The SESAME Colloquium

Title: Computational Literacy: The Very Idea

Andy diSessa, Distinguished Professor of the Graduate School

Abstract

My central claim is that achieving a new literacy with computation is the very best thing we can imagine for the future of computers and learning. That achievement will be transformative on par with the achievement of mass literacy for text; it will become fundamental to the intellectual life of future society.

Then, I must fill in:

• What is “computational literacy”?
• What will it look like? Is there an existence proof?
• How can we conceive of its emergence? How can we nurture its advancement?

For contrast, I’ll look briefly at a very visible competitor to the idea of computational literacy: computational thinking. I will argue that (1) the scientific basis for computational thinking is suspect, at best, and (2) while computational thinking has generated huge public visibility and funding, it is a detour to avoid on the track to achieving computational literacy.

I will close with an update to my own work, which includes re-developing, for wide-spread use, the computational medium we have used for experiments, and writing an exemplar of a “computationally literate” textbook, on physics.

About the speaker

Andrea “Andy” diSessa (disessa@berkeley.edu) holds degrees in physics from MIT (PhD) and Princeton (AB). He is a member of the National Academy of Education, a Fellow of the American Educational Research Association, Professor of the Graduate School, and Corey Professor Emeritus at UC Berkeley.

His research centers on the role of intuitive knowledge in learning scientific concepts, and computational literacies. He is the prime designer of Boxer, a medium to support computational literacy. diSessa has authored over 100 articles and chapters, and authored or edited seven volumes, including Changing Minds: Computers, Learning and Literacy, and Turtle Geometry: The Computer as a Medium for Exploring Mathematics.

Title: Computing and Data Science Education with Human Contexts

Lisa Yan

Abstract

There are many layers to an undergraduate classroom in CS/Data Science: a constantly evolving technical skill set, a plethora of real-world applications (and their accompanying sociopolitical conversations), and an increasingly large and diverse set of students. As educators, we therefore design not only a curriculum that addresses the shifting demands of a professional skilled workforce, but also a classroom experience that caters to the needs of both student and teacher.

In this talk, I share three initiatives towards constructing an ecosystem at UC Berkeley that centers human contexts in CS/Data Science education. (1) Curriculum: I build student cultural competence through peer-to-peer discussion assignments in a large, required ethics course for the EECS undergraduate degree program. (2) Course policies: The Flextensions tool increases flexibility in student accommodation with minimal additional course staff time; it automates “soft” assignment deadlines and leverages data to facilitate student support in large courses. (3) Teacher training: Undergraduate teaching assistants (TAs) are a core component of our interdisciplinary Data Science program; however, many are well-trained in STEM fields and less in the human contexts and ethics of data. I am developing a new pedagogy course for Data Science TAs that addresses the pedagogical needs of this interdisciplinary field alongside core values of student inclusion, social justice, and culturally relevant pedagogy.

About the speaker

Dr. Lisa Yan is an Assistant Teaching Professor in Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley, where she teaches undergraduate Data Science and Computer Science (CS). Her research is in computing and data education: equitable, inclusive classroom structures and data analytics tools to enhance teaching, learning, and conversation in large, higher-education computing courses.

Most recently, she has led the development of several new interdisciplinary courses: an introductory data science course that interleaves computing and social sciences; a cultural competence and computing ethics course; and a data science pedagogy course for primarily undergraduate teaching assistants. Prior to joining UC Berkeley, Yan was a Lecturer in CS at Stanford University; she received her Ph.D. and M.S. degrees in Electrical Engineering from Stanford and her B.S. degree in EECS from UC Berkeley.

Twelve Fast, Experimentally-Vetted Ways to Reduce Denial of Human-Caused Global Warming: Raising Both Conservatives’ and Liberals’ Climate Change Acceptance/Concern

Michael Ranney

Berkeley School of Education, University of California, Berkeley

Abstract

This talk’s content might transform your 2024 Thanksgiving conversations! Our experiments show that a dozen different brief (usually under-five-minute) “hunks” of scientific information increase acceptance (among conservatives and liberals alike; i.e., without polarization) that anthropogenic Global Warming (GW) is occurring/concerning (e.g., Ranney & Velautham, 2021). These interventions include videos, temperature (vs. stock-market) graphs, climate statistics (even mixed with misleading statistics), and texts explaining either GW’s physical-chemical mechanism or why climatologists deserve trust (e.g., Ranney & Clark, 2016; Senthilkumaran, Velautham, & Ranney, 2023). (Our public-outreach site, HowGlobalWarmingWorks.org, offers examples.)

