Sunghwan researches discourse and social interaction for teaching and learning mathematics, statistics, and data science. He is an Assistant Professor of Mathematics Education in the Department of STEM Education at NC State and is the Director of Educational Research in the NC State Data Science Academy. Prior to his academic career, he was a high school mathematics teacher and a National Board Certified Teacher. His current projects aim to enhance undergraduate data science and statistics instruction, and support instructors in facilitating productive and equitable learning opportunities for students with historically marginalized backgrounds.
- Doctoral Program Concentration: Mathematics & Statistics Education
- Master’s Concentration: Mathematics Education
- Undergraduate: Mathematics Education – Middle School or Secondary
Ph.D. Mathematics Education Michigan State University 2021
M.S. Statistics Michigan State University 2020
M.S. Mathematics Education Oregon State University 2008
B.S. Mathematics Education Kyungpook National University 2007
Area(s) of Expertise
Equity & Diversity
Mathematics & Statistics Education
Teacher Education & Professional Development
- Correction to: Interactional practices of inviting minoritized students to whole‑class mathematics discussions , Educational Studies in Mathematics (2024)
- Interactional practices of inviting minoritized students to whole-class mathematics discussions , EDUCATIONAL STUDIES IN MATHEMATICS (2024)
- Race/ethnicity in mathematics education: what topics appear and how do they change over time? , INTERNATIONAL JOURNAL OF MATHEMATICAL EDUCATION IN SCIENCE AND TECHNOLOGY (2024)
- A Participatory Turn in Mathematics Education Research: Possibilities and Tensions , JOURNAL FOR RESEARCH IN MATHEMATICS EDUCATION (2023)
- When Only White Students Talk: EQUIP-ing Prospective Teachers to Notice Inequitable Participation , Mathematics Teacher Educator (2023)
- How do you eat an elephant? How problem solving informs computational instruction in high school physics , 2022 PHYSICS EDUCATION RESEARCH CONFERENCE (PERC) (2022)
- Interactional Work of Advanced Placement Mathematics Teachers: Toward Productive and Equitable Mathematics Discussions , (2021)
- Interactional production of deficit talk in a professional development for mathematics teachers , JOURNAL OF MATHEMATICS TEACHER EDUCATION (2021)
- Tensions and Failures in an Analysis of Whiteness among a Racially and Socially Diverse Group of Mathematics Teacher Educators , Applying Critical Mathematics Education (2021)
- Racial hierarchy and masculine space: Participatory in/equity in computational physics classrooms , Computer Science Education (2020)
In this Level I early stage Design and Development study on Teaching, using design-based research, we aim to design, implement, investigate, and iteratively refine a video-based coaching model to develop mathematics teachersâ€™ responsive pedagogies for linguistically marginalized students. Building from cutting edge research on linguistically responsive mathematics pedagogies (Adler, 2021; de Araujo & Smith, 2021; Marshall et al., in press; Song & Coppersmith, 2020), this project addresses the persistent need to foster mathematics teachersâ€™ learning about supporting linguistically marginalized students (Lucas & Villegas, 2010; Prediger, 2019). Our novel model centers the experiences of students through video clips as rich tools for teacher learning. Our approach builds from key findings from our small prior study: that video-based coaching can support teachers in learning justice-oriented pedagogies such as social justice mathematics (Marshall, 2022) and learning to disrupt racialized patterns of exclusion in mathematics classrooms (Marshall, 2020) by supporting teachersâ€™ sensemaking about their own studentsâ€™ unique experiences in mathematics classrooms and giving timely, formative feedback as teachers encounter problems of practice (Horn et al., 2022). Central to our model is this core insight: that classroom video holds potential for supporting teacher learning of responsive pedagogies because of its opening of a window into studentsâ€™ experiences, proximity to practice, context-embeddedness, and affordances for troubleshooting such pedagogies soon after teachers try them in their classrooms. The scholars collaborating to lead this project have strong histories of work designing and investigating professional development for educational equity, and complementary expertise to build a powerful and scalable model for mathematics teachers learning of responsive pedagogies. Our overarching research question is: How do secondary mathematics teachers learn about supporting linguistically marginalized students? The primary outcomes of this research include: a portrait of the challenges and opportunities that mathematics teachers face in supporting linguistically marginalized students, an iteratively refined model of professional development for teachersâ€™ learning of responsive pedagogies, and an empirically-grounded theory of teachersâ€™ learning to support linguistically marginalized students.
The following proposal, Modules for Statistics Graduate Teaching Assistants Learning to Teach Equitably with Authentic Data (GTAs-LEAD), is submitted for consideration as a Level 1, Track 1: Engaged Student Learning proposal seeking to develop, implement, and research evidenced-based professional development modules for statistics graduate teaching assistants (GTAs). Developing data acumen is necessary for every citizen to harness the data revolution in their workplaces and everyday lives (NASEM, 2018). There have been longstanding recommendations to reform introductory statistics courses to address the growing need for expanding opportunities to investigate authentic data (GAISE College Report ASA Revision Committee, 2016; Ridgway, 2016). Despite the numerous efforts to develop curricular resources to bring authentic data investigation into introductory statistics classrooms, the instruction often focuses on procedural aspects of statistical skills. Moreover, in large universities, such as North Carolina State University (NCSU) and Michigan State University (MSU), the responsibility of leading these active learning opportunities often falls on graduate teaching assistants (GTAs) who may not have any formal education in teaching (Justice et al., 2017). Without discipline-specific professional development for statistics GTAs, innovative curricular resources are unlikely to reach the fruition of equitable learning outcomes of developing data acumen. The GTAs-LEAD project will address this urgent need to facilitate teacher learning of statistics GTAs. The project team hypothesizes that with carefully organized discipline-specific teacher learning modules, statistics GTAs can learn to teach equitably with authentic data while working with their GTA communities. Guided by a design and development research approach, the GTAs-LEAD project will: (1) design a set of four research-informed modules for statistics GTAs learning to teach equitably with authentic data (LEAD Modules), (2) implement LEAD Modules with two GTA communities teaching introductory statistics courses at NCSU and MSU, and (3) further refine LEAD Modules based on design-based research that examines GTA development and their communities. By drawing on the interdisciplinary expertise of the PI team, the GTAs-LEAD project infuses knowledge bases and resources from statistics education and mathematics teacher education to support statistics GTAs learning to teach equitably with authentic data.