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

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. Keywords., Learning Objects, Feature Selection, Association Rule Mining, Empirical Usage Metadata, SCORM

• Type: Conference
• Year: 2009
• Where: AIED
• Authors: S. A. Riley, Lee Dee Miller, Leen-Kiat Soh, Ashok Samal, Gwen Nugent
• Keywords: AIED 2009, Artificial Intelligence, Empirical Usage Metadata, Learning Objects, Meaningful Empirical Usage