Autonomously Generating Hints by Inferring Problem Solving Policies

@article{Piech2015AutonomouslyGH,
  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},
  year={2015}
}
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 Code.org `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

Figures, Tables, and Topics from this paper

Use expert knowledge instead of data: generating hints for hour of code exercises
Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python Programming Tutor
Automated Prediction of Novice Programmer Performance Using Programming Trajectories
Deep Knowledge Tracing On Programming Exercises
Identifying typical approaches and errors in Prolog programming with argument-based machine learning
Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning
Generating Data-driven Hints for Open-ended Programming
...
1
2
3
4
5
...