Skip to main content

Advertisement

SpringerLink
Log in

Modeling individual and collaborative problem-solving in medical problem-based learning

  • Original Paper
  • Published:
User Modeling and User-Adapted Interaction Aims and scope Submit manuscript

Abstract

Today a great many medical schools have turned to a problem-based learning (PBL) approach to teaching as an alternative to traditional didactic medical education to teach clinical-reasoning skills at the early stages of medical education. While PBL has many strengths, effective PBL tutoring is time-intensive and requires the tutor to provide a high degree of personal attention to the students, which is difficult in the current academic environment of increasing demands on faculty time. This paper describes the student modeling approach used in the COMET intelligent tutoring system for collaborative medical PBL. To generate appropriate tutorial actions, COMET uses a model of each student’s clinical reasoning for the problem domain. In addition, since problem solving in group PBL is a collaborative process, COMET uses a group model that enables it to do things like focus the group discussion, promote collaboration, and suggest peer helpers. Bayesian networks are used to model individual student knowledge and activity, as well as that of the group. The validity of the modeling approach has been tested with student models in the areas of head injury, stroke, and heart attack. Receiver operating characteristic (ROC) curve analysis shows that the models are highly accurate in predicting individual student actions. Comparison with human tutors shows that the focus of group activity determined by the model agrees with that suggested by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p  =  0.774, Kappa  =  0.823).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Anderson J.R. (1993). Problem solving and learning’. Am. Psychol. 48(1): 35–44

    Article  Google Scholar 

  • Anderson J.R., Jeffries R. (1985). Novice LISP Errors: Undetected losses of information from working memory. Human Comp. Interact. 1(2): 107–131

    Article  Google Scholar 

  • Arocha J.F., Patel V.L., Patel Y.C. (1993). Hypothesis generation and the coordination of theory and evidence in novice diagnostic reasoning. Med. Decis. Making 13(3), 198–211

    Google Scholar 

  • Barrows H.S. (1986).A taxonomy of problem-based learning method s. Med. Educ. 20(6): 481–486

    Google Scholar 

  • Barrows H.S. (1996). Problem-based learning in medicine and beyond: a brief overview. In: Wilkerson L., Gislaers W.H (eds). Bringing Problem-based Learning to Higher Education: Theory and Practice. Jossey-Bass, San Francisco, CA pp 3–12

    Google Scholar 

  • Barrows H.S., Tamblyn R.M. (1980). Problem-based Learning: an Approach to Medical Education. Springer, New York

    Google Scholar 

  • Bonar J., Cunningham R. (1988). Bridge: An intelligent tutor for thinking about programming. In: Self J. (eds). Artificial Intelligence and Human Learning: Intelligent Computer-aided Instruction. Chapman and Hall, London, pp. 391–409

    Google Scholar 

  • Bradley A.P. (1995). The use of area under ROC curve in the evaluation oflearning algorithms. Pattern Recognit. 30(6): 1145–1159

    Google Scholar 

  • Chi M.T.H., Siler S.A., Jeong H., Yamauchi T., Hausmann R.G. (2001). Learning from human tutoring. Cogn. Sci. 25(4): 471–533

    Article  Google Scholar 

  • Clancey W.J. (1983). Guidon. J. Comp. Based Instruct. 10(1): 8–14

    Google Scholar 

  • Conati C., Gertner A., VanLehn K. (2002). Using Bayesian networks to manage uncertainty in student modeling. User Model. User-Adapt. Interact. 12(4): 371–417

    Article  MATH  Google Scholar 

  • Corbett A.T., Anderson J.R. (1995). Knowledge tracing: modeling the acquisition of procedural knowledge. User Model. User-Adapt. Interact. 4(4): 253–278

    Article  Google Scholar 

  • Cote, R.A., Rothwel, D.J., Beckett, R.S., Palotay, J.L. (eds.) SNOMED International: The Systematized Nomenclature of Human and Veterinary Medicine. College of American Pathologists Northfield (1993)

  • Crowley, R., Medvedeva, O. A general architecture for intelligent tutoring of diagnostic classification problem solving. In: American Medical Informatics Association Symposium, pp. 185–189. Washington, D.C. (2003)

  • Das M., Mpofu D.F.S., Hasan M.Y., Stewart T.S. (2002). Student perceptions of tutor skills in problem-based learning tutorials. Med. Educ. 36(3): 272–278

    Article  Google Scholar 

  • Dawson B., Trapp R.G. (eds.) (2001). Basic and Clinical Biostatistics. McGraw-Hill, New York

    Google Scholar 

  • Dolmans D.H., Wolfhagen I.H., Scherpbier A.J. Vleuten C.P. (2001). Relationship of tutors’ group-dynamics skills to their performance rating in problem-based learning. Acad. Med. 76(5): 473–476

    Article  Google Scholar 

  • Elstein A.L., Shulman L.S., Sprafka S.A. (eds) (1978). Medical Problem Solving – an Analysis of Clinical Reasoning. Harvard University Press, M.A.

