by Tingting (Rachel) Chung (she/her/hers)
Personal Background
As a first-generation, Asian, female, international graduate student, I have a deeply personal understanding of the challenges faced by underrepresented groups in academia. My own journey through higher education has shaped my commitment to creating inclusive learning environments where every student can thrive regardless of their background.
With over twenty years of diversity-focused participation in academic and professional settings, I bring both lived experience and deliberate practice to my approach to inclusive teaching.
Diversity in Teaching
My commitment to diversity is woven into every aspect of my teaching practice. The following approaches reflect how I create an inclusive and equitable classroom environment:
Diverse Names in Examples
Using diverse names across examples and assignments so that all students see themselves reflected in the course materials.
Current Student Names
Updating slides each semester with current student names, fostering a sense of belonging and personal connection with course content.
Diverse Voices
Incorporating diverse voices and perspectives in course materials, ensuring students encounter a wide range of viewpoints and experiences.
Multiple Learning Styles
Supporting multiple learning styles through varied instructional methods, including visual, auditory, kinesthetic, and reading/writing approaches.
Recorded Lectures with Transcripts
Recording lectures with transcripts for non-native English speakers, enabling students to review material at their own pace and with language support.
Free and Accessible Resources
Using free textbooks and library resources whenever possible to reduce financial barriers to learning and ensure equitable access to course materials.
Flexible Office Hours
Offering flexible office hours to accommodate students with diverse schedules, including those who work, have caregiving responsibilities, or are in different time zones.
Connecting Diversity to Analytics
Connecting diversity concepts to analytical topics, demonstrating how bias can appear in data and algorithms and why diverse perspectives are essential in data science.