Course Description
This course provides an introduction to several major areas of research in Human-Computer Interaction (HCI). It is based in a combination of readings and discussion, a small statistics lab, and a self-defined project. The course readings will require preparing reports on a combination of historic framing papers and more current results. This will help you examine what the HCI community considers a contribution across a variety of problems, thus preparing you to understand and make meaningful contributions in these and other areas of HCI.
This course is explicitly not focused on the design methods commonly used in HCI practice. We do not assume a strong background in HCI (i.e., there is no undergraduate HCI prerequisite), but students seeking an introduction to effective design or the design process will be better served by CSE 440.
The course project will require hands-on experience with HCI, while remaining open to different possibilities. You might choose to design and implement a new piece of HCI technology, or you might choose to design and execute an appropriately compelling study with HCI research implications.
We will emphasize open discussion and feedback in all aspects of the course.
Basic Information
Contact: Email all instructors at cse510-staff [at] cs.washington.edu
Class Time & Location: Tuesdays & Thursdays, 10:00-11:20, LOW 105
Office Hours: By appointment, and as scheduled for project meetings.
Course Staff:
Readings
Assigned readings will focus on research topics, generally consisting of:
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A historic framing paper: presenting a theory, language, or understanding that can help in understanding and contextualizing the contributions of additional research.
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Two papers that provide more recent or specific contributions: presenting the type of contribution you might initially be expected to attempt in your research.
You are expected to read: (1) the historical framing paper, and (2) either of the more current papers (in other words, whichever seems more compelling or interesting to you). You obviously may choose to read all three. The calendar will link to assigned readings and provide any day-specific revisions to this reading structure.
You are expected to have read and considered the assigned readings prior to class, as the in-class discussions are a critical component of this course.
Reading Reports
To help prepare for an engaging and meaningful discussion, we require posting thoughts and questions beforehand. You can start a new discussion, participate in an existing discussion, or do a bit of both. You can discuss all of the assigned readings, or focus on a portion of the reading that you found most interesting. The important part is that we can see intellectual effort in your forum participation, not just a simple paper summary.
Reading reports are generally expected to be approximately 200 to 400 words. which may be in a single post or distributed across several posts related to a day’s readings. We expect most will be short and focused on discussion points or questions, and posts that exceed the upper limit or primarily summarize the reading itself will receive low grades. This aims to strike a balance between: (1) enough text to convey a meaningful response, and (2) succinct enough to allow review before class.
We will create Canvas discussion threads for each paper. If you have a thought or question that relates to multiple papers, post it wherever seems more appropriate.
Your participation in the forum discussion for each day will be graded on a scale from 0 to 3.
- 0: If you do not participate.
- 1: If your participation seems weak and does not convince us you read, understood, and considered the readings.
- 2: If your participation shows you read and understood the readings and had something interesting to say. This will be the most common grade.
- 3: Reserved for especially insightful participation.
It is generally easy to find something to criticize in any piece of research. But focusing exclusively on the potential flaws of research is generally not productive. You will generally find it more intellectually worthwhile to focus on aspects of work that are particularly well done, new ideas are prompted by a piece of work, or what you might have done differently if you conducted the research. This will also lead to much more valuable discussions.
Potential topics for discussion are:
- What idea or innovation enabled this, what more might be done based on that idea or innovation?
- What new questions or research agendas are suggested by this research?
- How might this research have informed some other research you have seen?
- What aspects of this work were particularly well done or effective?
- If you had conducted this research, what would you have done differently?
We also note that some papers will be presented by authors of those papers. Although we want everybody to be comfortable with open discussion, and we do not expect posts to be overly formal, this is another reason to be thoughtful in how you approach paper discussion.
Discussions will be coordinated using Canvas, with a post for each day:
https://canvas.uw.edu/courses/1249568/discussion_topics
Reading reports are due at 11:59pm the night before each class meeting. This ensures time the next morning to review discussion before class. Submitting the day of class, just before class, or in class is therefore unacceptable, risking zero credit. But feel free to continue a discussion after this, even after class.
Project
A course project will be a major component of your work. Details of the project are here:
https://courses.cs.washington.edu/courses/cse510/18au/assignments/project.html
Dates are also linked from the course calendar.
Some sample project ideas, gathered from faculty and researchers, have been made available here:
https://canvas.uw.edu/courses/1249568/discussion_topics/4437368
You can also use that discussion identify potential project partners and to post your own ideas.
Statistics Lab
To aid in developing necessary skills, you will complete a small statistics lab using R. The lab will walk you through analyzing an example data set, and you will then analyze two datasets from published research papers.
You will gain basic familiarity with analyzing experiments using mixed‑model analyses of variance. Consistent with lecture, this assignment is not intended to provide complete knowledge of how to design or analyze experiments, which is far beyond the scope of one lecture or assignment. This assignment is instead focused on a pragmatic introduction to analyzing experiments based in designs you might later find useful. Please consider this assignment in the context of the material covered in lecture, as not all of it is repeated here.
In addition to the lecture material and the contents of this assignment, you might benefit from working through all or part of Jacob Wobbrock’s independent study in Practical Statistics for Human-Computer Interaction.
http://depts.washington.edu/aimgroup/proj/ps4hci/
Download
The assignment is available for download:
https://courses.cs.washington.edu/courses/cse510/18au/assignments/statisticslab.zip
Submission
Due: Uploaded by 11:59am Monday, November 26, 2018.
Submit a ZIP archive including your document in PDF format and any additional files:
https://canvas.uw.edu/courses/1249568/assignments/4380322
Exam
A take home exam will give an opportunity to demonstrate and apply your understanding of course material in a more substantial format. It requires you to connect concepts across papers, serving as an evaluation of your understanding and critical thinking about course concepts. If you have kept pace with the readings, you will find this exam much easier to approach (e.g., simply referring back to readings, rather than needing to understand them from scratch). You may reference any of the articles, slides, notes, discussion posts, or other material readily available on the web. You may consult the course staff with any questions, but this is strictly an individual assignment (i.e., do not discuss it with others).
Submission
Due: Uploaded by 11:59pm Thursday, December 13, 2018.
https://canvas.uw.edu/courses/1249568/assignments/4380323
Grading
Grading will roughly correspond to:
- 20%: Readings
- 45%: Group Project
- 10%: Statistics Lab
- 15%: Exam
- 10%: Participation
Much of the grading in this course is necessarily subjective. We will attempt to communicate expectations and feedback throughout the course, but it is your responsibility to communicate with us if you would like guidance in this regard.
Project milestone grading will emphasize progress and preparation to engage with direction and feedback. Final project report grading will then consider overall execution.
Contributing
This course website lives on GitHub: