Research Methods

This goal of this course is to introduce and discuss concepts in research methodology, empirical analysis, and the scientific enterprise in computing. This course will prepare students for conducting research by examining how to plan, conduct, and report on empirical investigations. The course will cover techniques applicable to each of the steps of a research project, including formulating research questions, theory building, data analysis (using both qualitative and quantitative methods), building evidence, assessing validity, and publishing. The course will cover the principal research methods used to study human interaction with computer technology: controlled experiment, case studies, surveys, archival analysis, action research and ethnographies. We will also cover topics in peer review, ethical obligations involving human subjects research, how to give a scientific presentation, and how to write research papers, survey papers, and funding proposals.

Prerequisites

Enrolled as a Graduate Student in CSE or by instructor permission.

Logistics

Class Information

Lecture:
T/R 9:30am – 10:45am

206 Debartolo Hall

Instructor

Dr. Tim Weninger (tweninge@nd.edu)

Office Hours:
Tue 11:00am-12:00pm in 380 Fitzpatrick Hall
or by appointment

Teaching Assistants

None

Course Format and Activities

WeekDateTopicDiscussion LeadersPre-ReadingAssignments
101/16IntroductionNone
101/18History and Philosophy of ScienceOkasha, Ch 1-3
201/23Critical Reading of ResearchChandrasekharan, Eshwar, et al. “You can’t stay here: The efficacy of reddit’s 2015 ban examined through hate speech.CSCW (2017): 1-22.
201/25Critical Reading of ResearchMuchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: A randomized experimentScience341(6146), 647-651.
301/30Peer ReviewBohannon, John. “Who’s afraid of peer review?.” Science. (2013): 60-65.

Tomkins, A., Zhang, M. and Heavlin, W.D., 2017. Reviewer bias in single-versus double-blind peer review. Proceedings of the National Academy of Sciences, 114(48), pp.12708-12713.

http://blog.mrtz.org/2014/12/15/the-nips-experiment.html
302/01How to Write a Peer Review
402/06Morphology of a Paper and Technical WritingTim Weninger PPTWeekly Review: Glenski, M., Stoddard, G., Resnick, P., & Weninger, T. (2018). Guessthekarma: A game to assess social rating systemsProceedings of the ACM on Human-Computer Interaction2(CSCW), 1-15.Weekly Review Due
402/08LaTeX and BibTeXWeninger Example
502/13How to Write your Research
502/15How to Write a Survey
602/20How to Make a Research Presentation
602/22 How to Make a Research PresentationPPT1
PPT2
PPT3

Discussion Leader: Byron Dowling
Weekly Review: Boyd, A., Tinsley, P., Bowyer, K., & Czajka, A. (2021). Cyborg: Blending human saliency into the loss improves deep learningarXiv preprint arXiv:2112.00686.
702/27 Revising and Publishing ResearchDiscussion Leader:
Tasha Januszewicz
Weekly Review: Roy Chowdhury, A., Ding, B., Jha, S., Liu, W., & Zhou, J. (2022, November). Strengthening order preserving encryption with differential privacy. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (pp. 2519-2533).Literature Review Due
702/29Computing as a DisciplineDiscussion Leader: Ellen JoyceDouble Blind Who’s Harry Potter? Approximate Unlearning in LLMs. (under review)
803/05Research Funding and Proposal Writing
Discussion leader: Maria Dhakal
Weekly Review: Ahmed, T., & Devanbu, P. (2022, October). Few-shot training LLMs for project-specific code-summarization. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (pp. 1-5).
803/07 IRB, Ethics, and Research Malpractice
903/12 Spring Break
903/14Spring Break
1003/19Basics of Research, Theory BuildingWeekly Review:
1003/21Study DesignIntroduction Due
1103/26Experiment Design, Controls, Confounders

Weekly Review:
1103/38Laboratory, quasi and natural experiments
1204/02What do we mean when we we say that we know a thing?


