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Computers and Student Learning:Bivariate and Multivariate Evidence on the Availability and Use of Computers at Home and at School

  • Thomas Fuchs

    ()

  • Ludger Wößmann

    ()

We estimate the relationship between computers and students’ educational achievement in the international student-level PISA database. Bivariate analyses show a positive correlation between achievement and computer availability both at home and at school. However, once we control extensively for family background and school characteristics, the relationship gets negative for home computers and insignificant for school computers. Thus, mere availability of computers at home seems to distract students from effective learning. But achievement shows a positive conditional relationship with computer use for education and communication at home and an inverted U-shaped relationship with computer and internet use at school.

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File URL: http://www.cesifo-group.de/portal/page/portal/DocBase_Content/WP/WP-Ifo_Working_Papers/wp-ifo-2005-2010/IfoWorkingPaper-8.pdf
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Paper provided by Ifo Institute for Economic Research at the University of Munich in its series Ifo Working Paper Series with number Ifo Working Paper No. 8.

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Date of creation: 2005
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Handle: RePEc:ces:ifowps:_8
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  1. Fuchs, Thomas & Woessmann, Ludger, 2004. "What Accounts for International Differences in Student Performance? A Re-Examination Using PISA Data," IZA Discussion Papers 1287, Institute for the Study of Labor (IZA).
  2. Lindbeck, Assar & Snower, Dennis J., 1999. "Multi-Task Learning and the Reorganization of Work. From Tayloristic to Holistic Organization," IZA Discussion Papers 39, Institute for the Study of Labor (IZA).
  3. Ludger Woessmann, 2004. "How Equal Are Educational Opportunities? Family Background and Student Achievement in Europe and the United States," CESifo Working Paper Series 1162, CESifo Group Munich.
  4. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
  5. H, Entorf & Michel Gollac & Francis Kramarz, 1997. "New Technologies, Wages and Worker Selection," Working Papers 97-25, Centre de Recherche en Economie et Statistique.
  6. Angrist, Joshua & Lavy, Victor, 2001. "New Evidence on Classroom Computers and Pupil Learning," IZA Discussion Papers 362, Institute for the Study of Labor (IZA).
  7. Ludger Woesmann, 2003. "Schooling Resources, Educational Institutions and Student Performance: the International Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(2), pages 117-170, 05.
  8. Dinardo, J.E. & Pischke, J.S., 1996. "The Returns to Computer Use Revisited: Have Pencils Changed the Wage Structure Too?," Working papers 96-12, Massachusetts Institute of Technology (MIT), Department of Economics.
  9. Borghans, Lex & ter Weel, Bas, 2004. "Are computer skills the new basic skills? The returns to computer, writing and math skills in Britain," Labour Economics, Elsevier, vol. 11(1), pages 85-98, February.
  10. Bruce Sacerdote, 2002. "The Nature and Nurture of Economic Outcomes," American Economic Review, American Economic Association, vol. 92(2), pages 344-348, May.
  11. Rouse, Cecilia Elena & Krueger, Alan B., 2004. "Putting computerized instruction to the test: a randomized evaluation of a "scientifically based" reading program," Economics of Education Review, Elsevier, vol. 23(4), pages 323-338, August.
  12. Jere R. Behrman & Mark R. Rosenzweig, 2002. "Does Increasing Women's Schooling Raise the Schooling of the Next Generation?," American Economic Review, American Economic Association, vol. 92(1), pages 323-334, March.
  13. Wooldridge, Jeffrey M., 2001. "Asymptotic Properties Of Weighted M-Estimators For Standard Stratified Samples," Econometric Theory, Cambridge University Press, vol. 17(02), pages 451-470, April.
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