@inproceedings{bbc1302390c14ab2a3db2ed12f6f7fac,
title = "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 = "Association Rule Mining, Empirical Usage Metadata, Feature Selection, Learning Objects, SCORM",
author = "Riley, {S. A.} and Miller, {L. D.} and Soh, {L. K.} and A. Samal and G. Nugent",
year = "2009",
doi = "10.3233/978-1-60750-028-5-515",
language = "English (US)",
isbn = "9781607500285",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
number = "1",
pages = "515--522",
booktitle = "Frontiers in Artificial Intelligence and Applications",
edition = "1",
}