Building Games to Learn from Their Players: Generating Hints in a Serious Game

  • Andrew Hicks
  • Barry PeddycordIII
  • Tiffany Barnes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)


This paper presents a method for generating hints based on observed world states in a serious game. BOTS is an educational puzzle game designed to teach programming fundamentals. To incorporate intelligent feedback in the form of personalized hints, we apply data-driven hint-generation methods. This is especially challenging for games like BOTS because of the open-ended nature of the problems. By using a modified representation of player data focused on outputs rather than actions, we are able to generate hints for players who are in similar (rather than identical) states, creating hints for multiple cases without requiring expert knowledge. Our contributions in this work are twofold. Firstly, we generalize techniques from the ITS community in hint generation to an educational game. Secondly, we introduce a novel approach to modeling student states for open-ended problems, like programming in BOTS. These techniques are potentially generalizable to programming tutors for mainstream languages.


Serious Games Hint Generation Data-Driven Methods 


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  1. 1.
    Anderson, J.R., Reiser, B.J.: The lisp tutor. Byte 10(4), 159–175 (1985)Google Scholar
  2. 2.
    Barnes, T., Stamper, J.C.: Automatic hint generation for logic proof tutoring using historical data. Educational Technology & Society 13(1), 3–12 (2010)Google Scholar
  3. 3.
    Corbett, A.T., Anderson, J.R.: Student modeling and mastery learning in a computer-based programming tutor. In: Frasson, C., McCalla, G.I., Gauthier, G. (eds.) ITS 1992. LNCS, vol. 608, pp. 413–420. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  4. 4.
    Eagle, M., Johnson, M., Barnes, T.: Interaction networks: Generating high level hints based on network community clusterings. In: EDM, pp. 164–167 (2012)Google Scholar
  5. 5.
    Fossati, D., Di Eugenio, B., Ohlsson, S., Brown, C.W., Chen, L., Cosejo, D.G.: I learn from you, you learn from me: How to make iList learn from students. In: AIED, pp. 491–498 (2009)Google Scholar
  6. 6.
    Hicks, A.: Creation, evaluation, and presentation of user-generated content in community game-based tutors. In: Proceedings of the International Conference on the Foundations of Digital Games, FDG 2012, pp. 276–278. ACM, New York (2012)CrossRefGoogle Scholar
  7. 7.
    Jin, W., Barnes, T., Stamper, J., Eagle, M.J., Johnson, M.W., Lehmann, L.: Program representation for automatic hint generation for a data-driven novice programming tutor. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 304–309. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.A., et al.: Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education (IJAIED) 8, 30–43 (1997)Google Scholar
  9. 9.
    Murray, T.: An overview of intelligent tutoring system authoring tools: Updated analysis of the state of the art. In: Authoring Tools for Advanced Technology Learning Environments, pp. 491–544. Springer (2003)Google Scholar
  10. 10.
    Nathan, M.J., Koedinger, K.R., Alibali, M.W.: Expert blind spot: When content knowledge eclipses pedagogical content knowledge. In: Proceedings of the Third International Conference on Cognitive Science, pp. 644–648 (2001)Google Scholar
  11. 11.
    Rivers, K., Koedinger, K.R.: Automatic generation of programming feedback: A data-driven approach. In: The First Workshop on AI-supported Education for Computer Science (AIEDCS 2013), p. 50 (2013)Google Scholar
  12. 12.
    Stamper, J., Barnes, T., Lehmann, L., Croy, M.: The hint factory: Automatic generation of contextualized help for existing computer aided instruction. In: Proceedings of the 9th International Conference on Intelligent Tutoring Systems Young Researchers Track, pp. 71–78 (2008)Google Scholar
  13. 13.
    Stamper, J., Koedinger, K., Baker, R.S.J.d., Skogsholm, A., Leber, B., Rankin, J., Demi, S.: PSLC datashop: A data analysis service for the learning science community. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part II. LNCS, vol. 6095, pp. 455–455. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Andrew Hicks
    • 1
  • Barry PeddycordIII
    • 1
  • Tiffany Barnes
    • 1
  1. 1.North Carolina State UniversityRaleighUSA

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