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Access to high-quality education at scale is limited by the difficulty of providing student feedback on open-ended assignments in structured domains like computer programming, graphics, and short response questions.
Repair theory: A generative theory of bugs in procedural skills
J. S. Brown and K. VanLehn · 1980
Earlier work this paper cites.
Eliciting expert beliefs in substantial practical applications
A. O’Hagan · 1998
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Efficient, multiple-range random walk algorithm to calculate the density of states
F. Wang and D. Landau · 2001
Earlier work this paper cites.
An adaptive sampling algorithm for solving markov decision processes
H. S. Chang, M. C. Fu, J. Hu, and S. I. Marcus · 2005
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
Lightweight implementations of probabilistic programming languages via transformational compilation
D. Wingate, A. Stuhlmueller, and N. Goodman · 2011
Earlier work this paper cites.
The ‘cost disease’in higher education: is technology the answer?
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Earlier work this paper cites.
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N. Goodman, V. Mansinghka, D. M. Roy, K. Bonawitz, and J. B. Tenenbaum · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
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