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Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Oyerinde O. D., Chia P. A.
10.5120/ijca2017912671

Oyerinde O D. and Chia P A.. Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression. International Journal of Computer Applications 157(4):37-44, January 2017. BibTeX

@article{10.5120/ijca2017912671,
	author = {Oyerinde O. D. and Chia P. A.},
	title = {Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {157},
	number = {4},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {37-44},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume157/number4/26822-2017912671},
	doi = {10.5120/ijca2017912671},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Learning Analytics is an area of Information Systems research that integrates data analytics and data mining techniques with the aim of enhancing knowledge management and learning delivery in education management..The current research proposes a framework to administer prediction of Students Academic Performance using Learning Analytics techniques. The research illustrates how this model is used effectively on secondary data collected from the Department of Computer Science, University of Jos, Nigeria.Multiple Linear Regression was used with the aid of the Statistical Package for Social Sciences (SPSS) analysis tool. Statistical Hypothesis testing was then used to validate the model with a 5% level of significance.

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Keywords

Learning Analytics, Educational Data Mining, Students Academic Performance, Multiple Linear Regression.