A Model for Content Sequencing in Intelligent Tutoring Systems Based on the Ecological Approach and Its Validation Through Simulated Students

Abstract

In this paper, we present an algorithm for reasoning about the sequencing of content for students in an intelligent tutoring system. Our motivating influence is McCalla’s ecological approach which advocates attaching models of learners to the learning objects they interact with, and then mining these models for patterns that are useful for various purposes. In particular, we record with each learning object those students who experienced the object, together with their initial and final states of knowledge, and then use these interactions to reason about the most effective lesson to show future students based on their similarity to previous students. We validate our approach in a context of simulated students, providing details of the model of learning used in the simulation and the results obtained in order to demonstrate the value of our model. As a result we offer a novel approach for peer-to-peer intelligent tutoring from repositories of learning objects.