Autonomously Generating Hints by Inferring Problem Solving Policies

  title={Autonomously Generating Hints by Inferring Problem Solving Policies},
  author={Chris Piech and M. Sahami and Jonathan Huang and L. Guibas},
  journal={Proceedings of the Second (2015) ACM Conference on Learning @ Scale},
Exploring the whole sequence of steps a student takes to produce work, and the patterns that emerge from thousands of such sequences is fertile ground for a richer understanding of learning. In this paper we autonomously generate hints for the `Hour of Code,' (which is to the best of our knowledge the largest online course to date) using historical student data. We first develop a family of algorithms that can predict the way an expert teacher would encourage a student to make forward… Expand

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