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User Modeling and User-Adapted Interaction

, Volume 4, Issue 3, pp 149–196 | Cite as

The um toolkit for cooperative user modelling

  • Judy Kay
Article

Abstract

This paper gives an overview of the um toolkit: the philosophy underlying its design, examples of its use and discussion of the way it deals with some major issues in creating user modelling shells. The um toolkit has been developed to provide support for a variety of cooperative agents. An important element of its cooperativeness is due to its capacity to give users an understanding of their own user models. This paper describes two substantial but very different uses of the toolkit. The first involves a collection of coaching systems that help users learn more about their text editor. Experimental results suggest that the user model is associated with users learning more. The second is a movie advisor that uses a range of tools to construct and refine the user model and to filter a database of movies. Both these systems are built from combining tools in um. The paper describes several of the tools for constructing and refining user models. In addition it describes the user-model viewing tools and the way that these help users ensure their user models are correct. The paper also discusses the two central themes of the um work, the application of a tools approach to the design of a user modelling toolkit and the implications of making the user model accessible to its owner, the person modelled.

Key words

student model user model cooperative systems accessible user models visualisation of user models 

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References

  1. Brajnik, G., G. Guida, and C. Tasso: 1990, ‘User modeling in expert man-machine interfaces: a case study in intelligent information retrieval’.IEEE Trans. on Systems, Man and Cybernetics,20(1), 166–185.Google Scholar
  2. Brajnik, G. and C. Tasso: 1992, ‘A flexible tool for developing user modelling applications with non-monotonic reasoning capabilities’. In: E. Andre, R. Cohen, W. Graf, B. Kass, C. Paris and W. Wahlster (eds.):Proc. of UM92: Third International Workshop on User Modeling, pp. 42–66, Deutsches Forschungszentrum fur Kunstliche Intelligenz.Google Scholar
  3. Brown, J. S., R. R. Burton and K. M. Larkin: 1977, ‘Representing and using procedural bugs for educational purposes’.Proc. Annual Conference ACM, pp. 247–255, Seattle.Google Scholar
  4. Browne, D., P. Totterdell and M. Norman: 1990. In:Adaptive user interfaces, Academic Press, San Diego, California.Google Scholar
  5. Bullock, A. and O. Stallybrass: 1977. In:The Fontana Dictionary of Modern Thought.Google Scholar
  6. Butler, G.: 1992,Unix coach, Honours Thesis, Basser Dept. of Computer Science, University of Sydney.Google Scholar
  7. Carroll, J. M.: 1990,The Nurnberg Funnel: designing minimalist instruction for practical computer skill, MIT Press, Cambridge, Mass.Google Scholar
  8. Cohen, P. R.: 1985. In:Heuristic reasoning about uncertainty: an artificial intelligence approach, Morgan Kaufmann, Los Altos, California.Google Scholar
  9. Cook, R., J. Kay, G. Ryan and R. Thomas: 1994,A toolkit for appraising the long term usability of a text editor. (Available as ftp://ftp.cs.su.oz.au/pub/tr/TR94_489_toolkit_appraising_text_editor.ps.Z.)Google Scholar
  10. Cook, R.: 1991,Viewable individual user models for a text editor, Honours Thesis, Basser Dept. of Computer Science, University of Sydney.Google Scholar
  11. Cook, R. and J. Kay: 1993,Tools for viewing umuser models, SSRG Report 93/3/50.1, Dept. of Computer Science, University of Sydney, Australia.Google Scholar
  12. Crawford, K. and J. Kay: 1993, ‘Metacognitive processes and learning with intelligent educational systems’. In: P. Slezak (ed.):Cognitive Science Down Under, pp. 63–77, Ablex. SSRG 91/1/3-1 Dept. of Computer Science, University of Sydney.Google Scholar
  13. Duda, R. O., P. E. Hart and N. Nilsson: 1976, ‘Subjective bayesian methods for rule-based inference systems’. Technical Note 124, Artificial Intelligence Centre, SRI Intl., Menlo Park, Ca.Google Scholar
  14. Finin, T. W.: 1989, ‘GUMS — a general user modeling shell’. In: A. Kobsa and W. Wahlster (eds.):User models in dialog systems, pp. 411–431, Springer-Verlag, Berlin.Google Scholar
