Classifying Interaction Behaviors of Students and Conversational Agents Through Dialog Analysis

  • Michael Procter
  • Robert Heller
  • Fuhua Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10858)


E-learning systems based on a conversational agent (CA) provide the basis of an intuitive and engaging interface for the student. The goal of this paper is to propose a method for detecting conversational interaction behaviors of learners and CAs, using an agent-based framework, for the purpose of improving the communication between students and CA-based intelligent tutoring systems. Our framework models both the student and the CA and uses agents to represent data sources for each. We show how the framework uses the detection of conversational behaviors to initiate interventions to improve student conversational engagement. The results of initial user testing are reported.


  1. 1.
    Kerry, A., Ellis, R., Bull, S.: Conversational agents in E-Learning. In: Allen, T., Ellis, R., Petridis, M. (eds.) Applications and Innovations in Intelligent Systems, vol. XVI, pp. 169–182. Springer London (2009). Scholar
  2. 2.
    Szafir, D., Mutlu, B.: Pay attention!: designing adaptive agents that monitor and improve user engagement. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 11–20. ACM, New York (2012)Google Scholar
  3. 3.
    Nakano, Y.I., Ishii, R.: Estimating user’s engagement from eye-gaze behaviors in human-agent conversations. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, pp. 139–148. ACM, New York (2010)Google Scholar
  4. 4.
    Xu, Q., Li, L., Wang, G.: Designing engagement-aware agents for multiparty conversations. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2233–2242. ACM, New York (2013)Google Scholar
  5. 5.
    Asteriadis, S., Karpouzis, K., Kollias, S.: Feature extraction and selection for inferring user engagement in an HCI environment. In: Jacko, J.A. (ed.) HCI 2009. LNCS, vol. 5610, pp. 22–29. Springer, Heidelberg (2009). Scholar
  6. 6.
    Paquette, L., Baker, R.S.J.D., Sao Pedro, M.A., Gobert, J.D., Rossi, L., Nakama, A., Kauffman-Rogoff, Z.: Sensor-free affect detection for a simulation-based science inquiry learning environment. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 1–10. Springer, Cham (2014). Scholar
  7. 7.
    Wen, M., Yang, D., Rose, C.P.: Linguistic reflections of student engagement in massive open online courses. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)Google Scholar
  8. 8.
    Turney, P.D., Neuman, Y., Assaf, D., Cohen, Y.: Literal and metaphorical sense identification through concrete and abstract context. In: Proceedings of the 2011 Conference on the Empirical Methods in Natural Language Processing, pp. 680–690 (2011)Google Scholar
  9. 9.
    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
  10. 10.
    Procter, M., Lin, F., Heller, R.: Improving conversation engagement through data-driven agent behavior modification. In: Khoury, R., Drummond, C. (eds.) AI 2016. LNCS (LNAI), vol. 9673, pp. 270–275. Springer, Cham (2016). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

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

Personalised recommendations