Cluster Analysis for Users' Modeling in Intelligent E-Learning Systems
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Using Clustering Technique for Students' Grouping in Intelligent E-Learning Systems
USAB '08 Proceedings of the 4th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society on HCI and Usability for Education and Work
| Hi-index | 0.00 |
Many researchers agree that considering learning styles increases the learning progress and makes learning easier for students. Learning management systems (LMS) are very successful in e-education but do not incorporate learning styles. As a requirement for taking learning styles into consideration in LMS, the behavior of students in online courses needs to be investigated. In this paper, we analyze the behavior of 43 students based on their learning styles and predefined patterns of behavior. Firstly, we concentrated on whether students with different learning style preferences act differently in the course. This information can be used to create courses that include features for each learning style. Secondly, we investigated correlations between the learning style preferences and the behavior of students during the course. These correlations can be use to develop an approach for identifying learning styles in LMS based on students' behavior.