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

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  • Thomas Fuchs
  • Ludger Woessmann

Abstract

We estimate the relationship between students’ educational achievement and the availability and use of computers at home and at school in the international student-level PISA database. Bivariate analyses show a positive correlation between student achievement and the availability of computers both at home and at schools. However, once we control extensively for family background and school characteristics, the relationship gets negative for home computers and insignificant for school computers. Thus, the mere availability of computers at home seems to distract students from effective learning. But measures of computer use for education and communication at home show a positive conditional relationship with student achievement. The conditional relationship between student achievement and computer and internet use at school has an inverted U-shape, which may reflect either ability bias combined with negative effects of computerized instruction or a low optimal level of computerized instruction.

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Bibliographic Info

Paper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 1321.

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Date of creation: 2004
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Handle: RePEc:ces:ceswps:_1321

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Keywords: computers at home; computers at school; student achievement; educational production; PISA; education;

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  1. Ludger Wößmann, 2000. "Schooling Resources, Educational Institutions, and Student Performance: The International Evidence," Kiel Working Papers 983, Kiel Institute for the World Economy.
  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. 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. 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.
  5. Bruce Sacerdote, 2000. "The Nature and Nurture of Economic Outcomes," NBER Working Papers 7949, National Bureau of Economic Research, Inc.
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  7. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
  8. 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.
  9. 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).
  10. 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.
  11. 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.
  12. Angrist, Joshua & Lavy, Victor, 2001. "New Evidence on Classroom Computers and Pupil Learning," IZA Discussion Papers 362, Institute for the Study of Labor (IZA).
  13. 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).
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