Reading and Writing the World with Data: Teaching Statistics and Data Science for Social Justice to Pre-Service Math Teachers

Kevin Pelaez

The widespread availability of data and the emerging field of data science has focused attention on how we prepare teachers to teach statistics. It is particularly important that teacher preparation programs provide preservice mathematics teachers (PSMTs) opportunities to learn about data science as well as how data can be used to identify, analyze, and challenge social injustices. In my dissertation, I use a teaching statistics for social justice (TSSJ) frameworks approach to develop a teaching experiment for PSMTs that aims to develop (a) traditional statistical content knowledge, (b) statistical practices, and (c) understandings of race, racism, and educational equity in the context of statistics. Data includes surveys, task-based interviews, and classroom data (e.g., observations, student journals, classwork). Quantitative and qualitative findings from this dissertation will inform the design of statistics and data science curriculum for teacher preparation programs, design principles for teaching race and racism in the context of statistics and data science, and will contribute to theories of statistical knowledge for teaching. More broadly, the goal of this dissertation is to help prepare the next generation of statistically and critically literate teachers that can read the world with data (learn about social inequities through data) and write the world with data (engage in actions with data that challenge inequities).

About Kevin Pelaez

Kevin Pelaez is a doctoral candidate in the Mathematics and Science Education joint doctoral program at San Diego State University and University of California, San Diego. Kevin’s dissertation is at the intersection of statistics and data science education, teacher education, and critical pedagogies. Particularly, Kevin is using design-based research to develop and study a data science class for prospective mathematics teachers that aims to develop statistical and critical literacies in preparation for teaching data science. For the past three years, he has also worked on an NSF-funded project focused on designing secondary mathematics classrooms that create opportunities for all students, especially multilingual students, to learn mathematics.Prior to his doctoral studies, Kevin taught mathematics at the high school and college level as well as high school AVID and statistics. He received an M.S. in Statistics from San Diego State University and a B.A. in Mathematics with an Applied Emphasis from the University of San Diego. As a local San Diegan, Kevin continues to enjoy working with families, schools, and communities where he grew up. When he’s not coding (either in MaxQDA or R Studio), Kevin spends time attending to his collection of plants that grew while in quarantine. His favorite plant genus is monstera and his favorite species is the monstera adansonii.