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Data Mining Approach towards Students Behavior Assessment Methods for Higher Studies

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 30
Year of Publication: 2018
Authors:
Sharad Gangele, Kirti Soni, Sunil Patil
10.5120/ijca2018918099

Sharad Gangele, Kirti Soni and Sunil Patil. Data Mining Approach towards Students Behavior Assessment Methods for Higher Studies. International Journal of Computer Applications 181(30):11-14, November 2018. BibTeX

@article{10.5120/ijca2018918099,
	author = {Sharad Gangele and Kirti Soni and Sunil Patil},
	title = {Data Mining Approach towards Students Behavior Assessment Methods for Higher Studies},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2018},
	volume = {181},
	number = {30},
	month = {Nov},
	year = {2018},
	issn = {0975-8887},
	pages = {11-14},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume181/number30/30171-2018918099},
	doi = {10.5120/ijca2018918099},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The quality education to the students is primary point of higher educational institutions. One of the method to achieve high quality education in higher education system is analysis of students behavior with reference to examination performance, mark sheets, abnormal values and other students activities. Data mining methods discovers knowledge from these records for analysis and prediction about students behavior. In this paper, data mining techniques such as association rules and classification are applied to analyze and present a behavior model of students. The students behavior assessment framework is proposed as model for analysis using data mining technique, the model presents the indication to the critical quantities that regulate the students behavior on learning method. The proposed framework can be applied to extract valuable data that shows all characteristic of student behavior by clustering and subdivision of the student behavior large data set.

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Keywords

Behavior Analysis, Data Mining Technique, Prediction Analysis, Association, Classification