Preschool Mathematics Intervention Can Significantly Improve Student Learning Trajectories Through Elementary School

Abstract

Perhaps more than at any other time in history, the development of mathematical skill is critical for the long-term success of students. Unfortunately, on average, U.S. students lag behind their peers in other developed countries on mathematics outcomes, and within the United States, an entrenched mathematics achievement gap exists between students from more highly resourced and socially dominant groups, and minority students. To begin to remedy this situation, educational researchers have created instructional interventions designed to support the mathematical learning of young students, some of which have demonstrated efficacy at improving student mathematical skills in preschool, as compared with a business-as-usual control group. However, the degree to which these effects last or fade out in elementary school has been the subject of substantial research and debate, and differences in scholarly viewpoints have prevented researchers from making clear and consistent policy recommendations to educational decision makers and stakeholders. In this article, we use a relatively novel statistical framework, Dynamic Measurement Modeling, that takes both intra- and interindividual student differences across time into account, to demonstrate that while students who receive a short-term intervention in preschool may not differ from a control group in terms of their long-term mathematics outcomes at the end of elementary school, they do exhibit significantly steeper growth curves as they approach their eventual skill level. In addition, this significant improvement of learning rate in elementary school benefited minority (i.e., Black or Latinx) students most, highlighting the critical societal need for research-based mathematics curricula in preschool.

Expectancy-value theory & preschool parental involvement in informal STEM learning

“…By the time children enter preschool there is tremendous variability in their math and science knowledge (e.g., Bullock, Sodian, & Koerber, 2009;McWayne, Cheung, Wright & Hahs-Vaughn, 2012;von Hippel, Workman & Downey, 2018) that influences their future rates of learning (Dumas, McNeish, Sarama & Clements, 2019). Both the frequency and quality of parental involvement in math and science activities explains some of this variability (e.g., Levine, Suriyakham, Rowe, Huttenlocher & Gunderson, 2010;Ramani, Rowe, Eason & Leech, 2015;Skwarchuk, 2009).…”

Modeling individual differences in the timing of change onset and offset.

“…In the absence of intervention effects that persist throughout elementary school, there is debate about the policy ramifications of implementing interventions on a large scale (Bailey et al, 2018). Dumas et al (2019) applied nonlinear dynamic measurement growth models to data from a clustered randomized trial in children followed from preschool to Grade 5 and similarly found that the difference between the intervention and control groups diminishes over time. To probe aspects of timing, they applied a Michaelis-Menten model which estimated the time at which the outcome had developed to the midpoint between the intercept and asymptote.…”

Learning Progression–Based Assessments: A Systematic Review of Student and Teacher Uses

“…Twelve studies included LP-based assessments designed for summative purposes; research is required in this area as such tasks might more accurately measure learning growth promoted in an LP approach. Another limitation was that studies frequently relied on just two measurements of learning; Dumas et al (2019) argued that, as student learning is not linear, more complex models (e.g., dynamic measurement modeling) based on data collected across multiple time points are needed to understand learner trajectories and identify if learning growth is consistent over time.…”