ICT Supported Learning Rises Math Achievement in Low Socio Economic Status Schools

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9307)


Sustained improvement in student achievement on national standardized tests for low socio economic status (SES) districts is critical for reducing gaps in educational inequality. We report the results of 3 years of implementation of an ICT web-based learning environment in all 11 public schools of a low SES urban district in Chile. This includes 43 fourth grade classes and 1,355 students. This is a Computer Aided Instruction program that promotes whole class collaborative learning with peer support. Effect size on the national standardized fourth grade math test is 0.33, which is three times the national improvement level over the same period and five times the improvement made by a neighboring district with a similar population. On the other hand, the same students did not make any improvements on the national standardized language test. Since each class was taught by the same teacher, only without ICT, we can therefore discount the teacher effect.


Computer aided instruction Web-based learning Effect sizes 



To Gonzalo Navarrete, President of the Education Commission for the Chilean Association of Municipalities; to Maximiliano Ríos, Director of the Lo Prado Education Department; to Basal Funds for the Centers of Excellence Project FB 0003 from the Associative Research Program of CONICYT.


  1. 1.
    Araya, R., Van der Molen, J.: Impact of a blended ICT adoption model on Chilean vulnerable schools correlates with amount of on online practice. In: Proceedings of the Workshops at the 16th International Conference on Artificial Intelligence in Education AIED 2013, Memphis, USA, 9–13 July 2013Google Scholar
  2. 2.
    Banerjee, A., Cole, S., Duflo, E., Linden, L.: Remedying education: evidence from two randomized experiments in India. Q. J. Econ. 122, 1235–1264 (2007)CrossRefGoogle Scholar
  3. 3.
    Banilower, E.R., Boyd, S.E., Pasley, J.D., Weiss, I.R.: Lessons from a decade of mathematics and science reform. A capstone report for the local systemic change through teacher enhancement initiative (2006)Google Scholar
  4. 4.
    Berlinski, S., Busso, M., Cristia, J., Severin, E.: Computers in schools: why governments should do their homework. In: Chong, A. (ed.) Development Connections: Unveiling the Impact of New Information Technologies. Palgrave MacMillan, London (2011)Google Scholar
  5. 5.
    Cheung, A., Slavin, R.: The effectiveness of educational technology applications on mathematics achievement in K-12 classrooms: a meta-analysis. Educ. Res. Rev. 9, 88–111 (2013)CrossRefGoogle Scholar
  6. 6.
    Cuban, L.: Inside the Black Box of Classroom Practice: Change without Reform in American Education. Harvard Education Press, Cambridge (2013)Google Scholar
  7. 7.
    Doran, H., Izumi, L.: Putting Education to the Test: A Value-Added Model for California. Pacific Research Institute, San Francisco (2004)Google Scholar
  8. 8.
    Garet, M., Wayne, A., Stancavage, F., Taylor, J., Walters, K., Song, M., Brown, S., Hurlburt, S., Zhu, P., Sepanik, S., Doolitle, F., Warner, E.: Middle School Mathematics Professional Development Impact Study Findings after the First Year of Implementation. US Department of Education, Washington, DC (2010)Google Scholar
  9. 9.
    Fuchs, T., Woessmann, L.: Computers and Student Learning: Bivariate and Multivariate Evidence on the Availability and Use of Computers at Home and at School. Ifo Institute for Economic Research, Munich (2004)Google Scholar
  10. 10.
    Harris, D., Sass, T.: Teacher Training: Teacher Quality and Student Achievement. University of Wisconsin and Florida State University, Tallahassee (2008)Google Scholar
  11. 11.
    Hattie, J., Timperley, H.: The power of feedback. Rev. Educ. Res. 77(1), 81–112 (2007)CrossRefGoogle Scholar
  12. 12.
    Kane, T.J., Rockoff, J., Staiger, D.O.: What does certification tell us about teacher effectiveness? Evidence from New York City. Econ. Educ. Rev. 27, 615–631 (2008)CrossRefGoogle Scholar
  13. 13.
    Kane, T.J.: Do Value-Added Estimates Identify Causal Effects of Teachers and Schools?. The Brown Center Chalkboard, Brookings Institution, Washington, DC (2014)Google Scholar
  14. 14.
    Labaree, D.: Someone Has to Fail. The Zero-Sum Game of Public Schooling. Harvard University Press, Cambridge (2010)Google Scholar
  15. 15.
    Lai, F., Zhang, L., Qu, Q., Hu, X., Shi, Y., Boswell, M., Rozelle, S.: Does computer-assisted learning improve learning outcomes? Evidence from a randomized experiment in public schools in rural minority areas in Qinghai. Working document Nº 237. Rural Education Action Project, Stanford, CA (2012)Google Scholar
  16. 16.
    Lai, F., Luo, R., Zhang, L., Huang, X., Rozelle, S.: Does computer-assisted learning improve learning outcomes? Evidence from a randomized experiment in migrant schools in Beijing. Working document Nº 228. Rural Education Action Project, Stanford, CA (2011)Google Scholar
  17. 17.
    Louis, K., Leithwood, K., Wahlstrom, K., Anderson, S.: Learning from leadership: investigating the links to improved student learning. Final report of research to the Wallace Foundation (2010)Google Scholar
  18. 18.
    Mayfield, K., Chase, P.: The effects of cumulative practice on mathematics problem solving. J. Appl. Behav. Anal. 35, 105–123 (2002)CrossRefGoogle Scholar
  19. 19.
    Mo, D., Zhang, L., Lui, R., Qu, Q., Huang, W., Wang, J., Qiao, Y., Boswell, M., Rozelle, S.: Integrating computer assisted learning into a regular curriculum: evidence from a randomized experiment in rural schools in Shaanxi. Working document Nº 248. Rural Education Action Project, Stanford, CA (2013)Google Scholar
  20. 20.
    National Mathematics Advisory Panel: Report of the Task Group on Learning Processes, chap. 4. U.S. Department of Education, Washington, DC (2008)Google Scholar
  21. 21.
    Ortiz, E., Cristia, J.: The IDB and technology in education: how to promote effective programs? Tehnical Note IDB-TN_670 (2014)Google Scholar
  22. 22.
    Slavin, R., Lake, C.: Effective programs in elementary mathematics: a best-evidence synthesis. Rev. Educ. Res. 78(3), 427–515 (2008)CrossRefGoogle Scholar
  23. 23.
    Steenbergen-Hu, S., Cooper, H.: A meta-analysis of the effectiveness of intelligent tutoring systems on K-12 students’ mathematical learning. J. Educ. Psychol. 105(4), 970–987 (2013)CrossRefGoogle Scholar
  24. 24.
    U.S. Department of Education: Effectiveness of Reading and Mathematics Software Products Findings from Two Student Cohorts. Institute of Education Sciences National Center for Education Evaluation and Regional Assistance, Washington, DC (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (, which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.Centro de Investigación Avanzada en EducaciónUniversidad de ChileSantiagoChile

Personalised recommendations