personalized feedback that requires the existence of a large amount of previous student submissions, which is not always the case [27]....
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...Another approach to providing personalized feedback consists of comparing student submissions with one or more reference solutions [14], [15], [27], [28], [33], [36], [40]....
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...[27] require the number of variables to be fixed beforehand, which may be not beneficial for novice students developing their problem-solving skills [12]....
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...Furthermore, some of them also have scalability problems that prevent them from being used in real-world settings [15], or require a large amount of pre-existing correct submissions [15], [27], [28]....
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References
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...Learning rates are set using Adagrad (Duchi et al., 2011)....
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6,935 citations
"Learning Program Embeddings to Prop..." refers methods in this paper
...We use random search (Bergstra & Bengio, 2012) to optimize over hyperparameters (e.g, regularization parameters, matrix dimensions, and minibatch size)....
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6,792 citations
"Learning Program Embeddings to Prop..." refers background or methods in this paper
...The programs for these assignments operate in maze worlds where an agent can move, turn, and test for conditions of its current location....
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...Our models are related to recent work from the NLP and deep learning communities on recursive neural networks, particularly for modeling semantics in sentences or symbolic expressions (Socher et al., 2013; 2011; Zaremba et al., 2014; Bowman, 2013)....
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...…on recursive neural networks (called the NPM-RNN model) in which we parametrize a matrix MA in this new model with an RNN whose architecture follows the abstract syntax tree (similar to the way in which RNN architectures might take the form of a parse tree in an NLP setting (Socher et al., 2013))....
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5,171 citations