Bending the Curve: Using Growth Trajectories to Understand Mathematics Instruction Intervention Effects in Early Childhood

Denis Dumas

The mathematical development of young children is of critical importance both in terms of the long-term success of individual students, and the readiness of our society to meet the challenges of the future. Unfortunately, U.S. students currently lag-behind their same-age peers around the world in mathematics achievement, and within the U.S., a sizable and widening achievement gap exists between highly-resourced students from historically-dominant social groups and their less privileged counterparts. For this reason, school-leaders and educational policy-makers must identify practices that support the ongoing mathematics learning trajectories of diverse students at a young age. However, although some research-based mathematics instructional interventions administered in preschool show promising efficacy immediately following the instruction, much less is known about the effect of such interventions on student growth trajectories in elementary school. In this study, data from a large-scale randomized-control-trial of a preschool mathematics instructional intervention are analyzed using a nonlinear growth paradigm to uncover the way in which the preschool intervention impacts the shape of student-specific learning curves over elementary school. The influence of student contextual and socioeconomic factors is also considered, with the goal of producing actionable and policy-relevant knowledge about mathematical development within a highly diverse group of U.S. students.

About Denis Dumas

Denis Dumas is an assistant professor of Research Methods and Statistics at the University of Denver’s Morgridge College of Education. In general, his work focuses on understanding student learning, cognition, and creativity through the application and refinement of latent variable methods, especially multidimensional item response theory and non-linear growth models. He is the co-creator of a psychometric framework called dynamic measurement modeling—a mixed-effects paradigm for quantifying the ability of students to learn in response to particular instruction—and is widely interested in the mental attributes that contribute to students’ academic success across domains and contexts. He completed his doctoral work in Educational Psychology, and Master’s degree in Educational Measurement and Statistics, at the University of Maryland-College Park, and was a post-doctoral researcher at the American Educational Research Association. His work has also been previously funded by the U.S. Department of Education Institute for Educational Sciences, and the Hewlett Foundation.