Institute for Computational and Experimental Research in Mathematics (ICERM)

Abstract

While the importance of data science training has been highlighted by stakeholders across the mathematical sciences, the fusion of data skills with core parts of the curriculum has yet to be fully realized. In parallel to this challenge, the mathematical sciences are struggling with issues of equity, diversity, and inclusion. This talk presents pathways for incorporating data skills into undergraduate mathematics education in ways that support social justice. One pathway is through the classroom. I will present selected examples of course activities such as creation of computational art designed to celebrate artists from marginalized identity groups. A second pathway is through undergraduate research. I will showcase data-intensive student projects related to criminal justice. Finally, I will highlight resources for instructors who themselves want to grow their data skills and/or their understanding of social justice.

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Abstract

Today’s world is more thoroughly quantified than ever before. We are awash in numbers, statistics, and data-hungry algorithms. Quantitative information is power: it is used to convey objectivity, to strengthen arguments, and to inform decisions—and all too often it is used by humans and human-like chatbots to impress, overwhelm, and persuade without any conviction to truth. Fortunately, one doesn’t need an advanced degree in data science (although quite helpful) to see through the vast majority of quantitative misinformation. Drawing on examples from current events of misleading claims and misuses of artificial intelligence, this talk aims to empower data scientists and the everyday information consumer to push back when confronted by numerical arguments and data-hungry technologies. By focusing attention on the data sources and their weaknesses, rather than on the complex and often obscure algorithms and statistical procedures, we can see through the hype and hold our own against those selling quantitative snake-oil.

Abstract

We use “Data Science for Social Justice” as a tool to broaden the participation of students from historically marginalized communities (HMC) in STEM by infusing social justice into STEM learning as well as to broaden the participation of students from non-STEM disciplines (where underrepresentation is not as big a problem) in data science by infusing data science into discussions on social justice issues relevant to their respective disciplines. In this talk I will discuss our methodology and experiences with past and on-going projects.
This is joint work with faculty and researchers from NC Central University (Alade Tokuta, and Rakesh Malhotra), Cynosure Consulting (Rebecca Zulli Lowe, and Adrienne Smith), Fayetteville State University (Sambit Bhattacharya), Winston-Salem State University (Debzani Deb), and Worcester Polytechnic Institute (Elisabeth Stoddard, and Marja Bakermans).

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Abstract

In this presentation, the authors will talk about some of their strategies and approaches to incorporating justice-related themes in data science and math modeling education at the middle school, high school, and undergraduate (school) level. These include: developing extracurricular data science activities centering on climate change; developing community partnerships and involvement; engaging students through math modeling competitions; and hosting datathons centered on justice and activism.

Abstract

The world often looks to philosophy and the social sciences as opposed to the mathematical sciences to discuss and explore topics related to equity, collective action, and justice. Since the late 19th and early 20th centuries, however, African American scholars have taken social theories, which are strong in intuitive content, and expressed them in more formal terms using mathematics and data. Despite recent advances in discourses at the intersections of mathematics and social justice, the narratives and legacies of many of these African Americans who used mathematics and real-world data to understand society, especially in the American South, have been lost. This talk will explore the foundations of models used by African Americans to examine inequities in history with the intention of developing more just futures. More specifically, this talk will connect the intuitive aspects of a global experience to understand the means through which mathematics served as an integral tool for African Americans to not only speak to their experiences but also to the logic of democracy, race, and racism.

Abstract

This talk will cover the collaboration between a Data Science Student team and the faculty of the American Cultures courses. The Data Science Modules student team is group of students who build interactive computational notebooks for faculty members. The American Cultures requirement is a university-wide requirement at Berkeley that all students take a class within a set of classes on American Cultures. The 5 year collaboration has led to the creation of innovative pedagogy in student explorations of applied datasets on topics such as mass incarceration, environmental racism, redlining, labor movements. etc. The talk will be paired with workshop opportunities of getting participants to interact with the computational notebooks and reflect on the pedagogical implications.

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Abstract

The purpose of this workshop will be to give educators hands-on experience building and executing Jupyter notebooks for classroom use. Workshop participants can work through some example notebooks with social justice applications that have been used in UC Berkeley classrooms. Then workshop participants can practice building their own instructional notebooks. Using a cloud-based workshop hub, no set up is necessary and the workshop deployment will simulate the classroom environment.

This is an optional event, ICERM staff including IT support will not be available for this event.

Abstract

In Mathematics and Democracy, Lynn Steen and collaborators called for a literacy that ``empowers people by giving them the tools to think for themselves, to ask intelligent questions of experts, and to confront authority confidently.'' This sets an inspiring goal for data science curricula infused with issues of social justice. We can engage and motivate students with entry points that include local or regional data and by encouraging students to raise questions of their own. In this session, we’ll touch on simple examples that can be introduced in the classroom as shorter exercises or scaled up for student projects and beyond, even taking the instructor on unexpected journeys. For traditionally-trained mathematical scientists, creating more open-ended assignments connected to societal issues also raises questions around assessment. We’ll invite a group discussion sharing ways to approach this, both of student work and of our own as instructors.

Abstract

The narrative of the mathematics profession is shaped by those elevated by institutions to leave a permanent record in the form of interviews, presentations, publications, reviews, etc. This begs the questions: Is access to this privileged discourse based on merit alone? If so, what are the pathways to gain such merit? Are they equitable? In this presentation, we will describe the effect of early access to mathematics on college performance. The analysis of national data describing access to Algebra II in high school, as well as the analysis of 5.5 million credit hours and 86k students over five years at The University of Texas at San Antonio, reveals that underperformance in mathematics is the single largest predictor of changing majors (including mathematics) or dropping out of college. This trend starts in K-12 with unevenness shaped by socioeconomic factors and continues through graduate education. These dynamics perpetuate a cycle of privilege and exclusion. A strategy based on data analytics was designed and implemented at UTSA. We will discuss substantial improvements in student performance and future directions that will guarantee that the face of success matches the face of society.

Abstract

In the last few years, there has been increasing focus on integrating data science into a wide range of higher education curricula through programs and pathways building from foundations in mathematics, statistics and computer science with applications to real-world problems arising from sectors including business, industry, government, health, education and much more. Along with this integration, it is also important to incorporate the importance of the power of data science methods to tackle pressing social justice challenges that can help to not only gain insights but also to improve decision-making and creation of sustainable and scalable solutions that will impact the society in an equitable and inclusive way. In this talk, we will describe examples of sustainable data science programs and pathways for developing innovative multi-disciplinary research and education opportunities to solve societal challenges with some relevant examples using various technology tools and problem-solving frameworks. We will also discuss potential opportunities for students and faculty to collaborate on new developments in data science to engage the next generation workforce.

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