Computational Social Science Forum
Date: Monday, October 19, 2020
Time: 12:00-1:30 PM Pacific Time
Location: Register to receive the schedule and access links.
Online education platforms scale college STEM instruction with equivalent learning outcomes at lower cost
Igor Chirikov, Center for Studies in Higher Education, Goldman School of Public Policy, UC Berkeley; and René Kizilcec, Assistant Professor, Computing and Information Science, Cornell University
Abstract: Meeting global demand for growing the science, technology, engineering, and mathematics (STEM) workforce requires solutions for the shortage of qualified instructors. We propose and evaluate a model for scaling up affordable access to effective STEM education through national online education platforms. These platforms allow resource-constrained higher education institutions to adopt online courses produced by the country’s top universities and departments. A multisite randomized controlled trial tested this model with fully online and blended instruction modalities in Russia’s online education platform. We find that online and blended instruction produce similar student learning outcomes as traditional in-person instruction at substantially lower costs. Adopting this model at scale reduces faculty compensation costs that can fund increases in STEM enrollment. Paper here: https://advances.sciencemag.org/content/6/15/eaay5324.
The Computational Social Science Forum provides an informal setting for the interdisciplinary exchange of ideas and scholarship at the intersection of social science and data science. Weekly meetings are hosted by researchers from BIDS and D-Lab, and participants engage in a variety of activities such as presentations of work in progress, discussions and critiques of recent papers, introductions to new tools and methods, discussions around ethics, fairness, inequality, and responsible conduct of research, as well as professional development. We welcome social scientists researchers with interests in data science methods and tools, and data scientists with applications or interests in public policy, social, behavioral, and health sciences. Participants include graduate students, postdocs, staff, and faculty, and members are encouraged to attend regularly in order to foster community around improving computational social science research, supporting the development and research of group members, and fostering new collaborations. This Forum is organized as part of the Computational Social Science Training Program. Meetings are currently held virtually on Mondays at 12:00-1:30 PM Pacific Time, and interested UC Berkeley community members are invited to use this registration form to receive the schedule and access links. Please contact css-t32@berkeley.edu for more information.
Speaker(s)
Igor Chirikov
Igor Chirikov is the Director of the Student Experience in the Research University (SERU) Consortium and Senior Researcher at CSHE. SERU Consortium is an academic and policy research collaboration based at Center for Studies in Higher Education at the UC Berkeley working in partnership with the University of Minnesota, the International Graduate Insight Group (i-graduate) and member universities. The Consortium is a group of leading research-intensive universities who increase student success by generating and analyzing comparative data on the student experience. As SERU Consortium Director Igor Chirikov has broad responsibilities for overall SERU Consortium coordination, research and development. This includes coordinating the SERU North American and International Divisions, directing the SERU research priorities and development projects, and exploring new approaches to the use of data to assess the student experience.
René Kizilcec
Rene Kizilcec is an Assistant Professor in the School of Computing and Information Science at Cornell University, where he directs the Future of Learning Lab. He studies the impact of technology in formal and informal learning environments (including college classes, online degree programs, mobile learning, professional development, MOOCs, and middle/high school classrooms) and scalable interventions to broaden participation and reduce achievement gaps. His research has been published in Science, Science Advances, PNAS, and leading human-computer interaction and education conferences, where it has received multiple ACM Best Paper awards. In 2020, he was Program Co-Chair for the 2020 ACM Learning at Scale conference. Kizilcec received a BA in Philosophy and Economics from University College London, and a MSc in Statistics and PhD in Communication from Stanford University.