Abstract
Internet addiction refers to excessive internet use that interferes with daily life. Due to its negative impact on college students’ study and life, discovering students’ internet addiction tendencies and making correct guidance for them timely are necessary. However, at present, the research methods used on analyzing students’ internet addiction are mainly questionnaire and statistical analysis which relays on the domain experts heavily. Fortunately, with the development of the smart campus, students’ behavior data such as consumption and trajectory information in the campus are stored. With this information, we can analyze students’ internet addiction level quantitatively. In this paper, we provide an approach to estimate college students’ internet addiction level using their behavior data in the campus. In detail, we consider students’ addiction towards internet is a hidden variable which affects students’ daily time online together with other behavior. By predicting students’ daily time online, we will find students’ internet addiction levels. Along this line, we develop a linear internet addiction (LIA) model and a neural network internet addiction (NIA) model to calculate students’ internet addiction level respectively. And several experiments are conducted on a real-world dataset. The experimental results show the effectiveness of our method, and it’s also consistent with some psychological findings.
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References
Internet addiction disorder. https://en.wikipedia.org/wiki/Internet_addiction_disorder (2019). Accessed 10 Apr 2019
Burlak, G.N., Hernandez, J.A., Ochoa, A., Munoz, J.: The use of data mining to determine cheating in online student assessment. In: Electronics, Robotics and Automotive Mechanics Conference (CERMA 2006), vol. 1, pp. 161–166. IEEE (2006)
Fumero, A., Marrero, R.J., Voltes, D., Peñate, W.: Personal and social factors involved in internet addiction among adolescents: a meta-analysis. Comput. Hum. Behav. 86, 387–400 (2018)
Guan, C., Lu, X., Li, X., Chen, E., Zhou, W., Xiong, H.: Discovery of college students in financial hardship. In: 2015 IEEE International Conference on Data Mining, pp. 141–150. IEEE (2015)
He, W., et al.: Abnormal reward and punishment sensitivity associated with internet addicts. Comput. Hum. Behav. 75, 678–683 (2017)
Liu, W., Bao, X.Y., Wen, B., Chen, Q.: College students’ internet addiction investigation and related causes analysis (in Chinese). Ph.D. thesis (2010)
Malak, M.Z., Khalifeh, A.H., Shuhaiber, A.H.: Prevalence of internet addiction and associated risk factors in Jordanian school students. Comput. Hum. Behav. 70, 556–563 (2017)
Upadhayay, N., Guragain, S.: Internet use and its addiction level in medical students. Adv. Med. Educ. Pract. 8, 641 (2017)
Wei, Y.Y., Huang, G.S., Xie, Z.B., Wang, J.H., et al.: A study on the relationship between college students’ network dependence and loneliness—taking Hubei institute of technology as an example (in Chinese). J. Liuzhou Vocat. Techn. Coll. 3, 38–43 (2018)
Xi, Y., Zhuang, X., Wang, X., Nie, R., Zhao, G.: A research and application based on gradient boosting decision tree. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds.) WISA 2018. LNCS, vol. 11242, pp. 15–26. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02934-0_2
Xue, Y., et al.: Investigating the impact of mobile SNS addiction on individual’s self-rated health. Internet Res. 28(2), 278–292 (2018)
Zhang, Y., Qin, X., Ren, P.: Adolescents’ academic engagement mediates the association between internet addiction and academic achievement: the moderating effect of classroom achievement norm. Comput. Hum. Behav. 89, 299–307 (2018)
Zhao, F., Zhang, Z.H., Bi, L., Wu, X.S., Wang, W.J., Li, Y.F., Sun, Y.H.: The association between life events and internet addiction among Chinese vocational school students: the mediating role of depression. Comput. Hum. Behav. 70, 30–38 (2017)
Zhu, Y., Zhu, H., Liu, Q., Chen, E., Li, H., Zhao, H.: Exploring the procrastination of college students: a data-driven behavioral perspective. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9642, pp. 258–273. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32025-0_17
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Peng, W., Zhang, X., Li, X. (2019). Using Behavior Data to Predict the Internet Addiction of College Students. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_17
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DOI: https://doi.org/10.1007/978-3-030-30952-7_17
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