Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE
User Modeling and User-Adapted Interaction
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
eTeacher: Providing personalized assistance to e-learning students
Computers & Education
A learning style classification mechanism for e-learning
Computers & Education
Automatic detection of learning styles for an e-learning system
Computers & Education
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Computer games and traditional CS courses
Communications of the ACM - Finding the Fun in Computer Science Education
Personalisation of Learning in Virtual Learning Environments
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Using Cognitive Traits for Improving the Detection of Learning Styles
DEXA '10 Proceedings of the 2010 Workshops on Database and Expert Systems Applications
Guest Editorial: Special Section on Game-Based Learning
IEEE Transactions on Learning Technologies
Dynamic Adaptive Mechanism in Learning Management System Based on Learning Styles
ICALT '11 Proceedings of the 2011 IEEE 11th International Conference on Advanced Learning Technologies
A conversational intelligent tutoring system to automatically predict learning styles
Computers & Education
Enhancing student learning through hypermedia courseware andincorporation of student learning styles
IEEE Transactions on Education
An Adaptive Course Generation Framework
International Journal of Distance Education Technologies
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Knowing students' learning styles allows us to improve their experience in an educational environment. Particularly, the perception style is one of the most important dimensions of the learning styles since it describes the way students perceive the world as well as the kind of learning content they prefer. Several approaches to detect students' perception style according to Felder's model have been proposed. However, these approaches exhibit several limitations that make their implementation difficult. Thus, we propose a novel approach to detect the perception style of a student by analyzing his/her interaction with games, namely puzzle games. To carry out this detection, we track how students play a puzzle game and extract information about this interaction. Then, we train a Naive Bayes Classifier to infer the students' perception style by using the information extracted. We have evaluated our proposed approach with 47 Computer Engineering students. Experimental results showed that the perception style was successfully predicted through the use of games, with an accuracy of 85%. Finally, we conclude that games are a promising environment where the students' perception style can be detected.