| Instructor: | Bruce N. Walker |
| Office: | Room 230, Psychology Building (JS Coon Building) |
| Telephone: |
(404) 894-8265 |
| Email: | bruce.walker@psych.gatech.edu |
| Course web page: | |
| Office Hours: | After class and by appointment |
| CLASS MEETING PLACE and TIME: | Check Oscar website |
An overview of many of the research methods, tools, metrics, and analyses used in the human factors, engineering psychology, human-computer interaction, and design fields. Covers qualitative and quantitative approaches to assessing performance, preference, and affective responses as part of the evidence-based design, development, and deployment of systems and services. Some examples include surveys, focus groups, interviews, usability studies, task analysis, modeling, physiological data collection, eye tracking, benchmark tasks, accessibility audits, web analytics, and more.
In this course, you will learn about common methods employed in user-centered and evidence-based design. You will also learn how to choose methods, plan studies, and perform research that is inclusive of users with a range of abilities. The objective of this course is to train you to use the appropriate methods, tools, metrics, and analyses for generating evidence to inform and reflect on design decisions. This course is different from traditional research methods because you will be expected to increase your awareness, understanding, and application of inclusive research practices.
Learning in this course will occur through lectures, structured discussions, readings, in class and out of class activities, and assignments. You are expected to complete the specified readings to contribute to discussions and effectively engage in course activities.
At the end of this course, you should be able to:
Throughout the course, each student and each team will be expected to consider issues of equity and diversity. These issues could include differing abilities, gender identity, racial identity, socio-economic standing, and others. Implications may include initial project/idea selection, identification of user groups/personas, test participant selection, and team dynamics.
In the team project, in individual assignments, and even exams, you will be asked to explain how you will, would, or did consider issues of equity and diversity, and then reflect on what you learned or discovered that relates to equity and diversity.
William (Bill) Albert & Thomas (Tom) Tullis (2022). Measuring the User Experience: Collecting, Analyzing, and Presenting UX Metrics (Third Edition). Cambridge, MA: Morgan Kaufmann/Elsevier. Note: Published in 2022 but Copyright in 2023.
Download free copy from Science Direct (with Georgia Tech login):
https://www.sciencedirect.com/book/9780128180808/measuring-the-user-experience
Purchase printed copy or eBook:
https://shop.elsevier.com/books/measuring-the-user-experience/albert/9780128180808 (or via Amazon, etc.)
Jonathan Lazar, Jinjuan Heidi Feng, & Harry Hochheiser (2017). Research Methods in Human Computer Interaction (Second Edition). Cambridge, MA: Morgan Kaufmann/Elsevier.
Download free copy from Science Direct (with Georgia Tech login):
https://www.sciencedirect.com/book/9780128053904/research-methods-in-human-computer-interaction
Purchase printed copy or eBook:
https://shop.elsevier.com/books/research-methods-in-human-computer-interaction/lazar/9780128053904 (or via Amazon, etc.)
Optional extra text (largely duplicates Lazar et al.):
Baxter, K., Courage, C., & Caine, K. (2015). Understanding Your Users: A Practical Guide to User Research Methods (Second Edition). Waltham, MA: Morgan Kaufmann/Elsevier.
Additional readings, typically research articles and book chapters, may be added during the semester. Canvas announcements will be sent out when these are assigned, and a PDF file or a link to the resource will typically be provided through Canvas. Students will be responsible for obtaining and reading all materials before the class in which they are to be discussed. Demos and examples may also be made available via Canvas.
If you are a student with a disability and you need academic accommodations, please see me and contact the Disability Services (404-894-2563), http://disabilityservices.gatech.edu/. Links to an external site. All academic accommodations must be arranged through that office. They will then contact me with instructions.
Students will be assigned onto teams of approximately 4 students. Assignment will be partially systematic (e.g., balancing diversity of background, skills, experience, etc.) and partially random. Team members will sit together in class throughout the semester, which will facilitate in-class activities and practice. For example, in the middle of a lecture on interviews, we may ask you to do a quick interview of one of your teammates; since you will be sitting together, it will be efficient to do that mini-practice, then return to the lecture with minimal time lost in the transitions. Teams will also complete Team Assignments together throughout the semester. It is expected that teams engage in good team formation and onboarding practices (including a team contract). At the end of the semester, each student will evaluate the contribution they, and each of their team members, made to the Team Assignments. Final scores may factor in this peer evaluation.
