No abstract available.

A learning diagnosis system collects data from a learner's learning process, and analyzes it to build a suitable model for the learner, which can then be incorporated into an intelligent tutoring system to provide customized tutoring services. However, if the collected data reflects inconsistent learner behaviors or unpredictable learning tendencies, then the reliability of the learner model is degraded. In this paper, the outliers in the learner's data are eliminated by a k-NN method. We apply this method to an experimental data set obtained using DOLLS-HI, a learner diagnosis system that uses housing interior learning contents to diagnose learning styles. The resulting diagnosis model shows improved reliability than before eliminating the outliers.

Visual reasoning is an essential skill for many disciplines in engineering and architecture. We describe an intelligent tutoring system for visual reasoning that uses the missing view problem, a learning contents model based on skills, lessons, and problems, and a learner model that measures domain competence as a set of skills. Learning contents and pedagogical teaching strategy are stored in ontologies, which can be customized by the teacher.

This paper is a summary of the results we presented at the Feature Panel Session of the 1997 ASME Computers in Engineering Conference. Five participating groups submitted a total of nine test parts for feature recognition. To these test parts, we have applied our feature recognition method using a convex decomposition method called Alternating Sum of Volumes with Partitioning (ASVP). By applying combination operations to the ASVP decomposition of a part boundary, we obtain a Form Feature Decomposition (FFD) consisting of volumetric form features. The FFD can be further converted into application-specific feature representations, including the Negative Feature Decomposition (NFD) for machining or cast-then-machined applications. We describe an additional application of the ASVP algorithm to identify and filter out cylindrical features from a part boundary.