Discovering Knowledge Models in an Open-Ended Educational Game using Concept Formation
Workshop Proceedings of AIED 2015
2015
Abstract
Developing models of the knowledge and skills being exercised in a task is an important component of the design of any instructional environment. Developing these models is a labor intensive process. When working in exploratory and open-ended environments (EOLEs) the difficulty of building a knowledge model is amplified by the amount of freedom afforded to learners within the environment. In this paper we demonstrate a way of accelerating the model development process by applying a concept formation algorithm called TRESTLE. This approach takes structural representations of problem states and integrates them into a hierarchical categorization, which can be used to assign concept labels to states at different grain sizes. We show that when applied to an open-ended educational game, knowledge models developed from concept labels using this process show a better fit to student data than basic hand-authored models. This work demonstrates that it is possible to use machine learning to automatically acquire a knowledge component model from student data in open-ended tasks.
BibTeX
@inproceedings{harpstead-aied-2015-ws,
title = {Discovering Knowledge Models in an Open-Ended Educational Game using Concept Formation},
author = {Harpstead, Erik and MacLellan, Christopher J. and Aleven, Vincent},
booktitle = {Workshop Proceedings of AIED 2015},
year = {2015},
}