Before joining the faculty at Syracuse University, Charlotte received her doctorate at Vanderbilt University. There she gained research experience studying teacher learning in a large-scale mixed-methods research project.
There she also developed knowledge and practice in teacher education, which has since become one of her research foci at Syracuse.
As a faculty member at Syracuse University, Charlotte regularly works with local elementary school teachers in their classrooms to co-plan, co-teach, and observe instruction. These classroom experiences are central to her goals for improving her own teaching practice, informing her research questions about teacher and student learning at the elementary level, and maintaining a close connection to learners in the Syracuse area.
Charlotte's research interests are in teacher learning in pre-service and in-service contexts. Her current projects include:
Teacher education and teacher learning in a variety of contexts.
Charlotte Sharpe's research focuses on what teachers need to know and be able to do in order to provide high quality mathematics instruction to diverse students, and how to supports teachers to develop these practices in both pre-service and in-service settings. She is particularly interested in how teacher educators can design experiences in hybrid spaces (e.g., teacher education courses taught in shared field settings) to support pre-service teachers to develop high-leverage teaching practices while also developing more productive views about the mathematical capabilities of students of color, emergent bilinguals, and students with disabilities.
Her current work in studying pre-service teacher learning is organized around the principles of design-based research (see Cobb, Jackson, and Dunlap, 2016), which attempts to create phenomenon of interest (e.g., pre-service teacher learning) in order to simultaneously study it. As a research methodology, design research aims to contribute both practical designs for supporting learning and a local theory of learning in situ which can inform subsequent designs. In data analysis she generally use mixed-methods approaches – for example, using regression analyses and hierarchichal linear models to identify trends across participants that can be used to select rich cases for more in-depth qualitative case study.
Top-level research questions:
Ongoing & Upcoming Projects:
Grad Student Mentoring Opportunities