Measuring knowledge transfer skills by using constrained-student modeler autonomous agent

Abstract

The authors propose a methodology for the design of a student modeler autonomous agent based on a constraint model. The function of the autonomous agent is to interpret the student's constraint violations and draw a behavioral schema of the student's knowledge transfer skills on a neural network concepts. We aimed to build a student modeler by taking advantage of both the theoretical framework of the constraint based model and the autonomous agent. This enables us to generate from a set of constraints desirable functions such as: task generation, hints, and domain application metaphors related to the student's learning goals. We present the fundamental issue of how to represent student knowledge transfer skills in an autonomous agent.