Mc. Desmarais et al., USER-EXPERTISE MODELING WITH EMPIRICALLY DERIVED PROBABILISTIC IMPLICATION NETWORKS, User modeling and user-adapted interaction, 5(3-4), 1995, pp. 283-315
Citations number
34
Categorie Soggetti
Controlo Theory & Cybernetics","Computer Science Cybernetics
The application of user-expertise modeling for adaptive interfaces is
confronted with a number of difficult challenges, namely, efficiency a
nd reliability, the cost-benefit ratio, and the practical usability of
user modeling techniques. We argue that many of these obstacles can b
e overcome by standard, automatic means of performing knowledge assess
ment. Within this perspective, we present the basis of a probabilistic
user modeling approach, the POKS technique, which could serve as a st
andard user-expertise modeling tool. The POKS technique is based on th
e cognitive theory of knowledge structures: a formalism for the repres
entation of the order in which we learn knowledge units (ICU). The tec
hnique permits the induction of knowledge structures from a small numb
er of empirical data cases. It uses an evidence propagation scheme wit
hin these structures to infer an individual's knowledge state from a s
ample of KU. The empirical induction technique is based, in part, on s
tatistical hypothesis testing over conditional probabilities that are
determined by the KUs' learning order. Experiments with this approach
show that the technique is successful in partially inferring an indivi
dual's knowledge state, either through the monitoring of a user's beha
vior, or through a selective questioning process. However, the selecti
ve process, based on entropy minimization, is shown to be much more ef
fective in reducing the standard error score of knowledge assessment t
han random sampling.