Automating the Mentor in a Serious Game: A Discourse Analysis Using Finite State Machines

  • Brent Morgan
  • Fazel Kehtkar
  • Athur Graesser
  • David Shaffer
Part of the Communications in Computer and Information Science book series (CCIS, volume 374)


Serious games are increasingly becoming a popular, effective supplement to standard classroom instruction [1]. Similar to recreational games, multi-party chat is a standard method of communication in serious games. As players collaborate in a serious game, mentoring is often needed to facilitate progress and learning [2, 3, 4]. This role is almost exclusively provided by a human at the present time. However, the cost incurred with training a human mentor represents a critical barrier for widespread use of a collaborative epistemic game. Although great strides have been made in automating one-on-one tutorial dialogues [5, 6], multi-party chat presents a significant challenge for natural language processing. The goal of this research, then, is to provide a preliminary understanding of player-mentor conversations in the context of an epistemic game, Land Science [7].


Natural Language Processing Finite State Machine Chat Room Planning Team Human Coder 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ritterfeld, U., Cody, M., Vorderer, P. (eds.): Serious games: Mechanisms and effects. Routledge, New York (2009)Google Scholar
  2. 2.
    Bagley, E.S., Shaffer, D.W.: When people get in the way: Promoting civic thinking through epistemic gameplay. International Journal of Gaming and Computer-Mediated Simulations 1(1), 36–52 (2009)CrossRefGoogle Scholar
  3. 3.
    Kirschner, P.A., Sweller, J., Clark, R.E.: Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist 41(2), 75–86 (2006)CrossRefGoogle Scholar
  4. 4.
    Nash, P., Shaffer, D.W.: Mentor modeling: The internalization of modeled professional thinking in an epistemic game. Journal of Computer Assisted Learning 27(2), 173–189 (2011)CrossRefGoogle Scholar
  5. 5.
    Graesser, A.C., D’Mello, S.K., Cade, W.: Instruction based on tutoring. In: Mayer, R.E., Alexander, P.A. (eds.) Handbook of research on learning and instruction, pp. 408–426. Routledge, New York (2011)Google Scholar
  6. 6.
    Graesser, A.C., D’Mello, S.K., Hu, X., Cai, Z., Olney, A., Morgan, B.: AutoTutor. In: McCarthy, P.M., Boonthum, C. (eds.) Applied Natural Language Processing and Content Analysis: Identification, Investigation and Resolution, pp. 169–187. IGI Global, Hershey (2012)Google Scholar
  7. 7.
    Bagley, E.: Epistemography of an urban and regional planning practicum: Appropriation in the face of eesistance. In: WCER Working Paper 2010-8. University of Wisconsin Center for Education Research, Madison (2010)Google Scholar
  8. 8.
    Moldovan, C., Rus, V., Graesser, A.C.: Automated Speech Act Classification For Online Chat. In: The 22nd Midwest Artificial Intelligence and Cognitive Science Conference (2011)Google Scholar
  9. 9.
    D’Andrade, R.G., Wish, M.: Speech act theory in quantitative research on interpersonal behavior. Discourse Processes 8(2), 229–259 (1985)CrossRefGoogle Scholar
  10. 10.
    Rus, V., Moldovan, C., Witherspoon, A., Graesser, A.C.: Automatic Identification of Speakers’ Intentions in A multi-Party Dialogue System. In: 21st Annual Meeting of the Society for Text and Discourse (2011)Google Scholar
  11. 11.
    Sinclair, J., Coulthart, M.: Towards an analysis of discourse: The English used by teachers and pupils. Oxford University Press, London (1975)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Brent Morgan
    • 1
  • Fazel Kehtkar
    • 1
  • Athur Graesser
    • 1
  • David Shaffer
    • 2
  1. 1.Psychology, Institute for Intelligent SystemsUniversity of MemphisMemphisUSA
  2. 2.University of Wisconsin-MadisonMadisonUSA

Personalised recommendations