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Classification of Students in a Web based Learning Environment

by Mohit Shroff, Prashant Kanade, Prashant Zaware
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 6 - Number 3
Year of Publication: 2013
Authors: Mohit Shroff, Prashant Kanade, Prashant Zaware
10.5120/ijais13-451029

Mohit Shroff, Prashant Kanade, Prashant Zaware . Classification of Students in a Web based Learning Environment. International Journal of Applied Information Systems. 6, 3 ( October 2013), 22-27. DOI=10.5120/ijais13-451029

@article{ 10.5120/ijais13-451029,
author = { Mohit Shroff, Prashant Kanade, Prashant Zaware },
title = { Classification of Students in a Web based Learning Environment },
journal = { International Journal of Applied Information Systems },
issue_date = { October 2013 },
volume = { 6 },
number = { 3 },
month = { October },
year = { 2013 },
issn = { 2249-0868 },
pages = { 22-27 },
numpages = {9},
url = { https://www.ijais.org/archives/volume6/number3/536-1029/ },
doi = { 10.5120/ijais13-451029 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:52:27.146586+05:30
%A Mohit Shroff
%A Prashant Kanade
%A Prashant Zaware
%T Classification of Students in a Web based Learning Environment
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 6
%N 3
%P 22-27
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Predicting academic performance and monitoring the progress of students in a web based learning environment is a critical issue. In this paper, K-means Clustering algorithm is implemented to predict student performance at the end of the semester. The results can be used to enhance the understanding of the course instructor to reform the syllabus, thereby increasing the chances of a higher score by lagging students. Higher education institutes offering distance learning courses through web can use this model to identify which area of their course can be improved by data mining technology to achieve higher student marks.

References
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  2. Naeimeh D. , Mohammad S. "A New Model for Using Data Mining Technology in Higher Educational Systems" Faculty of Information Technology, Multimedia University (MMU), Cyberjaya, Malaysia. 2004 IEEE.
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  5. "Predicting student performance: An Application of Data Mining methods with an Educational Web based system" 33rd ASEEIIEEE Frontiers in Education Conference, November 2008.
  6. Alsabti, Khaled; Ranka, Sanjay; and Singh, Vineet, "An efficient k-means clustering algorithm" (1997). Electrical Engineering and Computer Science. Paper no. 43.
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  8. Juntao Wang , Xiaolong Su. School of Computer Science and Technology China University of Mining & Technology Xuzhou, China "An improved K-Means clustering algorithm"2011. IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

Web based learning performance measures k means.