Di Lonardo Burr Sabrina M, Xu Chang, Douglas Heather, LeFevre Jo-Anne, Susperreguy María Inés
Department of Cognitive Science, Carleton University, Ottawa, Ontario K1S 5B6, Canada.
Department of Psychology, Carleton University, Ottawa, Ontario K1S 5B6, Canada.
J Exp Child Psychol. 2022 Oct;222:105478. doi: 10.1016/j.jecp.2022.105478. Epub 2022 Jun 14.
According to the Pathways to Mathematics model [LeFevre et al. (2010), Child Development, Vol. 81, pp. 1753-1767], children's cognitive skills in three domains-linguistic, attentional, and quantitative-predict concurrent and future mathematics achievement. We extended this model to include an additional cognitive skill, patterning, as measured by a non-numeric repeating patterning task. Chilean children who attended schools of low or high socioeconomic status (N = 98; 54% girls) completed cognitive measures in kindergarten (M = 71 months) and numeracy and mathematics outcomes 1 year later in Grade 1. Patterning and the original three pathways were correlated with the outcomes. Using Bayesian regressions, after including the original pathways and mother's education, we found that patterning skills predicted additional variability in applied problem solving and arithmetic fluency, but not number ordering, in Grade 1. Similarly, patterning skills were included in the best model for applied problem solving and arithmetic fluency, but not for number ordering, in Grade 1. In accord with the hypotheses of the original Pathways to Mathematics model, patterning varied in its unique and relative contributions to later mathematical performance, depending on the demands of the tasks. We conclude that patterning is a useful addition to the Pathways to Mathematics model, providing further insights into the range of cognitive precursors that are related to children's mathematical development.
根据“通向数学之路”模型[勒菲弗等人(2010年),《儿童发展》,第81卷,第1753 - 1767页],儿童在语言、注意力和数量这三个领域的认知技能能够预测其当前及未来的数学成绩。我们扩展了这个模型,纳入了一项额外的认知技能——模式识别,该技能通过一项非数字重复模式任务来衡量。来自社会经济地位低或高的学校的智利儿童(N = 98;54%为女孩)在幼儿园(平均年龄71个月)完成了认知测试,并在一年后的一年级完成了计算能力和数学成绩测试。模式识别以及最初的三条路径与测试结果相关。通过贝叶斯回归分析,在纳入最初的路径和母亲的教育程度后,我们发现模式识别技能在一年级时能够预测应用问题解决和算术流畅性方面的额外变异性,但不能预测数字排序方面的变异性。同样,在一年级应用问题解决和算术流畅性的最佳模型中包含了模式识别技能,但数字排序模型中未包含。与最初的“通向数学之路”模型的假设一致,模式识别对后期数学表现的独特贡献和相对贡献因任务要求而异。我们得出结论,模式识别是对“通向数学之路”模型的有益补充,它为与儿童数学发展相关的认知先兆范围提供了进一步的见解。