Week Three: Usability

This week we discussed product usability and conducted our own usability tests with microwaves. We had to test three users by asking them to complete three tasks and recording three different data points for each task. Our three tasks were to open and close the door, start the timer for two minutes and then add a minute and a half after ten seconds, and to defrost an item weighing four pounds and four ounces using the defrost preset. For each of these tasks we tested their completion rate, timed the user, and asked them to rate their satisfaction.

In this photo, I am asking one of the users to complete one of the tasks in our test.

Design Process

While designing our test, we had to take into consideration a number of key factors. For example, we had to make sure our tasks were not too difficult and that our directions were clear so that the user knew exactly what we were asking them to do. Also, we had to pick the appropriate data points that fit with our tasks. After coming up with our tasks and data points, we wrote up a simple script to ensure that we were giving each user the same directions. After this, all we had left to do was test the users and compute our results.

In this photo we are trying to decide which tasks and data points to use for our test.
In this photo we are working with a neighboring team by peer editing both of our test layouts.


However, we did encounter some unexpected problems throughout this test. For example, instead of using the defrost preset, we were originally going to ask the user to use the popcorn preset. Once we saw the actual microwave that we were going to use for our test, we realized that there was no popcorn preset so we had to adjust our test. Also, we did not realize that all of our users were college- level males until we were analyzing our results. This is not necessarily a problem, but it made me wonder, did this significantly effect our data? At that point, I realized how the demographics of the users is just as important as the data collected in these types of tests.

If I were to continue this test, I would most likely change my data points and user group. I would change my user group by also testing women and would most likely change the age group of the users. I would change my data points by adjusting the way we measured user satisfaction. In addition to basing it on a numerical scale, I would ask for the users own feedback and comments. To further expand my test I would add more tasks of varying difficulties to help me see what about the microwave needs to be changed.

Application in the Future

I can see myself applying these techniques in a future job setting where I am asked to test the usability of a product that I may have helped design or update. For example, a potential project that I would be testing could be a coffee maker. By using tasks and data points, I can test various scenarios that the users may face and can also represent the usability in different sets of data. I could ask users to make a cup of coffee or clean the machine as one of their tasks and I would time how long it takes them as one of my data points. This technique is very useful for products that are commonly used by every day people. However, this technique would not be useful if we were testing a specific product with a general group of users, such as testing medical technology with the same group of people tested for the coffee maker. This ties back to the realization I came across earlier about how important the demographics of the users are to these tests. If the users do not fit the product then the data will not be an accurate depiction of the usability of that product.

Here is the link to our video presentation: