More details will be added in the coming weeks, stay tuned!
Rebecca Nugent (Carnegie Mellon University)
Tuesday Panel (6/29):
Felicia Simpson (Winston-Salem State University), Jacqueline Hughes-Oliver (North Carolina State University), Jamylle Carter (Diablo Valley College), Prince Afriyie (University of Virginia), Samuel Echevarria-Cruz (Austin Community College)
Panel Title: Expanding Horizons and Fostering Diversity
Catherine D'Ignazio (Massachusetts Institute of Technology) and Lauren Klein (Emory University)
Title: Data Feminism
Abstract: As data are increasingly mobilized in the service of governments and corporations, their unequal conditions of production, their asymmetrical methods of application, and their unequal effects on both individuals and groups have become increasingly difficult for data scientists--and others who rely on data in their work--to ignore. But it is precisely this power that makes it worth asking: "Data science by whom? Data science for whom? Data science with whose interests in mind? These are some of the questions that emerge from what we call data feminism, a way of thinking about data science and its communication that is informed by the past several decades of intersectional feminist activism and critical thought. Illustrating data feminism in action, this talk will show how challenges to the male/female binary can help to challenge other hierarchical (and empirically wrong) classification systems; it will explain how an understanding of emotion can expand our ideas about effective data visualization; how the concept of invisible labor can expose the significant human efforts required by our automated systems; and why the data never, ever “speak for themselves.” The goal of this talk, as with the project of data feminism, is to model how scholarship can be transformed into action: how feminist thinking can be operationalized in order to imagine more ethical and equitable data practices.
Alana Unfried (California State University - Monterey Bay)
Title: Expanding Opportunities for Underprepared Statistics Students
Abstract: As educators, are there ways that we have systemically excluded students from the possibility of success at learning statistics? I will argue, YES! Nationally, almost 2 million students begin college in remediation each year, and only 22% of students who start in mathematics remediation complete their subsequent general education mathematics course, such as introductory statistics (completecollege.org/spanningthedivide). We can expand the opportunity for thousands of students to learn statistics each year through switching to a corequisite statistics model in which students enter directly into an introductory statistics course, instead of completing remediation first. However, a major challenge of this model is designing statistics courses where both traditionally prepared and underprepared students can succeed side-by-side. I will discuss the use of complex instruction, a combination of pedagogical strategies that attend to problems of social inequality in the classroom through intentionally designed participation structures and tasks, to support student learning in an environment of such diverse backgrounds. I will also discuss how these strategies can be used beyond the introductory statistics classroom, and will provide evidence of the effectiveness of the corequisite model and complex instruction.