Advertisement

Educational Studies in Mathematics

, Volume 102, Issue 2, pp 173–191 | Cite as

Early home numeracy activities and later mathematics achievement: early numeracy, interest, and self-efficacy as mediators

  • Jinxin ZhuEmail author
  • Ming Ming Chiu
Article
  • 216 Downloads

Abstract

Parents are their children’s most influential educators, and their joint activities can influence these children’s early learning. Past studies with small, non-representative samples do not show a consistent link between early numeracy activities at home and children’s mathematics achievement. Specifically, whether or how early numeracy activity at home (ENAH) enhances mathematics learning in upper primary school remains an open question. This study tests this link, its precursors (home resources for learning, gender), and its possible mechanisms (including early numeracy skills, mathematics interest, and mathematics self-efficacy) on a representative sample of 3,600 Hong Kong fourth-grade children, with a multilevel path analysis. The results showed that ENAH was linked to both early numeracy and fourth-grade mathematics achievement, and did not support the substitution hypothesis (that other factors such as school lessons substitute for ENAH). The results also support two ENAH mechanisms. Children’s early numeracy and mathematics self-efficacy both partially mediated the link between ENAH and children’s later mathematics achievement. After including these explanatory variables in the model, ENAH still retained a significant direct link to fourth-grade mathematics achievement, suggesting that ENAH also operates through one or more other mechanisms. Lastly, boys and children in families with more home resources for learning were more likely than other children to participate in ENAH.

Keywords

Early numeracy activities Mathematics self-efficacy Mathematics achievement Home resources for learning 

Notes

Funding information

The work was fully supported by the grants from the Central Reserve Allocation Committee and the Faculty of Education and Human Development of The Education University of Hong Kong (Project No. 03A28) on the project titled “Big data for school improvement: Identifying and analyzing multiple sources to support schools as learning communities.”

