Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Learning implicit user interest hierarchy for context in personalization
Proceedings of the 8th international conference on Intelligent user interfaces
Using a Learning Agent with a Student Model
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
High-Level Student Modeling with Machine Learning
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
A User Reputation Model for a User-Interactive Question Answering System
SKG '06 Proceedings of the Second International Conference on Semantics, Knowledge, and Grid
Using web based answer hunting system to promote collaborative learning
ICWL'05 Proceedings of the 4th international conference on Advances in Web-Based Learning
A predictive framework for retrieving the best answer
Proceedings of the 2008 ACM symposium on Applied computing
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A method for capturing the interest and authority of students about course content is proposed and implemented as a user modeling approach in a Web-based user-interactive question-answering (QA) system. An instructor has to define a topic ontology (or concept hierarchy) for the course content so that the system can generate the corresponding structure of boards to hold relevant questions. The students can interactively post questions, and browse, select, and answer others’ questions in their interested boards. The users’ log data are accumulated and organized as the users’ historical data, which are used to build the association space containing the association relations between the users’ historical data and the topic ontology. From the association space, the interest and authority of students about the questions in each board can be computed first and the interest and authority of students about each topic in the ontology can be computed based on the corresponding parameters of its offspring (sub-topics or questions). These user models (interest and authority) can be used to automatically and properly distribute relevant questions and answers to relevant students to enhance learning efficiency and help instructors design suitable teaching materials to enhance instruction efficiency.