As part of a project to develop an intelligent computer tutor for basic algebra, we have been investigating task sequencing. In this paper we present an approach to task sequencing that is based on a component-skills view of intelligence and learning. We postulate that tutors use inferences about past and present student performance to determine a current skill set that will be the new target for learning. The skill set is then used as a basis for generating tasks that should elicit those skills. Current skill sets are modified slowly over time so that lessons appear coherent and well-planned. We first describe the approach at a general level, where it can be viewed as a cognitive model of human task sequencing. Then we discuss the implementation of the model in our intelligent algebra tutoring system.
This paper presents results of experiments we conducted on a number of scheduling algorithms used in a multi-processing Time Warp system. Our results show that system performance can be improved by using indirect indicators of Time Warp progress without going to the expense of user specified scheduling or relying on dependency graphs. Our best algorithm is based on a composite measure of simulation advance rate, flow control, and the appearance of specific message types.
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