  15. Parandeh Gheibi, N. and J. Kay: 1993, ‘Supporting a coaching system with viewable learner models’. In: V. Petrushin and A. Dovgiallo (eds.):Proc. Intl. Conf.for Computers, Computer Technologies in Education, pp. 140–141, Kiev, Ukraine.Google Scholar
  16. Goldberg, D., D. Nichols, B. M. Oki and D. Terry: 1992, ‘Using collaborative filtering to weave an information tapestry’.Comm ACM,35(12) 61–70.Google Scholar
  17. Goldstein, I. P.: 1982, ‘The genetic graph: a representation for the evolution of procedural knowledge’ In: D. Sleeman and J. S. Brown (eds.):Intelligent tutoring systems, Academic Press, New York.Google Scholar
  18. Greer, J. E. and G. I. McCalla: 1989, ‘A computational framework for granularity and its application to educational diagnosis’. IJCAI, pp. 477–482, Detroit.Google Scholar
  19. Johnson W. L. and E. Soloway: 1985, ‘PROUST’,Byte, 10(4).Google Scholar
  20. Jordan, P. W., S. W. Draper, K. K. MacFarlane and S. McNulty: 1991, ‘Guessability, Learnability and Experienced User Performance’. In: D. Diaper and N. Hammond (eds.):HCI'91 People and Computers VI: Usability Now!, pp. 237–245, Cambridge University Press.Google Scholar
  21. Kass, R. J.: 1988, ‘Acquiring a model of the user's beliefs from a cooperative advisory dialog’. MS-CIS-88-104 LINC LAB 139, Dept. of Computer and Information Science, Univ. of Pennsylvania, Philadelphia.Google Scholar
  22. Kay, J.: 1991, ‘An explicit approach to acquiring models of student knowledge’. In: R. Lewis and S. Otsuki (eds.):Advanced Researchon Computers and Education, pp. 263–268, Elsevier, North Holland.Google Scholar
  23. Kobsa, A. and W. Pohl: 1995, ‘The user modeling shell system BGP-MS’.User Modeling and User-Adapted Interaction 4(2), 59–106.Google Scholar
  24. Mack, R.: 1990, ‘Understanding and learning text-editing skills: observations on the role of the new user expectations’. In: S. P. Robertson, W. Zachary and J. B. Black (eds.):Cognition, computing and cooperation, pp. 304–337, Ablex, Norwood, New Jersey.Google Scholar
  25. Miller, C. and K. Swift: 1990. In:The handbook of non-sexist writing for writers, editors and speakers, Lippincott and Crowell, New York.Google Scholar
  26. Neal, L. R.: 1987, ‘Cognition-sensitive design and user modelling for syntax-directed editors’. In: G. Salveny (ed.):Proc. 2nd Intl. Conf. on Human-Computer Interaction, August 10–14,11, 99–102, Honolulu.Google Scholar
  27. Orwant, J.: 1993, ‘Doppelganger goes to school: machine learning for user modeling’.MIT MS Thesis, MIT Media Laboratory.Google Scholar
  28. Orwant, J.: 1994, ‘Heterogenous learning in the Doppelganger user modeling system’.User Modeling and User-Adapted Interaction 4(2), 107–130.Google Scholar
  29. Pike, R.: 1987, ‘The Text Editor sam's.Software Practice and Experience,17, 813–845.Google Scholar
  30. Rich, R.: 1983, ‘Users are individuals: individualizing user models’,Intl. J. of Man-Machine Studies,18, 199–214.Google Scholar
  31. Rosson, M. B.: 1983, ‘Patterns of Experience in Text Editing’. In: A. Janda (ed.):Human Factors in Computing Systems, Proceedings of CHI'83 Conference, pp. 171–175, North-Holland.Google Scholar
  32. Shortliffe, E. H. and D. G. Buchanan: 1975, ‘A model of inexact reasoning in medicine’,Mathematical Biosciences,23, 351–379.Google Scholar
  33. Sleeman, D.: 1985, ‘UMFE: a user modelling front-end subsys tem’,Intl. J. of Man-Machine Studies,23, 71–88.Google Scholar
  34. Suchman, L.: 1987. In:Plans and situated actions, Cambridge University Press, Cambridge, UK.Google Scholar
  35. Szolovits, P. and S. G. Pauker: 1978, ‘Categorical and probablistic reasoning in medical diagnosis’.Artificial Intelligence,11, 115–144.Google Scholar
  36. Wenger, E.: 1987,Artificial Intelligence and tutoring systems — computational and cognitive approaches to the communication of knowledge, Morgan Kaufmann, Los Altos.Google Scholar
  37. Wilensky, R., D. N. Chin, M. Lurai, J. Martin, J. Mayfield and D. Wu: 1988, ‘The Berkely UNIX consultant project’.Computational Linguistics,14(4), 35–84.Google Scholar
  38. Zadeh, L. A.: 1978, ‘Fuzzy sets as a basis for a theory of possibility’. In:Fuzzy sets and systems, North-Holland, Amsterdam.Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Judy Kay
    • 1
  1. 1.Basser Department of Computer ScienceUniversity of SydneyAustralia

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