A Study on impact of smartphone addiction on academic performance

  • Authors

    • Pamela Chaudhury
    • Hrudaya Kumar Tripathy
    2018-03-11
    https://doi.org/10.14419/ijet.v7i2.6.10066
  • Academic performance, Classification, Internet connectivity, Pearson’s correlationcoefficient, Smartphone addiction.
  • Abstract

    Smartphone addiction is increasingly affecting the masses and is negatively impacting the younger generation. Several researches have been done to study the impact of internet and smartphone addiction. However no work has been done to predetermine academic performance from smartphone addiction using data mining techniques. A total of 222 University students participated in the questionnaire. The survey questionnaire consisted of demographic information, internet access pattern and smartphone addiction pattern. Data was analysed using machine learning techniques using classification models. The results further encouraged us to find the correlation between smartphone addiction and academic performance. Pearson’ correlation was used to establish that smartphone usage had a negative impact on academic performance. Additionally other attributes like internet connectivity and active involvement in outdoor sports activities were investigated. Experimental results confirmed a negative correlation of these attributes with academic performance. The findings were of immense use and could be used to reduce the internet addiction amongst the student community and also enhance their academic performance

  • References

    1. [1] Internet live stat, (2017) data from www.internetlivestats.com/internet-users.

      [2] L. Chen and R. Nath, “Understanding the underlying factors of Internet addiction across cultures: A comparison studyâ€, in Electronic Commerce Research and Applications, Vol. 17, 1, pp 38-48, 2016.

      [3] A. Lepp, L.Jian, B. Jacob, S. Saba, “Exploring the relationships between college students’ cell phone use, personality and leisureâ€, Computers in Human Behavior, Elsevier, Vol. 43, 1, pp. 210-219, 2015.

      [4] L. Liang, D. Zhou,C. Yuan, A. Shao, and Y. Bian, “Gender differences in the relationship between internet addiction and depression: A cross-lagged study in Chinese adolescentsâ€, Computers in Human Behavior, Vol. 63,1, pp. 463-470, October 2016.

      [5]Livemint, (2017) data from: http:/ /www.livemint.com/Technology/ Dml7WqfGXIjhpQobNjBtnN/ India-overtakes-US-to-become-second-largest-smartphone-marke.html

      [6] Smartinsights (2017) data from: https://www.smartinsights.com/ social-media-marketing/social-media-strategy/new-global-social-media-research.

      [7] P. Kirschner and A. Karpinski, “Facebook® and academic performanceâ€, Computers in Human Behavior, Vol. 26, 6, pp. 1237-1245, 2010.

      [8] M. Samahaand N.Hawi, “Relationships among smartphone addiction, stress, academic performance, and satisfaction with lifeâ€, Computers in Human Behavior, vol. 57, 1, pp. 321-325, 2016.

      [9] M. Joseph&L. Andrew, “Online Communication Attitudes as Predictors of Problematic Internet Use and Well-Being Outcomesâ€, Southern Communication Journal, 77(5), pp. 403-419, 2012.

      [10] M. Kwon, D. J. Kim, H. Cho, & S. Yang 2013, “The smartphone addiction scale: development and validation of a short version for adolescentsâ€, PLoS ONE 8(12): e83558. https://doi.org/ 10.1371/ journal.pone.0083558.

      [11] P. Chaudhury, S. Mishra, H.K.Tripathy, and B.K. Mishra, “Enhancing the capabilities of student result prediction systemâ€, in Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies (ICTCS ‘16),Udaipur, India, ACM, New York, NY, USA, Article 91, 04–05 March, 6pp.

      [12] J.Han, M. Kamber&J. Pei,“Data Preprocessingâ€,in The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann, Boston, 2012, Pages 83-124, Data Mining (Third Edition), ISBN 9780123814791. Elsevier, 2012.

      [13]R. Caruanaand A. Niculescu-Mizil,“An empirical comparison of supervised learning algorithmsâ€,in Proceedings of the 23rd international conference on Machine learning (ICML '06). ACM, pp. 161-168. DOI: https://doi.org/10.1145/1143844.1143865, 2006.

      [14] X. Wu, V.Kumar, J.Ross Quinlan, et al., “Top 10 algorithms in data miningâ€, Knowledge Information System,Vol. 14,1, pp. 1-37. DOI 10.1007/s10115-007-0114-2.

      [15] E. Frank, M. Hall, and H. Ian,Witten,“The WEKA Workbench. Online Appendix for, Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.

  • Downloads

  • How to Cite

    Chaudhury, P., & Kumar Tripathy, H. (2018). A Study on impact of smartphone addiction on academic performance. International Journal of Engineering & Technology, 7(2.6), 50-53. https://doi.org/10.14419/ijet.v7i2.6.10066

    Received date: 2018-03-11

    Accepted date: 2018-03-11

    Published date: 2018-03-11