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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. Wößmann, Ludger, 2003. "Schooling resources, educational institutions and student performance: The international evidence," Munich Reprints in Economics 19661, University of Munich, Department of Economics.
  2. 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.
  3. Thomas Fuchs & Ludger Woessmann, 2004. "What Accounts for International Differences in Student Performance? A Re-examination using PISA Data," Econometric Society 2004 Australasian Meetings 274, Econometric Society.
  4. John E. DiNardo & Jorn-Steffen Pischke, 1996. "The Returns to Computer Use Revisited: Have Pencils Changed the Wage Structure Too?," NBER Working Papers 5606, National Bureau of Economic Research, Inc.
  5. 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.
  6. Woessmann, Ludger, 2004. "How Equal Are Educational Opportunities? Family Background and Student Achievement in Europe and the United States," IZA Discussion Papers 1284, Institute for the Study of Labor (IZA).
  7. Borghans, Lex & ter Weel, Bas, 2003. "Are Computer Skills the New Basic Skills? The Returns to Computer, Writing and Math Skills in Britain," IZA Discussion Papers 751, Institute for the Study of Labor (IZA).
  8. H, Entorf & Michel Gollac & Francis Kramarz, 1997. "New Technologies, Wages and Worker Selection," Working Papers 97-25, Centre de Recherche en Economie et Statistique.
  9. 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.
  10. 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).
  11. Bruce Sacerdote, 2002. "The Nature and Nurture of Economic Outcomes," American Economic Review, American Economic Association, vol. 92(2), pages 344-348, May.
  12. Joshua Angrist & Victor Lavy, 1999. "New Evidence on Classroom Computers and Pupil Learning," NBER Working Papers 7424, National Bureau of Economic Research, Inc.
  13. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
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