User Modeling pp 301-312 | Cite as

Learner Modelling for Intelligent CALL

  • Maureen Murphy
  • Michael McTear
Part of the International Centre for Mechanical Sciences book series (CISM, volume 383)


The demand for software for Computer-Assisted Language Learning (CALL) is increasing considerably. However a drawback of most, if not all, of the currently available CALL software is that it cannot provide very helpful feedback to the learner. The aim of this project was to work towards providing a more adequate and user-oriented interface for CALL. The project focused on the design of an application called CASTLE which takes into account the strengths, weaknesses, preferences and level of proficiency of each individual student when tutoring. This was accomplished by developing a module that provides detailed linguistic analysis of the learner’s response to the exercises of the program, a module that creates a dynamic model of the learner, and a module that controls the system’s reactions to the learner’s input and the structure of the materials offered to the learner.


Learner Model Call System Context Model Learner Module Student Model 
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.


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  1. Benyon, D., and Murray, D. (1993). Applying user modeling to human-computer interaction design. Artificial Intelligence Review 6:43–69.Google Scholar
  2. Bull, S., Pain, H., and Brna, P. (1995). Mr. Collins: A collaboratively constructed, inspectable student model for intelligent computer assisted language learning. Instructional Science 23:65–87.CrossRefGoogle Scholar
  3. Laurillard, D. (1992). Principles for computer-based software design for language learning. Computer Assisted Language Learning 4(3): 141–152.CrossRefGoogle Scholar
  4. Merrill, D. C., Reiser, B. J., Ranney, M., and Trafton, J. G. (1992). Effective tutoring techniques: A comparison of human tutors and intelligent tutoring systems. The Journal of the Learning Sciences 2(3):277–305.CrossRefGoogle Scholar
  5. McCoy, K., Pennington, C. A., and Suri, L. Z. (1996). English error correction: A syntactic user model based on principled “mal-rule” scoring. In Proceedings of the Fifth International Conference on User Modeling, 59–66.Google Scholar
  6. Rich, E. (1983). Users are individuals: Individualizing user models. International Journal of Man-Machine Studies 18:199–214.CrossRefGoogle Scholar
  7. Thume, K. H. (1992). Studien zur Entwicklung und Effektivität von computergestütztem Frendsprachen-erwerb. Regensburg: Roderer.Google Scholar
  8. Schuster, E., and Finin, T. (1986). VP2: The role of user modelling in correcting errors in second language learning. In Cohn, A. G., and Thomas, J. R., eds., Artificial Intelligence and Its Applications. 197–209.Google Scholar

Copyright information

© Springer-Verlag Wien 1997

Authors and Affiliations

  • Maureen Murphy
    • 1
  • Michael McTear
    • 1
  1. 1.School of Information and Software EngineeringUniversity of UlsterN. Ireland

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