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In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret.
An approach to the psychology of instruction
R. Atkinson and J. A. Paulson · 1972
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Induction of multiscale temporal structure
M. C. Mozer · 1992
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Knowledge tracing: Modelling the acquisition of procedural knowledge
A. T. Corbett and J. R. Anderson · 1995
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Factorial hidden markov models
Z. Ghahramani and M. I. Jordan · 1996
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Generalized weighted chinese restaurant processes for species sampling mixture models
H. Ishwaran and L. F. James · 2003
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Explanatory Item Response Models: a Generalized Linear and Nonlinear Approach
P. De Boeck and M. Wilson · 2004
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More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian knowledge tracing
R. S. Baker, A. T. Corbett, and V. Aleven · 2008
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A data repository for the EDM community: The PSLC DataShop
K. Koedinger, R. Baker, K. Cunningham, A. Skogsholm, B. Leber, and J. Stamper · 2010
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Feature engineering and classifier ensemble for KDD cup 2010
H.-F. Yu and Others · 2010
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KT-IDEM: Introducing item difficulty to the knowledge tracing model
Z. A. Pardos and N. T. Heffernan · 2011
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Does time matter? modeling the effect of time with Bayesian knowledge tracing
Y. Qiu, Y. Qi, H. Lu, Z. A. Pardos, and N. T. Heffernan · 2011
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Sequential effects in response time reveal learning mechanisms and event representations
M. Jones, T. Curran, M. C. Mozer, and M. H. Wilder · 2013
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Integrating knowledge tracing and item response theory: A tale of two frameworks
M. Khajah, Y. Huang, J. P. Gonzales-Brenes, M. C. Mozer, and P. Brusilovsky · 2014
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Incorporating latent factors into knowledge tracing to predict individual differences in learning
M. Khajah, R. M. Wing, R. V. Lindsey, and M. C. Mozer · 2014
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Improving student’s long-term knowledge retention with personalized review
R. Lindsey, J. Shroyer, H. Pashler, and M. Mozer · 2014
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Modeling skill acquisition over time with sequence and topic modeling
J. P. Gonzales-Brenes · 2015
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DRAW: A recurrent neural network for image generation
K. Gregor, I. Danihelka, A. Graves, D. Rezende, and D. Wierstra · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, et al · 2015
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Deep knowledge tracing
C. Piech, J. Bassen, J. Huang, S. Ganguli, M. Sahami, L. J. Guibas, and J. Sohl-Dickstein · 2015
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Interleaved practice improves mathematics learning
D. Rohrer, R. F. Dedrick, and S. Stershic · 2015
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R. V. Lindsey, M. Khajah, and M. C. Mozer · 2014
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The benefit of interleaved mathematics practice is not limited to superficially similar kinds of problems
D. Rohrer, R. F. Dedrick, and K. Burgess · 2014
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OLI Engineering Statics – Fall 2011
P. Steif and N. Bier · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Move your lamp post: Recent data reflects learner knowledge better than older data
A. Galyardt and I. Goldin · 2015
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Deep learning in neural networks: An overview
J. Schmidhuber · 2015
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Show and tell: A neural image caption generator
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan · 2015
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How to optimize student learning using recurrent neural networks (educational technology)
R. Golden · 2016
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Mastering the game of go with deep neural networks and tree search
D. Sliver, A. Huang, C. J. Maddison, A. Guez, et al · 2016
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