Authors: John Champaign; Robin Cohen
Addresses: David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada ' David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
Abstract: In this paper, we present an algorithm for reasoning about the sequencing of content for students in an intelligent tutoring system, influenced by McCalla's ecological approach. 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 through a novel method of validation, providing details of the model of learning used in the simulation and the results obtained in order to demonstrate the value of our model. Beyond confirmation through simulations of student learning, we report on a study with human users and expand on a previous pilot study. We demonstrate the effectiveness of our algorithms for selection of learning objects to solidify the overall defence of our approach.
Keywords: peer-based intelligent tutoring; simulation; ecological content sequencing; instructional design; learning models; modelling; intelligent tutoring systems; ITS; learning objects.
International Journal of Learning Technology, 2013 Vol.8 No.4, pp.337 - 361
Available online: 04 Feb 2014Full-text access for editors Access for subscribers Purchase this article Comment on this article