Affiliations: Department of Computing and Decision Sciences,
Stillman School of Business, Seton Hall University, South Orange, NJ 07079,
USA, weitzrob@shu.edu
Abstract: Two approaches to building intelligent tutoring systems are the
well-established model-tracing paradigm and the relatively newer
constraint-based paradigm. Proponents of the constraint-based paradigm claim
that it affords performance at levels comparable to that of model-tracing
tutors, but with significantly less development effort. We have built both a
model-tracing and constraint-based tutor for the same problem domain
(statistical hypothesis testing) and report on our findings with the goals of
evaluating proponents' claims, more generally contrasting and comparing the two
approaches, and providing guidance for others interested in building
intelligent tutoring systems. Principally we conclude that two characteristics
of the problem domain are key in distinguishing the appropriateness of the
approaches for a given problem domain. First, the constraint-based paradigm is
feasible only for domains in which the solution itself is rich in information.
There is no such restriction for model tracing. Second, model tracing
demonstrates superiority with respect to the ability to provide targeted,
high-quality remediation; this superiority increases with the complexity of the
solution process goal structure. Finally, we observe that the development
effort required to build a model-tracing tutor is greater than that for
building a constraint-based tutor. This increased effort is a function of
additional design requirements that are responsible for the improved
remediation.
Keywords: Intelligent Tutoring Systems, Model-tracing Tutors, Cognitive Tutors, Constraint-based Modelling, Constraint-based Tutors, Comparison of Intelligent Tutors, Knowledge-based Systems
