Skip to main content
Springer Nature Link
Log in

School Clusters Concerning Informatization Level and Their Relationship with Students’ Information Literacy: A Model-Based Cluster Analysis Approach

  • Conference paper
  • First Online:
Blended Learning. Education in a Smart Learning Environment (ICBL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12218))

Included in the following conference series:

  • 2140 Accesses

Abstract

For the concerning about how school utilize information technology to cultivate students adapted to the information age, numerous studies have examined the impacts of schools’ informatization on students’ information literacy. However, few researches have considered the individual uniqueness of each school. Thus, this study was to investigate the clusters of schools in terms of informatization level and the relationship between the clusters of schools and students’ information literacy. Model-based cluster analysis was used to explore the clusters of schools and ANOVA was used to investigate the relation between the clusters of schools and students’ information literacy. The results showed that the students of schools with high informatization level tended to perform better in the information literacy test than those of schools with low informatization level. Besides, the clusters of schools were significantly related to the regions of schools. Based on the findings, the authors proposed several suggestions for improving students’ information literacy from the perspective of schools.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Dunlea, R.: Ministerial Council on Education, Employment, Training and Youth Affairs (MCEETYA) (2005)

    Google Scholar 

  2. McKeever, C., Bates, J., Reilly, J.: School library staff perspectives on teacher information literacy and collaboration. J. Inf. Lit. 11, 51 (2017). https://doi.org/10.11645/11.2.2187

    Article  Google Scholar 

  3. American Library Association: Presidential committee on information literacy. https://libguides.ala.org/InformationEvaluation/Infolit. Accessed 02 July 2020

  4. Peacock, J.: Standards, curriculum and learning: implications for professional development. In: Australian and New Zealand Information Literacy Framework: Principles, Standards and Practice, pp. 29–32 (2004)

    Google Scholar 

  5. MOE: Notice of the Ministry of Education on printing and distributing the “Education Informatization 2.0 Action Plan”. http://www.moe.gov.cn/srcsite/A16/s3342/201804/t20180425_334188.html. Accessed 05 Feb 2020

  6. Lanning, S., Mallek, J.: Factors influencing information literacy competency of college students. J. Acad. Librariansh. 43, 443–450 (2017). https://doi.org/10.1016/j.acalib.2017.07.005

    Article  Google Scholar 

  7. Scherer, R., Rohatgi, A., Hatlevik, O.E.: Students’ profiles of ICT use: identification, determinants, and relations to achievement in a computer and information literacy test. Comput. Human Behav. 70, 486–499 (2017). https://doi.org/10.1016/j.chb.2017.01.034

    Article  Google Scholar 

  8. Alkan, M., Meinck, S.: The relationship between students’ use of ICT for social communication and their computer and information literacy. Large-scale Assessments Educ. 4, 15 (2016). https://doi.org/10.1186/s40536-016-0029-z

    Article  Google Scholar 

  9. Gil-Flores, J., Rodríguez-Santero, J., Torres-Gordillo, J.J.: Factors that explain the use of ICT in secondary-education classrooms: the role of teacher characteristics and school infrastructure. Comput. Human Behav. 68, 441–449 (2017). https://doi.org/10.1016/j.chb.2016.11.057

    Article  Google Scholar 

  10. Wu, D., Li, C.C., Zhou, W.T., Tsai, C.C., Lu, C.: Relationship between ICT supporting conditions and ICT application in Chinese urban and rural basic education. Asia Pacific Educ. Rev. 20, 147–157 (2019). https://doi.org/10.1007/s12564-018-9568-z

    Article  Google Scholar 

  11. Fraillon, J., Ainley, J., Schulz, W., Friedman, T., Duckworth, D.: Preparing for Life in a Digital World: The IEA International Computer and Information Literacy Study 2018 International Report. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-38781-5

    Book  Google Scholar 

  12. Gerick, J., Eickelmann, B., Bos, W.: School-level predictors for the use of ICT in schools and students’ CIL in international comparison. Large-Scale Assessments Educ. 5 (2017). https://doi.org/10.1186/s40536-017-0037-7

  13. Gerick, J.: School level characteristics and students’ CIL in Europe – a latent class analysis approach. Comput. Educ. 120, 160–171 (2018). https://doi.org/10.1016/j.compedu.2018.01.013

    Article  Google Scholar 

  14. Wu, D., Li, C.C., Zhou, W.T., Tsai, C.C., Lu, C.: Relationship between ICT supporting conditions and ICT application in Chinese urban and rural basic education. Asia Pacific Educ. Rev. 20, 147–157 (2019). https://doi.org/10.1007/s12564-018-9568-z

    Article  Google Scholar 

  15. Mynbayeva, A., Anarbek, N.: Informatization of education in Kazakhstan: new challenges and further development of scientific schools. Int. Rev. Manag. Mark. 6, 259–264 (2016)

    Google Scholar 

  16. Lonyuduk, N.P., Begi, N.: The application of fuzzy comprehensive evaluations in the college education informationization level. IOSR J. Res. Method Educ. 8, 8–17 (2018). https://doi.org/10.9790/7388-0803040817

    Article  Google Scholar 

  17. Sekulovska, A., Mitrevski, P.: Informatization level assessment framework and educational policy implications. Int. J. Manag. Public Sect. Inf. Commun. Technol. 7, 11–22 (2016). https://doi.org/10.5121/ijmpict.2016.7402

  18. Department of Education and Science: ICT in Schools Inspectorate Evaluation Studies, pp. 1–226 (2008)

    Google Scholar 

  19. Zhang, X., Lu, C., Wu, D.: A model based on the factor analysis for assessing the ICT development in basic education and regional comparison. In: Proceedings - 5th International Conference on Educational Innovation through Technology, EITT 2016. pp. 227–231. Institute of Electrical and Electronics Engineers Inc. (2017). https://doi.org/10.1109/eitt.2016.52

  20. Zurkowski, P.G.: The information service environment relationships and priorities. Natl. Comm. Libr. Inf. Sci. 1–30 (1974). ERICNumber: ED100391

    Google Scholar 

  21. ACRL: Information literacy competency standards for higher education. Community Jr. Coll. Libr. 1–16 (2000). https://doi.org/10.1300/j107v09n04_09

  22. Fraillon, J., Schulz, W., Ainley, J.: International computer and information literacy study: assessment framework (2013). https://doi.org/10.15478/uuid:b9cdd888-6665-4e9f-a21e-61569845ed5b

  23. Fabbi, J.L.: Fortifying the pipeline: a quantitative exploration of high school factors impacting the information literacy of first-year college students. Coll. Res. Libr. 76, 31–42 (2015). https://doi.org/10.5860/crl.76.1.31

    Article  Google Scholar 

  24. Shi, Y., Peng, C., Wu, Y., Yang, H.: Research on the evaluating indicators system of information literacy for K-12 students. China Educ. Technol. 73–77 + 93 (2018)

    Google Scholar 

  25. Ministry of Education Educational Information Strategy Research Base: China Education Informatization Development Report (2017/2018)

    Google Scholar 

  26. Kassambara, A., Mundt, F.: factoextra: Extract and Visualize the Results of Multivariate Data Analyses (2019). https://cran.r-project.org/package=factoextra

  27. Banerjee, A., Davé, R.N.: Validating clusters using the Hopkins statistic. In: IEEE International Conference on Fuzzy Systems, pp. 149–153 (2004). https://doi.org/10.1109/FUZZY.2004.1375706

  28. Scrucca, L., Fop, M., Murphy, T.B., Raftery, A.E.: Mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 8, 289–317 (2016). https://doi.org/10.32614/rj-2016-021

  29. Petko, D., Prasse, D., Cantieni, A.: The interplay of school readiness and teacher readiness for educational technology integration: a structural equation model (2018). https://doi.org/10.1080/07380569.2018.1428007

  30. Pelgrum, W.J.: Obstacles to the integration of ICT in education: results from a worldwide educational assessment. Comput. Educ. 37, 163–178 (2001). https://doi.org/10.1016/S0360-1315(01)00045-8

    Article  Google Scholar 

Download references

Acknowledgments

The work was supported by the Fundamental Research Funds for the Central Universities (CCNU19Z02001).

Author information

Authors and Affiliations

  1. Central China Normal University, Wuhan, 430079, China

    Sha Zhu, Feixiong Chen, Di Wu, Jian Xu, Xujun Gui & Harrison Hao Yang

  2. State University of New York at Oswego, Oswego, 13126, USA

    Harrison Hao Yang

Authors
  1. Sha Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feixiong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Di Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jian Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xujun Gui
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Harrison Hao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Di Wu .

Editor information

Editors and Affiliations

  1. The Open University of Hong Kong, Hong Kong, China

    Simon K. S. Cheung

  2. University of Canterbury, Christchurch, New Zealand

    Richard Li

  3. Kasetsart University, Bangkok, Thailand

    Kongkiti Phusavat

  4. Kasetsart University, Bangkok, Thailand

    Naraphorn Paoprasert

  5. City University of Hong Kong, Kowloon, Hong Kong

    Lam‑For Kwok

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhu, S., Chen, F., Wu, D., Xu, J., Gui, X., Yang, H.H. (2020). School Clusters Concerning Informatization Level and Their Relationship with Students’ Information Literacy: A Model-Based Cluster Analysis Approach. In: Cheung, S., Li, R., Phusavat, K., Paoprasert, N., Kwok, L. (eds) Blended Learning. Education in a Smart Learning Environment. ICBL 2020. Lecture Notes in Computer Science(), vol 12218. Springer, Cham. https://doi.org/10.1007/978-3-030-51968-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-51968-1_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-51967-4

  • Online ISBN: 978-3-030-51968-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics