Advertisement

The Impact of Digital Divides on Student Mathematics Achievement in Confucian Heritage Cultures: a Critical Examination Using PISA 2012 Data

  • Cheng Yong TanEmail author
  • Khe Foon Hew
Article

Abstract

This study critically examines if digital divides, comprising access to and use of information technology (IT) in two spheres (schools and at home), affect student achievement in Confucian heritage cultures (CHCs). The sample comprised 38,158 students from 1030 schools in seven CHCs who participated in Program for International Student Assessment (PISA) 2012. Markov chain Monte Carlo multiple imputation, hierarchical linear modeling (HLM), and latent class analysis (LCA) were employed in the analysis. Results showed that home (but not school) IT use benefited student mathematics achievement, and students with the overall least IT resources were most academically successful. These results indicate the importance of understanding the nuanced effects of digital divides in different contexts.

Keywords

Confucian heritage cultures Digital divides Information technology Mathematics achievement 

Supplementary material

10763_2018_9917_MOESM1_ESM.docx (16 kb)
ESM 1 (DOCX 16 kb)

References

  1. Baker, D. E., Goesling, B., & Letendre, G. K. (2002). Socioeconomic status, school quality, and national economic development: A cross-national analysis of the “Heyneman-Loxley Effect” on mathematics and science achievement. Comparative Education Review, 46(3), 291–312.CrossRefGoogle Scholar
  2. Berker, T., Hartmann, M., Punie, Y., & Ward, K. J. (Eds.) (2006). Domestication of media and technology. Berkshire, England: Open University Press.Google Scholar
  3. Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). Westport, CT: Greenwood.Google Scholar
  4. Carless, D. (2011). From testing to productive student learning: Implementing formative assessment in Confucian-heritage settings. New York, NY: Routledge.Google Scholar
  5. Cheema, J. (2014). A review of missing data handling methods in education research. Review of Educational Research, 84(4), 487–508.CrossRefGoogle Scholar
  6. Cheung, A. C. K., & Slavin, R. E. (2013). The effectiveness of educational technology applications for enhancing mathematics achievement in K-12 classrooms: A meta-analysis. Educational Research Review, 9, 88–113.CrossRefGoogle Scholar
  7. Chiu, M. M., & Khoo, L. (2005). Effects of resources, inequality, and privilege bias on achievement: Country, school, and student level analyses. American Educational Research Journal, 42(4), 575–603.CrossRefGoogle Scholar
  8. Collins, A., & Halverson, R. (2010). The second educational revolution: Rethinking education in the age of technology. Journal of Computer Assisted Learning, 26, 18–27.CrossRefGoogle Scholar
  9. Dedrick, R., Ferron, J., Hess, M., Hogarty, K., Kromrey, J., Lang, T., . . . Lee, R. S. (2009). Multilevel modelling: A review of methodological issues and applications. Review of Educational Research, 79(1), 69–102.Google Scholar
  10. Delen, E., & Bulut, O. (2011). The relationship between students’ exposure to technology and their achievement in science and math. The Turkish Online Journal of Educational Technology, 10(3), 311–317.Google Scholar
  11. Demir, I., Unal, H., & Kilic, S. (2010). The effect of quality of educational resources on mathematics achievement: Turkish case from PISA 2006. Procedia - Social and Behavioral Science s, 2(2), 1855–1859.Google Scholar
  12. Du, J., Havard, B., Yu, C., & Adams, J. (2004). The impact of technology use on low-income and minority students’ academic achievement: Educational Longitudinal Study of 2002. Journal of Educational Research & Policy Studies, 4(2), 21–38.Google Scholar
  13. French, J. J., French, A., & Li, W.-X. (2015). The relationship among cultural dimensions, education expenditure, and PISA performance. International Journal of Educational Development, 44, 25–34.CrossRefGoogle Scholar
  14. Giacquinta, J. B., Bauer, J. A., & Levin, J. E. (1993). Beyond technology’s promise: An examination of children’s educational computing at home. New York, NY: Cambridge University Press.Google Scholar
  15. Gil-Flores, J., Rodriguez-Santero, J., & Torres-Gordillo, J.-J. (2017). Factors that explain the use of ICT in secondary-education classrooms: The role of teacher characteristics and school infrastructure. Computers in Human Behavior, 68, 441–449.CrossRefGoogle Scholar
  16. Goldhaber, D. D., & Brewer, D. J. (2000). Does teacher certification matter? High school teacher certification status and student achievement. Educational Evaluation and Policy Analysis, 22(2), 129–145.CrossRefGoogle Scholar
  17. Güzeller, C. O., & Akın, A. (2014). Relationship between ICT variables and mathematics achievement based on PISA 2006 database: International evidence. Turkish Online Journal of Educational Technology, 13(1), 184–192.Google Scholar
  18. Han, S., & Makino, A. (2013). Learning cities in East Asia: Japan, the Republic of Korea and China. International Review of Education, 59, 443–468.CrossRefGoogle Scholar
  19. Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to Achievement. Oxon, & New York: Routledge.Google Scholar
  20. Hew, K. F., & Brush, T. (2007). Integrating technology into K‐12 teaching and learning: Current knowledge gaps and recommendations for future research. Educational Technology Research and Development, 55(3), 223–252.Google Scholar
  21. Hollingworth, S., Mansaray, A., Allen, K., & Rose, A. (2011). Parents’ perspectives on technology and children’s learning in the home: Social class and the role of the habitus. Journal of Computer Assisted Learning, 27, 347–360.CrossRefGoogle Scholar
  22. Ker, H. W. (2016). The impacts of student-, teacher- and school-level factors on mathematics achievement: An exploratory comparative investigation of Singaporean students and the USA students. Educational Psychology, 36(2), 254–276.CrossRefGoogle Scholar
  23. Kim, S., & Chang, M. (2010). Does computer use promote the mathematical proficiency of ELL students? Journal of Educational Computing Research, 42(3), 285–305.CrossRefGoogle Scholar
  24. Kirkwood, A. (2009). E-learning: You don’t always get what you hope for. Technology, Pedagogy and Education, 18(2), 107–121.CrossRefGoogle Scholar
  25. Klein, H. K., & Kleinman, D. L. (2002). The social construction of technology: Structural considerations. Science, Technology, and Human Values, 27(1), 28–52.CrossRefGoogle Scholar
  26. Korupp, S. E., & Szydlik, M. (2005). Causes and trends of the digital divide. European Sociological Review, 21(4), 409–422.CrossRefGoogle Scholar
  27. Lawless, K. A., & Pellegrino, J. W. (2007). Professional development in integrating technology into teaching and learning: Knowns, unknowns, and ways to pursue better questions and answers. Review of Educational Research, 77(4), 575–614.CrossRefGoogle Scholar
  28. Lee, Y. (2010). Views on education and achievement: Finland’s story of success and South Korea’s story of decline. KEDI Journal of Educational Policy, 7(2), 379–401.Google Scholar
  29. Li, Q., & Ma, X. (2010). A meta-analysis of the effects of computer technology on school students’ mathematics learning. Educational Psychology Review, 22(3), 215–243.CrossRefGoogle Scholar
  30. Livingstone, S. (2012). Critical reflections on the benefits of ICT in education. Oxford Review of Education, 38(1), 9–24.CrossRefGoogle Scholar
  31. Machin, S., McNally, S., & Silva, O. (2007). New technology in schools: Is there a payoff? Economic Journal, 117(522), 1145–1167.CrossRefGoogle Scholar
  32. MORI (1999). The British and technology. Basingstoke, England: Motorola.Google Scholar
  33. Muthen, B. (2001). Latent variable mixture modelling. In G. A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modelling (pp. 1–33). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  34. National Statistics. (2001). Internet access: First quarter 2001. London, England: National Statistics.Google Scholar
  35. National Statistics. (2002). Internet access: First quarter 2002. London, England: National Statistics.Google Scholar
  36. Organization for Economic Cooperation and Development. (2010). Are the new millennium learners making the grade? Technology use and educational performance in PISA. Paris, France: Centre for Educational Research and Innovation, OECD.Google Scholar
  37. Organization for Economic Cooperation and Development. (2013a). PISA 2012 results in focus: What 15-year-olds know and what they can do with what they know. Retrieved 11 September 2017 from http://www.oecd.org/pisa/keyfindings/pisa-2012-results-overview.pdf.
  38. Organization for Economic Cooperation and Development. (2013b). PISA 2012 assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy. Retrieved 2 July 2018 from https://www.oecd.org/pisa/pisaproducts/PISA%202012%20framework%20e-book_final.pdf
  39. Organization for Economic Cooperation and Development. (2014). PISA 2012 technical report. Retrieved 11 September 2017 from http://www.oecd.org/pisa/pisaproducts/PISA-2012-technical-report-final.pdf
  40. Organization for Economic Cooperation and Development. (2015). Students, computers and learning: Making the connection. Retrieved 11 September 2017 from http://www.keepeek.com/Digital-Asset-Management/oecd/education/students-computers-and-learning_9789264239555-en#page3
