Hsiaolin Hsieh (she/her)
I am a doctoral candidate at the Stanford Graduate School of Education. My research focuses on ensuring equitable opportunity and access to learning for multilingual learners. Specifically, I am using natural language processing and machine learning to analyze complex student dialogic participation in the classroom. I have developed LogoSearch, an online repository for collecting, archiving, and evaluating student conversations to support research and teacher professional development opportunities.
Formally trained in educational measurement and assessment, I have extensive experience in the design, implementation, and evaluation of tests in the K-12 context. My involvement in RCT research supporting upper elementary students’ literacy has equipped me with the knowledge and skills for large-scale data collection and advanced quantitative methods. My background in educational technology allows me to examine and leverage alternative tools to assist student learning. Through long-term research-practice partnerships, I have supported school districts in identifying and removing barriers that limit multilingual learners’ equitable access to mathematics courses.
I am a mixed-methods researcher, using both qualitative approaches (interviews, think-aloud, collaborative/independent coding, and classroom observation) and quantitative techniques (surveys, statistical modeling, natural language processing, and deep learning) to generate research findings and present those findings with clear and creative graphical representations. I have worked with stakeholders in local education systems, including teachers, instructional coaches, school and district administrators, and philanthropic foundations.