Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE
User Modeling and User-Adapted Interaction
Adapting to intelligence profile in an adaptive educational system
Interacting with Computers
Evaluating Bayesian networks' precision for detecting students' learning styles
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Using a style-based ant colony system for adaptive learning
Expert Systems with Applications: An International Journal
"How do you know that I don't understand?" A look at the future of intelligent tutoring systems
Computers in Human Behavior
Adaptive and Intelligent Web-based Educational Systems
International Journal of Artificial Intelligence in Education
An Intelligent SQL Tutor on the Web
International Journal of Artificial Intelligence in Education
Automatic detection of learning styles for an e-learning system
Computers & Education
ICWL '009 Proceedings of the 8th International Conference on Advances in Web Based Learning
Using linguistic cues for the automatic recognition of personality in conversation and text
Journal of Artificial Intelligence Research
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Oscar: an intelligent adaptive conversational agent tutoring system
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
A time for emoting: when affect-sensitivity is and isn't effective at promoting deep learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Combining ITS and elearning technologies: opportunities and challenges
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Adaptive tutoring in an intelligent conversational agent system
Transactions on Computational Collective Intelligence VIII
Detecting students' perception style by using games
Computers & Education
An adaptation algorithm for an intelligent natural language tutoring system
Computers & Education
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This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student's learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman, 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100%. Participants also found Oscar's tutoring helpful and achieved an average learning gain of 13%.