Course Overview

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 meaningful contribution across a variety of problems, thus helping prepare 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]

Class Time & Location: Tuesdays & Thursdays, 11:00-12:20, MGH 287

Office Hours: By appointment, and as scheduled for project meetings.

Course Staff:


Assigned readings will focus on research topics, generally consisting of:

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.

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.

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:

Discussions will be coordinated using Canvas, with a post for each day:

Reading reports are due the night before each class meeting. This ensures time to review discussion before class the next morning. 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.


A course project will be a major component of your work. Details of the project are here:

Dates are also linked from the course calendar.

Some sample project ideas, gathered from faculty and researchers, have been made available here:

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 statistics lab. We recommend using R for this lab. 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 the first four sections of Jacob Wobbrock’s independent study in Practical Statistics for Human-Computer Interaction.


The assignment is available for download:


Due: Uploaded by end of day Sunday, February 18, 2018.

Submit a ZIP archive including your document in PDF format and any additional files:


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).


Due: Uploaded by end of day Friday, March 16, 2018.


Grading will roughly correspond to:

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.


This course website lives on GitHub: