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

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.

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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.

  2. Berker, T., Hartmann, M., Punie, Y., & Ward, K. J. (Eds.) (2006). Domestication of media and technology. Berkshire, England: Open University Press.

  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.

  4. Carless, D. (2011). From testing to productive student learning: Implementing formative assessment in Confucian-heritage settings. New York, NY: Routledge.

  5. Cheema, J. (2014). A review of missing data handling methods in education research. Review of Educational Research, 84(4), 487–508.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  19. Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to Achievement. Oxon, & New York: Routledge.

  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.

  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.

  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.

  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.

  24. Kirkwood, A. (2009). E-learning: You don’t always get what you hope for. Technology, Pedagogy and Education, 18(2), 107–121.

  25. Klein, H. K., & Kleinman, D. L. (2002). The social construction of technology: Structural considerations. Science, Technology, and Human Values, 27(1), 28–52.

  26. Korupp, S. E., & Szydlik, M. (2005). Causes and trends of the digital divide. European Sociological Review, 21(4), 409–422.

  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.

  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.

  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.

  30. Livingstone, S. (2012). Critical reflections on the benefits of ICT in education. Oxford Review of Education, 38(1), 9–24.

  31. Machin, S., McNally, S., & Silva, O. (2007). New technology in schools: Is there a payoff? Economic Journal, 117(522), 1145–1167.

  32. MORI (1999). The British and technology. Basingstoke, England: Motorola.

  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.

  34. National Statistics. (2001). Internet access: First quarter 2001. London, England: National Statistics.

  35. National Statistics. (2002). Internet access: First quarter 2002. London, England: National Statistics.

  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.

  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.

  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.

  43. Raudenbush, S., & Bryk, A. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.

  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.

  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.

  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.

  47. Schleicher, A. (2009). Securing quality and equity in education: Lessons from PISA. Prospects, 39, 251–263.

  48. Selwyn, N. (1999). Differences in educational computer use: The influences of subject cultures. The Curriculum Journal, 10(1), 29–48.

  49. Selwyn, N. (2004). Reconsidering political and popular understandings of the digital divide. New Media & Society, 6(3), 341–362.

  50. Silverstone, R., & Hirsch, E. (Eds.) (1992). Consuming technologies: Media and information in domestic spaces. London, England: Routledge.

  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.

  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.

  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.

  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.

  56. Van Dijk, J., & Hacker, K. (2003). The digital divided as a complex and dynamic phenomenon. Information Society, 19, 315–326.

  57. Van Dijk, J. (2012). The network society (3rd ed.). London, England: Sage Publications Ltd.

  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.

  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.

  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.

  61. Wittwer, J., & Senkbeil, M. (2008). Is students’ computer use at home related to their mathematical performance at school? Computers & Education, 50, 1558–1571.

  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.

  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.

  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.

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Correspondence to Cheng Yong Tan.

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Tan, C.Y., Hew, K.F. The Impact of Digital Divides on Student Mathematics Achievement in Confucian Heritage Cultures: a Critical Examination Using PISA 2012 Data. Int J of Sci and Math Educ 17, 1213–1232 (2019). https://doi.org/10.1007/s10763-018-9917-8

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Keywords

  • Confucian heritage cultures
  • Digital divides
  • Information technology
  • Mathematics achievement