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International Conference on Recent Trends in Image Processing and Pattern Recognition

RTIP2R 2018: Recent Trends in Image Processing and Pattern Recognition pp 541–549Cite as

Data Mining Learning of Behavioral Pattern of Internet User Students

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1037))

Abstract

This study focuses on the students internet use in their personal life. Various aspects has been assumed with the help of data mining technique and tried to obtain some hidden outcomes of student’s internet behavior. The special focus is to test the significance based the on gender wise, location wise and different financial income group perspective to discriminate the behavioral pattern. Here, online survey is carrying out and 217 students information is gathered. The random sampling is performed for collection of data. The unsupervised and supervised learning analysis was carried out with SPSS 22.0v software package. The obtained result helps in future planning the direction of appropriate use of internet by students.

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References

  1. Achana, R.A., Hegadi, R.S., Manjunath, T.N.: A novel data security framework using E-MOD for big data. In: IEEE International WIE Conference on Electrical and Computer Engineering, pp. 546–551 (2015)

    Google Scholar 

  2. Adeyemi, O.: Measures of association for research in educational planning and administration. Res. J. Math. Stat. 3(3), 82–90 (2011)

    Google Scholar 

  3. Ahad, M.A., Tripathi, G., Agarwal, P.: Learning analytics for IoE based educational model using deep learning techniques: architecture, challenges and applications. Smart Learn. Environ. 5(1), 7 (2018)

    Article  Google Scholar 

  4. Ali, S., Haider, Z., Munir, F., Khan, H., Ahmed, A.: Factors contributing to the students academic performance: a case study of Islamia University Sub-Campus. Am. J. Educ. Res. 1(8), 283–289 (2013)

    Article  Google Scholar 

  5. Anderson, K.J.: Internet use among college students: an exploratory study. J. Am. Coll. Health 50(1), 21–26 (2001)

    Article  Google Scholar 

  6. Bratti, M., Staffolani, S.: Student time allocation and educational production functions, University of Ancona Department of Economics Working Paper No. 170 (2002)

    Google Scholar 

  7. Ceyhan, A.A.: Predictors of problematic internet use on Turkish university students. Cyberpsychol. Behav. 11(3), 363–366 (2008)

    Article  Google Scholar 

  8. Chou, C., Condron, L., Belland, J.C.: A review of the research on internet addiction. Educ. Psychol. Rev. 17(4), 363–388 (2005)

    Article  Google Scholar 

  9. Considine, G., Zappala, G.: Influence of social and economic disadvantage in the academic performance of school students in Australia. J. Sociol. 38, 129–148 (2002)

    Article  Google Scholar 

  10. Cooper, D.T., Klein, J.L.: College students’ online pornography use: contrasting general and specific structural variables with social learning variables. Am. J. Crim. Justice. 43(3), 551–569 (2018)

    Article  Google Scholar 

  11. Divya, M., Manjunath, T.N., Hegadi, R.S.: A study on developing analytical model for groundnut pest management using data mining techniques. In: IEEE’s International Conference on Computational Intelligence and Communication Networks, pp. 691–696 (2014)

    Google Scholar 

  12. Field, A.: Discovering Statistics using R for Windows. Sage publications, Thousand Oaks (2000)

    MATH  Google Scholar 

  13. Graetz, B.: Socio-economic status in education research and policy in John A., et al., socio-economic status and school education DEET/ACER Canberra. J. Pediatr. Psychol. 20(2), 205–216 (1995)

    Article  Google Scholar 

  14. Gross, E.F.: Adolescent internet use: what we expect, what teens report. J. Appl. Dev. Psychol. 25(6), 633–649 (2004)

    Article  Google Scholar 

  15. Gupta, S.L., Hitesh, G.: SPSS 17.0 for Researchers. International book house, Pvt. Ltd. (2011)

    Google Scholar 

  16. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, New York (2011)

    MATH  Google Scholar 

  17. Jang, Y., Kim, J., Lee, W.: Development and application of internet of things educational tool based on peer to peer network. Peer-to-Peer Network. Appl. 11(6), 1217–1229 (2018)

