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].


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