BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Intelligent Student Profiling with Fuzzy Models
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 3 - Volume 3
A Learner's Style and Profile Recognition via Fuzzy Cognitive Map
ICALT '04 Proceedings of the IEEE International Conference on Advanced Learning Technologies
An Approach for Detecting Learning Styles in Learning Management Systems
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
Evaluating Bayesian networks' precision for detecting students' learning styles
Computers & Education
Adaptive and Intelligent Web-based Educational Systems
International Journal of Artificial Intelligence in Education
Student Modelling Based on Belief Networks
International Journal of Artificial Intelligence in Education
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Using learning styles and viewing styles in streaming video
Computers & Education
International Journal of Advanced Intelligence Paradigms
Personalized Learning Course Planner with E-learning DSS using user profile
Expert Systems with Applications: An International Journal
Unifying heterogeneous e-learning modalities in a single platform: CADI, a case study
Computers & Education
A conversational intelligent tutoring system to automatically predict learning styles
Computers & Education
Expert Systems with Applications: An International Journal
International Journal of Artificial Intelligence in Education
Detecting students' perception style by using games
Computers & Education
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A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user's browsing history and knowledge factors like user's prior knowledge. In this paper, we address the problem of extracting the learner model based on Felder-Silverman learning style model. The target learners in this problem are the ones studying basic science. Using NBTree classification algorithm in conjunction with Binary Relevance classifier, the learners are classified based on their interests. Then, learners' learning styles are detected using these classification results. Experimental results are also conducted to evaluate the performance of the proposed automated learner modeling approach. The results show that the match ratio between the obtained learner's learning style using the proposed learner model and those obtained by the questionnaires traditionally used for learning style assessment is consistent for most of the dimensions of Felder-Silverman learning style.