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Minimize RSR Award Detail

Research Spending & Results

Award Detail

  • Ryan S Jones
Award Date:01/16/2020
Estimated Total Award Amount: $ 703,903
Funds Obligated to Date: $ 167,499
  • FY 2020=$167,499
Start Date:02/01/2020
End Date:01/31/2025
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.076
Primary Program Source:040106 NSF Education & Human Resource
Award Title or Description:CAREER: Supporting Model Based Inference as an Integrated Effort Between Mathematics and Science
Federal Award ID Number:1942770
DUNS ID:077648780
Parent DUNS ID:878135631
Program:Discovery Research K-12
Program Officer:
  • Celestine Pea
  • (703) 292-5186

Awardee Location

Street:1301 E. Main
Awardee Cong. District:04

Primary Place of Performance

Organization Name:Middle Tennessee State University
Street:1301 East Main St
Cong. District:04

Abstract at Time of Award

This project will design opportunities for mathematics and science teachers to coordinate their instruction to support a more coherent approach to teaching statistical model-based inference in middle school. It will prepare teachers to help more students develop a deeper understanding of ideas and practices related to measurement, data, variability, and inference. Since there is little research to show how to productively coordinate learning experiences across disciplinary boundaries of mathematics and science education, this project will address this gap by: (1) creating design principles for integrating instruction about statistical model-based inference in middle grades that coordinates data modeling instruction in mathematics classes with ecology instruction in science classes; (2) generating longitudinal (2 years) evidence about how mathematical and scientific ideas co-develop as students make use of increasingly sophisticated modeling and inferential practices; and (3) designing four integrated units that coordinate instruction across mathematics and science classes in 6th and 7th grade to support statistical model-based inference. This project will use a multi-phase design-based research approach that will begin by observing teachers' current practices related to statistical model-based inference. Information from this phase will help guide researchers, mathematics teachers, and science teachers in co-designing units that integrate data modeling instruction in mathematics classes with ecological investigations in science classes. This project will directly observe students’ thinking and learning across 6th and 7th grades through sample classroom lessons, written assessment items, and interviews. Data from these aspects of the study will generate evidence about how students make use of mathematical ideas in science class and how their ecological investigations in science class provoke a need for new mathematical tools to make inferences. The resulting model will integrate mathematics and science learning in productive ways that are sensitive to both specific disciplinary learning goals and the ways that these ideas and practices can provide a better approximation for students to knowledge generating practices in STEM disciplines. The CAREER program is a National Science Foundation (NSF)-wide activity that offers awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education, and the integration of education and research within the context of the mission of their organizations. This project is supported by NSF's Discovery Research PreK-12 (DRK-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. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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