, Volume 46, Issue 2, pp 301–315 | Cite as

Cognitive abilities that mediate SES’s effect on elementary mathematics learning: The Uruguayan tablet-based intervention

  • Juan Valle-LisboaEmail author
  • Álvaro Cabana
  • Robert Eisinger
  • Álvaro Mailhos
  • Mario Luzardo
  • Justin Halberda
  • Alejandro Maiche


In unequal societies the effectiveness of formal education depends on the socioeconomic status (SES) of students. Studies have shown that poverty affects the development of the brain in ways that might compromise future learning, thus increasing the differences between groups with different SES. Interest is growing in the development of tools that might change this state of affairs. This article presents a tablet-based study aimed at determining the cognitive abilities related to primary school children’s math learning. The study followed the students’ changes during a short intervention, the purpose of which was to improve students’ performance of some of the core components of mathematical cognition; in particular, of the approximate number system (ANS), a system that supports one’s ability to estimate quantities and to compare time intervals. The article presents the study’s characteristics and shows how the variables that were evaluated—ANS precision, time discrimination accuracy, digit span, and mathematical achievement—depend on SES. We employ multiple regressions to show that the variance in mathematics performance attributed to SES can be explained by differences in underlying cognitive factors. The study also indicates that those students of low-SES schools who participated in more tablet activities increased their performance more than students who did fewer activities. Although the intervention’s initial objective was to influence mathematical development and the study is not a randomized double-blind study, we argue that training the ANS can have positive effects in mathematics learning, and that this might benefit children living in low-SES contexts more than those in the general population, perhaps because of the former’s initially low levels of performance in school mathematics.


Math learning Cognitive abilities Socio-economic status (SES) Uruguay 


  1. ANEP [Administración Nacional de Educación Primaria] (2012). Relevamiento de contexto sociocultural de las escuelas de educación primaria [Survey of primary school’s socio-cultural context]. Montevideo: ANEP.
  2. Banerjee, A., Duflo, E., Goldberg, N., Karlan, D., Osei, R., Parienté, W., et al. (2015). A multifaceted program causes lasting progress for the very poor: Evidence from six countries. Science, 348(6236). doi: 10.1126/science.1260799.
  3. Barth, H., La Mont, K., Lipton, J., & Spelke, E. S. (2005). Abstract number and arithmetic in preschool children. Proceedings of the National Academy of Sciences of the United States of America, 102, 14116–14121.CrossRefGoogle Scholar
  4. Carey, S. (2001). Cognitive foundations of arithmetic: Evolution and ontogenesis. Mind and Language, 16(1), 37–55. doi: 10.1111/1468-0017.00155.CrossRefGoogle Scholar
  5. Chen, Q., & Li, J. (2014). Association between individual differences in non-symbolic number acuity and math performance: A meta-analysis. Acta Psychologica, 148, 163–172. doi: 10.1016/j.actpsy.2014.01.016.CrossRefGoogle Scholar
  6. de Hevia, M. D., Izard, V., Coubart, A., Spelke, E. S., & Streri, A. (2014). Representations of space, time, and number in neonates. Proceedings of the National Academy of Sciences of the United States of America, 111, 4809–4813.CrossRefGoogle Scholar
  7. Dehaene, S. (1992). Varieties of numerical abilities. Cognition, 44, 1–42.CrossRefGoogle Scholar
  8. DeWind, N. K., & Brannon, E. M. (2012). Malleability of the approximate number system: Effects of feedback and training. Frontiers in Human Neuroscience, 6, 1–10.CrossRefGoogle Scholar
  9. Fazio, L. K., Bailey, D. H., Thompson, C. A., & Siegler, R. S. (2014). Relations of different types of numerical magnitude representations to each other and to mathematics achievement. Journal of Experimental Child Psychology, 123(1), 53–72. doi: 10.1016/j.jecp.2014.01.013.CrossRefGoogle Scholar
  10. Feigenson, L., Dehaene, S., & Spelke, E. S. (2004). Core systems of number. Trends in Cognitive Science, 8(7), 307–314. doi: 10.1016/j.tics.2004.05.002.CrossRefGoogle Scholar
  11. Gathercole, S. E., Woolgar, F., Kievit, R. A., Astle, D., Manly, T., & Holmes, J. (2016). How common are WM deficits in children with difficulties in reading and mathematics? Journal of Applied Research in Memory and Cognition (in press). doi: 10.1016/j.jarmac.2016.07.013.Google Scholar
  12. Ginsburg, H. P., & Baroody, A. J. (2003). Test of early mathematics ability (3rd ed.). Austin, TX: Pro-Ed.Google Scholar
  13. Goldin, A. P., Hermida, M. J., Shalom, D. E., Costa, M. E., Lopez-Rosenfeld, M., Segretin, M. S., et al. (2014). Far transfer to language and math of a short software-based gaming intervention. Proceedings of the National Academy of Sciences of the United States of America, 111(17), 6443–6448. doi: 10.1073/pnas.1320217111.CrossRefGoogle Scholar
  14. Halberda, J., & Feigenson, L. (2008). Developmental change in the acuity of the “Number Sense”: The approximate number system in 3-, 4-, 5-, 6-year-olds and adults. Developmental Psychology, 44(5), 1457–1465.CrossRefGoogle Scholar
  15. Halberda, J., & Ly, R. (2015). PANAmath: The psychophysical assessment of number-sense acuity. (Manuscript in Preparation)Google Scholar
  16. Halberda, J., Ly, R., Wilmer, J., Naiman, D., & Germine, L. (2012). Number sense across the lifespan as revealed by a massive internet-based sample. Proceedings of the National Academy of Sciences of the United States of America, 109(28), 11116–11120.CrossRefGoogle Scholar
  17. Halberda, J., Mazzocco, M., & Feigenson, L. (2008). Individual differences in nonverbal number acuity predict maths achievement. Nature, 455, 665–668.CrossRefGoogle Scholar
  18. Hyde, D. C., Khanum, S., & Spelke, E. S. (2014). Brief non-symbolic, approximate number practice enhances subsequent exact symbolic arithmetic in children. Cognition, 131(1), 92–107.CrossRefGoogle Scholar
  19. Izard, V., Sann, C., Spelke, E. S., & Streri, A. (2009). Newborn infants perceive abstract numbers. Proceedings of the National Academy of Sciences of the United States of America, 106(25), 10382–10385. doi: 10.1073/pnas.0812142106.CrossRefGoogle Scholar
  20. Jordan, N. C., Kaplan, D., Ramineni, C., & Locuniak, M. N. (2009). Early math matters: Kindergarten number competence and later mathematics outcomes. Developmental Psychology, 45, 850–867.CrossRefGoogle Scholar
  21. Kirkland, L. D., Manning, M., Osaki, K., & Hicks, D. (2015). Increasing logico-mathematical thinking in low SES preschoolers. Journal of Research in Childhood Education, 29(3), 275–286.CrossRefGoogle Scholar
  22. Lefevre, J. A., Kwarchuk, S. L., Smith-Chant, B. L., Fast, L., Kamawar, D., & Bisanz, J. (2009). Home numeracy experiences and children’s math performance in the early school years. Canadian Journal of Behavioural Science, 41(2), 55–66. doi: 10.1037/a0014532.CrossRefGoogle Scholar
  23. Libertus, M. E. (2015). The role of intuitive approximation skills for school math abilities. Mind, Brain, and Education, 9(2), 112–120.CrossRefGoogle Scholar
  24. Libertus, M. E., & Brannon, E. M. (2010). Stable individual differences in number discrimination in infancy. Developmental Science, 13(6), 900–906. doi: 10.1111/j.1467-7687.2009.00948.x.CrossRefGoogle Scholar
  25. Lipina, S. J., & Colombo, J. A. (2009). Poverty and brain development during childhood: An approach from cognitive psychology and neuroscience. Washington, DC: American Psychological Association.CrossRefGoogle Scholar
  26. Lipina, S. J., & Posner, M. I. (2012). The impact of poverty on the development of brain networks. Frontiers in Human Neuroscience, 6, 238–250.CrossRefGoogle Scholar
  27. Mazzocco, M. M. M., Feigenson, L., & Halberda, J. (2011). Impaired acuity of the approximate number system underlies mathematical learning disability. Child Development, 82, 1224–1237.CrossRefGoogle Scholar
  28. National Mathematics Advisory Panel (2008). Foundations for success: The final report of the National Mathematics Advisory Panel. Washington, DC: U.S. Department of Education.Google Scholar
  29. Nieder, A., & Dehaene, S. (2009). Representation of number in the brain. Annual Review of Neuroscience, 32, 185–208. doi: 10.1146/annurev.neuro.051508.135550.CrossRefGoogle Scholar
  30. Odic, D., Hock, H., & Halberda, J. (2014). Hysteresis affects approximate number discrimination in young children. Journal of Experimental Psychology: General, 143(1), 255–265. doi: 10.1037/a0030825.CrossRefGoogle Scholar
  31. Odic, D., Libertus, M., Feigenson, L., & Halberda, J. (2013). Developmental change in the acuity of approximate number and area representations. Developmental Psychology, 49(6), 1103.CrossRefGoogle Scholar
  32. Odic, D., Valle Lisboa, J., Eisinger, R., Gonzalez, M., Maiche, A., & Halberda, J. (2016). Approximate number and approximate time discrimination each correlate with school math abilities in young children. Acta Psychologica, 163, 17–26.CrossRefGoogle Scholar
  33. Park, J., & Brannon, E. M. (2013). Training the approximate number system improves math proficiency. Psychological Science, 24(10), 2013–2019. doi: 10.1177/0956797613482944.CrossRefGoogle Scholar
  34. Schneider, M., Beeres, K., Coban, L., Merz, S., Schmidt, S., Stricker, J., et al. (2016). Associations of non-symbolic and symbolic numerical magnitude processing with mathematical competence: A meta-analysis. Developmental Science (in press). doi: 10.1111/desc.12372.Google Scholar
  35. Seo, K. H., & Ginsburg, H. P. (2004). What is developmentally appropriate in early childhood mathematics education? Lessons from new research. In D. H. Clements, J. Sarama, & A. M. DiBiase (Eds.), Engaging young children in mathematics: Standards for early childhood mathematics education (pp. 91–104). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  36. Sigman, M., Peña, M., Goldin, A. P., & Ribeiro, S. (2014). Neuroscience and education: Prime time to build the bridge. Nature Neuroscience, 17, 497–502.CrossRefGoogle Scholar
  37. Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453.CrossRefGoogle Scholar
  38. Tosto, M. G., Petrill, S. A., Halberda, J., Trzaskowski, M., Tikhomirova, T. N., Bogdanova, O., et al. (2014). Why do we differ in number sense? Evidence from a genetically sensitive investigation. Intelligence, 43(1), 35–46. doi: 10.1016/j.intell.2013.12.007.CrossRefGoogle Scholar
  39. Valle Lisboa, J., Mailhos, A., Eisinger, R., Halberda, J., González, M., Luzardo, M., et al. (2016). Estimulación a escala poblacional utilizando tablets: Del sistema numérico aproximado a la matemática simbólica [Population-scale stimulation using tablets: From the approximate number system to symbolic mathematics]. In S. Lipina, M. Sigman, & D. Fernnández-Slezak (Eds.), Pensar las TICs desde las ciencias cognitivas y la neurociencia (pp. 147–172). Buenos Aires: Gedisa.Google Scholar
  40. Walsh, V. (2003). A theory of magnitude: Common cortical metrics of time, space and quantity. Trends in Cognitive Science, 7(11), 483–488. doi: 10.1016/j.tics.2003.09.002.CrossRefGoogle Scholar
  41. Wilkinson, R., & Pickett, K. (2010). The spirit level: Why equality is better for everyone. London: Penguin.Google Scholar

Copyright information


Authors and Affiliations

  • Juan Valle-Lisboa
    • 1
    • 2
    Email author
  • Álvaro Cabana
    • 1
  • Robert Eisinger
    • 3
  • Álvaro Mailhos
    • 1
  • Mario Luzardo
    • 1
    • 4
  • Justin Halberda
    • 3
  • Alejandro Maiche
    • 1
  1. 1.Facultad de PsicologíaUniversidad de la RepúblicaMontevideoUruguay
  2. 2.Facultad de CienciasUniversidad de la RepúblicaMontevideoUruguay
  3. 3.Johns Hopkins UniversityBaltimoreUSA
  4. 4.CUREUniversidad de la RepúblicaMaldonadoUruguay

Personalised recommendations