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Novel Approach to evaluate Student performance using Data Mining

Published on June 2014 by Rahul Raghavan, Sagar Wahal, Manas Saxena, Anil Vasoya
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International Conference and workshop on Advanced Computing 2014
Foundation of Computer Science USA
ICWAC2014 - Number 2
June 2014
Authors: Rahul Raghavan, Sagar Wahal, Manas Saxena, Anil Vasoya
e385367e-8782-4ade-9abd-ef7b00a27875

Rahul Raghavan, Sagar Wahal, Manas Saxena, Anil Vasoya . Novel Approach to evaluate Student performance using Data Mining. International Conference and workshop on Advanced Computing 2014. ICWAC2014, 2 (June 2014), 0-0.

@article{
author = { Rahul Raghavan, Sagar Wahal, Manas Saxena, Anil Vasoya },
title = { Novel Approach to evaluate Student performance using Data Mining },
journal = { International Conference and workshop on Advanced Computing 2014 },
issue_date = { June 2014 },
volume = { ICWAC2014 },
number = { 2 },
month = { June },
year = { 2014 },
issn = 2249-0868,
pages = { 0-0 },
numpages = 1,
url = { /proceedings/icwac2014/number2/647-1429/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and workshop on Advanced Computing 2014
%A Rahul Raghavan
%A Sagar Wahal
%A Manas Saxena
%A Anil Vasoya
%T Novel Approach to evaluate Student performance using Data Mining
%J International Conference and workshop on Advanced Computing 2014
%@ 2249-0868
%V ICWAC2014
%N 2
%P 0-0
%D 2014
%I International Journal of Applied Information Systems
Abstract

Data mining is a process of extracting hidden information from huge volumes of data. The various data mining techniques used are Classification, Clustering and Association mining. All these techniques can be applied to educational data to predict a student's academic performance and also to determine the areas he is currently lacking in. The student can evaluate his performance and find out area to improve. While calculating a student's performance a student's marks in previous semesters and his term test marks, attendance and other factors. This paper proposes the use of One R algorithm and Frequency table to predict the "score" which determines how important a particular area is. The accuracy of this algorithm can be measured by comparing the predicted score with the actual score. Teachers can forward the result of student's report. They can also determine which students are currently lacking based on their marks and other factors. Using this data teacher can motivate a student to improve his performance in a particular area. Also students can view the report themselves and can make improvements based on area which they are lacking in.

References
  1. Suchita Borkar and K. Rajeswari. 1997 Attributes Selection for Predicting Students' Academic Performance Performance using Education Data Mining and Artificial Neural Network.
  2. R. R. Kabra, R. S. Bichkar 2011,Performance Prediction of Engineering Students using Decision Trees.
  3. http://en. wikipedia. org/wiki/Weka
  4. V. Ramesh,P. Parkavi, K. Ramar ,2014. Predicting Student Performance: A Statistical and Data Mining Approach.
  5. http://www. saedsayad. com/oner. htm
  6. C. Romero, S. Ventura, "Educational data mining: A survey from 1995 to 2005", Expert system with applications 33(2007), 135-146.
  7. Singh, Randhir. An Empirical Study of Applications of Data Mining Techniques for Predicting Student Performance in Higher Education, 2014. Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  8. Jiawei Han and Micheline Kamber, "datamining Concepts and Techniques", Elsevier Second Edition.
  9. http://www. soc. napier. ac. uk/~peter/vldb/dm/node8. html.
Index Terms

Computer Science
Information Sciences

Keywords

One R Algorithm Score Decision Tree Neural Networks Knowledge Discovery.