Confirmation-guided discovery of first-order rules with tertius
Machine Learning
Principles of data mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Automating metadata generation: the simple indexing interface
WWW '05 Proceedings of the 14th international conference on World Wide Web
Ontologies for Reusing Learning Object Content
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
AICT-SAPIR-ELETE '05 Proceedings of the Advanced Industrial Conference on Telecommunications/Service Assurance with Partial and Intermittent Resources Conference/E-Learning on Telecommunications Workshop
Strategies for automatic LOM metadata generating in a web-based CSCL tool
WebMedia '05 Proceedings of the 11th Brazilian Symposium on Multimedia and the web
Automatic Generation of Metadata for Learning Objects
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Adaptive feedback generation to support teachers in web-based distance education
User Modeling and User-Adapted Interaction
Defining clusters from a hierarchical cluster tree
Bioinformatics
Automatic Extraction of Pedagogic Metadata from Learning Content
International Journal of Artificial Intelligence in Education
User Modeling and User-Adapted Interaction
Evaluating Web Based Instructional Models Using Association Rule Mining
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
FIE'09 Proceedings of the 39th IEEE international conference on Frontiers in education conference
Hi-index | 0.00 |
Learning objects (LOs) are digital or non-digital entities used for learning, education or training commonly stored in repositories searchable by their associated metadata. Unfortunately, based on the current standards, such metadata is often missing or incorrectly entered making search difficult or impossible. In this paper, we investigate automating metadata generation for SCORM-complaint LOs based on user interactions with the LO and static information about LOs and users. Our framework, called the Intelligent Learning Object Guide (iLOG), involves real-time tracking of each user sessions (an LO Wrapper), offline data mining to identify key attributes or patterns on how the LOs have been used as well as characteristics of the users (MetaGen), and the selection of these findings as metadata. Mechanisms used in the data mining include data imputation via clustering, association rule mining, and feature selection ensemble. This paper describes the methodology of automatic annotation, presents the results on the evaluation and validation of the algorithms, and discusses the resulting metadata. We have deployed our iLOG implementation for five LOs in introductory computer science topics and collected data for over 1400 sessions. We demonstrate that iLOG successfully tracks user interactions that can be used to automate the generation of meaningful empirical usage metadata for different stakeholder groups including learners and instructors, LO developers, and researchers.