Topics in the theoretical, methodological, and practical aspects of information studies. Repeatable with Different Topics.
This course provides an overview of the major subject areas, ideas, concepts, and theories of Information Studies and introduces the basics of research, publication, and academic conventions in Information Studies. Prerequisites: Admission to the doctoral program and consent of the graduate advisor.
An overview of the nature and purposes of research, and common methods and methodologies in information studies.
An overview of concepts, results, and perspectives from philosophical, social science, humanistic, design, and technological disciplines that provide important underpinnings for information studies.
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This course starts by discussing broad landscape of epistemological and theoretical perspectives and styles of reasoning and by situating in it quantitative research. It introduces you to the foundational concepts in quantitative research methods, such as causality, conceptualization, operationalization, measurement and sampling. It presents experimental design, survey design, and basic descriptive and inferential (frequentist) statistics, as well as a brief introduction to Bayesian inference and statistics.
Explores a variety of approaches to qualitative methods including ethnography, participant observation, case studies, grounded theory, phenomenology, action research, and so forth. Students will have a hands-on opportunity to conduct their own research project in which they will learn, discuss, and reflect upon the procedures of qualitative research.
Intensive writing, critique, and rewriting to assist senior doctoral students with refining their research writing in preparation for qualifying papers, dissertation proposals, and formal publications. May be repeated for credit.
The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the iSchool Registration/Course Administrator for registration.
The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the iSchool Registration/Course Administrator for registration.
The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the iSchool Registration/Course Administrator for registration.
The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the iSchool Registration/Course Administrator for registration.
The individual student works under supervision of a member of the graduate faculty. Students must present the faculty member's name to the iSchool Registration/Course Administrator for registration.
Part 1 of the iRISE program sequence for PhD students. Participants will present their potential research projects, for which undergraduates will self-select select into teams. Together, teams and their advisors will compile a project management plan, design their study, identify intended audience beyond academia, and potential impact deliverable to communicate results.
Part 2 of the iRISE program sequence for PhD students. Once they receive university IRB approval to ensure their research meets ethics requirements, teams will begin and complete data collection and analysis. They’ll end with finalized results (i.e. answers to their research questions).
AI hype is everywhere—from classrooms to courtrooms, boardrooms to borders, protestors to investors. In this course, we’ll take a “production view” to get past this hype and critically examine the infrastructure, capital, and labor that goes into building AI, and study how AI is transforming global politics, policy, and economies. We will learn to think critically about major political economic actors in AI—the state, universities, and Big Tech—and how AI affects war fighting, borders, policing, and the environment.Over the semester, students will build towards a final project aligned with their ongoing research agenda by analyzing qualitative or quantitative data to support novel political economic analysis, and increase their work’s impact by situating it within live tech policy debates. Students will iteratively build towards this project via in-class reading reflections, discussions, and a mid-semester project plan. Out of class work will primarily consist of readings, preparing for an assigned day in which they lead discussion, and independent work towards the final project. The course will include select guest lectures and discussion with prominent thinkers in this field, helping build students' wider scholarly network.