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Reseach Article

Prediction of Student's Performance based on Incremental Learning

by Pallavi Kulkarni, Roshani Ade
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 14
Year of Publication: 2014
Authors: Pallavi Kulkarni, Roshani Ade
10.5120/17440-8211

Pallavi Kulkarni, Roshani Ade . Prediction of Student's Performance based on Incremental Learning. International Journal of Computer Applications. 99, 14 ( August 2014), 10-16. DOI=10.5120/17440-8211

@article{ 10.5120/17440-8211,
author = { Pallavi Kulkarni, Roshani Ade },
title = { Prediction of Student's Performance based on Incremental Learning },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 14 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 10-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number14/17440-8211/ },
doi = { 10.5120/17440-8211 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:28:11.127417+05:30
%A Pallavi Kulkarni
%A Roshani Ade
%T Prediction of Student's Performance based on Incremental Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 14
%P 10-16
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is necessary to use Student dataset in order to analyze student's performance for future improvements in study methods and overall curricular. Incremental learning methods are becoming popular nowadays since amount of data and information is rising day by day. There is need to update classifier in order to scale up learning to manage more training data. Incremental learning technique is a way in which data is processed in chunks and the results are merged so as to possess less memory. For this reason, in this paper, four classifiers that can run incrementally: the Naive Bayes, KStar, IBK and Nearest neighbor (KNN) have been compared. It is observed that nearest neighbor algorithm gives better accuracy compared to others if applied on Student Evaluation dataset which has been used.

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Index Terms

Computer Science
Information Sciences

Keywords

Student prediction KStar NNGe IBK Naïve Bayes