EXAM REVIEW

Exam Overview

  • The exam consists of both multiple choice and short answer questions.
    • Multiple Choice Questions
    • Total of 20 questions.
    • Worth 25 points total, with some questions worth 2 points each instead of just 1.
    • Short Answer Question
    • Worth 5 points.
    • Requires a response of a couple of paragraphs, no more than 200 words.
    • Grading
    • The instructor cannot auto-grade due to evaluating the short answer response manually.
    • Grades expected to be returned by the end of the week after spring break.

Logistics for the Test

  • The exam is not proctored and can be taken at home.
  • Online system (Canvas) may have technical issues; contact the instructor via email if problems arise during the exam.
  • The instructor will be traveling for a conference but will respond to emails to resolve any issues timely.

Course Content Coverage

  • The exam covers the first half of the course.
  • Topics span from Chris Anderson to Mimi Anwar and Chito Stirral.
  • Exclusions:
    • Content marked as browse will not be examined (e.g., punch card archives, open syllabus).
    • Content marked as optional will be included.

Upcoming Classes

  • Class After Spring Break: Case studies will be discussed, synthesizing material previously covered (history and theory of cultural data).

Review and Data Logs

  • Review session scheduled for Thursday.
  • Activity resembles the first round of data log preparation.
  • Students will work in groups on data logs and documentation pages.
    • The documentation page is a living document that will describe their data logs and outlined goals.

Class Organization and Key Concepts

  • Students were asked to identify key concepts that encapsulate the readings from the past weeks.
  • Noteworthy topics discussed include:
    • Data Proxies
    • Understanding how disparate variables are assembled to create scientific knowledge.
    • Issues around data proxies, including examples and applications discussed in the context of readings, particularly O'Neil's work.
    • Epistemological Considerations
    • Questions around knowledge assembly and data interpretation.
    • Technocratic Governance and Automation
    • Implications of algorithmic governance on social structures and power relations.

Group Discussions

  • Students will be broken out into groups focusing on key topics:
    • Group 1: Raw data as an oxymoron.
    • Group 2: The black box concept.
    • Group 3: Data proxy discussion.
    • Group 4: Power relations and domain expertise.
    • Group 5: Personalization in data contexts.

Insights from Class Readings

  • Various readings referenced for further exploration of discussed topics:
    • Kathy O'Neil's arguments regarding the dangers of data proxies and algorithmic governance.
    • Chris Anderson's perspectives on data abundance and analysis.
    • Additional readings from Gittleman and Jackson regarding data interpretation and aggregation, and how it impacts representation.

Preparation for the Exam

  • Students are encouraged to prepare to discuss key concepts from readings and synthesize ideas on how they relate to larger themes presented in class.
  • In-class discussions will help solidify understanding of topics such as data proxies, black boxes, and algorithmic governance.
  • The instructor will facilitate the interaction to establish connections between personal observations and course materials.

Importance of Ethical and Governance Considerations

  • Students will need to analyze how data and its interpretation can shape social and political decisions.
    • Discussions will address how data methodologies influence governance decisions and broader societal impacts.