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Computer technology in education: Evidence from a pooled study of computer assisted learning programs among rural students in China

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  • Mo, Di
  • Huang, Weiming
  • Shi, Yaojiang
  • Zhang, Linxiu
  • Boswell, Matthew
  • Rozelle, Scott

Abstract

There is a great degree of heterogeneity among the studies that investigate whether computer technologies improve education and how students benefit from them – if at all. The overall goal of this study is to assess the effectiveness of computing technologies to raise educational performance and non-cognitive outcomes and identify what program components are most effective in doing so. To achieve this aim we pool the data sets of five separate studies about computer technology programs that include observations of 16,856 students from 171 primary schools across three provinces in China. We find that overall computing technologies have positive and significant impacts on student academic achievement in both math and in Chinese. The programs are found to be more effective if they are implemented out-of-school, avoiding what appear to be substitution effects when programs are run during school. The programs also have heterogeneous effects by gender. Specifically, boys gain more than girls in Chinese. We did not find heterogeneous effects by student initial achievement levels. We also found that the programs that help students learn math—but not Chinese—have positive impacts on student self-efficacy.

Suggested Citation

  • Mo, Di & Huang, Weiming & Shi, Yaojiang & Zhang, Linxiu & Boswell, Matthew & Rozelle, Scott, 2015. "Computer technology in education: Evidence from a pooled study of computer assisted learning programs among rural students in China," China Economic Review, Elsevier, vol. 36(C), pages 131-145.
    • Handle: RePEc:eee:chieco:v:36:y:2015:i:c:p:131-145
    DOI: 10.1016/j.chieco.2015.09.001
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    1. Qiang Fu & Qiang Ren, 2010. "Educational Inequality under China's Rural–Urban Divide: The Hukou System and Return to Education," Environment and Planning A, , vol. 42(3), pages 592-610, March.
    2. Abhijit V. Banerjee & Shawn Cole & Esther Duflo & Leigh Linden, 2007. "Remedying Education: Evidence from Two Randomized Experiments in India," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(3), pages 1235-1264.
    3. Lisa Barrow & Lisa Markman & Cecilia Elena Rouse, 2009. "Technology's Edge: The Educational Benefits of Computer-Aided Instruction," American Economic Journal: Economic Policy, American Economic Association, vol. 1(1), pages 52-74, February.
    4. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 238-249, April.
    5. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(3), pages 1071-1102.
    6. Ofer Malamud & Cristian Pop-Eleches, 2011. "Home Computer Use and the Development of Human Capital," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(2), pages 987-1027.
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    Cited by:

    1. M. Niaz Asadullah & Anindita Bhattacharjee, 2022. "Digital Divide or Digital Provide? Technology, Time Use, and Learning Loss during COVID-19," Journal of Development Studies, Taylor & Francis Journals, vol. 58(10), pages 1934-1957, October.
    2. Eric Bettinger & Robert Fairlie & Anastasia Kapuza & Elena Kardanova & Prashant Loyalka & Andrey Zakharov, 2023. "Diminishing Marginal Returns to Computer‐Assisted Learning," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(2), pages 552-570, March.
    3. NAKAMURO Makiko & ITO Hirotake, 2020. "The Effect of Computer Assisted Learning on Children's Cognitive and Noncognitive Skills: Evidence from a Randomized Experiment in Cambodia," Discussion papers 20074, Research Institute of Economy, Trade and Industry (RIETI).
    4. Yue Ma & Robert W. Fairlie & Prashant Loyalka & Scott Rozelle, 2020. "Isolating the "Tech" from EdTech: Experimental Evidence on Computer Assisted Learning in China," CESifo Working Paper Series 8186, CESifo.
    5. Bin Tang & Te-Tien Ting & Chyi-In Wu & Yue Ma & Di Mo & Wei-Ting Hung & Scott Rozelle, 2020. "The Impact of Online Computer Assisted Learning at Home for Disadvantaged Children in Taiwan: Evidence from a Randomized Experiment," Sustainability, MDPI, vol. 12(23), pages 1-16, December.
    6. Mo, Di & Bai, Yu & Shi, Yaojiang & Abbey, Cody & Zhang, Linxiu & Rozelle, Scott & Loyalka, Prashant, 2020. "Institutions, implementation, and program effectiveness: Evidence from a randomized evaluation of computer-assisted learning in rural China," Journal of Development Economics, Elsevier, vol. 146(C).
    7. Gupta, Shriyam & Liu, Chengfang & Li, Shaoping & Chang, Fang & Shi, Yaojiang, 2023. "Association between ability tracking and student’s academic and non-academic outcomes: Empirical evidence from junior high schools in rural China," International Journal of Educational Development, Elsevier, vol. 103(C).

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    More about this item

    Keywords

    Education; Computer assisted learning; Pooled study; China;
    All these keywords.

    JEL classification:

    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality

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