Poor achievement in math is a national concern. Advanced study in math serves as a gateway for vocations in STEM (science, technology, engineering and mathematics) disciplines. Unfortunately, many U.S. students do not develop foundations for succeeding in advanced math. Moreover, national statistics reveal persistent income gaps in math achievement across grade levels, with low-income students scoring substantially lower than middle-income students. Although math difficulties are widespread, they are largely overlooked in primary school. However, recent longitudinal studies show that kindergarten number sense is highly predictive of meaningful math outcomes between first and third grades. The overarching goal of this application is to develop and test a number sense intervention for children at risk for math learning disabilities (MLD). The basic premise of the proposed project is that (a) weaknesses in number sense underlie many MLD, (b) number sense can be developed through explicit and clear instruction, and (c) gains in number sense lead to positive math outcomes in elementary school. The project will focus on low-income kindergartners -- children who enter kindergarten with serious lags in number sense and are most at risk for math failure. A kindergarten intervention will be developed in the first year of the project, along with a general language intervention for comparison purposes. In the subsequent four years, rigorous, evidence-based standards of scientific research will be used to evaluate the efficacy of number sense and language treatments, using randomized controlled trials. A total of 252 kindergartners will be randomly assigned to the number sense or to the comparison language condition (n = 126 in each group). An 8-week program will be run in three yearly waves. Children's performance in number sense, as well as their general math and reading achievement will be screened on four occasions (pre, post, end of kindergarten, first grade) in each wave. Age, gender, attention, language, working memory, and nonverbal IQ will be entered as covariates in the analyses. The primary analytic strategy will be growth curve modeling, a special case of hierarchical linear modeling.
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