Adaptive Content in an Online Lecture System

  • Mia K. Stern
  • Beverly Park Woolf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1892)


This paper discusses techniques for adapting the content in an online lecture system for a specific user. A two pass method is used: 1) determine the appropriate level of difficulty for the student and 2) consider the student’s learning style preferences. A simple grading scheme is used to determine the student’s knowledge and a Naïve Bayes Classifier is used to reason about the student’s preferences in terms of explanations, examples, and graphics. A technique for gathering and using population data is also discussed.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    D. Billsus and M. Pazzani. Learning Probabilistic User Models. In Proceedings of the Workshop on Machine Learning for User Models, Sixth International Conference on User Modeling, ChiaLaguna, Sardinia, June 1997.Google Scholar
  2. [2]
    C. Boyle and A.O. Encarnacion. MetaDoc: an Adaptive Hypertext Reading System. In P. Brusilovsky, A. Kobsa, and J. Vassileva, editors, Adaptive Hypertext and Hypermedia, chapter 3, pages 71–89. Kluwer Academic Publishers, The Netherlands, 1998.Google Scholar
  3. [3]
    P. Brusilovsky. Methods and Techniques of Adaptive Hypermedia. User Modeling and User-Adapted Interaction, 6:87–129, 1996.CrossRefGoogle Scholar
  4. [4]
    P. De Bra. Design Issues in Adaptive Hypermedia Application Development. In Proceedings of the Second Workshop on Adaptive Systems and User Modeling on the World Wide Web, Banff, Canada, June 1999.Google Scholar
  5. [5]
    P. De Bra and L. Calvi. Creating Adaptive Hyperdocuments for and on the Web. In Proceedings of Webnet, pages 189–201, 1997.Google Scholar
  6. [6]
    A. Kobsa, D. Müller, and A. Nill. KN-AHS: An Adpative Hypertext Klient of the User Modelling System BGP-MS. In 4th International Conference on User Modeling, pages 1–36, Hyannis, MA, 1994.Google Scholar
  7. [7]
    P. Langley, W. Iba, and K. Thompson. An Analysis of Bayesian Classifiers. In Proceedings of the Tenth Conference on Artificial Intelligence, San Jose, CA, 1992. AAAI Press.Google Scholar
  8. [8]
    T. Mitchell. Machine Learning, chapter 6, pages 177–179. WCB McGraw-Hill, Boston, MA, 1997.zbMATHGoogle Scholar
  9. [9]
    M. Stern, J. Steinberg, H.I. Lee, J. Padhye, and J. Kurose. MANIC: Multimedia Asynchronous Networked Individualized Courseware. In Educational Media and Hypermedia, 1997.Google Scholar
  10. [10]
    M. Stern, B.P. Woolf, and J. F. Kurose. Intelligence on the Web? In Artificial Intelligence in Education, 1997.Google Scholar
  11. [11]
    M. K. Stern and B. P. Woolf. Curriculum Sequencing in a Web-Based Tutor. In Proceedings of Intelligent Tutoring Systems, San Antonio, Texas, August 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Mia K. Stern
    • 1
  • Beverly Park Woolf
    • 1
  1. 1.Center for Knowledge Communication Department of Computer ScienceUniversity of MassachusettsAmherstUSA

Personalised recommendations