    Article  Google Scholar 

  18. Jenaro, C., Flores, N., Gómez-Vela, M., González-Gil, F., Caballo, C.: Problematic internet and cell-phone use: psychological, behavioural, and health correlates. Addict. Res. Theor. 15(3), 309–320 (2007)

    Article  Google Scholar 

  19. Joiner, R., Gavin, J., Duffield, J., Brosnan, M., Crook, C., Durndell, A., Lovatt, P.: Gender, internet identification, and internet anxiety: correlates of internet use. Cyber Psychol. Behav. 8(4), 371–378 (2005)

    Article  Google Scholar 

  20. Kim, Y., Sohn, D., Choi, S.M.: Cultural difference in motivations for using social network sites: a comparative study of American and Korean college students. Comput. Hum. Behav. 27(1), 365–372 (2011)

    Article  Google Scholar 

  21. Kimmons, R., Veletsianos, G.: Public internet data mining methods in instructional design, educational technology, and online learning research. TechTrends. 62(5), 492–500 (2018)

    Article  Google Scholar 

  22. Liñán, L.C., Pérez, Á.A.J.: Educational data mining and learning analytics: differences, similarities, and time evolution. Int. J. Educ. Technol. High. Educ. 12(3), 98–112 (2015)

    Google Scholar 

  23. Manjunath, T.N., Hegadi, R.S.: Statistical data quality model for data migration business enterprise. Int. J. Soft Comput. 8(5), 340–351 (2013)

    Google Scholar 

  24. Dunham, M.: Data Mining: Introductory and Advanced Topics. Pearson publications, USA (2002)

    Google Scholar 

  25. Metzger, M.J., Flanagin, A.J., Zwarun, L.: College student web use, perceptions of information credibility, and verification behaviour. Comput. Educ. 41(3), 271–290 (2003)

    Article  Google Scholar 

  26. Nalwa, K., Anand, A.P.: Internet addiction in students: a cause of concern. Cyberpsychol. Behav. 6(6), 653–656 (2003)

    Article  Google Scholar 

  27. Ogata, H., Oi, M., Mohri, K., Okubo, F., Shimada, A., Yamada, M., Wang, J., Hirokawa, S.: Learning analytics for e-book-based educational big data in higher education. In: Yasuura, H., Kyung, C.-M., Liu, Y., Lin, Y.-L. (eds.) Smart Sensors at the IoT Frontier, pp. 327–350. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55345-0_13

    Chapter  Google Scholar 

  28. Özcan, N.K., Buzlu, S.: Internet use and its relation with the psychosocial situation for a sample of university students. Cyber Psychol. Behav. 10(6), 767–772 (2007)

    Article  Google Scholar 

  29. Pritchard, M.E., Wilson, G.S.: Using emotional and social factors to predict student success. J. Coll. Student Dev. 44(1), 18–28 (2003)

    Article  Google Scholar 

  30. Raman, R., Vachharajani, H., Achuthan, K.: Students motivation for adopting programming contests: innovation-diffusion perspective. Educ. Inf. Technol. 23(5), 1919–1932 (2018)

    Article  Google Scholar 

  31. Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. World scientific, Singapore (2014)

    Book  Google Scholar 

  32. Tsitsika, A., et al.: Internet use and misuse: a multivariate regression analysis of the predictive factors of internet use among Greek adolescents. Eur. J. Pediatr. 168(6), 655 (2009)

    Article  Google Scholar 

  33. Weiser, E.B.: Gender differences in internet use patterns and Internet application preferences: a two-sample comparison. Cyber Psychol. Behav. 3(2), 167–178 (2000)

    Article  Google Scholar 

  34. Yogish, D., Manjunath, T.N., Hegadi, R.S.: Survey on trends and methods of an intelligent answering system. In: IEEE’s International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques, pp. 346–353 (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Mathematical Sciences, SRTM University, Nanded, MS, India

    Aniket Muley & Atish Tangawade

Authors
  1. Aniket Muley
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  2. Atish Tangawade
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Correspondence to Aniket Muley .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Muley, A., Tangawade, A. (2019). Data Mining Learning of Behavioral Pattern of Internet User Students. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_48

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  • DOI: https://doi.org/10.1007/978-981-13-9187-3_48

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9186-6

  • Online ISBN: 978-981-13-9187-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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