Application of Componential IRT Model for Diagnostic Test in a Standard-Conformant eLearning System

Authors:
Feng-Hsu Wang
Affiliations:
Ming Chuan University, Taiwan
Venue:
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
Year:
2006

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Design a personalized e-learning system based on item response theory and artificial neural network approach

Expert Systems with Applications: An International Journal

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Abstract

Diagnostic test plays an important role in personalized eLearning by providing information about cognitive levels of students' learning states. While many diagnosis algorithms have been proposed, most of them lack a solid theory base. On the other hand, item response theory (IRT) is a widely-accepted test theory and has been shown very effective in estimating a learner's latent ability. However, it did not tell much about conceptual cognitive states. This paper proposes an extension of IRT model for diagnostic test by extending it into a componential IRT model. This diagnostic test feature has been added into a standard-conformant eLearning system, called IDEAL, where a model-based diagnosis and remediation architecture is implemented. Preliminary results show that the proposed approach is effective.