Pixie: a shell for developing intelligent tutoring systems
Artificial intelligence and education; vol. 1: learning environments and tutoring systems
Instance-Based Learning Algorithms
Machine Learning
Deductive error diagnosis and inductive error generalization for intelligent tutoring systems
Journal of Artificial Intelligence in Education
Refinement-based student modeling and automated bug library construction
Journal of Artificial Intelligence in Education
Exploiting learning techniques for the acquisition of user stereotypes and communities
UM '99 Proceedings of the seventh international conference on User modeling
Multistrategy Discovery and Detection of Novice Programmer Errors
Machine Learning - Special issue on multistrategy learning
SmexWeb: An adaptive web-based hypermedia teaching system
Journal of Interactive Learning Research - Special double issue on intelligent systems/tools in training and lifelong learning
Machine Learning
Using Decision Trees for Agent Modeling: Improving Prediction Performance
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Human Plausible Reasoning for Intelligent Help
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Empirical Evaluation of User Models and User-Adapted Systems
User Modeling and User-Adapted Interaction
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Simultaneous Evaluation of Multiple Topics in SIETTE
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Hierarchical Representation and Evaluation of the Student in an Intelligent Tutoring System
ITS '02 Proceedings of the 6th 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
Stereotypes, Student Models and Scrutability
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Theoretical and Practical Considerations for Web-Based Intelligent Language Tutoring Systems
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
CLARISSE: A Machine Learning Tool to Initialize Student Models
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Web Passive Voice Tutor: An Intelligent Computer Assisted Language Learning System over the WWW
ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Personalised hypermedia presentation techniques for improving online customer relationships
The Knowledge Engineering Review
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Cluster-based predictive modeling to improve pedagogic reasoning
Computers in Human Behavior
Cooperative CBR System for Sharing Student Models in Cooperative Intelligent Tutoring Systems
CEEMAS '07 Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V
Predicting High-level Student Responses Using Conceptual Clustering
Proceedings of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation
Computer Speech and Language
Machine learning based learner modeling for adaptive web-based learning
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
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Initializing a student model for individualized tutoring in educational applications is a difficult task, since very little is known about a new student. On the other hand, fast and efficient initialization of the student model is necessary. Otherwise the tutoring system may lose its credibility in the first interactions with the student. In this paper we describe a framework for the initialization of student models in Web-based educational applications. The framework is called ISM. The basic idea of ISM is to set initial values for all aspects of student models using an innovative combination of stereotypes and the distance weighted k-nearest neighbor algorithm. In particular, a student is first assigned to a stereotype category concerning her/his knowledge level of the domain being taught. Then, the model of the new student is initialized by applying the distance weighted k-nearest neighbor algorithm among the students that belong to the same stereotype category with the new student. ISM has been applied in a language learning system, which has been used as a test-bed. The quality of the student models created using ISM has been evaluated in an experiment involving classroom students and their teachers. The results from this experiment showed that the initialization of student models was improved using the ISM framework.