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

References

  1. Aunio, P., & Niemivirta, M. (2010). Predicting children’s mathematical performance in grade one by early numeracy. Learning and Individual Differences, 20(5), 427–435.  https://doi.org/10.1016/j.lindif.2010.06.003 CrossRefGoogle Scholar
  2. Baker, D. P., Goesling, B., & LeTendre, G. K. (2002). Socioeconomic status, school quality, and national economic development: A cross-national analysis of the “Heyneman-Loxley Effect” on mathematics and science achievement. Comparative Education Review, 46(3), 291–312.  https://doi.org/10.1086/341159 CrossRefGoogle Scholar
  3. Bandura, A. (1997). Developmental analysis of self-efficacy. Self-efficacy: The exercise of control, 162–211.Google Scholar
  4. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.CrossRefGoogle Scholar
  5. Byrnes, J. P., & Miller, D. C. (2007). The relative importance of predictors of math and science achievement: An opportunity-propensity analysis. Contemporary Educational Psychology, 32(4), 599–629.  https://doi.org/10.1016/j.cedpsych.2006.09.002 CrossRefGoogle Scholar
  6. Byrnes, J. P., & Wasik, B. A. (2009). Factors predictive of mathematics achievement in kindergarten, first and third grades: An opportunity-propensity analysis. Contemporary Educational Psychology, 34(2), 167–183.  https://doi.org/10.1016/j.cedpsych.2009.01.002 CrossRefGoogle Scholar
  7. Castro, M., Expósito-casas, E., López-martín, E., & Lizasoain, L. (2015). Parental involvement on student academic achievement: A meta-analysis. Educational Research Review, 14, 33–46.  https://doi.org/10.1016/j.edurev.2015.01.002 CrossRefGoogle Scholar
  8. Chiu, M. M. (2007). Families, economies, cultures, and science achievement in 41 countries: Country-, school-, and student-level analyses. Journal of Family Psychology, 21(3), 510–519.  https://doi.org/10.1037/0893-3200.21.3.510 CrossRefGoogle Scholar
  9. Chiu, M. M. (2010). Effects of inequality, family and school on mathematics achievement: Country and student differences. Social Forces, 88(4), 1645–1676.  https://doi.org/10.1353/sof.2010.0019 CrossRefGoogle Scholar
  10. Chiu, M. M., & Chow, B. W. Y. (2015). Classmate characteristics and student achievement in 33 countries: Classmates’ past achievement, family socioeconomic status, educational resources and attitudes toward reading. Journal of Educational Psychology, 107(1), 152–169.  https://doi.org/10.1037/a0036897 CrossRefGoogle Scholar
  11. Chiu, M. M., & Khoo, L. (2005). Effects of resources, inequality, and privilege bias on achievement: Country, school, and student level analyses. American Educational Research Journal, 42(4), 575–603.  https://doi.org/10.3102/00028312042004575 CrossRefGoogle Scholar
  12. Chiu, M. M., & Zeng, X. (2008). Family and motivation effects on mathematics achievement: Analyses of students in 41 countries. Learning and Instruction, 18(4), 321–336.  https://doi.org/10.1016/j.learninstruc.2007.06.003 CrossRefGoogle Scholar
  13. Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic rewards and intrinsic motivation in education: Reconsidered once again. Review of Educational Research, 71(1), 1–27.  https://doi.org/10.3102/00346543071001001 CrossRefGoogle Scholar
  14. Dufur, M. J., Parcel, T. L., & Troutman, K. P. (2013). Does capital at home matter more than capital at school? Social capital effects on academic achievement. Research in Social Stratification and Mobility, 31(1), 1–21.  https://doi.org/10.1016/j.rssm.2012.08.002 CrossRefGoogle Scholar
  15. Fan, W., & Williams, C. M. (2010). The effects of parental involvement on students’ academic self-efficacy, engagement and intrinsic motivation. Educational Psychology, 30(1), 53–74.  https://doi.org/10.1080/01443410903353302 CrossRefGoogle Scholar
  16. Fast, L. A., Lewis, J. L., Bryant, M. J., Bocian, K. A., Cardullo, R. A., Rettig, M., & Hammond, K. A. (2010). Does math self-efficacy mediate the effect of the perceived classroom environment on standardized math test performance? Journal of Educational Psychology, 102(3), 729–740.  https://doi.org/10.1037/a0018863 CrossRefGoogle Scholar
  17. Flore, P. C., & Wicherts, J. M. (2015). Does stereotype threat influence performance of girls in stereotyped domains? A meta-analysis. Journal of School Psychology, 53(1), 25–44.  https://doi.org/10.1016/j.jsp.2014.10.002 CrossRefGoogle Scholar
  18. Ganley, C. M., & Lubienski, S. T. (2016). Mathematics confidence, interest, and performance: Examining gender patterns and reciprocal relations. Learning and Individual Differences, 47, 182–193.  https://doi.org/10.1016/j.lindif.2016.01.002 CrossRefGoogle Scholar
  19. Gottfried, A. E., Marcoulides, G. A., Gottfried, A. W., & Oliver, P. H. (2009). A latent curve model of parental motivational practices and developmental decline in math and science academic intrinsic motivation. Journal of Educational Psychology, 101(3), 729–739.  https://doi.org/10.1037/a0015084 CrossRefGoogle Scholar
  20. Gunderson, E. A., Ramirez, G., Levine, S. C., & Beilock, S. L. (2012). The role of parents and teachers in the development of gender-related math attitudes. Sex Roles, 66(3–4), 153–166.  https://doi.org/10.1007/s11199-011-9996-2 CrossRefGoogle Scholar
  21. Halpern, D. F. (2012). Sex differences in cognitive abilities (4th ed.). New York, NY: Taylor & Francis.Google Scholar
  22. Hidi, S., Berndorff, D., & Ainley, M. (2002). Children’s argument writing, interest and self- efficacy: An intervention study. Learning and Instruction, 12(4), 429–446.  https://doi.org/10.1016/S0959-4752(01)00009-3 CrossRefGoogle Scholar
  23. Hill, C. J., Bloom, H. S., Black, A. R., & Lipsey, M. W. (2008). Empirical benchmarks for interpreting effect sizes in research. Child Development Perspectives, 2(3), 172–177.  https://doi.org/10.1111/j.1750-8606.2008.00061.x CrossRefGoogle Scholar
  24. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.  https://doi.org/10.1080/10705519909540118 CrossRefGoogle Scholar
