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Reseach Article

Hypothesis-Testing Factors Affecting Students’ Academic Performance

by Urmika Kasi, Shreyas Prasad
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 30
Year of Publication: 2020
Authors: Urmika Kasi, Shreyas Prasad
10.5120/ijca2020920846

Urmika Kasi, Shreyas Prasad . Hypothesis-Testing Factors Affecting Students’ Academic Performance. International Journal of Computer Applications. 175, 30 ( Nov 2020), 32-36. DOI=10.5120/ijca2020920846

@article{ 10.5120/ijca2020920846,
author = { Urmika Kasi, Shreyas Prasad },
title = { Hypothesis-Testing Factors Affecting Students’ Academic Performance },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2020 },
volume = { 175 },
number = { 30 },
month = { Nov },
year = { 2020 },
issn = { 0975-8887 },
pages = { 32-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number30/31643-2020920846/ },
doi = { 10.5120/ijca2020920846 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:39:54.382286+05:30
%A Urmika Kasi
%A Shreyas Prasad
%T Hypothesis-Testing Factors Affecting Students’ Academic Performance
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 30
%P 32-36
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Examining the factors affecting students’ academic performance is a significant aspect of consideration as it can improve teaching and learning processes. Numerous academic and non-academic facets affect student performance, such as study time, frequency of absences, recreational activities, and interpersonal relationships. Educational data mining (EDM) and learning analytics (LA) are two closely related fields that reveal useful information from educational databases to generate actionable insights. This paper investigates the aforementioned feature sets by hypothesizing the impact of these factors on student performance based on existing studies and employs a combination of LA and EDM techniques to test the hypotheses. Experimental results show that the presented hypotheses were consistent on nearly all accounts. The insights of this study can be used to bolster student performance even beyond an academic scope by educational policy improvement.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Educational Data Mining Learning Analytics Student Performance