International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 13, No. 1, 8 Feb. 2023
Cover page and Table of Contents: PDF (size: 378KB)
Intelligent Model for Smartphone Addiction Assessment in University Students using Android Application and Smartphone Addiction Scale
Full Text (PDF, 378KB), PP.29-34
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Author(s)
Anshika Arora
1,*
Pinaki Chakraborty
1
M.P.S. Bhatia
1
Aditya Puri
2
Index Terms
Smartphone addiction, machine learning, linear classification, android application.
Abstract
Smartphones have been owned and used ubiquitously in all facets of society utilized for a wide number of tasks such as calling and messaging, social media, surfing as well as for entertainment. Spending a large amount of time on smartphone might lead to a dependence on it for a variety of purposes. This study uses objective measures of real time smartphone usage features to assess smartphone addiction. A purpose built android application to collect real time smartphone usage has been developed and linear classification models namely Support Vector Machine and Logistic Regression are used to predict smartphone addiction among university students. Furthermore, correlation and information gain measures are used to identify most vital features of smartphone usage which contribute maximum in assessment of smartphone addiction. It has been observed that both the linear models give worthy performance with more than 80% of accuracy. Also, the most important technical features impacting smartphone addiction are longest session spent for entertainment, total time used for communication, longest session spent for communication, longest session spent for work, total time used for entertainment, longest session for news and surfing, and data usage in other activities.
Cite This Paper
Anshika Arora, Pinaki Chakraborty, M.P.S. Bhatia, Aditya Puri, "Intelligent Model for Smartphone Addiction Assessment in University Students using Android Application and Smartphone Addiction Scale", International Journal of Education and Management Engineering (IJEME), Vol.13, No.1, pp. 29-34, 2023. DOI:10.5815/ijeme.2023.01.04
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