Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Data Mining Techniques in Education to Improve Adaptation of Learning in E-Learning System


Affiliations
1 Department of Computer Science, PSG CAS, Coimbatore, India
     

   Subscribe/Renew Journal


The Apply of data mining technique in to domain specific applications is being drastically improved more and more by the researchers to improve the best decision making recommendation systems like e-learning, e-commerce and etc., In this research work we discuss how the data mining techniques will improve the learning style adaptation, learning content organization and learning objects recommendations based on the instantaneous data collected through the web based learning management system like module. This paper focus the techniques like data classification and clustering techniques to predict the learning style of the peer learners based on the activities they have completed in the teaching learning activity of a particular course. The Experimental results show the clear snapshot of the analysis.

Keywords

E-Learning, Intelligent Tutor System, Data Classification, Multi Agent, Clustering.
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 247

PDF Views: 3




  • Data Mining Techniques in Education to Improve Adaptation of Learning in E-Learning System

Abstract Views: 247  |  PDF Views: 3

Authors

P. Nithya
Department of Computer Science, PSG CAS, Coimbatore, India
M. Poorani
Department of Computer Science, PSG CAS, Coimbatore, India
B. Uma Maheshwari
Department of Computer Science, PSG CAS, Coimbatore, India

Abstract


The Apply of data mining technique in to domain specific applications is being drastically improved more and more by the researchers to improve the best decision making recommendation systems like e-learning, e-commerce and etc., In this research work we discuss how the data mining techniques will improve the learning style adaptation, learning content organization and learning objects recommendations based on the instantaneous data collected through the web based learning management system like module. This paper focus the techniques like data classification and clustering techniques to predict the learning style of the peer learners based on the activities they have completed in the teaching learning activity of a particular course. The Experimental results show the clear snapshot of the analysis.

Keywords


E-Learning, Intelligent Tutor System, Data Classification, Multi Agent, Clustering.