  25. Huang, Q., Zhang, X., Liu, Y., Yang, W., & Song, Z. (2017). The contribution of parent–child numeracy activities to young Chinese children’s mathematical ability. British Journal of Educational Psychology, 87(3), 328–344.  https://doi.org/10.1111/bjep.12152 CrossRefGoogle Scholar
  26. Kennedy, P. (2008). A guide to econometrics (6th ed.). New York, NY: Wiley-Blackwell.Google Scholar
  27. Kleemans, T., Peeters, M., Segers, E., & Verhoeven, L. (2012). Child and home predictors of early numeracy skills in kindergarten. Early Childhood Research Quarterly, 27(3), 471–477.  https://doi.org/10.1016/j.ecresq.2011.12.004 CrossRefGoogle Scholar
  28. Konstantopoulos, S. (2008). Methodological studies: The power of the test for treatment effects in three-level block randomized designs. Journal of Research on Educational Effectiveness, 1(4), 265–288.  https://doi.org/10.1080/19345740802328216 CrossRefGoogle Scholar
  29. Lefevre, J.-A., Polyzoi, E., Skwarchuk, S.-L., Fast, L., & Sowinski, C. (2010). Do home numeracy and literacy practices of Greek and Canadian parents predict the numeracy skills of kindergarten children? International Journal of Early Years Education, 18(1), 55–70.  https://doi.org/10.1080/09669761003693926 CrossRefGoogle Scholar
  30. Lefevre, J.-A., Skwarchuk, S.-L., Smith-Chant, B. L., & 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.  https://doi.org/10.1037/a0014532 CrossRefGoogle Scholar
  31. Ma, X., Shen, J., Krenn, H. Y., Hu, S., & Yuan, J. (2015). A meta-analysis of the relationship between learning outcomes and parental involvement during early childhood education and early elementary education. Educational Psychology Review, 28(4), 771–801.  https://doi.org/10.1007/s10648-015-9351-1 CrossRefGoogle Scholar
  32. Mankiw, N. G. (2014). Principles of macroeconomics (7th ed.). Nashville, TN: Southwestern Publishing.Google Scholar
  33. Manolitsis, G., Georgiou, G. K., & Tziraki, N. (2013). Examining the effects of home literacy and numeracy environment on early reading and math acquisition. Early Childhood Research Quarterly, 28(4), 692–703.  https://doi.org/10.1016/j.ecresq.2013.05.004 CrossRefGoogle Scholar
  34. Martin, M. O., Mullis, I. V. S., & Hooper, M. (2016). Methods and procedures in TIMSS 2015. Lynch School of Education, Boston College: TIMSS & PIRLS International Study Center.Google Scholar
  35. Masters, G. N., & Wright, B. D. (1993). The partial credit model. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of polytomous item response theory models (pp. 101–121). New York: Springer.  https://doi.org/10.4324/9780203861264.ch5 CrossRefGoogle Scholar
  36. Missall, K., Hojnoski, R. L., Caskie, G. I. L., & Repasky, P. (2015). Home numeracy environments of preschoolers: Examining relations among mathematical activities, parent mathematical beliefs, and early mathematical skills. Early Education and Development, 26(3), 356–376.  https://doi.org/10.1080/10409289.2015.968243 CrossRefGoogle Scholar
  37. Mok, M. M. C., Zhu, J., & Law, C. L. K. (2017). Cross-lagged cross-subject bidirectional predictions among achievements in mathematics, English language and Chinese language of school children. Educational Psychology, 37(10), 1259–1280.  https://doi.org/10.1080/01443410.2017.1334875 CrossRefGoogle Scholar
  38. Moon, U. J., & Hofferth, S. L. (2016). Parental involvement, child effort, and the development of immigrant boys’ and girls’ reading and mathematics skills: A latent difference score growth model. Learning and Individual Differences, 47, 136–144.  https://doi.org/10.1016/j.lindif.2016.01.001 CrossRefGoogle Scholar
  39. Mullis, I. V. S., & Martin, M. O. (2013). TIMSS 2015 Assessment Frameworks. International Association for the Evaluation of Educational Achievement (IEA). TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.Google Scholar
  40. Muthén, B. O., & Satorra, A. (1995). Complex sample data in Structural Equation Modeling. Sociological Methodology, 25(1995), 267–316.  https://doi.org/10.2307/271070 CrossRefGoogle Scholar
  41. Muthén, L. K., & Muthén, B. O. (2018). Mplus user’s guide (8th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
  42. Niklas, F., Cohrssen, C., & Tayler, C. (2016). Parents supporting learning: A non-intensive intervention supporting literacy and numeracy in the home learning environment. International Journal of Early Years Education, 9760(April), 1–22.  https://doi.org/10.1080/09669760.2016.1155147 CrossRefGoogle Scholar
  43. Purpura, D. J., Reid, E. E., Eiland, M. D., & Baroody, A. J. (2015). Using a brief preschool early numeracy skills screener to identify young children with mathematics difficulties. School Psychology Review, 44(1), 41–59.  https://doi.org/10.17105/SPR44-1.41-59 CrossRefGoogle Scholar
  44. Sandberg, J. F., & Hofferth, S. L. (2001). Changes in children’s time with parents: United States, 1981-1997. Demography, 38(3), 423–436.CrossRefGoogle Scholar
  45. Schunk, D. H., & Pajares, F. (2009). Self-efficacy theory. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 35–53). New York, NY: Routledge.Google Scholar
  46. Sirin, S. R., & Rogers-Sirin, L. (2005). Components of school engagement among African American adolescents. Applied Developmental Science, 9(1), 5–13.  https://doi.org/10.1207/s1532480xads0901_2 CrossRefGoogle Scholar
  47. Skwarchuk, S. L., Sowinski, C., & LeFevre, J. A. (2014). Formal and informal home learning activities in relation to children’s early numeracy and literacy skills: The development of a home numeracy model. Journal of Experimental Child Psychology, 121(1), 63–84.  https://doi.org/10.1016/j.jecp.2013.11.006 CrossRefGoogle Scholar
  48. Steiger, J. H., & Lind, J. C. (1989). Statistically based tests for the number of common factors. Paper presented at the annual meeting of the Psychometric Society, Iowa City, IA.Google Scholar
  49. Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1–10.  https://doi.org/10.1007/BF02291170 CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.The Education University of Hong KongTai PoHong Kong

Personalised recommendations