Blog Update #3a – Further Updated Task Examples
The task examples have not changed since Milestone II. The task examples can be found here: https://cs444cafeohyay.home.blog/2019/02/05/cpsc-444-blog-post-2/
Blog Update #3b – Low-Fidelity Prototype Demonstration
Blog Update #3c – Additional Information about Prototype
Our low-fidelity prototype is designed to support all our task examples. The scope includes a central listing of cafés and a detail view for each café. Not included in the prototype are a map for navigating to the café (could simply be a link to google maps), and an interface for receiving user reviews/information (omitted to first focus efforts on properly displaying information to users).
The list view shows cafés, represented by pictures of their interiors, sorted by distance from the user’s current location, and sized (width) based on overall rating– better cafés are bigger. We decided to use images the represent the cafés in the central listing to help users get a feel for the styles of different cafés, reduce mistakes by presenting large touch targets, and be visually interesting to the user. We chose to use images of the interior of the cafés to signal that our app gives users access to inside information before they arrive at a café– touching a cafe to view more details is metaphorically like stepping inside to have a look around. The distance-sorting is because distance traveled is a key cost to be minimized in all of our task examples. We tried to use the size of each image to represent that café’s quality (in terms of user ratings possibly), so that users could immediately judge the trade-off between distance to a café and its value.
There is a location search bar near the top so that a user could look for cafés near a different location. The location search bar was included because some of the participants in our field study mentioned that they would want to find a café not just in their immediate area, but e.g. near a mall they were going to, or on the way home from work. It also signals to the user that our main listing is based on their location.
Also included is a detail view for each café, showing a volume meter for the café’s current volume level, its distance from the user’s current location, and key notes about its general features. The volume meter helps meet the needs of task example 1, the user looking for a less noisy café. Touching the volume meter would play a clip of background audio recorded in the café, helping users determine if the café has loud music (task example 1), or a lively atmosphere (task example 3). The key notes show the features that other users found most salient about the café, with the idea that if some important amenity is missing, or if something is especially good about the café, it would be mentioned by users and appear here.
Blog Update #3c – Walkthrough Report
We performed two cognitive walkthroughs of our low fidelity prototype, to evaluate our prototype in terms of our task examples. We learned from the walkthroughs that our prototype lacks some features required by our task examples: viewing a café’s menus, reviews, and hours of operation. One design feature of our prototype was to list cafes sorted by distance, with nearer cafés at the top, and show larger images for better rated cafés. However, these features were not apparent in the walkthrough, suggesting a need for better signifiers. We also found that our descriptions for the cafés were vague, and that users may need to know the location of a café, rather than just how far away it is from them. On the other hand, basic features like getting a list of nearby cafés, viewing café attributes, and the noise indicator, were all easily noticed and utilized by users during our walkthrough.
Task Example 1:
For this task example, our walkthrough covers tasks of finding a café near their current location which has a great atmosphere for studying, is not too busy or loud, and has more than only couches available. Our walkthrough showed that users are able to get a list of cafés near their current location and could get the attributes of a café, with an indication of the noise level, as well as a picture, which indicates the café’s atmosphere for studying. Users were able to perform these tasks without any errors, however, they did not get appropriate feedback on whether the café has non-couch seating available nor on how busy the café is at the moment.
Task Example 2:
Similarly for this task example, our walkthrough touched upon the attributes of a given café. This time the user was asked to find out whether “Great Dane”, a café given by us, is busy, small and has a cozy atmosphere. The users were able to find Great Dane and access its attributes without any errors but did not get appropriate feedback on how small, busy, or cozy the café is, as the descriptions were vague and pictures were lacking. The users were also asked to find a café that has chai mocha lattes but were unable to locate menus of cafés.
Task Example 3:
Our walkthrough again used café attributes for this task example. Users were asked to find a café with an outlet, bathroom, and with long business hours. Users were able to access the attributes of selected cafés without any errors but did not get appropriate feedback as our attributes did not include whether a café has an outlet or bathroom and there was no option for users to see the business hours of cafés.
Blog Update #3e – Proposed goals of experiment
Proposed goals ranked by importance and feasibility:
- Is our design more effective than Google Maps based on the time taken for the user to complete specific tasks?
- Is our design more effective than Google Maps based on the number of errors made by the user to complete specific tasks?
- How easy for new users to recognize the different features of the design without external help? (Whether new users are able to recognize and understand the functionalities of the design easily, or they need some sort of tutorial?)
- How easy is the new system to learn?
- Once the user has learnt how the system works, is it fast to use?
- What design of the details page maximizes the amount of information the can glean about a café in a short amount of time? (Numbers, “star ratings”, or text descriptions).
- The inclusion of which details on the main listing page minimizes the number of times a user has to open the details page to view the full info when deciding on a café to go to? (Which details are important to have on the main listing page?)
Chosen goal to address in evaluation:
Our experiment goal is to determine if our design is more effective than Google Maps at completing the main tasks drawn from our task examples. As measures of effectiveness, we will record the amount of time taken and the errors made for each task. We have identified Google Maps as the primary competitor to our system, as every single one of our field study participants used Google Maps to find a café to study in.
The key challenge of our system design is to identify if it is an alternative that is at least as good as Google Maps, if not better, for finding specific pieces of information.
CS444 Cafe Oh Yay 