  41. Ono, H., & Zavodny, M. (2007). Digital inequality: A five country comparison using microdata. Social Science Research, 36, 1135–1155.CrossRefGoogle Scholar
  42. Papanastasiou, E. C., & Ferdig, R. E. (2006). Computer use and mathematical literacy: An analysis of existing and potential relationships. Journal of Computers in Mathematics and Science Teaching, 25(4), 361–371.Google Scholar
  43. Raudenbush, S., & Bryk, A. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
  44. Raudenbush, S., Bryk, A., Cheong, Y. F., Congdon, R., & du Tolt, M. (2011). HLM7: Hierarchical linear and nonlinear modeling. Lincolnwood, IL: Scientific Software International.Google Scholar
  45. Research Surveys of Great Britain (RSGB). (2001). ICT access and use: Report on the benchmark survey—DfEE research report 252. London, England: Department for Education and Employment.Google Scholar
  46. Richter, T. (2006). What is wrong with ANOVA and multiple regression? Analyzing sentence reading times with hierarchical linear models. Discourse Processes, 41(3), 221–250.CrossRefGoogle Scholar
  47. Schleicher, A. (2009). Securing quality and equity in education: Lessons from PISA. Prospects, 39, 251–263.CrossRefGoogle Scholar
  48. Selwyn, N. (1999). Differences in educational computer use: The influences of subject cultures. The Curriculum Journal, 10(1), 29–48.CrossRefGoogle Scholar
  49. Selwyn, N. (2004). Reconsidering political and popular understandings of the digital divide. New Media & Society, 6(3), 341–362.CrossRefGoogle Scholar
  50. Silverstone, R., & Hirsch, E. (Eds.) (1992). Consuming technologies: Media and information in domestic spaces. London, England: Routledge.Google Scholar
  51. Silverstone, R., Hirsch, E., & Morley, D. (1992). Information and communication technologies and the moral economy of the household. In R. Silverstone & E. Hirsch (Eds.), Consuming technologies: Media and information in domestic spaces (pp. 15–31). London, England: Routledge.Google Scholar
  52. Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., . . . Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. BMJ, 338, b2393.  https://doi.org/10.1136/bmj.b2393.
  53. Tan, C. (2015). Teacher-directed and learner-engaged: Exploring a Confucian conception of education. Ethics and Education, 10(3), 302–312.CrossRefGoogle Scholar
  54. Tan, C. (2016). Constructivism and pedagogical reform in China: Issues and challenges. Globalisation, Societies and Education, 15, 238–247.  https://doi.org/10.1080/14767724.2015.1105737.CrossRefGoogle Scholar
  55. Tran, T. T. (2013). Is the learning approach of students from the Confucian heritage culture problematic? Educational Research for Policy and Practice, 12(1), 57–65.CrossRefGoogle Scholar
  56. Van Dijk, J., & Hacker, K. (2003). The digital divided as a complex and dynamic phenomenon. Information Society, 19, 315–326.CrossRefGoogle Scholar
  57. Van Dijk, J. (2012). The network society (3rd ed.). London, England: Sage Publications Ltd.Google Scholar
  58. Visser, M., Juan, A., & Feza, N. (2015). Home and school resources as predictors of mathematics performance in South Africa. South African Journal of Education, 35(1), 1–10.CrossRefGoogle Scholar
  59. Waithaka, E. N. (2014). Family capital: Conceptual model to unpack the intergenerational transfer of advantage in transitions to adulthood. Journal of Research on Adolescence, 24(3), 471–484.CrossRefGoogle Scholar
  60. Werblow, J., & Duesbery, L. (2009). The impact of high school size on math achievement and dropout rate. The High School Journal, 92(3), 14–23.CrossRefGoogle Scholar
  61. Wittwer, J., & Senkbeil, M. (2008). Is students’ computer use at home related to their mathematical performance at school? Computers & Education, 50, 1558–1571.CrossRefGoogle Scholar
  62. Yuen, A. H. K., Lau, W. W. F., Park, J. H., Lau, G. K. K., & Chan, A. K. M. (2016). Digital equity and students’ home computing: A Hong Kong study. Asia-Pacific Educational Research, 25(4), 509–518.CrossRefGoogle Scholar
  63. Zbiek, R. M., Heid, M. K., & Blume, G. (2007). Research on technology in mathematics education: The perspective of constructs. In F. K. Lester Jr. (Ed.), Second handbook of research on mathematics teaching and learning: A project of the National Council of Teachers of Mathematics (pp. 1169–1207). Charlotte, NC: Information Age.Google Scholar
  64. Zhang, L., Khan, G., & Tahirsylaj, A. (2015). Student performance, school differentiation, and world cultures: Evidence from PISA 2009. International Journal of Educational Development, 42, 43–53.CrossRefGoogle Scholar

Copyright information

© Ministry of Science and Technology, Taiwan 2018

Authors and Affiliations

  1. 1.Faculty of EducationThe University of Hong KongPokfulamHong Kong

Personalised recommendations