Information Technology
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This presentation uses Russian social media as a lens for exploring the relationship between individuals, society and online communication technologies.  Alexanyan proposes a framework for comparative international analysis that leverages three interconnected elements – history, network structure and media ecology.  She examines Russia’s social media ecology and its relationship to Russia’s broader socio-political environment on the basis of this framework.  This includes a case study and social network analysis of Russian social network platforms, articulating the various factors that have influenced the specific evolution and network structure of social media in Russia, its role within the broader online and traditional media ecologies, and its implications for Russian society and politics in general.

Speaker Bio

karina alexanyan 182x218 Karina Alexanyan
Karina Alexanyan’s research focuses on global social media, with an emphasis on Russia. She is currently a post doctoral scholar at mediaX at Stanford University, and an affiliate of the Berkman Center for Internet and Society and the Social Media Research Foundation.  Alexanyan received her PhD in Communications from Columbia University in 2013, an M.A. in Communication from NYU and a B.A. in Linguistics and Modern Languages (French and Russian) from the Claremont Colleges.

Recent publications include a chapter on Citizen Journalism in Russia in "Citizen Journalism-Global Perspectives," a chapter on Russian Blogging in “International Blogging – Identity, Politics and Networked Publics,” and articles on  Russian Social Networks in the journal Digital Icons, and in the Russian Analytical Digest.  In 2008, with the support of Harvard’s Berkman Center and Columbia’s Harriman Institute, Karina organized the "RussiaOnline" Conference at Columbia University, bringing together scholars and practitioners from Russia, the U.S. and Europe.

This event is part of the Liberation Technology Seminar Series. 

****NOTE LOCATION****

School of Education 

Room 128

Karina Alexanyan Postdoctoral Scholar; Project Manager, Media X, Stanford University
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Crowdsourcing is an increasingly powerful method for combining amateurs' efforts to recreate an expert's abilities. However, across domains from design to engineering to art, few goals are truly the effort of just one person — even one expert. If we can now crowdsource simple tasks such as image labeling, how might we coordinate many peoples' abilities toward far more complex and interdependent goals? In this talk, I present computational systems for gathering and guiding crowds of experts --- including professional programmers, designers, singers and artists. The resulting collectives tackle problems modularly and at scale, dynamically grow and shrink depending on task demands, and combine into larger organizations. I'll demonstrate how these expert crowds, which we call Flash Teams, can pursue goals such as designing new user experiences overnight and producing animated shorts in two days.
 
As new forms of online work such as Flash Teams emerge, new forms of digital labor challenges also arise. I will introduce our work creating an online platform to facilitate collective action for workers on the Amazon Mechanical Turk marketplace. Our reflection from this effort: the nature of online communities make it easy to form new forms of publics to form, but equally easy for them to stall, face internal friction, and ultimately disintegrate.

Speaker Bio

msb hoover Michael Bernstein

Michael Bernstein is an Assistant Professor of Computer Science at Stanford University, where he co-directs the Human-Computer Interaction group and is a Robert N. Noyce Family Faculty Scholar. His research in human-computer interaction focuses on the design of crowdsourcing and social computing systems. This work has received Best Paper awards and nominations at premier venues in human-computer interaction and social computing (ACM UIST, ACM CHI, ACM CSCW, AAAI ISWSM). Michael has been recognized with the NSF CAREER award, as well as the George M. Sprowls Award for best doctoral thesis in Computer Science at MIT. He holds Ph.D. and M.S. degrees in Computer Science from MIT, and a B.S. in Symbolic Systems from Stanford University.

This event is part of the Liberation Technology Seminar Series. 

School of Education

Room 206

Michael Bernstein Assistant Professor, Computer Science; Co-Director, Human-Computer Interaction Group, Stanford
Seminars
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Abstract

Substantial systematic differences exist in children’s home learning experiences. The few existing parenting programs that have shown promise often are not widely accessible, either due to the demands they place on parents’ time and effort or cost. In this study, we evaluate the effects of READY4K!, a text messaging program for parents of preschoolers designed to help them support their children’s literacy development. The program targets the behavioral barriers to good parenting by breaking down the complexity of parenting into small steps that are easy-to-achieve and providing continuous support for an entire school year. We find that READY4K! positively affected the extent to which parents engaged in home literacy activities with their children by 0.22 to 0.34 standard deviations, as well as parental involvement at school by 0.13 to 0.19 standard deviations. Increases in parental activity at home and school translated into student learning gains in some areas of early literacy, ranging from approximately 0.21 to 0.34 standard deviations. The widespread use, low cost, and ease of scalability of text messaging make texting an attractive approach to supporting parenting practices.

Speaker Bio

susanna loeb Susanna Loeb

Susanna Loeb

Barnett Family Professor of Education, Stanford University

Faculty Director, Center for Education Policy Analysis

Co-Director, Policy Analysis for California Education

 

 

 

This event is part of the Liberation Technology Seminar Series. 


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School of Education

Room 206

Susanna Loeb Faculty Director Faculty Director, Center for Education Policy Analysis
Seminars
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