CMS.S60
Special Subject: Comparative Media Studies
Unpacking \\\*Super Serious\\\* Serious Games
Konstantin Mitgutsch (Singapore-MIT GAMBIT Game Lab), Narda Alvarado
Tue-Thu, Jan 10-12, 17-19, 10am-03:00pm, NE25-375
Pre-register on WebSIS and attend first class.
Limited to 15 participants.
Listeners allowed, space permitting
Prereq: Permission of instructor
Level: U 3 units Standard A - F Grading Can be repeated for credit
Seminar or lecture on a topic that is not covered in the regular curriculum.
Once upon a time games were played for enjoyment and to engage players in social, physical and cultural activities. But in recent years a new trend of "serious games" with "serious purposes" arose. These games claim to raise awareness about social and political issues such as injustice, poverty, racism, sexism, exploitation, oppression, and global problems, reaching specific purposes beyond pure entertainment (raise funds, sign petitions, form opinion, etc). In this workshop, best practice examples of serious games for social change will be played, discussed and analyzed.
Game designers will be invited and recent research papers on the impact of serious games will be discussed. The workshop will focus on the question of what serious games are, what their potential is and where they reach their instructional limit. The question being, do serious games meet the high expectations that designers put into them? Participants with an interest in Super Serious Serious Games, no matter what the level of experience and expertise in video games is, are all welcome.
Contact: Konstantin Mitgutsch (Singapore-MIT GAMBIT Game Lab), k_mitgut@MIT.EDU
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CMS.S61
Special Subject: Comparative Media Studies
The Rise of Film Noir
Martin Marks
Tue, Thu, Jan 10, 12, 17, 19, 24, 26, 31, 2, 01-02:30pm, 4-364
Pre-register on WebSIS and attend first class.
Listeners allowed, space permitting
Prereq: Permission of instructor
Level: U 3 units Standard A - F Grading Can be repeated for credit
Seminar or lecture on a topic that is not covered in the regular curriculum.
This class will explore Hollywood Film Noir as it developed during the 1930s-50s. The focus will be on important émigré directors who moved to Hollywood when the Nazis rose to power, including Fritz Lang, Billy Wilder, Otto Preminger, Michael Curtiz, and Fred Zinnemann. Also to be considered are contributions by cinematographers, set designers, and composers. Class activities include a field trip to the Sackler Museum at Harvard to look at émigré artworks from this period, a forum and concert by the Boston Chamber Music Society in Kresge on Jan. 21, and a concurrent series of six great examples of film noir from the 1940s. (See separate IAP listings for the film series and concert.) Assignments to include short response papers about three of the films in the series, and reports on examples of “neo-noir” films from recent decades.
Contact: Martin Marks, mmmarks@MIT.EDU
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CMS.S62
Special Subject: Comparative Media Studies
Time Machine: Building a model for Predicting Culture
Grant McCracken
Wed-Thu, Jan 18-19, 25-26, 1-2, 03-05:00pm, 4-231
Pre-register on WebSIS and attend first class.
Listeners allowed, space permitting
Prereq: Permission of instructor Qualitative and quantitative skills.
Level: U 3 units Standard A - F Grading Can be repeated for credit
Seminar or lecture on a topic that is not covered in the regular curriculum.
As our culture becomes more diverse and changeable, cultural prediction becomes more urgent and difficult. The point of this course is to build a model for making predictions. We will proceed in a practical way, taking on "real world problems." How quickly could we have seen the influence Alice Waters and Chez Panisse were to have on American culture? Could we have predicted a shift in Hollywood that demoted the likes of Schwarzenegger and promoted the likes of Michael Cera? To build the model, aka “big board” or “time machine”, we have to solve theoretical and methodological problems: what is the unit of analysis, what are the best markers of adoption, what are the best metrics, how can we make and monitor predictions, how can we represent data according to best “infographic” practice? To my knowledge, a model like this has no precedent. Think of the course as something out of the early Soviet space program. The engineering will be dodgy. Failure is not unlikely. The process will be messy and frustrating. But the outcome is sure to be illuminating and instructive. Plus your heroism is guaranteed.
Contact: Grant McCracken, grant27@gmail.com
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