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Predicting Student’s Performance using Machine Learning

Vrushali A. Sungar, Pooja D. Shinde, Monali V. Rupnar. Published in Information Sciences.

Communications on Applied Electronics
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Vrushali A. Sungar, Pooja D. Shinde, Monali V. Rupnar
10.5120/cae2017652730

Vrushali A Sungar, Pooja D Shinde and Monali V Rupnar. Predicting Student’s Performance using Machine Learning. Communications on Applied Electronics 7(11):11-15, December 2017. BibTeX

@article{10.5120/cae2017652730,
	author = {Vrushali A. Sungar and Pooja D. Shinde and Monali V. Rupnar},
	title = {Predicting Student’s Performance using Machine Learning},
	journal = {Communications on Applied Electronics},
	issue_date = {December 2017},
	volume = {7},
	number = {11},
	month = {Dec},
	year = {2017},
	issn = {2394-4714},
	pages = {11-15},
	numpages = {5},
	url = {http://www.caeaccess.org/archives/volume7/number11/789-2017652730},
	doi = {10.5120/cae2017652730},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Education plays vital role in a student’s life. While choosing any field, number of options available in front of student. Student’s marks, aptitude, family background, educational environment are main essential factors while selecting a career path and these factors act as a training set to the learning system for classification. With time educational records are accumulating and increasing rapidly. To handle this data along with new features without forgetting previously learnt knowledge, incremental learning technique is introduced by machine learning. Incremental learning algorithm handles previous knowledge to take future decisions and update the system. Knowledge is represented by combining different classifiers for identification of student’s features for his/her career growth. In this paper, ensemble technique is used with incremental algorithm for student’s career choice and results over real world data sets are used to validate the effectiveness of this method.

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Keywords

Incremental learning, classifiers, machine learning, knowledge