Student Modeling with Atomic Bayesian Networks

  • Fang Wei
  • Glenn D. Blank
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)


Atomic Bayesian Networks (ABNs) combine several valuable features in student models: prerequisite relationships, concept to solution step relationships, and real time responsiveness. Recent work addresses some of these features but have not combined them, which we believe is necessary in an ITS that helps students learn in a complex domain, in our case, object-oriented design. A refined representation of prerequisite relationships considers relationships between concepts as explicit knowledge units. Theorems show how to reduce the number of parameters required to a small constant, so that each ABN can guarantee a real time response. We evaluated ABN-based student models with 240 simulated students, investigating their behavior for different types of students and different slip and guess values. Holding slip and guess to equal, small values, ABNs are able to produce accurate diagnostic rates for student knowledge states.


Bayesian Network Knowledge Level Knowledge State Intelligent Tutoring System 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Albacete, P.L., VanLehn, K.: The Conceptual Helper: An Intelligent Tutoring System for Teaching Fundamental Physics Concepts. In: Gauthier, G., VanLehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 564–573. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    Blank, G.D., Parvez, S., Wei, F., Moritz, S.: A Web-Based ITS for OO Design. In: Poster for 12th International Conference on AIED, Workshop of Adaptive Systems for Web-Based Education: Tools and reusability, Amsterdam, The Netherlands (June 2005)Google Scholar
  3. 3.
    Butz, C.J., Hua, S., Maguire, R.B.: A Web-based Intelligent Tutoring System for Computer Programming. In: Proceedings of the IEEE/WIC/ACM Conference on Web Intelligence, pp. 159–165 (2004)Google Scholar
  4. 4.
    Carmona, C., Millán, E., Pérez-de-la-Cruz, J.-L., Trella, M., Conejo, R.: Introducing Prerequisite Relations in a Multi-layered Bayesian Student Model. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS, vol. 3538, pp. 347–356. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Chi, M.T.H., Koeske, R.D.: Network Representation of a Child’s Dinosaur Knowledge. Developmental Psychology 19(1), 29–39 (1983)CrossRefGoogle Scholar
  6. 6.
    Collins, J.A., Greer, J.E., Huang, S.H.: Adaptive assessment using granularity hierarchies and Bayesian nets. In: Lesgold, A.M., Frasson, C., Gauthier, G. (eds.) ITS 1996. LNCS, vol. 1086, pp. 569–577. Springer, Heidelberg (1996)Google Scholar
  7. 7.
    Millán, E., Pérez-de-la-Cruz, J.L.: A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation. User Modeling and User-Adapted Interaction 12, 230–281 (2002)CrossRefGoogle Scholar
  8. 8.
    Millán, E., Agosta, J.M., Pérez-de-la-Cruz, J.L.: Bayesian Student Modeling and the Problem of Parameter Specification. Brithish Journal of Educational Technology 32(2), 171–181 (2001)CrossRefGoogle Scholar
  9. 9.
    Moritz, S., Blank, G.D.: A Design-First Curriculum for Teaching Java in a CS1 Course. ACM SIGCSE Bulletin archive 37(2), 89–93 (2005)CrossRefGoogle Scholar
  10. 10.
    Moritz, S., Wei, F., Parvez, S., Blank, G.D.: From Objects-First to Design-First with Multimedia and Intelligent Tutoring. In: The Tenth ITiCSE, Monte da Caparica, Portugal (June 2005)Google Scholar
  11. 11.
    Murray, W.R.: A Practical Approach to Bayesian Student Modeling. In: Goettl, B.P., Halff, H.M., Redfield, C.L., Shute, V.J. (eds.) ITS 1998. LNCS, vol. 1452, pp. 424–433. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  12. 12.
    Peral, J.: Probabilistic Reasoning in Intelligence Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)Google Scholar
  13. 13.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Upper Saddle River (1995)MATHGoogle Scholar
  14. 14.
    Shute, V.J.: SMART: Student Modeling Approach for Responsive Tutoring. User Modeling and User-Adapted Interaction 5, 1–44 (1995)CrossRefGoogle Scholar
  15. 15.
    VanLehn, K., Lynch, C., Schulze, K., Shapiro, J.A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., Wintersgill, M.: The Andes Physics Tutoring System: Lessons Learned. International Journal of Artificial Intelligence and Education 15(3) (2005)Google Scholar
  16. 16.
    VanLehn, K., Niu, Z.: Bayesian student modeling, user interfaces and feedback: A sensitivity analysis. International Journal of Artificial Intelligence in Education 12(2), 154–184 (2001)Google Scholar
  17. 17.
    VanLehn, K., Niu, Z., Siler, S., Gertner, A.S.: Student Modeling from Conventional Test Data: A Bayesian Approach without Priors. In: Goettl, B.P., Halff, H.M., Redfield, C.L., Shute, V.J. (eds.) ITS 1998. LNCS, vol. 1452, pp. 434–443. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  18. 18.
    Wei, F., Moritz, S., Parvez, S., Blank, G.D.: A Student Model for Object-Oriented Design and Programming. The Journal of Computing Sciences in Colleges (CCSC) 20, 260–273 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fang Wei
    • 1
  • Glenn D. Blank
    • 1
  1. 1.Computer Science & EngineeringLehigh UniversityBethlehemUSA

Personalised recommendations