Program Representation for Automatic Hint Generation for a Data-Driven Novice Programming Tutor

  • Wei Jin
  • Tiffany Barnes
  • John Stamper
  • Michael John Eagle
  • Matthew W. Johnson
  • Lorrie Lehmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7315)

Abstract

We describe a new technique to represent, classify, and use programs written by novices as a base for automatic hint generation for programming tutors. The proposed linkage graph representation is used to record and reuse student work as a domain model, and we use an overlay comparison to compare in-progress work with complete solutions in a twist on the classic approach to hint generation. Hint annotation is a time consuming component of developing intelligent tutoring systems. Our approach uses educational data mining and machine learning techniques to automate the creation of a domain model and hints from student problem-solving data. We evaluate the approach with a sample of partial and complete, novice programs and show that our algorithms can be used to generate hints over 80 percent of the time. This promising rate shows that the approach has potential to be a source for automatically generated hints for novice programmers.

Keywords

Intelligent tutoring systems automatic hint generation programming tutors educational data mining and data clustering 

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References

  1. Barnes, T., Stamper, J.: Automatic hint generation for logic proof tutoring using historical data. Journal Educational Technology & Society, Special Issue on Intelligent Tutoring Systems 13(1), 3–12 (2010)Google Scholar
  2. Barnes, T., Stamper, J.: Using Markov decision processes for student problem-solving visualization and automatic hint generation. In: Handbook on Educational Data Mining. CRC Press (2010)Google Scholar
  3. Jin, W., Lehmann, L., Johnson, M., Eagle, M., Mostafavi, B., Barnes, T., Stamper, J.: Towards Automatic Hint Generation for a Data-Driven Novice Programming Tutor. In: Workshop on Knowledge Discovery in Educational Data, 17th ACM Conference on Knowledge Discovery and Data Mining (2011)Google Scholar
  4. Stamper, J., Barnes, T., Croy, M.: Enhancing the automatic generation of hints with expert seeding. To appear in Intl. Journal of AI in Education, Special Issue “Best of ITS” (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wei Jin
    • 1
  • Tiffany Barnes
    • 2
  • John Stamper
    • 3
  • Michael John Eagle
    • 2
  • Matthew W. Johnson
    • 2
  • Lorrie Lehmann
    • 2
  1. 1.Shaw UniversityRaleighUSA
  2. 2.University of North CarolinaCharlotteUSA
  3. 3.Carnegie Mellon UniversityPittsburghUSA

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