A course instructor was guided to focus on a practical, evidenced based mode to rehumanize their secondary mathematics methods course in the United States of America by their own past K-12 teaching experiences and intentions to incorporate issues of diversity, equity, inclusion, and social justice using the Street Data framework, specifically the deep learning section using small talks.Small talks are approximately 5-minute, once per 50 minutes of class time, student lead discussions that introduce their classmates to the life stories of identities that have been oppressed within STEM, particularly mathematics.To reduce bias, the instructor explains their positionality and how a community was created that allowed students to be the experts and gain provide the insights to others into their course and learning through deep learning.As such, the following research questions developed: 1) After engaging in the humanizing mathematics activity, what shifts did PTs experience? and 2) How do PTs anticipate humanizing (incorporating a community with equity and justice) within their future mathematics courses?To evoke deep learning, small talks were positioned on three specific foci that aligned with deep learning from Safir and Dugan (2021): redefining success, building coherence, and making learning public.Results indicate PTs intend to pass the opportunity of small talks on, giving voice to their future students, families, fellow teachers, their school, and their community leaders one day.
Amanda Meiners (Mon,) studied this question.