A collaborative intelligent tutoring system for medical problem-based learning

ABSTRACT

This paper describes COMET, a collaborative intelligent tutoring system for medical problem-based learning. The system uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. It incorporates a multi-modal interface that integrates text and graphics so as to provide a rich communication channel between the students and the system, as well as among students in the group. Students can sketch directly on medical images, search for medical concepts, and sketch hypotheses on a shared workspace. The prototype system incorporates substantial domain knowledge in the area of head injury diagnosis. A major challenge in building COMET has been to develop algorithms for generating tutoring hints. Tutoring in PBL is particularly challenging since the tutor should provide as little guidance as possible while at the same time not allowing the students to get lost. From studies of PBL sessions at a local medical school, we have identified and implemented eight commonly used hinting strategies. We compared the tutoring hints generated by COMET with those of experienced human tutors. Our results show that COMET's hints agree with the hints of the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.652, Kappa = 0.773).

REFERENCES

• 1: Barrows, H. S. A taxonomy of problem-based learning methods. Medical Education, 20, 481--486, 1986.
• 2: Dirk E. Mahling , Bryan B. Sorrows , Ingjerd Skogseid, A collaborative environment for semi-structured medical problem based learning, The first international conference on Computer support for collaborative learning, p.218-225, October 1995, Indiana Univ., Bloomington, Indiana, United States
• 3: Suthers, D., Toth, E., and Weiner, A. An Integrated Approach to Implementing Collaborative Inquiry in the Classroom. Proceedings of Computer-Supported Collaborative Learning, 272--279, 1997.
• 4: Martha, W., et al. CIRCSIM-Tutor: An Intelligent Tutoring System Using Natural Language Dialogue. Proceedings of Midwest AI and Cognitive Science Conference, 16--23, 2001.
• 5: Martens, A., Bernauer, J., Illmann, T., and Seitz, A. Docs 'n Drugs - The Virtual Polyclinic. An Intelligent Tutoring System for Web-Based and Case-Oriented Training in Medicine. Proceedings of the AMIA Fall Symposium, 2001.
• 6: Jameson, A., Baldes, S., and Kleinbauer, T. Generative Models of Group Members as Support for Group Collaboration. Workshop on User and Group Models for Web-Based Adaptive Collaborative Environments Proceedings of the International Conference on User Modeling, 1--14, 2003.
• 7: Lock, Z., and Kudenko, D. Multi-component User Models of Team Members. Workshop on User and Group Models for Web-Based Adaptive Collaborative Environments, in International Conference on User Modeling, 25--34, 2003.
• 8: Masthoff, J. Modeling a group of television viewers. Proceedings of the Workshop Future tv, in Intelligent Tutoring Systems Conference, 34--42, 2002.
• 9: Diana, H. J. M., et. al. Relationship of tutors' group-dynamics skills to their performance rating in problem-based learning. Academic Medicine, 76, 473--476, 2001.
• 10: Das, M., Mpofu, D. F. S., Hasan, M. Y., and Stewart, T. S. Student perceptions of tutor skills in problem-based learning tutorials. Medical Education 36, 272--278, 2002.
• 11: Feltovich, P. J., and Barrows, H. S. Issues of generality in medical problem solving. In H. G. Schmidt and M. L. De Volder (Eds.) Tutorials in problem-based learning: A new direction in teaching the health professions. The Netherlands: Van Gorcum, 1984.
• 12: Cristina Conati , Abigail Gertner , Kurt Vanlehn, Using Bayesian Networks to Manage Uncertainty in Student Modeling, User Modeling and User-Adapted Interaction, v.12 n.4, p.371-417, November 2002 [doi>10.1023/A:1021258506583]
• 13: Spirtes, P., Glymour, C., and Scheines, R. Causation, prediction, and search. Cambridge, MA: MIT Press, 2000.
• 14: Dawson, B., and Trapp, R. G. Basic & Clinical Biostatistics. 3rd ed. McGraw-Hill, 2001.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems

Additional Classification:
  J. Computer Applications
  J.3 LIFE AND MEDICAL SCIENCES
      Subjects: Medical information systems

  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.1 Computer Uses in Education
          Subjects: Collaborative learning

General Terms:
Design, Human Factors

Keywords:
bayesian networks, collaborative environments, intelligent tutoring systems, medicine, multi-modal interfaces, problem-based learning