Improving Conversation Engagement Through Data-Driven Agent Behavior Modification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9673)

Abstract

E-learning systems based on a conversational agent (CA) provide the basis of an intuitive, engaging interface for the student. The goal of this paper is to propose an agent-based framework for providing an improved interaction between students and CA-based e-learning applications. Our framework models both the student and the CA and uses agents to represent data sources for each. We describe an implementation of the framework based on BDI (Belief-Desire-Intention) architecture and results of initial testing.

References

  1. 1.
    Heller, R., Procter, M.: Animated pedagogical agents: the effect of visual information on a historical figure application. Int. J. Web-Based Learn. Teach. Technol. 4, 54–65 (2009)CrossRefGoogle Scholar
  2. 2.
    Kumar, R., Rosé, C.P.: Architecture for building conversational agents that support collaborative learning. IEEE Trans. Learn. Technol. 4, 21–34 (2011)CrossRefGoogle Scholar
  3. 3.
    D’Mello, S., Craig, S., Witherspoon, A., McDaniel, B., Graesser, A.: Automatic detection of learner’s affect from conversational cues. User Model. User-Adap. Inter. 18, 45–80 (2008)CrossRefGoogle Scholar
  4. 4.
    McClure, G., Chang, M., Lin, F.: MAS controlled NPCs in 3D virtual learning environment. In: 2013 International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), pp. 1026–1033. IEEE (2013)Google Scholar
  5. 5.
    Löckelt, M.: Design and implementation issues for convincing conversational agents. Conversational Agents and Natural Language Interaction: Techniques and Effective Practices: Techniques and Effective Practices. p. 156 (2011)Google Scholar
  6. 6.
    Bellotti, F., Berta, R., De Gloria, A., Lavagnino, E.: Towards a conversational agent architecture to favor knowledge discovery in serious games. In: Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology, pp. 1–7. ACM, New York, NY, USA (2011)Google Scholar
  7. 7.
    Nunamaker Jr., J.F., Derrick, D.C., Elkins, A.C., Burgoon, J.K., Patton, M.W.: Embodied conversational agent-based kiosk for automated interviewing. J. Manag. Inf. Syst. 28, 17–48 (2011)CrossRefGoogle Scholar
  8. 8.
    Chrysafiadi, K., Virvou, M.: Student modeling approaches: a literature review for the last decade. Expert Syst. Appl. 40, 4715–4729 (2013)CrossRefGoogle Scholar
  9. 9.
    Callejas, Z., López-Cózar, R., Ábalos, N., Griol, D.: Affective conversational agents: the role of personality and emotion in spoken interactions. In: Perez-Marin, D., Pascual-Nieto, I. (eds.) Conversational Agents and Natural Language Interaction, pp. 203–222. IGI Global, Hershey (2011)CrossRefGoogle Scholar
  10. 10.
    Calvo, R.A., D’Mello, S.: Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1, 18–37 (2010)CrossRefGoogle Scholar
  11. 11.
    Bordini, R.H., Hübner, J.F., Wooldridge, M.: Programming Multi-agent Systems in AgentSpeak using Jason. Wiley, New York (2007)CrossRefMATHGoogle Scholar
  12. 12.
    Soliman, M., Guetl, C.: Experiences with BDI-based design and implementation of intelligent pedagogical agents. In: 2012 15th International Conference on Interactive Collaborative Learning (ICL), pp. 1–5 (2012)Google Scholar
  13. 13.
    Gonzalez-Sanchez, J., Chavez-Echeagaray, M.E., Atkinson, R., Burleson, W.: ABE: An agent-based software architecture for a multimodal emotion recognition framework. In: 2011 9th Working IEEE/IFIP Conference on Software Architecture (WICSA), pp. 187–193 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Computing and Information SystemsAthabasca UniversityAthabascaCanada
  2. 2.Faculty of Humanities and Social SciencesAthabasca UniversityAthabascaCanada

Personalised recommendations