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School of Engineering
- Undergraduate major in Mechanical Engineering
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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
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School of Environment and Society
- Department of Architecture and Building Engineering
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- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Social and Human Sciences
- Instructor(s)
- Kezuka Kazuhiro
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Mon5-6()
- Group
- -
- Course number
- SHS.M442
- Credits
- 1
- Academic year
- 2022
- Offered quarter
- 2Q
- Syllabus updated
- 2022/3/16
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
In recent years, computational social science has been emerging. Computational social science is an area that collects traces of people's behavior on the Internet ("digital footprint") and analyzes them. In this course, students will learn how to analyze the social survey and the vast amount of data in the digital era.
Student learning outcomes
By the end of this course, students will be able to:
1) understand and practice the social survey methodology in the digital age.
2) analyze data which you collected.
Keywords
social survey, big data, statistical analysis
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | ✔ Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Students summarize the textbook on paper and report it. Students discuss based on the paper. At the last class, students must plan a social survey and make a presentation of it.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Guidance | Understand what computational social science is. |
| Class 2 | Observing behavior | Understand big data and strategy of analyzing them. |
| Class 3 | Asking questions | Understand social surveys in digital era. |
| Class 4 | Running experiments | Understand experiments of social sciences in digital era. |
| Class 5 | Creating mass collaboration | Understand the strategies to develop our research with collaboration. |
| Class 6 | Ethics | Understand the ethics of social surveys in digital era. |
| Class 7 | Presentation of survey plan | Make a persuasive presentation of survey plan. |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Salganik, M. J., 2017, Bit by Bit: Social Research in the Digital Age, Princeton University Press.
Reference books, course materials, etc.
None required.
Assessment criteria and methods
a) Evaluation for those who make summary resume:
summary resume: 30%, final presentation: 30%, commitment: 40%
b) Evaluation for those who don't make summary resume:
final presentation: 50%, commitment: 50%
Related courses
- SHS.M443 ： Graduate Lecture in Cognition, Mathematics and Information F1A
- SHS.M444 ： Graduate Lecture in Cognition, Mathematics and Information F1B
- SHS.M461 ： Graduate Methodologies in Cognition, Mathematics and Information S1
Prerequisites (i.e., required knowledge, skills, courses, etc.)
None required.
Contact information (e-mail and phone) Notice : Please replace from "[at]" to "@"(half-width character).
kkezuka[at]ila.titech.ac.jp
