AHAM: a Dexter-based reference model for adaptive hypermedia
Proceedings of the tenth ACM Conference on Hypertext and hypermedia : returning to our diverse roots: returning to our diverse roots
Machine Learning
A Web Based Adaptive Educational System
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Local distance-based classification
Knowledge-Based Systems
A framework for WWW user activity analysis based on user interest
Knowledge-Based Systems
A User Modeling Approach to Web Based Adaptive Educational Hypermedia Systems
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Describing User Interactions in Adaptive Interactive Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
A hybrid genetic algorithm for classification
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
A Taxonomy of Similarity Mechanisms for Case-Based Reasoning
IEEE Transactions on Knowledge and Data Engineering
A classification-based review recommender
Knowledge-Based Systems
User models for adaptive hypermedia and adaptive educational systems
The adaptive web
Modeling individualization in a bayesian networks implementation of knowledge tracing
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
User modeling based on emergent domain semantics
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Task-Based user modelling for knowledge work support
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
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Creating an efficient user knowledge model is a crucial task for web-based adaptive learning environments in different domains. It is often a challenge to determine exactly what type of domain dependent data will be stored and how it will be evaluated by a user modeling system. The most important disadvantage of these models is that they classify the knowledge of users without taking into account the weight differences among the domain dependent data of users. For this purpose, both the probabilistic and the instance-based models have been developed and commonly used in the user modeling systems. In this study a powerful, efficient and simple 'Intuitive Knowledge Classifier' method is proposed and presented to model the domain dependent data of users. A domain independent object model, the user modeling approach and the weight-tuning method are combined with instance-based classification algorithm to improve classification performances of well-known the Bayes and the k-nearest neighbor-based methods. The proposed knowledge classifier intuitively explores the optimum weight values of students' features on their knowledge class first. Then it measures the distances among the students depending on their data and the values of weights. Finally, it uses the dissimilarities in the classification process to determine their knowledge class. The experimental studies have shown that the weighting of domain dependent data of students and combination of user modeling algorithms and population-based searching approach play an essential role in classifying performance of user modeling system. The proposed system improves the classification accuracy of instance-based user modeling approach for all distance metrics and different k-values.