The purpose of this 4-year project is to improve student mathematics achievement by developing a mathematics intervention focused on key measurement and data analysis skills. The PM intervention will be designed for first and second grade students who are experiencing mathematics difficulties. To increase student mathematics achievement, the intervention will include: (a) a technology-based component and (b) hands-on activities.
The Discovery Research K-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.
Strong knowledge of measurement and data analysis is essential to ensure competiveness of the nation as a whole and full access to educational and work opportunities for all students. Despite this importance, a considerable number of U.S. students, particularly students from poor and minority backgrounds, struggle with these two areas of mathematics. The purpose of this 4-year Research and Development project, Precision Mathematics (PM): Building Student Proficiency in the Foundational Concepts and Problem Solving Skills of Measurement and Data Analysis, is to improve student mathematics achievement by developing a mathematics intervention focused on key measurement and data analysis skills. The PM intervention will be designed for first and second grade students who are experiencing mathematics difficulties. To increase student mathematics achievement, the intervention will include: (a) a technology-based component that will provide students with individualized instruction and (b) hands-on activities that will offer opportunities for students to interact with their teacher and peers around critical measurement and data analysis concepts. Primary activities of the project will include intervention development, pilot testing, data analysis, and intervention revision. One primary benefit of PM is that it will provide struggling learners with meaningful access to critical concepts and skills identified in the Common Core State Standards Initiative. Another benefit is that will be designed to serve as a foundation for students to understand more advanced mathematical concepts introduced in the later grades. PM has the potential to address a concerning gap in U.S. education. To date, intervention research focused on measurement and data analysis is scant.
Proficiency with measurement and data analysis is essential for obtaining occupations in the STEM fields. A primary aim of this project is to develop PM, a mathematics intervention designed to teach key concepts of measurement and data analysis to at-risk 1st and 2nd grade students. Comprising the intervention will be technology-based and collaborative problem-solving activities. At each grade, the intervention will provide 20 hours of instruction focused on topics identified in the Common Core State Standards. A primary aim of the project is to develop the intervention using a design science approach, including a mix of qualitative and quantitative research methods that will guide iterative testing and revision cycles. A second primary aim is to test the promise of the intervention to improve student mathematics achievement. Rigorous pilot studies (i.e., randomized controlled trials) will be conducted in 1st and 2nd grade classrooms involving over 700 at-risk students. Within classrooms, students will be randomly assigned to treatment (PM) or control conditions (business as usual). Two research questions will be addressed: (a) What is the potential promise of the intervention when delivered in authentic education settings? (b) Based on empirical evidence, are revisions to the intervention's theory of change necessary? Tests of main effects of intervention effects will be conducted using analysis of covariance models, adjusting for pretest scores. Generated findings are anticipated to contribute to the knowledge base on early STEM learning for at-risk learners.