    Google Scholar 

  • Feltovich P.J., Barrows H.S. (1984). Issues of generality in medical problem solving. In: Schmidt H.G., De Volder M.L. (eds). Tutorials in Problem-based Learning: A New Direction in Teaching the Health Professions. Van Gorcum, The Netherlands, pp. 128–142

    Google Scholar 

  • Franz T.M., Larson J.R. (2002). The impact of experts on information sharing during group discussion. Small Group Res. 33(4): 383–411

    Google Scholar 

  • Grant J., Marsden P. (1988). Primary knowledge, medical education and consultant expertise. Med. Educ. 22(3): 173–179

    Article  Google Scholar 

  • Hanley J., McNeil B. (1983). A method of comparing the areas under receiver operating characteristic curves derived from the same case. Radiology 148(3): 839–43

    Google Scholar 

  • Inaba, A., Mizoguchi, R.: Learners roles and predictable educational benefits in collaborative learning: an ontological approach to support design and analysis of CSCL. In: Seventh International Conference on Intelligent Tutoring Systems, pp. 285–294. Alagoas, Brazil (2004)

  • Jameson, A., Baldes, S., Kleinbauer, T.: Generative models of group members as support for group collaboration. Ninth International Conference on User Modeling, pp. 1–14. Johnstown, Pennsylvania (2003)

  • Kahn C.E. Jr., Roberts L.M., Shaffer K.A., Haddawy P. (1997). Construction of a Bayesian network for mammographic diagnosis of breast cancer. Comp. Biol. Medi. 27(1): 19–29

    Article  Google Scholar 

  • Koschmann T., Kelson A., Feltovich P., Barrows H.S. (1996). Computer-supported problem-based learning: a principled approach to the use of computers in collaborative learning. In: Koschmann T. (eds) CSCL: Theory and Practice of an Emerging Paradigm. Erlbaum, Mahwah, NJ pp. 83–124

    Google Scholar 

  • Larson J.R., Jr., Christensen C. (1993). Groups as problem-solving units: toward a new meaning of social cognition. Br. J. Soc. Psychol. 32(2): 5–30

    Google Scholar 

  • Lauritzen S.L. (1995). The EM-algorithm for graphical association models with missing data. Computat. Stat. Data Anal. 19(2): 191–201

    Article  MATH  Google Scholar 

  • Levine J.M., Moreland R.L.: Small groups In: Gilbert, D., Fiske, S., Lindzey, G. (eds.) The Handbook of Social Psychology, pp. 415–469. McGraw-Hill, Boston, MA

  • Levine J.M., Moreland R.L. (2004). Collaboration: The social context of theory development. Pers. Soc. Psychol. Rev. 8(2): 164–172

    Article  Google Scholar 

  • Lock, Z., Kudenko, D.: Multi-component user models of team members. In: Ninth International Conference on User Modeling, pp. 25–34. Johnstown, Pennsylvania

  • Mclaren, B.M., Walker, E., Harrer, A., Bollen, L., Sewall, J.: Creating cognitive tutors for collaborative learning: steps toward realization. In this issue (2006)

  • Mahling, D.E., Sorrows, B.B., Skogseid, I.: A Collaborative environment for semi-structured medical problem based learning. In: First International Conference on Computer-Supported Collaborative Learning, pp. 218–225 Bloomington, IIN (1995)

  • Martens, A., Bernauer, J., Illmann, T.: Seitz, A.: A. Docs ’n Drugs – The virtual polyclinic: an intelligent tutoring system for web-based and case-oriented training in medicine. In: American Medical Informatics Association Fall Symposium, pp. 433–437. Washington, DC (2001)

  • Martha, W. et al.: CIRCSIM-tutor: an intelligent tutoring system using natural language dialogue. In: Twelfth Midwest AI and Cognitive Science Conference, pp. 16–23. Oxford, OH (2001)

  • Masthoff J. (2004). Group modeling: selecting a sequence of television items to suit a group of viewers. User Model. User-Adapt. Interact. 14(1): 37–85

    Article  Google Scholar 

  • Milliken F.J., Bartel C.A., Kurtzberg T.R. (2003). Diversity and creativity in work groups: a dynamic perspective on the affective and cognitive processes that link diversity and performance. In: Paulus P.B., Nijstad B.A. (eds). Group Creativity: Innovation through Collaboration. Oxford University Press, New York, pp. 32–62

    Google Scholar 

  • Mitrovic, A.: Experiences in implementing constraint-based modeling in SQL-tutor. In: Fourth International Conference in Intelligent Tutoring Systems, pp. 414–423 San Antonio, TX (1998)

  • Moore K.L., Dalley A.F. (eds) (1999) Clinically oriented anatomy Lippincott. William & Willkins, Philadelphia

    Google Scholar 

  • Nathan M.J., Kintsch W., Young E. (1992). A theory of algebra-word-problem comprehension and its implications for the design of learning environments. Cognition and Instruction 9(4): 329–389

    Article  Google Scholar 

  • Neale M.A., Mannix E.A., Gruenfeld D.H. (eds.) (1998) Research on managing groups and teams: Composition. JAI, Greenwich CT

