A Study on Efficiency of Data Mining Approaches to Online -Learning Methods

Authors(2) :-Chinaguravaiah Makkena, K Anuradha

Now days, on-line learning systems increase student's ability to be told on their own. the use of information Mining in education system has become a significant analysis area, and it's accustomed collect info expeditiously from electronic learning systems. The academic systems face numerous issues like static delivery of the fabric identification of student desires and checking the standard of student interaction level. This paper surveys academic data processing approaches like pattern mining, clustering, classification, and AI. The goal of this paper is to get economical information from web-based learning systems. This work provides specific web-based courses, well-known adjective environment, and intelligent learning systems. The comparison of electronic learning systems and elaborated analysis modify students to enhance the training expertise. This paper presents the previously performed analysis connected studies, techniques that may be used to improve the scholar information and tutorial progress in an E-Learning system.

Authors and Affiliations

Chinaguravaiah Makkena
Department of Computer Science and Engineering, Sri Indu College of Engineering and Technology, JNTU Hyderabad, Hyderabad, India
K Anuradha
Department of Computer Science and Engineering, KMIT, Affiliated Jntu Hyderabad, Hyderabad, Andhra Pradesh, India

Online-Learning, Adaptive, Data Mining, Learning System

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 808-816
Manuscript Number : CSEIT11724104
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Chinaguravaiah Makkena, K Anuradha, "A Study on Efficiency of Data Mining Approaches to Online -Learning Methods ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.808-816, July-August-2017.
Journal URL : http://ijsrcseit.com/CSEIT11724104

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