Automating Hint Generation with Solution Space Path Construction

  title={Automating Hint Generation with Solution Space Path Construction},
  author={Kelly Rivers and K. Koedinger},
  booktitle={Intelligent Tutoring Systems},
Developing intelligent tutoring systems from student solution data is a promising approach to facilitating more widespread application of tutors. In principle, tutor feedback can be generated by matching student solution attempts to stored intermediate solution states, and next-step hints can be generated by finding a path from a student's current state to a correct solution state. However, exact matching of states and paths does not work for many domains, like programming, where the number of… Expand

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Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data
Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces
The Behavior of Tutoring Systems
  • K. VanLehn
  • Computer Science
  • Int. J. Artif. Intell. Educ.
  • 2006
A Canonicalizing Model for Building Programming Tutors
Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC
Using learning analytics to assess students' behavior in open-ended programming tasks
DomainIndependent Proximity Measures in Intelligent Tutoring Systems
  • In Proceedings of the 6th International Conference on Educational Data Mining (EDM) (pp. 334-335)
  • 2013