    Google Scholar 

  • Newble, D., Norman, G., Vleuten, C.: Assessing clinical reasoning. In: Jones, J.H.M. (ed.): Clinical Reasoning in the Health Professions, pp. 156–168. Butterworth-Heinemann, Oxford (2000)

  • Nijstad B.A., Diehl M., Stroebe W. (2003). Cognitive stimulation and interference in idea-generating groups. In: Paulus P.B., Nijstad B.A. (eds). Group Creativity: Innovation through Collaboration. Oxford University Press, New York, pp. 137–159

    Google Scholar 

  • Read, T., Barros, B., Bárcena, E., Pancorbo, J.: Coalescing individual and collaborative learning to model user linguistic competences. In this issue (2006)

  • Reusser K. (1993). Tutoring systems and pedagogical theory: Representational tools for understanding, planning, and reflection in problem solving. In: Lajoie S.P., Derry S.J. (eds). Computers as Cognitive Tools. Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 143–177

    Google Scholar 

  • Schmidt H.G., Norman G.R., Boshuizen H.P.A. (1990). A cognitive perspective on medical expertise: theory and implications. Acad. Med. 65(10): 611–621

    Article  Google Scholar 

  • Sharples M., Jeffery N., du Boulay B., Teather B., Teather D., du Boulay G. (2000).Structured computer-based training in the interpretation of neuroradiological images. Int. J. Med. Inform. 60(3): 263–280

    Article  Google Scholar 

  • Shortliffe E.H. (1976).Computer-Based Medical Consultations: MYCIN. Elsevier, New York

    Google Scholar 

  • Shute V.J. (1995). SMART: Student modeling approach for responsive tutoring. User Model. User-Adapt. Interact. 5(1): 1–44

    Article  Google Scholar 

  • Sison R.C., Numao M., Shimura M. (1998). Discovering error classes from discrepancies in novice behaviors via multistrategy conceptual clustering. User Model. User-Adapt. Interact. 8(1–2): 103–129

    Article  Google Scholar 

  • Soller A. (2004).Computational modeling and analysis of knowledge sharing in collaborative distance learning. User Model. User-Adapt. Interact. 14(4): 351–381

    Article  Google Scholar 

  • Suebnukarn S., Haddawy P.: A collaborative intelligent tutoring system for medical problem-based learning. In: Ninth International Conference on Intelligent User Interface, pp. 14–21. Madeira, Portugal (2004)

  • Suebnukarn, S., Haddawy, P.: Modeling individual and collaborative problem-solving in medical problem-based learning. In: Tenth International Conference on User Modeling, pp. 377–386. Edinburgh, UK (2005)

  • Suebnukarn S., Haddawy, P.: A Bayesian approach to generating tutorial hints in a collaborative medical problem-based learning system. Artifi. Intell. Med. (In press)

  • Suthers, D., Girardeau, L., Hundhausen, C.: Deictic roles of external representations in face-to-face and online collaboration. In: Fifth International Conference in Computer-Supported Collaborative Learning, pp. 173–182. Bergen, Norway (2003)

  • Suthers, D., Toth, E., Weiner, A.: An integrated approach to implementing collaborative inquiry in the classroom. In: Second International Conference on Computer Supported Collaborative Learning, pp. 272–279. Toronto, Canada (1997)

  • Tindale R.S., Meisenhelde H.M., Dykema-Engblade A.A., Hogg M.A. (2001). Shared cognition in small groups. In: Hogg M.A., Tindale R.S. (eds). Blackwell Handbook of Social Psychology: Group Processes. Blackwell, Malden, MA pp. 1–30

    Google Scholar 

  • VanLehn K., Martin J. (1998). Evaluation of an assessment system based on Bayesian student modeling. Int. J. Artif. Intell. Educ. 8(2): 179–221

    Google Scholar 

  • Webb G.I., Kuzmycz M. (1996).Feature based modelling: a methodology for producing coherent, consistent, dynamically changing models of agent’s competencies. User Model. User-Adapt. Interact. 5(2): 117–150

    Article  Google Scholar 

  • Wenger E. (1987). Artificial Intelligence and Tutoring systems: Computational and Cognitive Approaches to the Communication of Knowledge. Morgan Kaufmann. Los Altos, CA

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Clinical Skill Development Unit, Thammasat University Dental School, Pathumthani, 12121, Thailand

    Siriwan Suebnukarn

  2. Computer Science and Information Management Program, Asian Institute of Technology, Pathumthani, 12120, Thailand

    Peter Haddawy

  3. 101/38 Soi 9A, Moo 4, Chonlada House, Branggui Sainoi Road, Bangbuathong, Nonthaburi, 1110, Thailand

    Siriwan Suebnukarn

Authors
  1. Siriwan Suebnukarn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Haddawy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siriwan Suebnukarn.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Suebnukarn, S., Haddawy, P. Modeling individual and collaborative problem-solving in medical problem-based learning. User Model User-Adap Inter 16, 211–248 (2006). https://doi.org/10.1007/s11257-006-9011-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11257-006-9011-8

Keywords

Search

Navigation