Research on college students’ information literacy based on big data

  • Yao Ying


With the rapid development of network technology and information technology, the information literacy situation of college students has attracted more and more attention of the society. With the emergence and development of big data, the development of technology changes rapidly in an era of information, It’s the important problem how to fit the data and get effective training and study the development of the times on the background of the information literacy of the students, but it does not get enough attention. In urgent need of society it attaches great importance to the cultivation of information literacy, which pays attention to the research on College Students’ information literacy. In this paper, under the background of big data development of College Students’ information literacy as a starting point, a detailed analysis of students should possess in the context of the present era of information literacy and its training methods, through visiting, comprehensive analysis of questionnaires and interviewing, the aim of this research is to further explore the situation under the background of big data challenges and corresponding solutions for large, it can provide a solution for reference of the training and development of students’ information literacy.


Big data Information literacy College students Research Solution 



Shandong province education science planning Projects in 13th Five-Year “College Students Information Literacy Survey and Enhancement Strategies” (YC2017051).


  1. 1.
    Bhatt, M.: The abolishment of colleges and its implications on ELT in Nigeria. Procedia 242(25), 167–169 (2017)Google Scholar
  2. 2.
    Hu, Y.: Research on the application of fault tree analysis for building fire safety of hotels. Proc. Eng. 135(1), 45–46 (2017)Google Scholar
  3. 3.
    Flaherty, G.T.: Research on the move: the potential applications of mobile health technology in travel medicine. J. Travel Med. 23(6), 49–55 (2016)CrossRefGoogle Scholar
  4. 4.
    Jelača, M.S., Bjekić, R., Leković, B.: A proposal for research framework based on the theoretical analysis and practical application of MLQ questionnaire. Econ. Themes 54(4), 45–46 (2016)Google Scholar
  5. 5.
    Taoukis, P., Stoforos, N.: Editorial to the IFSET Special Issue “Advances in research and applications of nonthermal technologies for food processing and preservation” based on the 2015 International Nonthermal Processing Workshop. Innov. Food Sci. Emerg. Technol. 38(1), 220–227 (2016)Google Scholar
  6. 6.
    Bello, O., Holzmann, J., Yaqoob, T., Teodoriu, C.: Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art. J. Artif. Intell. Soft Comput. Res. 5(2), 45–46 (2015)CrossRefGoogle Scholar
  7. 7.
    Colchester, K., Hagras, H., Alghazzawi, D.: A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. J. Artif. Intell. Soft Comput. Res. 7(1), 220–227 (2017)CrossRefGoogle Scholar
  8. 8.
    Wiederhold, B.K., Riva, G., Wiederhold, M.D., Cipresso, P., Riva, G.: Virtual reality for artificial intelligence: human-centered simulation for social science. Stud. Health Technol. Inform. 219, 89–102 (2015)Google Scholar
  9. 9.
    Asker Firoozjaee, R., Khamehchi, E.: A novel approach to assist history matching using artificial intelligence. Chem. Eng. Commun. 202(4), 567–569 (2015)Google Scholar
  10. 10.
    Pizoń, J., Lipski, J.: Manufacturing process support using artificial intelligence. Appl. Mech. Mater. 4171(791), 294–304 (2015)Google Scholar
  11. 11.
    Leung, C.: The “social” in big data teaching: abstracted norms versus situated enactments. J. English Lingua Franca 2(2), 43–47 (2013)MathSciNetGoogle Scholar
  12. 12.
    Oppermann, M., Brazil, L.: Use of artificial intelligence in diagnosis and clinical conduct of lumbar spinal stenosis. AR Bras Neurocir 1026(13), 47–56 (2015)Google Scholar
  13. 13.
    Hurst, N.: Doing it by the book: training student teachers at the faculty of letters, the University of Porto (FLUP) to evaluate big data teaching (ELT) materials. e-TEALS 6(1), 24–29 (2016)Google Scholar
  14. 14.
    Lieto, A., Bhatt, M., Oltramari, A., Vernon, D.: The role of cognitive architectures in general artificial intelligence. Cogn. Syst. Res. 1006(16), 45–46 (2017)Google Scholar
  15. 15.
    Juszczyk, M.: The challenges of nonparametric cost estimation of construction works with the use of artificial intelligence tools. Proc. Eng. 196(9), 49–55 (2017)Google Scholar
  16. 16.
    Agnes Ada Okpe: The abolishment of colleges and its implications on ELT in Nigeria. Procedia 232(24), 567–569 (2017)Google Scholar
  17. 17.
    Sun, S., Tallón-Ballesteros, A.J., Pamučar, D.S., Liu, F., Song, X., Huang, F.: Research on the application of data mining in the field of electronic commerce. Front. Artif. Intell. Appl. 293(27), 294–304 (2015)Google Scholar
  18. 18.
    Leung, C.: The “social” in Big Data Teaching: abstracted norms versus situated enactments. J. Engl. Lingua Franca 2(2), 43–47 (2013)MathSciNetGoogle Scholar
  19. 19.
    Gürkaynak, E.: Preparation for central common examination is not a Torment but Fun. Procedia 232(24), 47–56 (2016)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of EducationJiNing UniversityQufuChina

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