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Analysis and Prediction of Student’s Academic Performance in University Courses

by Garima Sharma, Santosh K. Vishwakarma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 160 - Number 4
Year of Publication: 2017
Authors: Garima Sharma, Santosh K. Vishwakarma
10.5120/ijca2017913045

Garima Sharma, Santosh K. Vishwakarma . Analysis and Prediction of Student’s Academic Performance in University Courses. International Journal of Computer Applications. 160, 4 ( Feb 2017), 40-44. DOI=10.5120/ijca2017913045

@article{ 10.5120/ijca2017913045,
author = { Garima Sharma, Santosh K. Vishwakarma },
title = { Analysis and Prediction of Student’s Academic Performance in University Courses },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2017 },
volume = { 160 },
number = { 4 },
month = { Feb },
year = { 2017 },
issn = { 0975-8887 },
pages = { 40-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume160/number4/27065-2017913045/ },
doi = { 10.5120/ijca2017913045 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:06:20.814349+05:30
%A Garima Sharma
%A Santosh K. Vishwakarma
%T Analysis and Prediction of Student’s Academic Performance in University Courses
%J International Journal of Computer Applications
%@ 0975-8887
%V 160
%N 4
%P 40-44
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Management of huge amount of data has always been a matter of concern. With the increase in awareness towards education, the amount of data in educational institutes is also increasing. The increasing growth of educational databases, have given rise to a new field of data mining, known as Educational Data Mining (EDM). With the help of this one can predict the academic performance of a student that can help the students, their instructors and also their guardians to take necessary actions beforehand to improve the future performance of a student. This paper deals with the implementation of ID3 decision tree algorithm to build a predictive model based on the previous performances of a student. The dataset used in this paper is the semester data of the students of a private institute of India. Rapidminer, an open source software platform is used to obtain the results.

References
  1. Han, J., Kamber, M., Pei, J.. Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann; 3rd ed.; 2012. ISBN 978-0-12- 381479-1.
  2. Pujari, K., A.. Data Mining Techniques. ISBN-10:8173713804.
  3. Scheuer, O. & McLaren, B.M. (2011). Educational Data Mining. In the Encyclopedia of the Sciences of Learning, Springer.
  4. Altujjar, Y., Altamimi,W., Al-Turaiki, I., Al-Razgan, M., Predicting Critical Courses Affecting Students Performance: A Case Study. Procedia Computer Science 82 ( 2016 ) 65 71.
  5. Badr, G., Algobail, A., Almutairi, H., Almutery, M., Predicting Students Performance in University Courses: A Case Study and Tool in KSU Mathematics Department. Procedia Computer Science 82 ( 2016 ) 80 89.
  6. Elakia, Gayathri, Aarthi, Naren J., Application of Data Mining in Educational Database for Predicting Behavioural Patterns of the Students. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4649-4652.
  7. Shahiri,A., M., Husain,W., Rashid,N.,A., A Review on Predicting Students Performance using Data Mining Techniques. Procedia Computer Science 72 ( 2015 ) 414 422.
  8. Rapidminer Studio Documentation http://docs.rapidminer.com/studio/
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining EDM KDD Classification Decision tree ID3 Student’s academic performance prediction

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