Mathematics Education Research
Leveraging Contrasting Cases to Investigate Integer Understanding
Faculty: Laura Bofferding
The purpose of this NSF CAREER project is to identify language factors and instructional sequences that contribute to improving elementary students’ understanding of addition and subtraction problems involving negative integers. A second objective is to identify how elementary teachers interpret their students’ integer understanding and use research findings to support their teaching of these concepts. This project is expected to contribute to theories regarding the development of integer understanding as well as what makes a useful contrasting case when learning new, related concepts. Moreover, the results of this project can contribute to our understanding of how to build on students’ prior number knowledge rather than contradict it.
This project (2014-2019) is funded by NSF Award DRL-1350281, $680,504 with a supplement of $16,205.
Understanding Data-Literacy of School Personnel for Data-Driven Decision Making
Faculty: Rachael Kenney, Yukiko Maeda, Ala Samarapungavan, Rachel Roegman
Data-driven decision-making (DDDM) has become a central focus for education policy and practice at all levels. Under the current climate of accountability as a result of No Child Left Behind (NCLB) in 2002 and Race to the Top in 2010, school administrators and teachers face unprecedented demands to use a wide range of data to inform educational decisions that promote student achievement. Accordingly, data-driven decision-making (DDDM) has become a central focus for educational policy and practice at all levels as an innovative strategy for school system and instructional reform. There is a critical need for educators and administrators to develop assessment and data literacy so that effective DDDM can take place in educational settings. Specific goals of the project are:
- To understand how educators (particularly secondary teachers and curriculum coordinators in mathematics and science) and administrators use assessments, select data, extract relevant information and construct actionable knowledge to make decisions in practice;
- To understand the extent of coherence of DDDM across different school system levels;
- To provide useful feedback on the data use in DDDM to educators and administrators.
This project is funded by the Launch the Future grant in the College of Education, Purdue
Other Faculty Research
- Jill Newton’s Research
- Signe Kastberg’s Research
- Rachael Kenney’s Research
- Laura Bofferding’s Research
CATALYST is an interdisciplinary research-oriented unit focused on building and supporting a community of educational professionals who are dedicated to advancing K- 12 STEM (science, technology, engineering, and mathematics) teaching and learning through research.