Scrutable Adaptation: Because We Can and Must

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4018)


Beginning with the motivations for scrutability, this paper introduces PLUS, a vision of Pervasive Lifelong User-models that are Scrutable. The foundation for PLUS is the Accretion/Resolution representation for active user models that can drive adaptive hypermedia, with support for scrutability. The paper illustrates PLUS in terms of its existing, implemented elements as well as some examples of applications built upon this approach. The concluding section is a research agenda for essential elements of this PLUS vision.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kobsa, A.: Personalized Hypermedia and International Privacy. Communications of the ACM 45, 64–67 (2002)CrossRefGoogle Scholar
  2. 2.
    Kobsa, A., Koenemann, J., Pohl, W.: Personalized hypermedia presentation techniques for improving online customer relationships. The Knowledge Engineering Review 16, 111–155 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Kay, J.: The um toolkit for cooperative user modelling. User Modeling and User-Adapted Interaction 4, 149–196 (1995)CrossRefGoogle Scholar
  4. 4.
    Kay, J., Kummerfeld, B., Lauder, P.: Personis: a server for user models. In: Bra, P.D., Brusilovsky, P., Conejo, R. (eds.) Adaptive Hypertext 2002, pp. 203–212. Springer, Heidelberg (2002)Google Scholar
  5. 5.
    Carmichael, D.J., Kay, J., Kummerfeld, B.: Consistent Modelling of Users, Devices and Sensors in a Ubiquitous Computing Environment. User Modeling and User-Adapted Interaction 15, 197–234 (2005)CrossRefGoogle Scholar
  6. 6.
    Cimolino, L., Kay, J.: Verified Concept Mapping for Eliciting Conceptual Understanding. In: Aroyo, L., Dicheva, D. (eds.): ICCE Workshop on Concepts and Ontologies in Web-based Educational Systems, ICCE 2002, International Conference on Computers in Education. CS-Report 02-15 Technische Universiteit Eindhoven, pp. 9–14 (2002)Google Scholar
  7. 7.
    Kay, J., Lum, A.: Exploiting readily available web data for scrutable student models. In: 12th International Conference on Artificial Intelligence in Education, The Netherlands, Amsterdam, pp. 338–345 (2005)Google Scholar
  8. 8.
    Uther, J., Kay, J.: VlUM, a web-based visualisation of large user models. User Modeling 2003, pp. 198–202 (2003)Google Scholar
  9. 9.
    Bull, S., Kay, J.: A framework for designing and analysing open learner modelling. In: Kay, J., Lum, A., Zapata-Rivera, D. (eds.): 12th International Conference on Artificial Intelligence in Education (AIED 2005) Workshop 11, Amsterdam, pp. 81–90 (2005)Google Scholar
  10. 10.
    Czarkowski, M., Kay, J.: Giving learners a real sense of control over adaptivity, even if they are not quite ready for it yet. In: Chen, S., Magoulas, G. (eds.) Advances in web-based education: Personalized learning environments. IDEA (2006), pp. 93–125 (2006)Google Scholar
  11. 11.
    Holden, S., Kay, J., Poon, J., Yacef, K.: Workflow-based personalised document delivery. International Journal on e-Learning 4, 131–148 (2005)Google Scholar
  12. 12.
    Li, L., Kay, J.: Assess: promoting learner reflection in student self-assessment. In: 12th International Conference on Artificial Intelligence in Education (AIED 2005) Workshop 11, The Netherlands, Amsterdam (2005)Google Scholar
  13. 13.
    Assad, M., Kay, J., Kummerfeld, B.: Models of people, places and devices for location-aware services. In: Pervasive 2006 (to appear, 2006)Google Scholar
  14. 14.
    Merceron, A., Yacef, K.: A web-based tutoring tool with mining facilities to improve learning and teaching. In: 11th International Conference on Artificial Intelligence in Education (AIED 2003). IOS Press, Sydney (2003)Google Scholar
  15. 15.
    Merceron, A., Yacef, K.: TADA-Ed for educational data mining. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning and Instruction 7 (2005)Google Scholar
  16. 16.
    Martin, E.M.: Learning scrutable user models: Inducing conceptual descriptions. In: Knstliche Intelligenz 2002 (2002)Google Scholar
  17. 17.
    Mazza, R., Dimitrova, V.: CourseVis: Externalising student information to facilitate instructors in distance learning. In: Hoppe, F.V., Kay, J. (eds.) Artificial Intelligence in Education, pp. 279–286. IOS Press, Amsterdam (2003)Google Scholar
  18. 18.
    Cheverst, K., Byun, H.E., Fitton, D., Sas, C., Kray, C., Villar, N.: Exploring Issues of User Model Transparency and Proactive Behaviour in an Office Environment Control System. User Modeling and User-Adapted Interaction 15, 235–273 (2005)CrossRefGoogle Scholar
  19. 19.
    Bull, S., Brna, P., Pain, H.: Extending the scope of the student model. User Modeling and User-Adapted Interaction 5, 45–65 (1995)CrossRefGoogle Scholar
  20. 20.
    Beck, J., Stern, M., Woolf, B.P.: Cooperative Student Models. In: du Boulay, B., Mizoguchi, R. (eds.) Artificial Intelligence in Education, pp. 127–134. IOS Press, Amsterdam (1997)Google Scholar
  21. 21.
    Mitrovic, A., Martin, B.: Evaluating the effects of open student models on learning. In: de Bra, P., Brusilovsky, P., Conejo, R. (eds.) The 2nd International Conference on Adaptive Hypermedia and Adaptive Web-based Systems, vol. 296-305. Springer, Heidelberg (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.Smart Internet Technology Research Group, School of Information TechnologiesUniversity of SydneyAustralia

Personalised recommendations