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Automating Hint Generation with Solution Space Path Construction

  • Kelly Rivers
  • Kenneth R. Koedinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)

Abstract

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 solution states and paths is too large to cover with data. It has previously been demonstrated that the state space can be substantially reduced using canonicalizing operations that abstract states. In this paper, we show how solution paths can be constructed from these abstract states that go beyond the paths directly observed in the data. We describe a domain-independent algorithm that can automate hint generation through use of these paths. Through path construction, less data is needed for more complete hint generation. We provide examples of hints generated by this algorithm in the domain of programming.

Keywords

automatic hint generation feedback learning path construction solution space programming tutor 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kelly Rivers
    • 1
  • Kenneth R. Koedinger
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

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