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University of Michigan School of Information


Special topics courses

Winter 2022 topics offered

SI 311-001: Social Movements & The Internet with Professor J. Sheng

Over the past few decades, social movements have increasingly relied on social movement participants leveraging internet technologies in mobilization, coordination, and public outreach to assist in their movement goals. How have new online tools such as social media and digital connectivity changed the processes of contemporary social movements? This course examines the ways social movements have adapted to online technologies to critically think about how the internet has altered traditional forms of social movement mobilization. We start with an introduction and review of traditional social movement literatures, building up to recent scholarship that examines how the internet has changed social movements. The last few weeks are focused on different contemporary social movement case studies where the internet played an important role, including the Arab Spring, Black Lives Matter, LGBTQ equality, feminism and the #MeToo movement, recent international social movements, and the internet’s role in the Jan. 6 storming of the U.S. Capitol. Students will be encouraged to think about the ways social movement processes have been accelerated and/or changed due to online technologies. 

SI 311-003: Sports Analytics with Professor T. Finholt

In this course students will work with the instructor and with training/coaching personnel in U-M Athletics to address a set of analyses related to athlete health, safety or performance, such as by using data gathered from: tracking devices worn in practice and competition (e.g., Catapult); cameras (e.g., TrackMan); or boxscore and other statistical data (e.g, Pro Football Focus).  These datasets can be large and complex. For example, wearables data typically consist of a hundred records per second with a dozen or so variables per record (e.g., distance traveled, direction of movement, number of explosive movements) – collected longitudinally across up to fifteen athletes per team per season.

Pre-requisites: Students should have completed (or be currently taking) an introductory level stats course (e.g., STATS 250) and an introductory programming course (e.g., EECS 183, ENGR 101/151 or INFO 106).