Automating Hint Generation with Solution Space Path Construction

@inproceedings{Rivers2014AutomatingHG,
  title={Automating Hint Generation with Solution Space Path Construction},
  author={Kelly Rivers and K. Koedinger},
  booktitle={Intelligent Tutoring Systems},
  year={2014}
}
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

Figures and Topics from this paper

Autonomously Generating Hints by Inferring Problem Solving Policies
Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python Programming Tutor
Using the Hint Factory to Compare Model-Based Tutoring Systems
A Survey of Automated Programming Hint Generation - The HINTS Framework
High-Coverage Hint Generation for Massive Courses: Do Automated Hints Help CS1 Students?
Authoring Tutors with Complex Solutions: A Comparative Analysis of Example Tracing and SimStudent
Current State and Next Steps on Automated Hints for Students Learning to Code
...
1
2
3
4
5
...

References

SHOWING 1-8 OF 8 REFERENCES
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