Basic Course Information
- Zeerak Ahmed: firstname.lastname@example.org
- Tallulah Axinn: email@example.com
- Nam Wook Kim: firstname.lastname@example.org
- Annie Schugart: email@example.com
- Karen Su: firstname.lastname@example.org
- Thursday @ 4pm: Maxwell Dworkin 221 (Karen)
- Thursday @ 5pm: Maxwell Dworkin 123 (Zeerak)
- Friday @ 10am: Maxwell Dworkin 221 (Nam)
- Friday @ 11am: Maxwell Dworkin 323 (Tallulah)
- Friday @ 1pm: Maxwell Dworkin 221 (Annie)
Krzysztof’s Office Hours: sign up here
A situation with a mixed initiative interaction is one in which the machine can initiate an interaction, but the user can also choose to initiate the interaction. For example, Luminere had the ability to suggest help without being prompted but the user could still have the same interaction by accessing the Help tab. Another example is Google Calendar as it can automatically add events by scraping emails but the user can also add events manually in their calendar sites.
In situations where people are expected to be aware and recognize an event that doesn’t occur very often, then often people will struggle to recognize the event. This is can be a problem in cases like TSA security checks; the TSA officer has to be vigilant in order to identify dangerous objects in people’s luggage, but as most luggage does not contain these objects it can be easy for the prevalence effect to occur and for the officer to miss dangerous objects when they do show up. The TSA combats this by regularly planting fake images of guns and then informing the officer that it was just a test when they identify them.
Personas refer to “archetypes” created by designers as a way to synthesize one’s observed data. There a numerous subconscious assumptions the different people on design teams may have. Focusing possible assumptions into a “proto-persona” (stereotype) or creating a “persona’ (from observation) can reveal to a team the choices a user might make based on the embodied characteristics in the persona. By revealing shortcomings and potential user choices, personas can serve as a way to focus and justify a design, as well as help keep a team on the same page throughout the design process.
Some important aspects to include in a persona: activities - what the user does and with what frequency, volume, etc.; goals, hopes, aspirations - what motivates the user with respect to the product domains; needs and frustrations - what gets in the user’s way with respect to their needs and aspirations; constraints - what restricts the user; skills and confidence - what the user is good at.
Contributed by Phillip Huang, Kemi Akenzua, and Nik Boström
Handicap is a general term for restrictions that cause difficulty for certain groups or communities of people. These restrictions hinder the user experience for these groups and causes a disparity between themselves and groups without the handicap. This is in contrast to a disability, which completely prohibits participation in an activity. The concept of a handicap gives rise to the design principle of having not only an accessible experience, but an equitable experience for all users.
There are many examples of handicaps we encounter each day. At MIT for example, people in wheelchairs must take a longer route and use an elevator to enter a building that people without this handicap do not need to do. In a Radcliffe building, there is a wheelchair ramp next to the stairs for people with and without wheelchairs to use. The latter is equitable.
Contributed by Phillip Huang, Kemi Akenzua, and Nik Boström
An Example to kick off class:
In the lecture, we looked at a device that can be used to make songs. It creates music. There are two sliders on the screen. The user can change the metrics. He or she can change the jazz factor and the happy factor. The design choice reflected the complexity of the underlying model. This example shows that the gulf of evaluation and the gulf of execution must be met. → This system did a good job of managing gulf of execution.
Human Motor Performance on Basic Input Tasks:
Next, in class, we completed an experiment using our laptops. Macs and PCs have different screen sizes. We viewed a model for a Mac screen and a model for a PC screen. We had to move the mouse across the screen. We had to move it horizontally. The results for the class were that the people in the PC condition took longer than the people in the Mac condition. The people in the PC condition took 865 milliseconds while the people in the Mac condition took 777 milliseconds. → Takes longer on PC because of the tendency to overshoot.
Index of Difficulty:
Fitts’ law connects the index of difficulty and the movement time. It is a function of both distance to travel and target size. log₂(D/W) represents the index of difficulty, in bits. D represents the width of the screen. W represents the width of a menu. There is a linear relationship between the index of difficulty and time. The movement time, in seconds, is equal to a+b*log₂(D/W).
The mouse was introduced 50 years ago and we are still using it. Interestingly, the mouse adds no extra noise to pointing, nor does it decrease efficiency, compared to the bare hand. This helps make the mouse the optimal input device for pointing. The ability of humans to transmit information through the neural motor system is 10.53 bits/second. Uniformly scaling everything does not make it more efficient.
One way to adjust menus is to change the size of various items depending on the frequency with which they are clicked. Items clicked more frequently will appear larger than items clicked less frequently. This makes it different from a static menu.
Another type of menu is a frequency-based menu. This menu has ordering based on frequency. More frequently clicked items will appear higher in the menu than other items.
Size of the cursor:
There may always be exactly one target in the bounds of a cursor. If you grow all of the buttons in the visual space, everything stays the same but in the clickable space everything is much larger → changes clickable space, not visual space.
Motor Performance versus Age:
Fine motor control peaks in the late teens, then slowly decreases. Strength peaks in the 30s, then decreases. Errors decrease as age increases. As we age, we end up choosing strategies that favor accuracy over speed.
Contributed by Dhruv Suri, Mason Hale, and Nicki Adler