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Deep Knowledge Tracing (DKT) models student learning behavior by using Recurrent Neural Networks (RNNs) to predict future performance based on historical interaction data.
Pytorch: High-performance computation and machine learning
Edward Yang, Antonio S. Rojas, Justin Z. Zhang, and David B. Knowles · 1901
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A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
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Finding structure in time
Jeffrey L. Elman · 1990
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Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi · 1994
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Knowledge tracing: Modeling the acquisition of procedural knowledge
Albert T. Corbett and John R. Anderson · 1994
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Performance factors analysis–a new alternative to knowledge tracing
Philip I Pavlik Jr, Hao Cen, and Kenneth R. Koedinger · 2009
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Lecture 6.5 - rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
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Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
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Training Recurrent Neural Networks
Ilya Sutskever · 2013
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Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Self-attentive knowledge tracing
Srijan Kumar Pandey and George Karypis · 2019
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Graph-based knowledge tracing: Modeling student learning with concepts and relations
Hiroki Nakagawa, Yusuke Iwasawa, and Yutaka Matsuo · 2019
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Context-aware attentive knowledge tracing
Arghya Ghosh and Neil Heffernan · 2020
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Qi Liu, Shuanghong Shen, Zhenya Huang, Enhong Chen, and Yonghe Zheng · 2021
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Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas Guibas, and Jascha Sohl-Dickstein · 2015
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, and Soumith Chintala · 2017
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Dynamic key-value memory networks for knowledge tracing
Jian Zhang, Xingjian Shi, Irwin King, and Dit-Yan Yeung · 2017
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Knowledge tracing: A survey
Ghodai Abdelrahman, Qing Wang, and Bernardo Nunes · 2023
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