Team Assignments: 49%
Individual Assignments: 49%
Final Exam (optional): 2%
In-person attendance is expected for this course. You should come to classes prepared -- that is, having read and made an attempt to understand the reading material that was assigned, and ready to engage in class discussion and activities. You should be ready to discuss and apply material covered in the lectures and reading. Be a good team member -- you should have equitable membership in your team. This means taking on a workload that is clearly similar to your team members’ workload, being knowledgeable about your team activities and plans, going to team meetings, etc. All students will provide feedback on the contributions of themselves and their teammates to the Team assignments. This may be factored into the final score a student gets for the Team assignments.
There will be approximately 4 team assignments. Each assignment will require the team to perform some task, use some suitable research methods, and write up a report on the activities. For example, a team may be prompted to plan and conduct an interview, then analyze the results of that interview, and write up a report on what they did. The report will document what they did, why, how, with whom, what choices were made along the way, and what they learned in the interview, and from the activity as a whole. The assessment of the Team Assignments will be largely about the process and products.
There will be approximately 4 individual assignments, to be completed individually. Each assignment will call for an analysis or critique or evaluation, leveraging the student’s knowledge in the field and critical thinking skills. As one example, the assignment might provide a research question and a set of survey questions. The student would be asked to evaluate the survey questions on various dimensions (especially how well they address the purpose of the research), and propose any improvements. The assessment of the Individual Assignments will be largely about the critical thinking and analysis.
In Spring 2025, the final exam will be optional. Students who opt in to take the final will be required to notify the instructor by the last day of classes. If a student takes the final exam, it will be held in person, during the scheduled exam period for this course, and will count for 2% of the overall grade. If the student does not opt to take the final exam, the Team and Individual Assignments will each count for 50% of the grade. For students who do not opt in to take the final exam, the final participation date will be the last day of classes.
This course will leverage the expertise and experience of industry professionals (and other practitioners) to connect class content with the day-to-day research-related activities that can occur in many HCI-related jobs. These professionals will bring real-world perspectives and case examples through lectures and activities. Engaging with professionals in class is also an opportunity for students to learn about a variety of workplace cultures and practices, and better understand the role of research in these workplaces.
Please, above all, be respectful and considerate of others in the class. It should go without saying, but this includes showing up on time for classes, meetings, exams, etc. Please mute all devices while in class. If you have an emergency phone call that you must take, please exit the class and take the phone call outside of the room. Consider that others sitting around you can see what is on your screen.
The course content, any recordings, exams, and materials provided by the instructor in this course are copyright and protected, and for the use of the students enrolled in the course and cannot be further disseminated. Electronic video/audio recordings initiated by students are not permitted unless an explicit permission is granted by faculty.
All students are assumed to have read the Honor Code and consented to be bound by it. Violations of the Honor Code are taken extremely seriously and will result in a failing grade for the course and referral to the Dean of Students for further action. Specific violations include (but are not limited to):
Unless explicitly stated on the official course Canvas page, all exams administered in this course are to be taken without the use of notes, books, or ancillary materials, and without the assistance of any other person or group, in the class or outside of the class. Texting or other use of electronic devices such as PDAs, cell phones, audio devices, or other mobile devices during scheduled exams is prohibited for reasons of exam integrity. Use of these devices during exams is viewed as a violation. If you have any questions, please ask. I will assume that all students enrolled in the course know and understand what constitutes academic misconduct and agree to be bound by these rules.
In this class we treat AI-based assistance, such as ChatGPT and Copilot, the same way we treat collaboration with other people: for both individual and team-based assignments, you are welcome to talk about your ideas and work with other people, both inside and outside the class, as well as with AI-based assistants.
However, all work you submit must be your own. You should never include in your assignment anything that was not written directly by you without proper citation (including quotation marks and in-line citation for direct quotes).
Including anything you did not write in your assignment without proper citation will be treated as an academic misconduct case. If you are unsure where the line is between collaborating with AI and copying AI, we recommend the following heuristics:
Heuristic 1: Never hit “Copy” within your conversation with an AI assistant. You can copy your own work into your own conversation, but do not copy anything from the conversation back into your assignment.
Instead, use your interaction with the AI assistant as a learning experience, then let your assignment reflect your improved understanding.
Heuristic 2: Do not have your assignment and the AI agent open at the same time. Similar to the above, use your conversation with the AI as a learning experience, then close the interaction down, open your assignment, and let your assignment reflect your revised knowledge.
This heuristic includes avoiding using AI directly integrated into your composition environment: just as you should not let a classmate write content or code directly into your submission, so also you should avoid using tools that directly add content to your submission.
Deviating from these heuristics does not automatically qualify as academic misconduct; however, following these heuristics essentially guarantees your collaboration will not cross the line into misconduct.