Applications of simulated students: an exploration
Journal of Artificial Intelligence in Education
Using a Simulated Student to Repair Difficulties in Collaborative Learning
ICCE '02 Proceedings of the International Conference on Computers in Education
Using Simulated Students to Evaluate an Adaptive Testing System
ICCE '02 Proceedings of the International Conference on Computers in Education
'First Aid for You': Getting to Know Your Learning Style Using Machine Learning
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
TIDES - Using Bayesian Networks for Student Modeling
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
The impact of learning styles on student grouping for collaborative learning: a case study
User Modeling and User-Adapted Interaction
Evaluating Bayesian networks' precision for detecting students' learning styles
Computers & Education
Bloom's taxonomy revisited: specifying assessable learning objectives in computer science
Proceedings of the 39th SIGCSE technical symposium on Computer science education
Designing a Dynamic Bayesian Network for Modeling Students' Learning Styles
ICALT '08 Proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies
ICALT '08 Proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies
ICALT '09 Proceedings of the 2009 Ninth IEEE International Conference on Advanced Learning Technologies
Advanced Adaptivity in Learning Management Systems by Considering Learning Styles
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Adaptive Learning with the LS-Plan System: A Field Evaluation
IEEE Transactions on Learning Technologies
A Fuzzy-Neural Network for Classifying Learning Styles in a Web 2.0 and Mobile Learning Environment
LA-WEB '09 Proceedings of the 2009 Latin American Web Congress (la-web 2009)
A Kohonen Network for Modeling Students' Learning Styles in Web 2.0 Collaborative Learning Systems
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Using Cognitive Traits for Improving the Detection of Learning Styles
DEXA '10 Proceedings of the 2010 Workshops on Database and Expert Systems Applications
An exploratory study of the relationship between learning styles and cognitive traits
EC-TEL'06 Proceedings of the First European conference on Technology Enhanced Learning: innovative Approaches for Learning and Knowledge Sharing
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Adaptive learning algorithm of self-organizing teams
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
A huge number of studies attest that learning is facilitated if teaching strategies are in accordance with students learning styles, making the learning process more effective and improving students performances. In this context, this paper presents an automatic, dynamic and probabilistic approach for modeling students learning styles based on reinforcement learning. Three different strategies for updating the student model are proposed and tested through experiments. The results obtained are analyzed, indicating the most effective strategy. Experiments have shown that our approach is able to automatically detect and precisely adjust students' learning styles, based on the non-deterministic and non-stationary aspects of learning styles. Because of the probabilistic and dynamic aspects enclosed in automatic detection of learning styles, our approach gradually and constantly adjusts the student model, taking into account students' performances, obtaining a fine-tuned student model.