Weekly Review:
1204/04What do we mean when we we say that we know a thing? pt2
https://towardsdatascience.com/lessons-from-how-to-lie-with-statistics-57060c0d2f19 
Research Design Due
1304/09Distributions and when statistics lie
Weekly Review:
1304/11Distributions and when statistics lie
1404/16Class Cancelled
Weekly Review:
1404/18OLS
1504/23My results are State of the Art, and other lies we tell ourselves.
Weekly Review:
Final Paper Due
1504/25How to Evaluate AI Systems
1604/30How to Evaluate AI SystemsReviews Due
1605/02Reading Day
17TBDFinal ExamTBDFinal Exam

This course will draw materials from research literature as well as lessons accumulated over decades of experience in computing research. Students will attend weekly classes, complete frequent readings and reviews, and formulate a short research review article.

This term we will be using Canvas for class discussion. The system is highly catered to getting you help fast and efficiently from classmates and myself.

Lectures and Class Participation

Students should attend all classes. Effective lectures rely on students’ participation to raise questions and contribute in discussions. We will strive to maintain interactive class discussions if possible.

Questions, Discussions, and Help

If you have any questions or need clarification of class material, what should you do? First, try to post your question to the Canvas forum whenever possible, or otherwise email the instructor. The forum is for you and your peers to discuss class-related materials and to help one another. The forum will be monitored closely, but please be aware that we may not be able to answer all questions on the forum in a timely manner, due to the overwhelming number of questions that such a forum sometimes generates. Also, there are obviously things that are not appropriate for the forum, such as solutions for assignments as well as comments or requests to the staff.

In any case, for more thorough discussion, come to our office hours if you can!  Don’t be shy. Use our office hours to their fullest extent to help your study.

Requirements

Coursework

Most class meetings will require pre-reading selected by discussion leaders. Those readings will be discussed during class.

Each weekly reading will result in a short writeup.

Discussion leaders will give a talk at the beginning of each class. Discussion leaders for each week are exempt from the readings.

Signup here: first come first served.

Pre-Candidacy Proposal

A term paper is due at the end of the term with several milestones throughout the semester.

Final Exam

A final exam covering the topics in this course will be administered during finals week.

Grade Breakdown

Discussion Leaders10
Weekly Readings/Reviews15
Literature Review20
Introduction10
Research Design5
Final Paper10
Peer Review5
Final Exam25

Grades

This table indicates minimum guaranteed grades. Under certain limited circumstances (e.g., an unreasonably hard exam), we may select more generous ranges or scale the scores to adjust.

Total Grade
90-100 A-, A
80-89 B-, B, B+
70-79 C-, C, C+
60-69 D

Polices

Textbooks

Textbooks are required, but generally very cheap or free.

Salganik, Matthew J. Bit by bit: Social research in the digital age. Princeton University Press, 2019.

Okasha, Samir. Philosophy of Science: Very Short Introduction. Oxford University Press, 2016.

Lectures

Students should attend all classes. Effective class meetings rely on students’ participation to raise questions and contribute in discussions. We will strive to maintain interactive class discussions if possible.

Lecture capture and Zoom will not be provided.

Regrading

All requests to change grading of any course work must be submitted to the instructor in writing within one week of when the grades are made available. Requests must be specific and explain why you feel your work deserves additional credit. Do not ask for a regrade until you have studied and understood our sample solution.

Late Work

All scheduled due dates/times are US Eastern Time. Homework is typically due at the beginning of class on the due date, but check each the assignment for specifics.

Due date/time will be strictly enforced. Missing or late and/or unannotated work gets zero credit. If you are unable to complete an assignment due to illness or family emergency, we will understand but please see the instructor as soon as possible to make special arrangements. All such exceptional cases must be fully documented.

Academic Integrity

Notre Dame Students are expected to abide by Academic Code of Honor Pledge:

As a member of the Notre Dame community, I acknowledge that it is my responsibility to learn and abide by principles of intellectual honesty and academic integrity, and therefore I will not participate in or tolerate academic dishonesty.

Authorship effort on any submitted work must be accurately documented and properly cited. Artificial Intelligence tools like ChatGPT represents a new paradigm in academic and scholarly writing. Use of such tools on submitted work must be documented.