Intelligent Learning Object Guide (iLOG): A Framework for Automatic Empirically-Based Metadata Generation

Authors:
S. A. Riley;L. D. Miller;L. -K. Soh;A. Samal;G. Nugent
Affiliations:
University of Nebraska---Lincoln: Department of Computer Science and Engineering;University of Nebraska---Lincoln: Department of Computer Science and Engineering;University of Nebraska---Lincoln: Department of Computer Science and Engineering;University of Nebraska---Lincoln: Department of Computer Science and Engineering;University of Nebraska---Lincoln: Center for Research on Children, Youth, Families and Schools
Venue:
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Year:
2009

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iLOG: a framework for automatic annotation of learning objects with empirical usage metadata

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Abstract

We present a framework for the automatic annotation of learning objects (LOs) with empirical usage metadata. Our implementation of the Intelligent Learning Object Guide (iLOG) was used to collect interaction data of over 200 students' interactions with eight LOs. We show that iLOG successfully tracks student interaction data that can be used to automate the creation of meaningful empirical usage metadata that is based on real-world usage and student outcomes.