Other interventions involve sea-level rise, climate change’s effects, supra-nationalistic statistics, and CO2’s cognitive harms (e.g., Kihiczak & Ranney, 2023; Ranney et al., 2019; Velautham, Ranney, & Brow, 2019). Velautham (2022) likewise increased GW acceptance using two hope-oriented interventions (re: the effectiveness/uptake of, or dyads selecting among, GW solutions). Our convincing information (perhaps even Ranney’s 13-word haiku; e.g., Ranney, et al., 2016, etc.) plausibly improves decisions about policies, politics, and candidates.

About the speaker

Professor Michael Ranney studies explanations and understandings––and how to improve them. His work fosters the incorporation of challenging information (e.g., on global climate change; see HowGlobalWarmingWorks.org). He and his collaborators study reasoning involving both supportive and contradictory relations. They also generate curricula, methods, and software designed to improve rational thinking.

Ranney's work on epistemic knowledge representations/reorganizations exhibits the fragmentary nature of scientific and societal knowledge––in arenas as diverse as physics, biology, abortion, and immigration. His projects often examine ruminations and policy-making involving socially crucial rates and statistics.

Ranney’s core training was mostly in Psychology but recruited broad backgrounds/interests: His first publications were in Applied Physics and Materials Science, and he double-majored in microbiology. Before coming to Cal, he was a Postdoctoral Fellow in Cognitive Science at Princeton University (working with philosophers, etc.). Prior to switching to the Climate Change Cognition, Ranney also researched animal learning, algebra recognition, artificial intelligence, intelligent tutoring systems, science education, environmental psychology, numeracy, journalism education, and cognition about evolution. He also created RTMD theory and the Numerically Driven Inferencing (NDI) paradigm with its EPIC procedure.

Title: TBA

Jodi Davenport

Senior Managing Director, WestEd

Abstract

TBA

About the speaker

TBA

Title: Supporting equitable STEM+CS teaching and learning: Reflections and future directions emerging from work with research-practice partnerships

Jennie Chiu

University of Virgina, School of Education and Human Development

About the speaker

Jennifer L. Chiu is an Associate Professor at the School of Education and Human Development at the University of Virginia. Her research includes studying how technology-enhanced environments can help learners engage in STEM and computational practices, the development of technologies to help teachers rehearse effective pedagogical strategies, and supporting teachers to integrate computation in their STEM classrooms.

She received her B.S. from Stanford University, and her M.A. and Ph.D. degrees from the University of California, Berkeley.

Title: Making Undergraduate STEM Education more Inclusive, Interpersonal, and Interdisciplinary through Bioinspired Design

Haider Ali Bhatti

Graduate Group in Science and Mathematics Education (SESAME), University of California, Berkeley

Abstract

We live in a society where educators inevitably face the task of preparing students for future careers that do not yet exist. Thus, we are faced with a pressing question—how do we prepare the students of today for the unknown jobs of tomorrow? In this talk, I will present a future-facing philosophy predicated on making undergraduate STEM education more inclusive, interpersonal, and interdisciplinary to meet the demands of the future workforce and society.

Specifically, I will show how our Bioinspired Design course—open to all majors, all years, with no prerequisites—fostered a truly unique educational context for students from diverse backgrounds to achieve various learning outcomes needed now and in the future. This course explicitly tasked all students to be innovators as they worked in interdisciplinary teams to translate authentic scientific discoveries from primary literature into societally beneficial bioinspired designs.

Based on Estrada's Tripartite Integration Model of Social Influence, I will first present assessment evidence supporting the development of students' Science Identity, Science Self-Efficacy, and Internalization of Scientific Community Values. I will then show how we expanded assessment in the self-efficacy domain by developing and validating a novel Innovation Skills construct through Wilson's Four Building Blocks of Assessment. Lastly, I will present preliminary results connecting student self-efficacy with student products from the course. Through this talk, I aim to inspire educators to embrace innovative, inclusive, and interdisciplinary approaches that empower students to become confident, socially responsible leaders, equipped with the adaptable skills and mindsets necessary to thrive in the future.

About the speaker

Ali is a PhD candidate in the SESAME program at the University of California, Berkeley. His research focuses on how we can make STEM education more inclusive, interpersonal, and interdisciplinary through design-based research informed by assessment.

He received a Bachelor's degree in Biological Sciences at Rutgers University–New Brunswick, followed by a Master's degree in Biological Science Education at the Rutgers Graduate School of Education. After graduating, he taught high school biology and also worked at Khan Academy as a biology content creator.